CN110694701A - Method and device for transporting liquid drops and method for cleaning liquid drop transport path - Google Patents

Abstract

The application discloses a liquid drop transmission method and device and a cleaning method of a liquid drop transportation path. Wherein, the method comprises the following steps: obtaining an undirected graph of the two-dimensional microfluidic array, wherein the undirected graph comprises a vertex set and an edge set, each vertex in the vertex set represents an electrode in the microfluidic array, and an edge between two adjacent vertices represents a path between the two adjacent vertices; determining mutually disjoint vertexes or mutually disjoint edges in the undirected graph; determining drop transport paths based on mutually disjoint vertices or mutually disjoint edges, wherein the drop transport paths in each set of drop transport paths are mutually disjoint; the droplets are transported along a droplet transport path. The cleaning method and the cleaning device solve the technical problems that cross contamination can occur in a liquid drop transmission path in the related art and the cleaning effect of cleaning cross contamination points is poor.

Description

Method and device for transporting liquid drops and method for cleaning liquid drop transport path

Technical Field

The application relates to the field of digital microfluid, in particular to a liquid drop transmission method and device and a cleaning method of a liquid drop transportation path.

Background

Digital microfluidics is an emerging technology that enables on-chip processing of fluids. This technology has advanced the automated development of biochemical laboratory procedures. The digital microfluidic biochip ensures that sample sampling and analysis are continuously available by reducing the consumption speed of samples and reagents, thereby completing real-time biochemical analysis. Under clocking on a two-dimensional electrode array, electrowetting methods are used to manipulate nano-scaled droplets ("cells") in a "digital" fashion. Currently, enzyme assays, DNA sequencing, cell experiments and immunoassays have been successfully implemented on digital microfluidic chips. While the digital microfluidic chip used for clinical diagnostics includes over 5000 electrodes, a commercial digital microfluidic chip can embed over 300,000 electrodes with integrated optical detectors at 20 μm x 20 μm. Advances in these technologies have driven the search for computer aided design tools to enable the design of such chips. At present, methods such as structural integration, physical design and liquid drop transport path have been developed for the automatic design of the digital microfluidic chip.

For example, a path algorithm based on network flow is proposed in the related art, which can simultaneously move a group of non-interfering networks along a specific path, thereby solving the problem of droplet transportation path on the biochip. In addition, the droplet conveyer in the related art can be used to convey droplets having a high bypass property, which is a method that is unlikely to prevent other droplets from moving. For microfluidic biochips, an automatic synthesis tool for droplet path sensing is proposed. However, it does not create any specific drop path during the synthesis process. To this end, a system droplet transport method is integrated with biochip synthesis, the proposed method minimizing the number of units for the droplet path while also satisfying the constraints imposed by considering throughput and fluid properties. However, since the paths in the microfluidic biochip are considered as virtual paths, the droplet paths may intersect or overlap each other at different time intervals. Therefore, cross-contamination between droplets may occur at the overlapping sites.

Most automated droplet transport methods have a drawback in that they are based on the unlimited sharing of cells on a biochip through various droplet paths. Therefore, cross-contamination in this case is unavoidable. The path of the sample and reagent droplets is referred to as the functional droplet path. Cross-contamination occurs when residue left by one functional droplet is transferred to another, which can lead to adverse consequences such as misleading assay results (false positives, false diagnoses, etc.). For example, in an in vitro diagnostic session with multiple reuses, if the paths of the urine and serum droplets are crossed, the residue left by the urine sample droplet is transferred to another serum sample droplet, which may contaminate the serum sample droplet.

When multiple drop paths intersect one another, the residue left by one drop passing at an earlier clock cycle may contaminate drops arriving at the intersection at a later clock cycle. Therefore, cross-contamination occurs at this intersection, which is referred to as the "cross-contamination point". Thus, the droplet routing problem of biochips must be considered to avoid cross-contamination during droplet transport.

However, current approaches to avoid cross-contamination do not work well, for example, assuming that droplet transport does not occur simultaneously with the bioassay operation (e.g., mixing). In practice, however, the droplet path and mixing operation are often performed simultaneously. Furthermore, in this scenario, the same set of wash droplets is used to wash the residue in multiple bioassay and pathway stages. However, because excessive residue within the cleaning droplets can reduce the ability to remove excess residue from the electrodes, a particular cleaning droplet cannot be used for multiple cleaning operations.

In view of the above problems, no effective solution has been proposed.

Disclosure of Invention

The embodiment of the application provides a droplet transmission method and device and a cleaning method of a droplet transportation path, so as to at least solve the technical problems that the droplet transmission path has cross contamination and the cleaning effect for cleaning cross contamination points is poor in the related art.

According to an aspect of an embodiment of the present application, there is provided a method of transporting droplets, including: obtaining an undirected graph of the two-dimensional microfluidic array, wherein the undirected graph comprises a vertex set and an edge set, each vertex in the vertex set represents an electrode in the microfluidic array, and an edge between two adjacent vertices represents a path between the two adjacent vertices; determining mutually disjoint vertexes or mutually disjoint edges in the undirected graph; determining a drop transport path based on mutually disjoint vertices or mutually disjoint edges; the droplets are transported along a droplet transport path.

According to another aspect of the embodiments of the present application, there is also provided a method of cleaning a droplet transport path, including: determining a cross-contamination point in the droplet transport path, wherein the cross-contamination point is a plurality of droplet path cross-points; determining the sequence of the cleaning liquid drops passing through the cross-contamination point and the sequence of the at least two functional liquid drops reaching the cross-contamination point, wherein the sequence of the cleaning liquid drops reaching the cross-contamination point is later than the sequence of one functional liquid drop of the at least two functional liquid drops and is earlier than the sequence of the other functional liquid drops of the at least two functional liquid drops reaching the cross-contamination point; the cleaning droplets and the at least two functional droplets are transported in an order that the cleaning droplets and the at least two functional droplets reach the cross-contamination point.

According to another aspect of the embodiments of the present application, there is also provided a droplet delivery apparatus including: the device comprises an acquisition module, a detection module and a control module, wherein the acquisition module is used for acquiring an undirected graph of the two-dimensional microfluidic array, the undirected graph comprises a vertex set and an edge set, each vertex in the vertex set represents one electrode in the microfluidic array, and an edge between two adjacent vertices represents a path between the two adjacent vertices; the first determining module is used for determining mutually disjoint vertexes or mutually disjoint edges in the undirected graph; a second determination module to determine a drop transport path based on mutually disjoint vertices or mutually disjoint edges; the first transport module is used for transporting the liquid drops according to the liquid drop transport path.

According to another aspect of the embodiments of the present application, there is also provided a cleaning apparatus for a droplet transport path, including: a third determining module, configured to determine a cross-contamination point in the droplet transport path, where the cross-contamination point is a plurality of droplet path cross-points; the fourth determining module is used for determining the sequence of the cleaning liquid drops passing through the cross-contamination point and the sequence of the at least two functional liquid drops reaching the cross-contamination point, wherein the sequence of the cleaning liquid drops reaching the cross-contamination point is later than the arrival sequence of one functional liquid drop in the at least two functional liquid drops and earlier than the arrival sequence of the other functional liquid drops in the at least two functional liquid drops at the cross-contamination point; and the second transmission module is used for transmitting the cleaning liquid drops and the at least two functional liquid drops according to the sequence that the cleaning liquid drops and the at least two functional liquid drops reach the cross contamination point.

According to still another aspect of the embodiments of the present application, there is also provided a non-volatile storage medium including a stored program, wherein the program, when executed, controls a device in which the storage medium is located to perform the above-described cleaning method for a droplet transport path.

According to still another aspect of the embodiments of the present application, there is also provided a processor for executing a program, wherein the program executes the above-described cleaning method of the droplet transport path.

In the embodiment of the application, mutually-disjoint peaks and edges in an undirected graph are used for determining mutually-disjoint droplet transportation paths, or the sequence of the cleaning droplets arriving at the cross-contamination point is set to be later than the arrival sequence of one functional droplet in at least two functional droplets and earlier than the arrival sequence of other functional droplets arriving at the cross-contamination point in at least two functional droplets, so that the technical problems that cross contamination can exist in a droplet transportation path in the related art and the cleaning effect of cleaning the cross-contamination point is poor are solved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:

FIG. 1 is a schematic illustration of an alternative synchronization of cleaning drop paths and functional drop paths across a contamination point in accordance with embodiments of the present application;

FIG. 2 is a schematic illustration of an alternative two cross-contamination point cleaning drop path in synchronization with a functional drop path in accordance with an embodiment of the present application;

FIG. 3 is a schematic diagram of an alternative order of determining positional adjustments according to embodiments of the present application;

FIG. 4 is a schematic diagram of an alternative all possible drop transport paths according to an embodiment of the present application;

FIG. 5 is a schematic diagram of an alternative obstacle arrangement for a droplet transport path according to an embodiment of the present application;

FIG. 6 is a schematic diagram illustrating an alternative arrangement of disjoint paths and non-cross-contamination paths according to an embodiment of the present application;

FIG. 7 is a schematic diagram of an alternative path with or without a purge operation according to an embodiment of the present application;

FIG. 8 is a schematic illustration of an optional cleaning step in synchronization with the transport of functional liquid droplets in accordance with an embodiment of the present application;

FIG. 9 is a schematic flow chart diagram of a method of droplet delivery according to an embodiment of the present application;

FIG. 10 is a schematic diagram of a droplet delivery apparatus according to an embodiment of the present application;

FIG. 11 is a flow chart of a method of cleaning a droplet transport path according to an embodiment of the present application;

fig. 12 is a block diagram of a cleaning apparatus of a droplet transport path according to an embodiment of the present application.

Detailed Description

In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

The embodiment of the application provides a new solution for determining the droplet path to avoid cross contamination in the optimization of the droplet transportation path. This approach targets disjoint droplet transport paths, avoiding path overlap, and thus minimizing the possibility of cross-contamination. The embodiment of the application is a digital microfluidic biochip based on a direct access scheme, namely, each electrode in the chip is controlled by a separate control pin. The embodiment of the application also utilizes a cleaning operation synchronization method to operate the cleaning liquid drops, so that residues left by the sample and the reagent liquid drops are cleaned. By controlling the order in which the droplets reach the cross-contamination point, the transport of the wash droplets can be synchronized with the sample/reagent droplet transport step, thereby minimizing droplet transport time. In order to avoid cross-contamination between successive transport steps, embodiments of the present application incorporate cleaning operations between successive transport steps of functional droplets and propose an optimized method to minimize the number of cleaning operations. The details are as follows.

In the embodiments of the present application, the basic concept in the proposed path method is first introduced. Then, the restriction factors in the transport path of the droplets are introduced. Finally, the drop path problem is broken down into a series of sub-problems to be discussed.

A. Problem raising and restriction

1) The problems are solved: given a bioassay protocol (results from architecture-level synthesis methods), and the location of these modules on the biochip plan (the location of modules on the biochip plan can be determined by layout tools), the path layout determines the path of droplet transport by using the available cells in the microfluidic array. Droplets are transported along these paths between modules or between modules and fluid I/O ports, such as reservoirs and sensors on a chip.

The fluid ports on the microfluidic module boundary are referred to as "pins". The path of the droplets between the pins of different modules or on-chip memories is called the "net". Thus, the fluid path for a single droplet traveling between two terminals (i.e., a source and a destination) can be modeled as a net with two pins. Two droplets from different terminals are typically delivered to a common module (i.e., mixer or diluter) for mixing or dilution. To allow simultaneous mixing of the droplets during transport, a three pin mesh was used to generate this fluid path model.

Cross-contamination can occur when multiple drop paths cross or overlap each other. At the intersection of two drop paths, a drop arriving at a later clock cycle may be contaminated by residue left from another drop passing at an earlier clock cycle. The more elements that are shared by two drop paths, the higher the likelihood of cross-contamination.

It is an aim of embodiments of the present application to avoid cross-contamination between different droplet paths, a secondary objective being to minimise the time required for a droplet to travel along a path. Thus, embodiments of the present application focus on disjoint drop paths and minimization of the total length of all paths (measured by the number of cells in the path from the source to the destination).

In a set of disjoint paths, a drop path does not share any cells with other drop paths. This path would avoid this possibility: when one droplet is transported through a cell, another droplet has been transported through the cell in an earlier time interval. Thus, the non-intersecting paths avoid cross-contamination between different droplets. Minimization of the drop path length results in a reduction in drop transport time. It also frees up more redundant units for parallel fluid operation and fault tolerant operation.

2) The restriction factors are as follows: although the non-intersecting drop paths can avoid cross-contamination between different drops, if some drops are too close together during transport, mixing can occur inadvertently. Therefore, during droplet transport, a minimum spacing between droplets must be ensured to avoid accidental mixing. The functional areas of the microfluidic cartridge may be encapsulated by adding a separate area to avoid conflicts between the droplet path and concurrent assay operations. In addition, there is a need to meet fluid containment rules to avoid accidental mixing.

Another constraint in drop paths is that there is an upper limit on the number of electrodes per drop path. Since droplets containing macromolecules such as proteins tend to adsorb on and contaminate the electrodes, which may lead to loss of chemicals within the droplet and thus to erroneous analysis results, the length of the droplet path should be short, not exceeding the upper limit.

3) And (3) problem decomposition: because the digital microfluidic array can be dynamically reconfigured during operation, a series of two-dimensional configuration layouts of the modules can be obtained for different time spans during the module layout phase. Droplet transport is therefore broken down into a series of sub-problems. The sub-problem is a problem to be solved when determining a certain droplet transportation path in a bioassay process. In each sub-problem, the web to be transported between the source and destination is first determined. Microfluidic modules that remain active during the transport of droplets are considered a "barrier". By solving these sub-problems in turn, a complete droplet transport path solution is obtained.

B. Method for determining path

The scheme provided by the embodiment of the application mainly solves the problem of a liquid drop transportation path, so that cross contamination in one sub-problem is avoided. The problem of finding non-intersecting paths in a two-dimensional microfluidic array is first converted into the problem of finding non-intersecting (vertex non-intersecting or edge non-intersecting) paths in the figure. Next, a drop path algorithm is described for determining disjoint drop paths and minimizing the number of cells used in a drop path if constraints are met. Specifically, the method comprises the following steps:

1) graph model and disjoint paths: the problem of finding feasible disjoint paths for a two-pin or three-pin net in a two-dimensional microfluidic array can be directly translated into pairs of vertices in the figureThe problem of points finding disjoint paths (vertex disjoint or edge disjoint). Consider a planar undirected graph G ═ V, E, where V is the set of vertices and E is the set of edges. Each vertex in the figure represents an electrode in a two-dimensional microfluidic array, and if the electrodes corresponding to two vertices are adjacent, there is a side between the two vertices. Pin in two-pin net a pair of vertices (t) in graph_i,s_i) And (4) showing. The pins in a three-pin net are formed by a set of three vertices (t)_1,i,t_2,i,s_i) Is represented by the formula (I) in which t_1,iAnd t_2,iRepresenting two source pins, s_iRepresenting the target pin. Three-pin net (t)_1,i,t_2,iSi) are as follows. First, a two-pin net is generated (t)_1,i,t_2,i) A path. Then computing the net (t)_1,i,t_2,i) Each electrode in the path and node s_iThe Manhattan length (measured by the number of electrodes) between, the electrode with the smallest Manhattan length is selected as the mixing point m_i. Thus, a three-pin net (t)_1,i,t_2,i,s_i) The net is treated as 3 two-pin: (t)_1,i,m_i)，(t_2,i,m_i) And (m)_i,s_i). Routes, which are composed of a set of consecutive edges of the graph, represent paths of a net, wherein the end points of a route represent corresponding pins. Each edge in the route indicates: the electrodes whose two end points at the edge are represented are adjacent in the path of the drop.

Considering a set of disjoint paths, a certain drop path does not share any cell with any other drop path. Corresponding paths in the associated graph are mutually vertex disjoint in that a path does not share any vertex with other paths in the set. Similarly, in a set of disjoint paths, a drop path does not share any pair of adjacent cells with any other drop path, and their corresponding paths in the figure are mutually edge-disjoint in that the path does not share any edge with other paths in the set.

Because a three-pin net can be treated as a two-pin net, only a two-pin net is considered for disjoint paths. In a two-dimensional microfluidic arrayIn a column, given n two-pin nets, corresponding pins (t)₁,s₁)，(t₂,s₂),...,(t_n,s_n) The problem of finding feasible disjoint paths for these nets is equivalent to finding paths that are vertex disjoint or edge disjoint to each other in graph G, where the endpoints of each path in G represent the corresponding pins of each net. Given G ═ V, E and vertex pair (t)₁,s₁)，(t₂,s₂),...,(t_n,s_n) Determining whether there is a mutually vertex disjoint path P₁,P₂,...，P_nSo that P is_iHaving an end point t_iAnd s_iThis problem is NP-complete. Furthermore, even if graph G is a two-dimensional grid, the problem of determining whether there are mutually edge-disjoint paths is also an NP-complete problem. Therefore, a heuristic approach is used in the present invention. Furthermore, it is often difficult to find vertex-disjoint paths in the graph model to solve the drop path problem; such a path may not exist for a given set of nets. Therefore, in this case, it is more practical to relax the constraint and find edge disjoint paths. For edge-disjoint paths, only a single electrode at the intersection needs to be cleaned, rather than a large number of consecutive electrodes in multiple drop-sharing paths. Thus, by using edge-disjoint paths, the number of electrodes that need to be cleaned is reduced.

2) The routing method of the disjoint path comprises the following steps: next, a non-intersecting path based drop path algorithm is proposed that minimizes path length and avoids cross-contamination in sub-problems. The input to the algorithm is a list of nets to be transported in the sub-problem, and the output is a set of vertex disjoint (preferred) or edge disjoint (as a design tradeoff choice) paths that have the shortest length and obey constraints.

In one sub-problem, the Lee algorithm, a popular technique commonly used in grid routing, can be used to obtain a single drop path per net. In a two-pin net, the Lee algorithm ensures that the shortest path between two pins will be found. For a three-pin net, a two-pin net can be used to obtain a feasible path to connect the three pins. The interconnections obtained in this way at present cannot be guaranteed to be of minimum length. However, this is an ideal route in practice and may allow mixing to occur simultaneously during transport. However, the Lee algorithm does not avoid cross-contamination.

The active microfluidic cartridge in this sub-problem is considered to be a "barrier". A modified Lee algorithm is used to set a path for each net in the sub-problem in turn. After a path of a certain net is set, the unit occupied by the path is marked as an obstacle of the net without the path. Thus, the latter path is disjoint with respect to all previous paths. However, the path order in the sub-problem affects the path configurability of all nets. Even if each net can set a path individually, the path order may prevent the generation of paths for all nets.

Therefore, the Lee algorithm is modified to obtain the optimal order of paths for n nets in the subproblem: first, the bounding box of the net is set. Assuming that the two pins of a two-pin p-net are p1 and p2, the coordinates in the two-dimensional microfluidic array are (x1, y1) and (x2, y2), which correspond to the four vertex coordinates of the bounding box as (x1, y1), (x1, y2), (x2, y1) and (x2, y 2). The number of pins within the bounding box of a certain net p is then defined as pin (p). Xrange (p) ═ x1-x2| and yrange (p) ═ y1-y2 |. The priority of p-nets is given by the following equation, where A is a user-defined parameter. Here, the parameter a is set to 1:

priority(p)＝pin(p)+A·max{Xrange(p),Yrange(p)}

the larger the priority number, the lower the priority of the p-net in the routing order. The path setting process of the network is divided into two phases. In the first stage, a modified Lee algorithm is implemented for each net in the sequence of nets' paths. The cells occupied by the route are marked as obstacles for which no route is set. Each generated path should pass the Route Length Constraint Check (RLCC). In addition, each generated path also needs to pass a Fluid Constraint Rule Check (FCRC). The route length constraint rule specifies the upper limit value of the route length, so that the overlong route is avoided; fluid confinement rules specify the spacing between droplets during droplet delivery and must be followed to avoid inadvertent mixing. If the fluid constraint rules are violated, the delivery time of the drop is adjusted to counteract the consequences of the violation.

Shortest path-the path using the least number of cells is taken as the output item for each net. If all shortest paths of some net fail to satisfy the path length constraint, or violate the fluid constraint rules, and modification of drop movement fails, such net is put into a conflict list to eventually resolve it. Note that all paths obtained at this stage do not only intersect edges, nor do vertices.

In the second phase, for each net in the conflict list, a modified Lee algorithm is performed to generate several shortest paths without considering the previously generated paths as obstacles in the first phase. From these shortest paths, a path is selected as an output item that does not have a common pair of neighboring elements between it and the previously obtained path (i.e., for each net, several shortest paths are generated, and then a path that does not intersect the edges of the previous path is selected for output). Thus, the resulting path may intersect the previously obtained path, but the edges still do not. As before, this path also needs to be checked by way of a route length constraint check and a fluid constraint rule check.

In the second phase, disjoint routes for all nets have been obtained. When a feasible droplet path cannot be generated using the disjoint path approach, the path feasibility is improved using a layout modification approach to recomplex the bioassay. The feasibility of the drop path between two modules was quantified in terms of the number of electrodes in the drop delivery path (i.e., the drop path was quantified in terms of the number of electrodes in the drop delivery path). Re-synthesis of the bioassay using the layout modification method to obtain a synthesized result with higher path feasibility would lead to a modular layout that would generate feasible droplet paths.

The following describes the synchronization of the cleaning operation with the droplet path:

in an embodiment of the present application, the washing step is integrated into the droplet transport step of the target bioassay to mitigate cross-contamination problems. The path of the sample and reagent droplets is referred to as the functional droplet path. The path of the cleaning droplets is referred to as the cleaning droplet path. The cleaning droplets are manipulated to pass through all cross-contaminated sites and to remove residues left by the functional droplets. The path of the cleaning droplets is synchronized with the path of the functional droplets, thereby shortening the droplet transfer time. The order in which the droplets arrive at the sites, i.e., the cross-contamination points, can be adjusted according to table I.

TABLE I adjustment of drop arrival sequence

When there are multiple sites where different droplet paths cross each other, the problem of synchronizing the cleaning droplet path with the functional droplet path for one cross contamination point is first solved and the solution is extended to the synchronization of multiple cross contamination points.

A. One cross-contamination site case

The synchronization of the cleaning droplet path with the functional droplet path proposed for one cross-contamination site involves two steps. In a first step, a path is created for the wash droplet (or droplets) to traverse the cross-contamination site. The drop path is composed of two sub-paths. The first sub-path connects the reservoir (source point) for the cleaning droplets to the cross-contamination site (waste reservoir) and the second sub-path connects the cross-contamination site (source point) to the waste reservoir (sink). A modified Lee algorithm is employed to generate the two sub-paths.

For one cross-contamination site, the path of the wash droplet (or droplets) is synchronized with the paths of the two functional droplets. The droplets are simultaneously transported from the respective source points along the calculated droplet paths through the cross-contamination points to the waste reservoir. The time for the wash droplet to reach the cross-contamination position depends on the length of the first sub-path. Note that the arrival time of a droplet from the source to the waste reservoir is calculated based on the length of the corresponding path. If one droplet is transported across one electrode per clock cycle, the arrival time of the droplet is equal to the number of electrodes in the path. Between the arrival times of two functional droplets, the wash droplet (or droplets) will arrive at the site at the appropriate time.

Thus, in the second step, the arrival order of the cleaning drop and the functional drop is adjusted to ensure that the cleaning drop (or drops) arrives at the cross-contamination point later than one functional drop but earlier than another functional drop. Without loss of generality, a cleaning drop is considered here for a cross-contamination point. For one cross-contamination site S, droplet D₁Path and droplet D₂Where the paths intersect, D is first estimated based on the path lengths of the droplets connecting their source points and cross-contamination sites, respectively₁、D₂And the arrival order of the cleaning droplets W. The order of arrival of these three droplets was then adjusted according to table I. For example, if D₁、D₂And W continuously arrives at the cross-contamination site, D is delayed₂Such that W is at D₂Before reaching the contamination site. Thus, at D₂Before reaching, the liquid drop W will clean D₁Residue left at an earlier clock cycle. Note that the droplets with the arrival time delay are temporarily held (retained) in one chip memory cell. After adjustment of one cross-contamination point, droplet D was recorded₁、D₂And the update time of W crossing the site. Note that if a droplet must be stored in a chip memory unit, then the total transit time of the droplet is the sum of the droplet path time and the chip memory duration. Therefore, the droplet chip storage duration should be minimized.

Fig. 1 shows the synchronization of the cleaning droplet path with the functional droplet path for one cross-contamination point. Among them, fig. 1(a) shows a cleaning droplet path and a functional droplet path. Fig. 1(b) shows a snapshot of the 9 th clock cycle. Fig. 1(c) shows a snapshot of the 13 th clock cycle. Fig. 1(d) a snapshot of the 17 th clock cycle. As shown in FIG. 1(a), two kinds of functional liquid droplets D₁And D₂Intersects the cross-contamination site S. From washing liquid separation pondA cleaning droplet W is emitted to clean the residue on the spot S. The cleaning droplet path includes two sub-paths. The first sub-path connects the cleaning liquid separation tank with the S, and the second sub-path connects the S with the waste liquid tank. Assuming all drops start moving at the same time, estimate D₁、D₂And the order in which W reaches the cross-contamination point S. From FIG. 1(a), D was calculated based on the path length of the droplet connecting the source point and the intersection point₁At a fifth clock cycle, point S, D is reached₂And reaching the point S in the third clock cycle, and reaching the point S in the ninth clock cycle. Retardation of D according to Table 1₁Such that W is at D₁And reach the previous crossing site. At the ninth clock cycle, D, as shown in FIG. 1(b)₂Has reached the waste pool node, W reaches the cross-contamination site S for cleaning D₂The residue of (2). D1 is stored in a chip memory cell and is not transported along its drop path. D₁And W, to avoid accidental mixing. In FIG. 1(c), at clock cycle 13, W has left the cross-contamination point S and is transferred to the waste reservoir, and D₁To point S where it has been cleaned. In FIG. 1(D), at the 17 th clock cycle, 3 droplets D₁、D₂And W has reached the corresponding destination.

Thus, the maximum drop transport time is 17 clock cycles. When the functional liquid droplet D₁、D₂When passing through the cross-contamination point S-i.e. D₁At the 13 th clock cycle, pass through point S, D₂At clock cycle 3, pass S point-recording the delivery time information. If D is₁、D₂Other cross-contamination sites are also traversed in the same sub-problem, and the delivery time information is used when adjusting the order in which the wash and functional droplets reach these cross-contamination sites. Next, synchronization of the cleaning droplets and the functional droplets in which there are a plurality of cross-contamination points in one sub-problem is described.

B. Multiple cross contamination point conditions

In a sub-problem with one droplet transport path, there are typically multiple cross-contamination sites where the paths of pairs of functional droplets intersect. Each cross-contamination site requires a cleaning droplet (or even multiple cleaning droplets) to clean the residue from the contamination site. Here, it is assumed that one cleaning droplet is sufficient to clean one cross-contamination site. It is proposed to clean the droplet path and the functional droplet path simultaneously for each cross-contamination point. If these cross-contamination sites are independent, i.e., one droplet path does not cross multiple cross-contamination sites, then a simultaneous approach can simply be used for one cross-contamination site.

Next, consider the case where one drop path intersects multiple paths at different intersection contact sites. For example, as shown in FIG. 2(a), droplet D₁Respectively with the droplet D₂And D₃Is in position S_1,2And S_1,3And (4) intersecting. Droplet D₁Is conveyed along its path so as to pass first through S_1,2Then passes through S_1,3. Two cleaning droplets W_1,2And W_1,3Sending out from a cleaning liquid separation tank to respectively clean two cross-contamination sites S_1,2And S_1,3。

In order to synchronize the cleaning droplet path with the functional droplet path, first, W_1,2And W_1,3Generating droplet paths, respectively passing through S_1,2And S_1,3As shown in fig. 2 (b). Each cleaning drop path includes two sub-paths. The order of arrival of the wash droplet and the two functional droplets is then adjusted for each cross-contamination point. For S_1,2Sites based on the path of the slave droplets (the path connecting their origin with S)_1,2Site-linked) length derived W_1,2、D₁And D₂The estimated order of arrival of the first and second images, and their order of arrival adjusted. Similarly, adjust W_1,3、D₁And D₃For site S_1,3The order of arrival of.

Suppose that site S is adjusted first_1,3In the arrival order of (c), and then to site S_1,2And (6) adjusting. Thus, at the registration point S_1,3After adjustment, is S_1,2The site is adjusted. Because based on the distance from the source point of the droplet to S_1,2Estimation of the drop path length of (D)₂、D₁And W_1,2Reaches S continuously_1,2，D₁Must be temporarily stored for several clock cycles to ensure W_1,2First through S_1,2I.e. D₁By S_1,2The time of the site is delayed. However, due to D₁At a transport time of S_1,2Modified at site, W_1,3、D₁And D₃To S_1,3The order of the sites (the order has been based on their arrival from the source site to S_1,3The initial estimate of the drop path length of the locus is modified) will no longer be valid. Therefore, it must be in accordance with D₁Update delivery time (slave pair S)_1,2Adjustment obtained) to adjust S again_1,3The order of arrival at the site. A total of three adjustments were required, one for S_1,2Twice for S_1,3。

However, if S is adjusted first_1,2The order of arrival of the sites is S_1,3When the site is adjusted, D can be used₁The updated delivery time. Now only two adjustments are needed in total, one for S_1,2Site, another for S_1,3A site.

1) Calculation of the order of the regulatory sites: when cleaning the droplet path simultaneously with the functional droplet path for a plurality of cross-contamination sites, the arrival order must be adjusted for these sites in a predetermined order. The order of site adjustment was determined as follows: for a functional droplet that successively passes through multiple cross-contamination sites, the order of arrival of the cross-contamination sites is adjusted according to the order in which this functional droplet passes, where the order of the cross-contamination sites is the same as the order of the original list. Thus, for each functional droplet in a sub-problem, the cross-contamination points that it passes sequentially along the route from the source point to the waste reservoir are listed first. Next, these lists of different functional droplets are combined to form a site-adjusted sequence, such that the sequence of these cross-contaminated spots remains the same as that of their original list.

For a particular subproblem, given the order in which each functional droplet passes successively through a corresponding plurality of cross-contamination locations, a directed graph G ═ V, E is constructed: each vertex in the graph represents one cross-contamination point in a two-dimensional microfluidic array, with one directed edge from vertex m to n if a functional droplet passes consecutively through two cross-contamination points m and n. The problem of determining the order of the site adjustments can be directly translated into the problem of the topological order of graph G. The topological order of the directed graph G is the linear ordering of its vertices, with m preceding n for each directed edge e from vertex m to vertex n.

FIG. 3 shows an example of determining the order of site adjustment. In one particular sub-problem, D is shown in FIG. 3(a)₁-D₄Four functional droplet paths from S₁-S₅The five cross-contamination points of (a) intersect. D₁Continuously passes through S₁、S₃And S₂；D₂Continuously passes through S₁And S₄；D₃Continuously passes through S₅、S₄And S₂；D₄Continuously passes through S₃And S₅. The topological order of the corresponding directed graph G is shown in fig. 3 (b). For example, due to D₁Successively pass through S₁、S₃And S₂In graph G, then there is a slave S₁Point to S₃Another directed edge is a slave S₃Point to S₂。

And generating a sequencing result based on the directed graph G by adopting a topological sequencing algorithm, wherein the sequencing result is also a position adjusting sequence. The site adjustment sequence for the case of fig. 3 is as follows: s₁→S₃→S₅→S₄→S₂Wherein the order of these cross-contamination sites is the same as in its original list. The arrival order was adjusted for five sites in this site adjustment order.

For a directed graph G ═ V, E, the worst-case computational complexity of the topological ordering algorithm is O (| V | + | E |). Note that topological ordering exists if and only if graph G is a directed acyclic graph. If there is a directed loop in G. Such as droplets D₁Successively pass through S₁And S₂And the droplet D₂Successively pass through S₂And S₁There is no site adjustment sequence. In this case, the cleaning droplet path cannot be the same as the functional droplet pathCleaning operations must be inserted between successive functional droplet paths to clean the residue.

2) And (3) a synchronization program: the order of arrival was adjusted for all cross-contamination sites following the site adjustment sequence obtained in 1) above. After a site was adjusted, the updated delivery time for each functional droplet as it passed through the site was recorded. In this way, when the arrival order is adjusted for the next site, the latest delivery time information of the adjustment site in the previous site adjustment order can be used.

For an mxn microfluidic array, the worst case computational complexity of the path algorithm (for generating functional droplet or cleaning droplet paths) is o (mn). The worst-case time complexity of the synchronization of the cleaning step with the functional drop path step for k cross-contamination sites in a sub-problem is o (kmn).

The unified planning of disjoint paths and cleaning operations is described below:

in the embodiment of the application, a droplet path algorithm for avoiding cross contamination in a single subproblem is provided by synchronously unifying the non-intersected droplet paths and the cleaning operation.

In the first stage (disjoint path), vertex disjoint (preferred) or edge disjoint (as a design tradeoff) drop paths are obtained using a disjoint drop path approach. After the first stage, if there are edge-disjoint paths, these drop paths intersect at a cross-contamination site. In the second phase (cleaning operation synchronization), the cleaning operation is synchronized with the functional droplet path to avoid cross-contamination at these sites. If all the drop paths after the first stage are vertex disjoint, then no cleaning step is required in the second stage.

The output items of the above procedure are the drop route and the transit time of each functional drop through the cross-contamination point. The maximum droplet delivery time with the cleaning step in this sub-problem is also obtained. Since the number of cross-contamination sites is greatly reduced due to the non-intersecting paths in the first stage and fewer cleaning droplets are required in the second stage, the maximum droplet transport time for all webs with cleaning steps is also reduced.

For an mxn microfluidic array, the worst-case computational complexity of the Lee algorithm-based path algorithm for the first stage is o (mn). For the second stage, the worst case time complexity of the cleaning operation synchronization method for k cross-contamination sites is o (kmn). Therefore, the worst case time complexity of this procedure (synchronizing disjoint paths and flush operations is o (kmn)).

Among them, the scheme to avoid cross contamination in the continuous path sub-problem is as follows:

cross-contamination may occur within one sub-problem, or between two sub-problems, e.g., the drop path in one current sub-problem may share the same elements as the drop path in the previous sub-problem. To avoid cross-contamination between the two sub-problems, the drop path in the current sub-problem should avoid sharing the same unit as the path in the previous sub-problem. Thus, when generating a path in the current sub-problem, all paths in the previous sub-problem, along with the active modules, are considered "obstacles".

However, to bypass these additional obstacles, some paths will be lengthened and may not pass the route length constraint check. Furthermore, due to these additional obstacles, a network that was originally easy to route may become unable to find an available route. Therefore, it is necessary to introduce a cleaning operation step after the droplet transport process in one sub-problem. In a cleaning operation, cleaning droplets are routed through selected cells and clean the residue on the cells. The delay caused by the cleaning operation corresponds to the time required to set the cleaning droplet path. For a sub-problem involving a subsequent cleaning operation, the drop path is not considered an obstacle to the next sub-problem.

Suppose a bioassay can be broken down into K droplet path sub-problems. In the sub-problem (i-1), if the cleaning operation is not performed after the droplet path is set, Route _ T is employed_i(1. ltoreq. i.ltoreq.K) represents the drop path in the sub-problem i. Parameter T_iDefined as Route _ T_iThe maximum transmission time required in (1). Between the sonIn the problem (i-1), if the cleaning operation is performed after the droplet path is set, Route _ T is used_i ^*(1. ltoreq. i.ltoreq.K) represents the drop path in the sub-problem i. Parameter T_i ^*Defined as Route _ T_i ^*The maximum transmission time required in (1). In the sub-problem (i-1), the time required for the cleaning operation after the droplet path is set is Tw_i(i is more than or equal to 1 and less than or equal to K). Thus, (T)_i ^*+Tw_i) Represents the total time of droplet delivery, including the time required for the cleaning step after sub-problem (i-1) and the time to set the path for sub-problem i. If a scrubbing operation is performed between sub-problem (i-1) and sub-problem i, the path setup time for sub-problem i is increased, but this also frees up more cells for path setup and reduces the path setup time in sub-problem i. As a result, in most cases, T_i ^*<T_i. Whether a washing step is added between two subproblems therefore depends on the path setup time for each subproblem and the nature of these paths.

An optimization model is described next for determining when to perform a cleaning operation between successive path steps. x is the number of_iIs a binary variable defined as follows:

the solution provided by the embodiments of the present application is to minimize the total time required for setting the droplet path in the subproblem. Thus, the objective function of the optimization problem can be expressed as follows:

in determining the number, the number of cleaning operations is less than the number of sub-problems. Thus, the optimization problem will now be set forth as follows. Minimum F to comply

F represents the time required for setting the drop path, MinimizeF representsMinimizing the time. To solve this problem, it is only noted that to minimize F, if T_i ^*+Tw_i<T_iThen xi needs to be set to 1, otherwise xi is 0. If the above formula were not used, but merely a cleaning operation was interposed between all of the successive path sub-problems, then the total drop path time would be longer, which is also unacceptable.

In the present embodiment, the droplet path problem including three sub-problems (K — 3) lists all possibilities of the droplet path, as shown in fig. 4. For the first sub-problem, since there is no former sub-problem, Route _ T₁And Route _ T₁ ^*Are the same.

For the second sub-problem, if Route _ T₁(and Route _ T₁ ^*Same) without intervening cleaning operations, they would be an obstacle in the second sub-problem. In the second sub-problem (the sub-problem assumes Route _ T)₁(Route_T₁ ^*) Becomes an obstacle) is represented as Route _ T₂. Route _ T if a flush operation is inserted after the first sub-problem₁(Route_T₁ ^*) Will not be an obstacle to the second sub-problem in which the corresponding drop path is taken as Route _ T₂ ^*And (4) showing. Note that Route _ T₂And Route _ T₂ ^*Is different.

For the third sub-problem, if Route _ T₂Then no cleaning operation is inserted, then the third sub-problem (this sub-problem assumes Route _ T)₂Is an obstacle) is then denoted as Route _ T₃A; if Route _ T₂ ^*Then no cleaning operation is inserted, then the third sub-problem (this sub-problem assumes Route _ T)₂ ^*Is an obstacle) is then denoted as Route _ T₃B. If the second sub-problem (Route _ T)₂Or Route _ T₂ ^*) Then there is a cleaning operation, Route _ T₂And Route _ T₂ ^*Will not be an obstacle in the third sub-problem in which the corresponding drop paths are generatedThe path is denoted as Route _ T₃ ^*。

However, there is no guarantee that there is always a viable Route _ T_i. FIG. 5(a) shows the drop path Route _ T in the first sub-problem₁(Route_T₁ ^*). Route _ T if no clean operation is performed after the first sub-problem₁(Route_T₁ ^*) Will be considered as an obstacle in the second sub-problem. Route _ T as shown in FIG. 5(b)₂There is no solution because all possible paths from the source point to the waste reservoir are intercepted by the obstacle. Due to Route _ T₂Without a solution, it is not possible to continue determining Route _ T₃Whether or not there is a solution. Therefore, it is impossible to get from Route _ T₃A obtains T₃。

Due to Route _ T₂ ^*The inclusion of the droplet path during the cleaning operation after the droplet path setting in sub-problem 1 ensures that there is always a Route _ T₂ ^*As shown in fig. 5 (c). So that it can continuously identify whether there is Route _ T₃Solution of _b. Therefore, T3 can only be driven from Route _ T₃B get, not Route _ T₃A. Note that Route _ T₂ ^*Only with respect to the source and waste reservoir, and also with respect to the module layout in sub-problem 2. It has no relation to any drop path in sub-problem 1.

In summary, for sub-problem i, when Route _ T_i-1 ^*When no cleaning operation is performed later, the cleaning operation can be directly performed from a specific Route _ T_iObtaining T_i(e.g., Route _ T in FIG. 4)₃B). This is because, as previously described, it is guaranteed that there is always a Route _ T present_i-1 ^*A feasible solution of (1). Note that Route _ T_i-1 ^*Only the source and waste reservoir, and also the module layout in sub-problem (i-1). Thus, T can be seen_iDepending only on the sub-problem (i-1). Thus, the problem formulation is generic, not requiring a separate precedence relationship for all transmissions (i.e., indirection).

Similarly, Route _ T_i ^*With source and waste reservoir only, and also in sub-problem iThe module layout is relevant. Thus, T_i ^*Only problem i is relevant. Furthermore, because of Tw_iIs the time required for the cleaning operation after the setting of the droplet path in the sub-problem (i-1), and it is only related to the sub-problem (i-1). In summary, for the sub-problem i, three constants T_i、T_i ^*And Tw_iDepends only on the sub-problems i and (i-1). The ILP model only examines successive path sub-problems, so its complexity is linear in the number of sub-problems.

Note that since the duration of a fluid operation is longer than the duration of a path sub-problem, multiple path sub-problems may be scheduled during the performance of a single fluid operation. Therefore, the fluid-operated cleaning step should be considered separately from the cleaning step of the path sub-problem. For each fluid operation, such as mixing, separating, and diluting, one or more wash droplets may be associated with the fluid operation before and after it occurs, and the wash droplets may clean corresponding fluidic modules in the array. On the one hand, the units used for fluid handling will be cleaned prior to fluid handling, thereby avoiding cross-contamination with the former path sub-problems. On the other hand, those units will be cleaned after fluid operation without becoming a contamination point for subsequent path sub-problems. Furthermore, because the droplet path takes less time than the fluid operation, the duration of cleaning the droplet path may be omitted compared to the duration of the fluid operation. However, since the cleaning drop path duration is comparable to the functional drop path, in the present embodiment, the cleaning step is considered to be interposed for the path sub-problem, thereby minimizing the total path time.

For a bioassay containing K path sub-problems, in the worst case, the number of variables in the ILP model is o (K) and the number of contaminations is o (K).

The following is detailed in connection with two specific examples: two bioassay methods were used, including multiplexed in vitro diagnostics and protein assays on human body fluids, which could be run on a 3.0GHz INTEL Xeon processor (with 12GB memory).

A. Reference method

Two reference methods are considered to avoid cross-contamination occurring within one sub-problem.

1) Reference method 1: a cleaning operation is inserted. The present referenced method inserts a cleaning operation between successive functional droplet path steps in one sub-problem for cleaning residues on the cross-contamination site. It is to be noted that in this case the washing droplets and the functional droplets are not transported simultaneously. The maximum droplet transport time for all webs is the sum of the droplet transport times for the cleaning operation step and the functional droplet path step.

2) Reference method 2: additional wash droplets. The reference method is to add one cleaning droplet to each functional droplet. For example, in FIG. 2(a), two cleaning droplets W are not used_1,2And W_1,3To clean two cross-contamination sites S_1,2And S_1,3Instead, a cleaning droplet is added to each functional droplet D₁、D₂And D₃The above. During the setting of the functional droplet path, the cleaning droplets follow the corresponding functional droplet path and clean the residue.

For this reference method, the maximum drop transport time for all webs can be divided into three sections. The first part is the droplet transport time taken to transport all the wash droplets from the wash droplet reservoir to their respective functional droplet source points. The second part is the droplet transit time to move all cleaning droplets from the source node to the waste reservoir node following their respective functional droplets. The third part is the transit time to move all wash droplets to the waste reservoir.

B. Case 1: multiple in vitro diagnostics

The proposed method for bioassays, i.e. the multiplex in vitro diagnosis of human body fluids, was evaluated here. Three human body fluids were sampled: i.e., urine, serum, and plasma, which are distributed to digital microfluidic biochips. Glucose and lactate determinations were performed for each body fluid.

This measured droplet transport problem is broken down into 11 sub-problems. By attempting to determine a set of vertex-or edge-disjoint drop paths and using those paths under all constraintsTo minimize the number of cells to solve these sub-problems in turn. For path length constraints, the length of each drop path should not exceed 20 electrodes. Thus, the path length constraint T_dEqual to 20 electrodes.

Sub-problem 3 is used herein to illustrate the previously proposed disjoint path and flush operation synchronization method. As shown in fig. 6(a), there are three two-pin nets and two three-pin nets. Route 1 is defined as Dt₁And M₁Route between (i.e., Net1), Route 2 is defined as DI₂And DI₃Route between (i.e., Net 2), Route 3 is defined as DI₂And between the waste reservoirs (i.e., Net 3). Route 4 is defined as a three-pin net (with pin R)₁、DI₁And M₄) Is defined as a three-pin Net (with pin R), and Route5 is defined as a three-pin Net (with pin R)₂、DI₁And M₃) Is routed (i.e., Net 5). Module M being active during this time interval₂Is considered to be an obstacle to the path. WR and WS are the separation reservoirs for the buffer droplets, wash droplets and waste droplets, respectively.

First, a non-intersecting path method is used to obtain the path of the droplet. A decision order is set for the paths of these nets. Net1 has a priority of 5 because the number of pins in its bounding box is 0, and the maximum value of Xrange and Yrange is 5. Net 4 has a priority of 13 because the number of pins in its bounding box is 2, and the maximum value of Xrange and Yrange is 11. The priorities of Net 2, Net 3, and Net 5 are 16, 3, and 15, respectively. Thus, the order of the Net paths is Net 3, Net1, Net 4, Net 2 and Net 5.

In generating the Net 2 path, DI is done because the routes of the previous paths Net 3, Net1 and Net 4 have been marked as obstacles₂And DI₃(i.e., Route 2) the shortest path between them violates the path length constraint, i.e., L (Route 2) ═ 35 (cell)>T_d20 (unit), as in fig. 6 (b). Therefore, Net 2 is put into the conflict list, and then a path is generated for Net 5. After Net 5 is generated, a path is generated for Net 2, without considering a previously generated path as an obstacle. Selecting a path without a common adjacency to a previous path routeAnd (5) unit pairs. The path of Net 2 satisfies the path length constraint, i.e., L (Route 2) ═ 18 (cell)<T_d20 (unit), as in fig. 6 (c). The paths of Net 2 intersect Net 4 and Net 5, respectively (S)_2,4And S_2,5). Thus, an ideal edge-disjoint drop path of 63 cells total is ultimately obtained. All paths both satisfy path length and obey fluid constraints, as shown in fig. 6 (c). In summary, the four paths (Net, Net 3, Net 4, and Net 5) are vertex-disjoint from each other, and one path (Net 2) is edge-disjoint from the other paths. Note that for droplets D in Route 4 and Route5, respectively₁And D₃If they start moving towards the destination at the same time, the fluid constraints may be violated. However, according to the modification rule, D is forced₁Stay at the current position until D₂Is transported to and mixed with the mixing site while continuing to deliver D₃Move to its destination, covering conflicts with constraints. Since the delay of Route 4 is according to D₂Is determined so that there is no additional delay in the process. The drop delivery time for all paths in the sub-problem is 19 clock cycles.

Figure 6 shows the disjoint path and the non-cross-contamination path of sub-problem 3. (a) Three two-pin nets and two three-pin nets. (b) Route 2 violates the path length constraint. (c) Feasible paths for all nets using disjoint paths approach. (d) And (4) obtaining a path result by adopting a path method without cross contamination.

The obtained disjoint path results (obtained by applying the method proposed for sub-problem 3) are compared with the method of improving the Lee algorithm to solve a sub-problem without considering cross-contamination between different paths. As in fig. 6(d), this method uses four path intersections, two more intersections than are obtained by the proposed method. The number of cross-contamination sites can be used to evaluate the likelihood of cross-contamination of a set of paths in a sub-problem.

As a second stage of the aforementioned method, a cleaning operation synchronization method is used to completely avoid cross-contamination during the droplet path setup. For the path result in FIG. 6(c), there are two cross-contamination bitsPoint S_2,4And S_2,5. Note that S_i,jIs a cross-contamination site for Route i and Route j. The order of site adjustment is determined next. FIG. 6(c) shows that the droplet on Route 2 passes through S continuously_2,5And S_2,4(ii) a Thus, the site adjustment sequence S was obtained_2,5→S_2,4。

The sequence is adjusted for the two sites to synchronize the cleaning step with the functional droplet delivery. For the first site in the site adjustment sequence S_2,5First, W is generated_2,5Cleaning the droplet path of (a) to clean the cross-contamination site S_2,5. Then adjust S according to Table I_2,5At site W_2,5The order of arrival of droplets of Route 2 and Route 5. Adjustment of S_2,5Thereafter, when droplets of Route 2 and Route5 pass through S_2,5At the site, the updated delivery time is recorded. In this way, when the next site S is adjusted_2,4When the arrival sequence of (1) is S_2,5Updating the delivery time information. Thus, the maximum drop delivery time is 22 clock cycles for all nets with a washing step. The results of the comparisons between the multiplex bioassay methods are shown in table II and described in detail below.

TABLE II comparison between multiplex bioassay methods (contamination inside the subproblems)

For the path results obtained in fig. 6(c) using the proposed method, the cleaning operation insertion method (reference method 1) is also used to completely avoid the occurrence of cross-contamination. First, droplets are transported along Route 1 and 3-5 simultaneously. Two wash droplets are then separately emitted from the wash vessel and transported to the waste reservoir through two cross-contamination sites. After the washing operation is complete, the droplets are transported along Route 2, through two sites that have been cleaned. The maximum drop delivery time with the cleaning step was 59 clock cycles. For the path results obtained using the modified Lee algorithm in fig. 6(d), the maximum drop delivery time (97 clock cycles) for the intervening cleaning step is much greater than the time results for the edge disjoint paths. This is because two cleaning operations must be inserted in sequence during the droplet path setup to clean the residue from the four cross-contamination sites.

In Table II, for each sub-question, the number of cross-contamination sites (N)_cs) Consumption of cleaning droplets (N)_wash) And maximum drop delivery time (such as T) for nets with or without a cleaning step in the subproblem_rwAnd T_r) A comparison was made. "DR" stands for the previously proposed disjoint drop path method and "NDR" stands for the modified Lee algorithm-it solves the sub-problem without considering cross-contamination between different paths. "WS" represents a previously proposed cleaning operation synchronization method, "WI" represents a cleaning operation insertion method (refer to method 1), and "AW" represents a method of appending a cleaning droplet after a functional droplet (refer to method 2). Thus, "DR + WS" represents the previously proposed method, which unifies disjoint path methods and cleaning operation synchronization.

N_cs0 means that the obtained routes are vertex disjoint. In sub-problems 4, 8, 9, 10 and 11, all methods obtained the same N_cs、N_wash、T_rAnd T_rwValues, since "DR" and "NDR" generated the same route without generating any cross-contamination sites. Therefore, no cleaning operation is required.

Here, the results of sub-problem 3 in table II are analyzed. For the "DR + WS" approach, since "DR" compares N with "NDR_csReducing from 4 to 2, the cleaning drop path is synchronized with the functional drop path, so there is a cleaning step (T)_rw) Is slightly longer than without the cleaning step (T)_r) Maximum drop transport time. For the "NDR + WS" method, T is present despite the presence of four cross-contamination sites_rwIs only slightly higher than the value of "DR + WS" because the cleaning operation is synchronized with the functional drop path step. For "DR + WI", T is the time required to perform a cleaning operation since it is interposed between successive functional droplet path setting steps_rwIs far greater in valueIn the values of the "DR + WS" and "NDR + WS" methods, there are more cross-contamination sites and more washing steps are required to be inserted. The "NDR + AW" method consumes more cleaning droplets than other methods because each functional droplet must be added to a cleaning droplet. Although T of "NDR + AW_rwThe value is lower than "NDR + WI" but still higher than "NDR + WS". The results show that the proposed "DR + WS" approach significantly reduces the total path time by generating disjoint paths and synchronizing the cleaning operations.

In fig. 7, the effect of cross-contamination on the drop path across two sub-problems is illustrated. Figure 7(a) shows the disjoint path solution of sub-problem 7. If no cleaning operation is performed between sub-problems 7 and 8, then all of the net paths in sub-problem 7 are considered obstacles in sub-problem 8, as in FIG. 7 (b). The disjoint paths of sub-problem 8 are also shown in FIG. 7 (b). Maximum transfer time T₈Is 76 clock cycles. If a cleaning operation is performed, the path of sub-problem 8 does not treat the path of sub-problem 7 as an obstacle, as in FIG. 7 (c). Maximum transfer time T₈ ^*Now reduced to 13 clock cycles. Furthermore, because the path of sub-problem 2 requires a large number of cells, if no purging operation is performed between sub-problem 2 and sub-problem 3, then a viable path for sub-problem 3 cannot be obtained. For this case, embodiments of the present invention will compare T_iSet to a larger value, i.e., 10000.

Table III lists T for each sub-problem with the "DR + WI" and "NDR + WI" methods_i、T_i ^*And Tw_iI is more than or equal to 1 and less than or equal to 11. For the "DR + WI" method, the optimization model proposed earlier is adopted, and the shortest path time for all sub-problems is obtained as 348 clock cycles. For this optimized path plan, a cleaning operation must be performed before sub-problems 2, 3, and 6-8. For the "NDR + WI" method, the minimum path time for all sub-problems is very long, 511 clock cycles, and the cleaning operation must be completed before sub-problems 2, 3, 4, 6, and 8. The CPU time of the multivariate in-vitro diagnosis ILP model is 0.5 s.

TABLE III parameters of the optimization model (expressed in clock cycles)

C. Case 2: protein assay

The proposed method was then evaluated for a protein assay (which has been performed on a digital microfluidic assay chip) whose path problem was broken down into 127 subproblems.

In sub-problem 82, there are five 2-pin nets for delivering functional droplets, Dlt₂₃Simultaneously, as shown in fig. 8 (a). As shown in FIG. 8(b), droplet D₁Along the path from DsB₂₈Is transmitted to the chip memory unit S₄(ii) a Droplet D₂From Dlt₂₉Is transferred to the chip storage unit S₂(ii) a Droplet D₃From Dlt₂₀Is transferred to a chip storage unit S₁(ii) a Droplet D₄From DsB₃₀Is transmitted to the chip memory unit S₃(ii) a Droplet D₅From DsB₂₄Is transmitted to Dlt₂₄The diluter of (1). Four functional droplets D₂-D₅The paths of (a) intersect at four cross-contamination points. S_i,jIs D_iAnd D_jCross-contamination sites of the pathway.

In fig. 8, (a) represents a five 2-pin net. (b) Showing a functional drop path. (c) Indicates cross contamination point S_4,5Cleaning the droplet path.

In the second stage of the method mentioned above, the cleaning operation is synchronized with the functional droplet path setting step. The order of site adjustment is determined. FIG. 8(b) shows D₃Continuously passes through S_3,5，S_2,3And S_3,4；D₄Continuously passes through S_4,5And S_3,4；D₅Continuously passes through S_4,5And S_3,5. Using topological sorting methodThese lists are combined with the order of site adjustment: s_4,5→S_3,5→S_2,3→S_3,4Wherein the order of these cross-contamination sites is maintained consistent with their original list order.

TABLE IV comparison between protein assay methods (contamination within sub-problems)

According to the position adjusting sequence, the cleaning step and the functional liquid drop transmission of the four positions are synchronized. For the first site in the site adjustment sequence S_4,5First, a cleaning liquid droplet W is generated_4,5To clean the cross-contamination site S_4,5As shown in fig. 8 (c). W_4,5Includes two sub-paths. The first sub-path will clean the containers (WR) and S_4,5Connected, the second sub-path will S_4,5Connected with a waste liquid pool (WS). Then adjust W_4,5，D₄And D₅Arrival site S_4,5The order of (a). According to tables I, D₅Is at W_4,5Through site S_4,5And then arrive. Thus, D₄，W_4,5And D₅Successively crossing S at 1, 5 and 9 clock cycles respectively_4,5Site, thereby ensuring W_4,5Can be at D₅Before arrival D₄At S_4,5The residue left behind at the site was cleaned up. Is at S_4,5After adjusting the arrival sequence of loci, record D₄And D₅Through S_4,5Updated delivery time of the site. In this way, for the next site S_3,5When the arrival sequence is adjusted, S can be used_4,5The latest delivery time information for the site. Repeating the above steps to adjust other sites in the sequence, i.e. S_3,5，S_2,3And S_3,4And (6) adjusting.

Table IV shows one subproblem fragment in the protein analysis, since the results of all 127 path subproblems could not be shown. For each sub-problem, N of the different methods are compared_cs，N_wash，T_rAnd T_rw. T is obtained by the method of' DR + WS_rwBecause it is directed to non-intersecting paths to reduce the number of cross-contamination sites, the cleaning operation is synchronized with the functional droplet path setup step, thereby minimizing overall path time. The "DR + WI" method is also directed to disjoint paths, so it gets the same N as the "DR + WS" method_csAnd N_washThe value is obtained. However, because it intervenes in a sub-problem, between successive functional droplet paths, a cleaning operation, T_rwThe value of (A) is much higher than that obtained by the "DR + WS" method. The "NDR + WS", "NDR + WI" and "NDR + AW" methods do not take into account the need to avoid cross-contamination that occurs when generating drop paths; thus, N_csAnd N_wasThe value of h is much higher than the values obtained by the "DR + WS" and "DR + WI" methods. In particular, the "NDR + AW" method T is based on the insertion of a cleaning step between successive functional droplet path steps_rwThe values are much higher than those obtained by other methods. The "NDR + AW" method consumes more cleaning droplets because one additional cleaning droplet must be added for each functional droplet. Although this method results in a maximum droplet transport time shorter than "NDR + WI", it is still higher than the "NDR + WS" method.

Based on the foregoing principles, the present application provides, in an embodiment, a method embodiment of a droplet transport method, it should be noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system, such as a set of computer-executable instructions, and that although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than that illustrated or described herein.

Fig. 9 is a schematic flow chart of a droplet transport method according to an embodiment of the present application, as shown in fig. 9, the method includes the following steps:

step S902, obtaining an undirected graph of the two-dimensional microfluidic array, wherein the undirected graph comprises a vertex set and an edge set, each vertex in the vertex set represents an electrode in the microfluidic array, and an edge between two adjacent vertices represents a path between the two adjacent vertices;

for example: consider a planar undirected graph G ═ V, E, where V is the set of vertices and E is the set of edges. Each vertex in the figure represents an electrode in a two-dimensional microfluidic array, and if the electrodes corresponding to two vertices are adjacent, there is a side between the two vertices. The pins in a two-pin net are represented by a pair of vertices (ti, si) in the graph. The pins in the three-pin net are represented by a set of three vertices (t1, i, t2, i, si), where t1, i and t2, i represent two source pins and si represents a target pin. The three-pin net (t1, i, t2, i, si) is processed as follows. First, a two-pin net (t1, i, t2, i) path is generated. The Manhattan length (measured by the number of electrodes) between each electrode in the path of the mesh (t1, i, t2, i) and the node si is then calculated, and the electrode with the smallest Manhattan length is selected as the mixing point mi. Thus, a three-pin net (t1, i, t2, i, si) will be treated as a 3-two-pin net: (t1, i, mi), (t2, i, mi) and (mi, si). Routes, which are composed of a set of consecutive edges of the graph, represent paths of a net, wherein the end points of a route represent corresponding pins. Each edge in the route indicates: the electrodes whose two end points at the edge are represented are adjacent in the path of the drop.

Step S904, determining mutually disjoint vertexes or mutually disjoint edges in the undirected graph;

step S906, determining liquid drop transportation paths based on mutually disjoint peaks or mutually disjoint edges, wherein the liquid drop transportation paths in a group of liquid drop transportation paths are mutually disjoint;

specifically, the method comprises the following steps: considering a set of disjoint paths, a certain drop path does not share any cell with any other drop path. Corresponding paths in the associated graph are mutually vertex disjoint in that a path does not share any vertex with other paths in the set. Similarly, in a set of disjoint paths, a drop path does not share any pair of adjacent cells with any other drop path, and their corresponding paths in the figure are mutually edge-disjoint in that the path does not share any edge with other paths in the set.

Because a three-pin net can be treated as a two-pin net, only two-lead routing considerations are taken into account for disjoint routingA net of feet. In a two-dimensional microfluidic array, given n two-pin nets, corresponding pins (t)₁,s₁)，(t₂,s₂),...,(t_n,s_n) The problem of finding feasible disjoint paths for these nets is equivalent to finding paths that are vertex disjoint or edge disjoint to each other in graph G, where the endpoints of each route in G represent the corresponding pins of each net. Given G ═ V, E and vertex pair (t)₁,s₁)，(t₂,s₂),...,(t_n,s_n) Determining whether there is a mutually vertex disjoint path P₁,P₂,...，P_nSo that P is_iHaving an end point t_iAnd s_iThis problem is NP-complete. Furthermore, even if graph G is a two-dimensional grid, the problem of determining whether there are mutually edge-disjoint paths is also an NP-complete problem. Therefore, a heuristic approach is used in the present invention. Furthermore, it is often difficult to find vertex-disjoint paths in the graph model to solve the drop path problem; such a path may not exist for a given set of nets. Therefore, in this case, it is more practical to relax the constraint and find edge disjoint paths. For edge-disjoint paths, only a single electrode at the intersection needs to be cleaned, rather than a large number of consecutive electrodes in multiple drop-sharing paths. Thus, by using edge-disjoint paths, the number of electrodes that need to be cleaned is reduced.

Step S908, the droplet is transported according to the droplet transport path.

The two-dimensional microfluidic array comprises a three-pin network, wherein pins in the three-pin network correspond to three vertexes in an undirected graph, and the three vertexes comprise: the device comprises vertexes corresponding to two source point pins and vertexes corresponding to one target pin, wherein the pins are fluid ports of the microfluidic array on the boundary of an undirected graph, and a three-pin network is a liquid drop path between any two pins of three pins on different modules or chips.

In order to ensure the shortest path, i.e. the shortest path transit time, in determining the droplet transit path based on mutually disjoint vertices or mutually disjoint edges, the following processing steps may also be performed: determining a target droplet transport path between two source point pins based on an undirected graph, wherein the two source point pins correspond to a pair of vertices in the undirected graph; determining a distance between each electrode in the target droplet transport path and the target pin to obtain a plurality of distances; determining a minimum distance from a plurality of distances; determining a mixing point based on a vertex where the electrode corresponding to the minimum distance is located, wherein the mixing point is an intersection point of a first path, a second path and a third path, the first path is a first path between the vertex where the electrode corresponding to the minimum distance is located and one source point pin of the two source point pins, and the second path is a path between the vertex where the electrode corresponding to the minimum distance is located and the other source point pin of the two source point pins; the third path is a third path between a vertex where the electrode corresponding to the minimum distance is located and a vertex corresponding to the target pin.

Step S906 may be implemented by, but is not limited to: the method comprises the steps of determining a liquid drop transportation path based on mutually-disjoint vertexes or mutually-disjoint edges, selecting a path with the shortest length from an available path list based on the mutually-disjoint vertexes or mutually-disjoint edges, and taking the path with the shortest length as the liquid drop transportation path, wherein the available path list is a path from a vertex corresponding to a source point pin to a vertex corresponding to a target pin.

Wherein the length of each available path in the available path list is determined based on the number of electrodes in each available path.

After the drop transport path is determined based on mutually disjoint vertices or mutually disjoint edges, the cells in the drop transport path are marked as obstacles to other paths in order to facilitate planning of subsequent paths.

In step S906, when determining the droplet transportation path, the following processing steps may be further performed: determining a priority of all nets in the two-dimensional microfluidic array, wherein each net in the all nets represents a path between any two pins on different modules or chips; selecting the path with the shortest length from the available paths of each net as a sub-path of the droplet transportation path; a droplet transport path is determined based on the plurality of sub-paths.

In some embodiments of the present application, in step S908, a route length constraint check and a fluid constraint rule check are performed on the plurality of sub-paths; determining sub-paths of the plurality of sub-paths which pass through the route length constraint check and the fluid constraint rule check to obtain an available sub-path set; determining sub-paths which do not pass the route length constraint check and the fluid constraint rule check in the plurality of sub-paths to obtain a conflict sub-path set; determining the shortest path of each network in the conflict sub-path set to obtain a plurality of shortest paths; selecting a sub-path which does not have a common adjacent unit with the paths in the available path set from the shortest paths to obtain a target sub-path; a drop transport path is generated based on the set of available sub-paths and the target sub-path.

When the droplets are transported according to the determined droplet transportation path, in order to avoid cross contamination of the transportation path, the path may also be cleaned by a corresponding cleaning strategy, specifically:

determining a cross-contamination point in the droplet transport path, wherein the cross-contamination point is a plurality of droplet path cross-points; determining the sequence of the cleaning liquid drops passing through the cross-contamination point and the sequence of at least two functional liquid drops reaching the cross-contamination point, wherein the sequence of the cleaning liquid drops reaching the cross-contamination point is later than the sequence of one functional liquid drop in the at least two functional liquid drops and earlier than the sequence of the other functional liquid drops in the at least two functional liquid drops reaching the cross-contamination point; and conveying the cleaning liquid drops and the at least two functional liquid drops according to the sequence of the cleaning liquid drops and the at least two functional liquid drops reaching the cross-contamination point.

Wherein, in determining the order of the cleaning droplets and the at least two functional droplets that pass through the cross-contamination point to reach the cross-contamination point, the following process may be performed: determining a first time for the wash droplet to reach the cross-contamination point and a second time for at least two functional droplets to reach the cross-contamination point, wherein the at least two functional droplets are sequentially consecutive functional droplets; adjusting the sequence of the cleaning liquid drops and the at least two functional liquid drops to reach the cross-contamination point according to a preset rule based on the first time and the second time, wherein the preset rule comprises: the time of the cleaning liquid drop reaching the cross-contamination point is between the time of any two sequentially continuous functional liquid drops in the at least two functional liquid drops reaching the cross-contamination point.

In some embodiments of the present application, the order in which the wash droplets and the at least two functional droplets reach the cross-contamination point may be adjusted according to a preset rule based on the first time and the second time by: and when any two continuous functional liquid drops are earlier than the time of the cleaning liquid drops reaching the cross-contamination point, delaying the arrival time of the functional liquid drop which arrives at the cross-contamination point later in any two continuous functional liquid drops, wherein the time of the delayed first functional liquid drop arriving at the cross-contamination point is later than the time of the cleaning liquid drop arriving at the cross-contamination point.

In some embodiments of the present application, delaying an arrival time of a functional droplet arriving at the cross-contamination point later in any two consecutive functional droplets comprises: storing the functional liquid drops arriving at the cross-contamination point later in a storage unit of a biochip, and setting the storage time of the functional liquid drops in the storage unit; and when the storage time is up, controlling the functional liquid drops arriving at the cross-contamination point later to move out of the storage unit and continue to move to the cross-contamination point, wherein the sum of the storage time and the transmission time of the functional liquid drops arriving at the cross-contamination point later on in the corresponding liquid drop transportation path is larger than the time of the cleaning liquid drops arriving at the cross-contamination point.

In some embodiments of the present application, when the cross-contamination point is plural, before the cleaning droplet and the at least two functional droplets are transferred in the adjusted order, the following processing steps may be further performed: determining an adjustment order for a plurality of the cross-contamination points; generating a target adjustment sequence by using a topological sorting algorithm; constructing a directed graph based on the adjustment order, wherein each vertex in the directed graph represents a cross-contamination point; based on the directed graph, sequentially adjusting the arrival sequence of the cleaning liquid drops and the functional liquid drops corresponding to each cross contamination point in the directed graph; wherein the transport time information is updated as each functional droplet passes the cross-contamination point, and the transport time of functional droplets that do not pass any cross-contamination point is adjusted based on the updated transport time information.

In some embodiments of the present application, before the cleaning droplets and the at least two functional droplets are transferred in the adjusted order, the following process steps may also be performed: determining a drop transport time for the drop transport path, wherein the drop transport time comprises a sum of: the time of the cleaning liquid drops from the liquid storage tank to the waste liquid tank through the cross contamination point and the transmission time of the functional liquid drops on the corresponding transportation path; determining whether to stop a cleaning operation of a specified sub-path in the droplet transport path based on the droplet transport time.

In some embodiments of the present application, the step of determining the order of the wash droplet passing through the cross-contamination point and the at least two functional droplets reaching the cross-contamination point is achieved by: and adding a cleaning liquid drop after each functional liquid drop, wherein the cleaning liquid drop moves synchronously with the functional liquid drop.

The embodiment of the present application further provides a droplet transportation device, which is used for implementing the droplet transportation method shown in fig. 9, and as shown in fig. 10, the device includes:

the acquisition module 102 is configured to acquire an undirected graph of a two-dimensional microfluidic array, where the undirected graph includes a vertex set and an edge set, each vertex in the vertex set represents an electrode in the microfluidic array, and an edge between two adjacent vertices represents a path between the two adjacent vertices;

a first determining module 104, configured to determine mutually disjoint vertices or mutually disjoint edges in the undirected graph;

a second determination module 106 for determining drop transport paths based on mutually disjoint vertices or mutually disjoint edges, wherein the drop transport paths in a set of drop transport paths are mutually disjoint;

a first transport module 108 for transporting the droplets in accordance with the droplet transport path.

It should be noted that, reference may be made to the relevant description in fig. 1 to 9 for a preferred implementation of the embodiment shown in fig. 10, and details are not repeated here.

It should be further noted that, the respective modules in fig. 10 may be represented in the form of hardware or software, and for the former, it may be represented in the following implementation form, but is not limited thereto: the modules are located in the same processor, or the modules are located in different processors. In the latter case, the respective modules are program modules for implementing the respective functions, and the respective program modules may be executed in one or more processors.

An embodiment of the present application further provides a method for cleaning a droplet transport path, as shown in fig. 11, the method includes:

step S1102, determining a cross-contamination point in the droplet transport path, wherein the cross-contamination point is a plurality of droplet path cross-points;

step S1104, determining the sequence of the cleaning liquid drops passing through the cross-contamination point and the arrival of the at least two functional liquid drops at the cross-contamination point, wherein the sequence of the cleaning liquid drops arriving at the cross-contamination point is later than the arrival sequence of one functional liquid drop of the at least two functional liquid drops and earlier than the arrival sequence of the other functional liquid drops of the at least two functional liquid drops at the cross-contamination point;

there are various implementations of step S1104, for example, in some embodiments of the present application, the following implementations may be implemented: determining a first time for the cleaning droplets to reach the cross-contamination point and a second time for the at least two functional droplets to reach the cross-contamination point, wherein the at least two functional droplets are sequential functional droplets; adjusting the sequence of the cleaning liquid drops and the at least two functional liquid drops to reach the cross-contamination point according to a preset rule based on the first time and the second time, wherein the preset rule comprises: the time of the cleaning liquid drop reaching the cross-contamination point is between the time of any two sequential functional liquid drops of the at least two functional liquid drops reaching the cross-contamination point.

Wherein the order of the cleaning droplets and the at least two functional droplets to reach the cross-contamination point can be adjusted by: and when any two continuous functional liquid drops are earlier than the time of the cleaning liquid drops reaching the cross-contamination point, delaying the arrival time of the functional liquid drop which arrives at the cross-contamination point later in any two continuous functional liquid drops, wherein the time of the delayed first functional liquid drop reaching the cross-contamination point is later than the time of the cleaning liquid drop reaching the cross-contamination point.

In some embodiments of the present application, the arrival time of the functional droplet arriving at the cross-contamination point later in any two consecutive functional droplets may be delayed by: storing the functional liquid drops arriving at the cross contamination point later into a storage unit of the biochip, and setting the storage time of the functional liquid drops in the storage unit; and when the storage time is up, controlling the functional liquid drops arriving at the cross-contamination point later to move out of the storage unit and continue to move to the cross-contamination point, wherein the sum of the storage time and the transmission time of the functional liquid drops arriving at the cross-contamination point later on in the corresponding liquid drop transportation path is greater than the time of the cleaning liquid drops arriving at the cross-contamination point.

In some optional embodiments of the present application, when there are a plurality of cross-contamination points, determining an adjustment order of the plurality of cross-contamination points before the cleaning liquid droplet and the at least two functional liquid droplets are transferred in the adjusted order; generating a target adjustment sequence by using a topological sorting algorithm; constructing a directed graph based on the adjustment order, wherein each vertex in the directed graph represents a cross-contamination point; based on the directed graph, sequentially adjusting the arrival sequence of the cleaning liquid drops and the functional liquid drops corresponding to each cross contamination point in the directed graph; wherein the transport time information is updated as each functional droplet passes a cross-contamination point, and the transport time of functional droplets that do not pass any cross-contamination point is adjusted based on the updated transport time information.

For another example, in other embodiments of the present application, the above order may also be determined by: one additional cleaning droplet follows each functional droplet, wherein the cleaning droplets move synchronously with the functional droplets. During the setting of the functional droplet path, the cleaning droplets follow the corresponding functional droplet path and clean the residue.

Step S1106, the cleaning droplets and the at least two functional droplets are transported in the order of the cleaning droplets and the at least two functional droplets to the cross-contamination point.

In order to prevent the cleaning operation from causing the total transfer time to be too long, in some embodiments of the present application, the following process steps may also be performed before the cleaning droplets and the at least two functional droplets are transferred in the adjusted order:

determining a drop transport time for the drop transport path, wherein the drop transport time comprises a sum of: the time of the cleaning liquid drops from the liquid storage tank to the waste liquid tank through the cross contamination point and the transmission time of the functional liquid drops on the corresponding transportation path; and determining whether to stop the cleaning operation of the designated sub-path in the droplet transportation path or not based on the droplet transportation time, wherein when the droplet transportation time is greater than a preset threshold value, the cleaning operation of the designated sub-path is stopped, and otherwise, the cleaning operation is continued. The designated sub-path may be any one of the droplet transport paths, or may be determined according to a certain principle, for example, according to a preset priority or according to the length of the sub-path, for example, when the sub-path is determined according to the length of the sub-path, the path with the shortest path is selected as the designated sub-path.

It should be noted that, reference may be made to the relevant description in fig. 1 to 9 for a preferred implementation of the embodiment shown in fig. 11, and details are not repeated here.

An embodiment of the present application further provides a cleaning apparatus for a droplet transport path, as shown in fig. 12, the apparatus including:

a third determining module 120 configured to determine a cross-contamination point in the droplet transportation path, wherein the cross-contamination point is a plurality of droplet path intersection points;

a fourth determining module 122, configured to determine an order in which the cleaning droplets passing through the cross-contamination point and the at least two functional droplets reach the cross-contamination point, where the order in which the cleaning droplets reach the cross-contamination point is later than an order in which one of the at least two functional droplets reaches the cross-contamination point and is earlier than an order in which the other of the at least two functional droplets reaches the cross-contamination point;

and a second transfer module 124 for transferring the cleaning liquid droplets and the at least two functional liquid droplets in the order of arrival of the cleaning liquid droplets and the at least two functional liquid droplets at the cross-contamination point.

It should be noted that, reference may be made to the relevant description in fig. 1 to 9 for a preferred implementation of the embodiment shown in fig. 12, and details are not repeated here.

The embodiment of the application also provides a nonvolatile storage medium, which comprises a stored program, wherein when the program runs, the device where the storage medium is located is controlled to execute the droplet transmission method.

The embodiment of the application also provides a processor, which is used for running the program, wherein the program runs to execute the droplet transportation method.

The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.

In the above embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.

The foregoing is only a preferred embodiment of the present application and it should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.

Claims (16)

1. A method of transporting droplets, comprising:

obtaining an undirected graph of a two-dimensional microfluidic array, wherein the undirected graph comprises a vertex set and an edge set, each vertex in the vertex set represents one electrode in the microfluidic array, and an edge between two adjacent vertices represents a path between the two adjacent vertices;

determining mutually disjoint vertices or mutually disjoint edges in the undirected graph;

determining drop transport paths based on the mutually disjoint vertices or mutually disjoint edges, wherein the drop transport paths in each set of drop transport paths are mutually disjoint;

transporting the droplets in accordance with the droplet transport path.

2. The method of claim 1, wherein the two-dimensional microfluidic array comprises a three-pin net, wherein pins in the three-pin net correspond to three vertices in the undirected graph, the three vertices comprising: the three-pin network comprises vertexes corresponding to two source point pins and a vertex corresponding to one target pin, wherein the pins are fluid ports of the microfluidic array on the boundary of the undirected graph, and the three-pin network is a liquid drop path between any two pins of three pins on different modules or chips.

3. The method of claim 2, wherein in determining a drop transport path based on the mutually disjoint vertices or mutually disjoint edges, the method further comprises:

determining a target droplet transport path between the two source point pins based on the undirected graph, wherein the two source point pins correspond to a pair of vertices in the undirected graph;

determining a distance between each electrode in the target droplet transport path and the target pin, resulting in a plurality of distances;

determining a minimum distance from the plurality of distances;

determining a mixing point based on a vertex where the electrode corresponding to the minimum distance is located, wherein the mixing point is an intersection point of a first path, a second path and a third path, the first path is a first path between the vertex where the electrode corresponding to the minimum distance is located and one of the two source point pins, and the second path is a path between the vertex where the electrode corresponding to the minimum distance is located and the other of the two source point pins; the third path is a third path between a vertex where the electrode corresponding to the minimum distance is located and a vertex corresponding to the target pin.

4. The method of claim 2, wherein determining a drop transport path based on the mutually disjoint vertices or mutually disjoint edges comprises:

and selecting a path with the shortest length from an available path list based on the mutually-intersected vertexes or mutually-intersected edges, and taking the path with the shortest length as the liquid drop transportation path, wherein the available path list is a path from a vertex corresponding to the source point pin to a vertex corresponding to the target pin.

5. The method of claim 1, wherein after determining a drop transport path based on the mutually disjoint vertices or mutually disjoint edges, the method further comprises:

marking cells in the droplet transport path as obstacles to other paths.

6. The method of claim 1, determining a drop transport path based on the mutually disjoint vertices or mutually disjoint edges, comprising:

prioritizing all nets in the two-dimensional microfluidic array, wherein each net in the all nets represents a path between any two pins on different modules or chips;

selecting a path of shortest length from the available paths of each net as a sub-path of the droplet transport path;

determining the drop transport path based on a plurality of the sub-paths.

7. The method of claim 6, determining the drop transport path based on a plurality of the sub-paths, comprising:

performing a route length constraint check and a fluid constraint rule check on a plurality of said sub-paths;

determining a sub-path of the plurality of sub-paths which passes the route length constraint check and the fluid constraint rule check to obtain an available sub-path set;

determining a sub-path of the plurality of sub-paths which does not pass the route length constraint check and the fluid constraint rule check to obtain a conflict sub-path set; determining the shortest path of each network in the conflict sub-path set to obtain a plurality of shortest paths; selecting a sub-path which does not have a common adjacent unit with the paths in the available sub-path set from the shortest paths to obtain a target sub-path;

generating the drop transport path based on the set of available sub-paths and the target sub-path.

8. A method of cleaning a droplet transport path, comprising:

determining a cross-contamination point in the droplet transport path, wherein the cross-contamination point is a plurality of droplet path cross-points;

determining the sequence of the cleaning liquid drops passing through the cross-contamination point and the sequence of at least two functional liquid drops reaching the cross-contamination point, wherein the sequence of the cleaning liquid drops reaching the cross-contamination point is later than the sequence of one functional liquid drop in the at least two functional liquid drops and earlier than the sequence of the other functional liquid drops in the at least two functional liquid drops reaching the cross-contamination point;

and conveying the cleaning liquid drops and the at least two functional liquid drops according to the sequence of the cleaning liquid drops and the at least two functional liquid drops reaching the cross-contamination point.

9. The method of claim 8, determining an order in which wash droplets that pass through the cross-contamination point and at least two functional droplets reach the cross-contamination point, comprising:

determining a first time for the wash droplet to reach the cross-contamination point and a second time for at least two functional droplets to reach the cross-contamination point, wherein the at least two functional droplets are sequentially consecutive functional droplets;

adjusting the sequence of the cleaning liquid drops and the at least two functional liquid drops to reach the cross-contamination point according to a preset rule based on the first time and the second time, wherein the preset rule comprises: the time of the cleaning liquid drop reaching the cross-contamination point is between the time of any two sequentially continuous functional liquid drops in the at least two functional liquid drops reaching the cross-contamination point.

10. The method of claim 9, adjusting an order of the wash droplets and at least two functional droplets to reach the cross-contamination point according to a preset rule based on the first time and the second time, comprising:

and when any two continuous functional liquid drops are earlier than the time of the cleaning liquid drops reaching the cross-contamination point, delaying the arrival time of the functional liquid drop which arrives at the cross-contamination point later in any two continuous functional liquid drops, wherein the time of the delayed first functional liquid drop arriving at the cross-contamination point is later than the time of the cleaning liquid drop arriving at the cross-contamination point.

11. The method of claim 10, delaying the arrival time of the functional droplet arriving at the cross-contamination point later in time of any two consecutive functional droplets, comprising:

storing the functional liquid drops arriving at the cross-contamination point later in a storage unit of a biochip, and setting the storage time of the functional liquid drops in the storage unit;

and when the storage time is up, controlling the functional liquid drops arriving at the cross-contamination point later to move out of the storage unit and continue to move to the cross-contamination point, wherein the sum of the storage time and the transmission time of the functional liquid drops arriving at the cross-contamination point later on in the corresponding liquid drop transportation path is larger than the time of the cleaning liquid drops arriving at the cross-contamination point.

12. The method of claim 10, wherein when the cross-contamination point is multiple, before delivering the wash droplet and the at least two functional droplets in the adjusted order, the method further comprises:

determining an adjustment order for a plurality of the cross-contamination points;

generating a target adjustment sequence by using a topological sorting algorithm;

constructing a directed graph based on the adjustment order, wherein each vertex in the directed graph represents a cross-contamination point;

based on the directed graph, sequentially adjusting the arrival sequence of the cleaning liquid drops and the functional liquid drops corresponding to each cross contamination point in the directed graph;

wherein the transport time information is updated as each functional droplet passes the cross-contamination point, and the transport time of functional droplets that do not pass any cross-contamination point is adjusted based on the updated transport time information.

13. The method of claim 8, prior to delivering the wash droplet and the at least two functional droplets in the adjusted order, the method further comprising:

determining a drop transport time for the drop transport path, wherein the drop transport time comprises a sum of: the time of the cleaning liquid drops from the liquid storage tank to the waste liquid tank through the cross contamination point and the transmission time of the functional liquid drops on the corresponding transportation path;

determining whether to stop a cleaning operation of a specified sub-path in the droplet transport path based on the droplet transport time.

14. The method of claim 8, determining an order in which wash droplets that pass through the cross-contamination point and at least two functional droplets reach the cross-contamination point, comprising: and adding a cleaning liquid drop after each functional liquid drop, wherein the cleaning liquid drop moves synchronously with the functional liquid drop.

15. A device for transporting droplets, comprising:

the device comprises an acquisition module, a detection module and a control module, wherein the acquisition module is used for acquiring an undirected graph of a two-dimensional microfluidic array, the undirected graph comprises a vertex set and an edge set, each vertex in the vertex set represents one electrode in the microfluidic array, and an edge between two adjacent vertices represents a path between the two adjacent vertices;

a first determining module, configured to determine mutually disjoint vertices or mutually disjoint edges in the undirected graph;

a second determination module for determining drop transport paths based on the mutually disjoint vertices or mutually disjoint edges, wherein the drop transport paths in each set of drop transport paths are mutually disjoint;

a first transport module for transporting the droplets according to the droplet transport path.

16. A cleaning apparatus for a transport path of droplets, comprising:

a third determining module, configured to determine a cross-contamination point in the droplet transport path, where the cross-contamination point is a plurality of droplet path cross-points;

a fourth determining module, configured to determine an order in which the cleaning droplets passing through the cross-contamination point and the at least two functional droplets reach the cross-contamination point, where the order in which the cleaning droplets reach the cross-contamination point is later than an order in which one of the at least two functional droplets reaches the cross-contamination point and earlier than an order in which the other of the at least two functional droplets reaches the cross-contamination point;

and the second transmission module is used for transmitting the cleaning liquid drops and the at least two functional liquid drops according to the sequence that the cleaning liquid drops and the at least two functional liquid drops reach the cross contamination point.

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