CN103995983A - Method for analyzing node importance in signal transduction network based on logic model - Google Patents
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Abstract
The invention discloses a method for analyzing node importance in a signal transduction network based on a logic model. The method includes the following steps of S1, setting up the signal transduction network according to a signal transduction access stored in a database; S2, expressing the signal transduction network as the logic serial-parallel model according to the connecting relation between nodes in the signal transduction network; S3, decomposing the overall structure of the signal transduction network into the most basic serial structure and the most basic parallel structure; S4, calculating the change value of probability that the signal transduction network fails when the probability that a single node or multiple nodes of the nodes fail is changed, and measuring the importance of the single node or the multiple nodes for keeping the functions of the signal transduction network through the change value of the probability that the signal transduction network fails. Modeling is conducted on the whole network structure through the logic model, and the importance of the single node can be easily inspected from the view of global network topological properties.
Description
Technical field
The present invention relates to bioinformatics technique field, in particular to the method for node importance in a kind of logic-based model analysis Signaling transduction networks.
Background technology
In Signaling transduction networks, the importance of weighing single protein contributes to find protein crucial in cell signalling process and the weak link of biosystem, the discovery of crucial protein and the weak link of biosystem will contribute to people to be better familiar with biosome, will contribute to screening and the research and development of some medicines.At present, also less in the method for Signaling transduction networks vacuum metrics protein importance.
Biologically, the gene necessity that further important node is corresponding is higher, and further important node has slower evolutionary rate, in this way weighs protein importance and need to carry out more experimental study, and be difficult for carrying out quantitative test.In protein interaction network, connection degree is the topological index of the most frequently used description protein importance.The connection degree of node refers to that the number of interactional node can occur all and this node.Conventionally the node that connects Du Genggao is more important for the one-piece construction and the function that ensure network, but this index has only been investigated the local characteristics of node, does not consider the whole topology of networks at node place.In fact, the lower node of part degree of connection likely becomes the bottleneck of network, and in the time that they break down, whole network structure is destroyed, and signal cannot effectively be transmitted, thereby causes the generation of disease.
Also have certain methods to carry out the prediction of node importance in network by the flow analysis of network.As article Liu W, et al.SigFlux:a novel network feature to evaluate the importance of proteins in signal transduction networks.BMC Bioinformatics, 2006,7:515 has proposed a kind of method of the minimal path collection prediction node importance in Network Based, and detailed step is as follows:
1) from document, download the Signaling transduction networks of hippocampus neurons in mice, as network to be studied;
2) adopt breadth-first search algorithm to generate all paths between input and output, adopt all backfeed loops in MFinder software search network, obtain the minimal path collection in network;
3) a kind of new feature SigFlux that measures single protein importance has been proposed, be defined as the ratio that the minimal path collection number that comprises this node accounts for all minimal path collection numbers, and calculate the SigFlux value of protein according to the distribution situation of path in network and backfeed loop;
4) necessity of SigFlux and little musculus cdna and evolutionary rate are carried out to correlation analysis, further compare SigFlux and degree of connection index, illustrate that SigFlux is as the validity of weighing protein importance index.
From above-mentioned prior art, can find out, its prediction node importance is mainly the importance that the part topological attribute based on Signaling transduction networks is predicted individual node in network, do not carry out the vital role of analysis node from the global structure of network, and lack fault analysis mechanism from probability, do not consider the impact that the raising of the fault rate of individual node causes whole network.
Summary of the invention
The present invention aims to provide the method for node importance in a kind of logic-based model analysis Signaling transduction networks, the technical matters with the raising that solves the fault rate of not considering individual node in prior art on the impact that whole network was caused.
To achieve these goals, according to an aspect of the present invention, provide the method for node importance in a kind of logic-based model analysis Signaling transduction networks.The method comprises the following steps: S1, according to the signal transduction pathway of storing in database, builds a Signaling transduction networks; S2, is expressed as Signaling transduction networks according to the annexation between node in Signaling transduction networks the connection in series-parallel model of logic; S3, is decomposed into the most basic series and parallel connections structure to the general structure of Signaling transduction networks; S4, in the time that the fault rate of the individual node in node or multiple nodes changes, calculate the probability change value that Signaling transduction networks breaks down, the probability breaking down by Signaling transduction networks changes value metric individual node or the importance of multiple node to holding signal transduction network function.
Further, the method also comprises: S5, the probability change value that Signaling transduction networks is broken down and the necessity of gene and the evolutionary rate of gene carry out correlation analysis, compare, and then the parameter in Signaling transduction networks is adjusted with default degree of connection index.
Further, in step S4, be set in trouble-proof situation, the probability that in Signaling transduction networks, each node breaks down is a fixed value.
Further, in step S4, be set under normal circumstances, the probability that each node in Signaling transduction networks is under normal circumstances broken down is set as a constant value, constant value is the mean value that in Signaling transduction networks, each node breaks down, and in the time that individual node breaks down, the corresponding probability that this individual node breaks down becomes 1, calculate the probability that each the most basic series and parallel connections structure breaks down, and then extrapolate the probability that Signaling transduction networks breaks down.
Further, in step S1, the various signal transduction pathways of various signal transduction pathway behaviours, mouse, fruit bat, nematode or the yeast of storing in existing database.
Apply technical scheme of the present invention, by logical model, whole network structure is carried out to modeling, be conducive to investigate from overall network topology attribute angle the importance of individual node; Analyze from probability the impact that the raising of the fault rate of individual node causes whole network, easily carry out quantitative test; Can easily the importance analysis of individual node be generalized to the associating importance analysis of multiple nodes, the weak module in discovering network.Can, by important node in the method discovery signals transduction network of analyzing, for biological experimental design provides guidance, help the function of research nodes.
Brief description of the drawings
The Figure of description that forms the application's a part is used to provide a further understanding of the present invention, and schematic description and description of the present invention is used for explaining the present invention, does not form inappropriate limitation of the present invention.In the accompanying drawings:
Fig. 1 shows the process flow diagram of the method for node importance in the logic-based model analysis Signaling transduction networks of one exemplary embodiment according to the present invention;
Fig. 2 shows the process flow diagram of the method for node importance in the logic-based model analysis Signaling transduction networks of another exemplary embodiment according to the present invention;
Fig. 3 A shows the part of real Apoptosis Signaling transduction networks;
Fig. 3 B shows the connection in series-parallel logical model that Fig. 3 A is corresponding;
Fig. 4 A shows the most basic cascaded structure; And
Fig. 4 B shows the most basic parallel-connection structure;
Fig. 5 shows people's apoptotic signal transduction network;
Fig. 6 shows the schematic diagram after Fig. 5 Module Division;
Fig. 7 shows the logic relation picture that Fig. 6 is corresponding;
Fig. 8 shows the middle sub-module of module 1 correspondence in Fig. 7.
Embodiment
It should be noted that, in the situation that not conflicting, the feature in embodiment and embodiment in the application can combine mutually.Describe below with reference to the accompanying drawings and in conjunction with the embodiments the present invention in detail.
A kind of typical embodiment according to the present invention, provides the method for node importance in a kind of logic-based model analysis Signaling transduction networks.The method comprises the following steps: S1, according to the signal transduction pathway of storing in database, builds a Signaling transduction networks; S2, is expressed as Signaling transduction networks according to the annexation between node in Signaling transduction networks the connection in series-parallel model of logic; S3, is decomposed into the most basic series and parallel connections structure to the general structure of Signaling transduction networks; S4, in the time that the fault rate of the individual node in above-mentioned node or multiple nodes changes, calculate the probability change value that Signaling transduction networks breaks down, the probability breaking down by Signaling transduction networks changes value metric individual node or the importance of multiple node to holding signal transduction network function, as shown in Figure 1.
Apply technical scheme of the present invention, by logical model, whole network structure is carried out to modeling, be conducive to investigate from overall network topology attribute angle the importance of individual node; Connection in series-parallel relation is the basis of computational grid fault rate, large network is resolved into step by step to basic series parallel structure and could calculate according to the probability of malfunction of all nodes the probability of malfunction of whole network, thereby analyze from probability the impact that the raising of the fault rate of individual node causes whole network, that a situation arises is more identical with actual disease, easily carries out quantitative test; Can easily the importance analysis of individual node be generalized to the associating importance analysis of multiple nodes, the weak module in discovering network.
For complicated Signaling transduction networks, it is more difficult being directly changed into logical relation model.Its basic ideas are that complex network is divided step by step: the first step is divided into separate multiple modules; Second step is that the module being obtained by the first step is continued to divide, distinguish its inner connection in series-parallel relation, if inside modules can be distinguished the connection in series-parallel relation of individual node, the logical relation model conversion of this module finishes, otherwise continue this module to divide, be divided into multiple independently fritters, repeat partiting step; Be finally that the logical relation model by each module is spliced, form final logical relation model.
Preferably, as shown in Figure 2, method further comprises: S5, the probability change value that Signaling transduction networks is broken down and the necessity of gene and the evolutionary rate of gene carry out correlation analysis, compare with default degree of connection index, and then the parameter in Signaling transduction networks is adjusted, thereby be more conducive to obtain gratifying result, match with biological data, as consistent with gene necessity.Wherein, the information in existing biological data storehouse for the data acquisition such as necessity and evolutionary rate of gene, as the data whose necessity of mouse is stored in MGD.Wherein, default connection degree index obtains according to the information in existing database.
According to the present invention, a kind of typical embodiment, in step S4, is set under normal circumstances, and the probability that in Signaling transduction networks, each node breaks down is a fixed value, facilitates like this calculating of follow-up probability.For example, in step S4, the probability that each node in Signaling transduction networks is under normal circumstances broken down is set as a constant value, constant value is the mean value that in Signaling transduction networks, each node breaks down, in the time that individual node breaks down, the corresponding probability that this individual node breaks down becomes 1, calculates the probability that each the most basic series and parallel connections structure breaks down, and then extrapolates the probability that Signaling transduction networks breaks down.Wherein, refer under normal circumstances in the situation that Signaling transduction networks can normally transduce.
In logic-based model analysis Signaling transduction networks of the present invention, the method for node importance can be for the assessment of signal transduction pathway in various species, the various signal transduction pathways of for example people, mouse, fruit bat, nematode or yeast.
In of the present invention one typical embodiment, in this logic-based model analysis Signaling transduction networks, the method for node importance comprises the following steps:
1. the structure of Signaling transduction networks and be expressed as logical model
For Signaling transduction networks, the annexation existing between the node in network, has formed topological structure.Ensure effective transmission of signal by whole network structure, particularly by the redundancy scheme of path, when the part of nodes of guarantee network makes a mistake (fault), network still can be brought into play function normally.In a path, according between node with or relation, can be converted into connection in series-parallel logical model.For example, if Fig. 3 A is a real Signaling transduction networks, its corresponding connection in series-parallel logical model as shown in Figure 3 B.Here do not distinguish the Activation and inhibition relation between node.
2. calculate the probability of malfunction of whole network
For complicated connection in series-parallel logical network, always can be divided into the most basic series parallel structure in Fig. 4 A and Fig. 4 B.Wherein, Fig. 4 A shows the most basic cascaded structure, and Fig. 4 B shows the most basic parallel-connection structure.
For cascaded structure, establish node A, B, fault rate that C is corresponding is respectively P (A), P (B) and P (C), the probability that the cascade system G being made up of these three nodes so breaks down is:
P(G)＝1-[1-P(A)][1-P(B)][1-P(C)] (1)
The probability that the parallel system R being made up of these three nodes breaks down is:
P(R)＝P(A)P(B)P(C) (2)
By complication system being divided into basic connection in series-parallel module, can calculate the probability that whole system breaks down according to the fault rate of individual node.
The step of being divided with prior module by the process of basic module calculated population probability of malfunction is contrary, to be divided into step by step little module by large network above, here start to calculate probability of malfunction by little module, extension module scope step by step, until comprise whole network calculations and go out the probability of malfunction of whole network.
3. the importance measures of individual node
Suppose under normal circumstances, the probability that in network, each node i breaks down is a fixed value, as P (i)=0.5, according to the logical model of network, can calculate whole system S
0general probability P (the S breaking down
0).
For the individual node i in network, in the time that it breaks down, its corresponding probability becomes P (i)=1.Think and now do not have other nodes to break down, can calculate with this understanding so, the probability of malfunction S of whole system
ip (S
1), P (S under normal circumstances
i)≤P (S
0).
The importance of node i is defined as:
F(i)＝P(S
i)-P(S
0) (3)
This value has characterized the node i impact probability that whole network is broken down that breaks down, thereby this node of assessment that can be quantitative is for the importance of whole network structure.
4. the importance measures of many nodes
Similar with the importance measures of individual node, suppose n node 1 ..., n} breaks down simultaneously, the probability of malfunction of its node all becomes P (i)=1, i=1 ... n, can calculate with this understanding, the P (Sn) of the probability of malfunction Sn of whole system.The associating importance of this n node is defined as:
F(n)＝P(S
n)-P(S
0) (4)
Notice, this associating importance is not equal to the stack of the independent importance of n node, and has close ties with distribution and the connection of this n node.
Further illustrate beneficial effect of the present invention below in conjunction with embodiment, the following step that there is no special description, all can adopt ordinary skill in the art means to realize.
Embodiment 1
1), according to the various signal transduction pathways of storing in existing database, build people's apoptotic signal transduction network, as shown in Figure 5.For people's as shown in Figure 5 apoptotic signal transduction network, it is carried out to Module Division and logical relation conversion.
2) Fig. 5 is divided into separate module 1 and module 2, as shown in Figure 6.
3) carry out logical relation division according to Fig. 6, obtain corresponding logic relation picture, as shown in Figure 7.
4) set under normal circumstances, the fault rate of individual node is a normal value (as 0.1), according to logic relation picture, can calculate the fault rate of whole network.Its computing method are the fault rates that first calculate in each module, then obtain the fault rate of whole network.For module 1, can mark off middle sub-module, as shown in Figure 8.
First calculate the fault rate P of middle sub-module
m,
P
m＝0.1[1-(1-0.1)(1-0.01)][1-(1-0.1)(1-0.01)]＝0.0012
Then comprehensively obtaining whole module probability is:
P(M
1)＝1-(1-0.1){1-P
m}(1-0.1)＝0.1910
For module 2, it is the series connection of 5 nodes, and corresponding probability of malfunction is:
P(M
2)＝1-(1-0.1)
5＝0.4095
Finally can obtain network failure probability of happening P (S always
0)=0.2241, calculating is herein more complicated, realization able to programme.
In the time that wherein certain node i breaks down, it is 1 that its fault changes, and repeats above computation process, obtains new fault rate P (S
i), then according to the importance of its difference measurement individual node.
For this network, Caspase3 node is of paramount importance, and its importance score value is 1.The importance probability of other nodes is all less than 1, and as for Caspase9, its importance score value is 0.5738.
Similarly, can computing module 1 and the importance score value of module 2.
Can find out from above-described embodiment, adopt technical scheme of the present invention at least to there is following beneficial effect:
1, by logical model, whole network structure is carried out to modeling, be conducive to investigate from overall network topology attribute angle the importance of individual node;
2, analyze from probability the impact that the raising of the fault rate of individual node causes whole network, that a situation arises is more identical with actual disease, easily carries out quantitative test;
3, can easily the importance analysis of individual node be generalized to the associating importance analysis of multiple nodes, the weak module in discovering network.
The foregoing is only the preferred embodiments of the present invention, be not limited to the present invention, for a person skilled in the art, the present invention can have various modifications and variations.Within the spirit and principles in the present invention all, any amendment of doing, be equal to replacement, improvement etc., within all should being included in protection scope of the present invention.
Claims (5)
1. a method for node importance in logic-based model analysis Signaling transduction networks, is characterized in that, comprises the following steps:
S1, according to the signal transduction pathway of storing in database, builds a Signaling transduction networks;
S2, is expressed as described Signaling transduction networks according to the annexation between node in described Signaling transduction networks the connection in series-parallel model of logic;
S3, is decomposed into the most basic series and parallel connections structure to the general structure of described Signaling transduction networks;
S4, in the time that the fault rate of the individual node in described node or multiple nodes changes, calculate the probability change value that described Signaling transduction networks breaks down, the probability breaking down by described Signaling transduction networks changes described in value metric individual node or described multiple node to keeping the importance of described Signaling transduction networks function.
2. method according to claim 1, is characterized in that, described method further comprises:
S5, the probability change value that described Signaling transduction networks is broken down and the necessity of gene and the evolutionary rate of gene carry out correlation analysis, compare, and then the parameter in described Signaling transduction networks is adjusted with default degree of connection index.
3. method according to claim 1, is characterized in that, in described step S4, is set in trouble-proof situation, and the probability that in described Signaling transduction networks, each node breaks down is a fixed value.
4. method according to claim 3, it is characterized in that, in described step S4, the probability that each node in described Signaling transduction networks is under normal circumstances broken down is set as a constant value, described constant value is the mean value that in described Signaling transduction networks, each node breaks down, in the time that individual node breaks down, the corresponding probability that this individual node breaks down becomes 1, calculate the probability that the most basic series and parallel connections structure breaks down described in each, and then extrapolate the probability that described Signaling transduction networks breaks down.
5. method according to claim 1, is characterized in that, in described step S1, and the various signal transduction pathways of various signal transduction pathway behaviours, mouse, fruit bat, nematode or the yeast of storing in described database.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN107133492A (en) * | 2017-05-02 | 2017-09-05 | 温州大学 | A kind of method that gene pathway is recognized based on PAGIS |
| CN111478854A (en) * | 2020-04-01 | 2020-07-31 | 中国人民解放军国防科技大学 | Real-time network node importance ordering method based on flow data |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN107133492A (en) * | 2017-05-02 | 2017-09-05 | 温州大学 | A kind of method that gene pathway is recognized based on PAGIS |
| CN107133492B (en) * | 2017-05-02 | 2020-08-25 | 温州大学 | Method for identifying gene pathway based on PAGES |
| CN111478854A (en) * | 2020-04-01 | 2020-07-31 | 中国人民解放军国防科技大学 | Real-time network node importance ordering method based on flow data |
| CN111478854B (en) * | 2020-04-01 | 2021-10-12 | 中国人民解放军国防科技大学 | Real-time network node importance ordering method based on flow data |
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