KR20180114733A - An optimized anti-cancer drug identification platform for personalized therapy - Google Patents

An optimized anti-cancer drug identification platform for personalized therapy Download PDF

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KR20180114733A
KR20180114733A KR1020170046796A KR20170046796A KR20180114733A KR 20180114733 A KR20180114733 A KR 20180114733A KR 1020170046796 A KR1020170046796 A KR 1020170046796A KR 20170046796 A KR20170046796 A KR 20170046796A KR 20180114733 A KR20180114733 A KR 20180114733A
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information
network
drug
nodes
cancer
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Korean (ko)
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KR102029297B1 (en
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조광현
송제훈
신동관
한영현
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한국과학기술원
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    • G06F19/706—
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/50—Molecular design, e.g. of drugs
    • G06F19/12—
    • G06F19/708—
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B5/00—ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/80—Data visualisation

Abstract

Disclosed is a method for processing information, comprising the following steps of: determining a reference network corresponding to a cancer related to a patient or a cell line among a plurality of cancer-networks related to a plurality of cancers; determining one or more target-nodes targeted by each of one or more drugs given among nodes included in the reference network; converting information on a change in a molecular level into one or more specific-model-parameters to reflect the information on the change in a molecular level, which is generated in a gene of a patient or a cell line, to the reference network; generating simulation-input information in which the reference network (5), one or more target-nodes, and one or more specific-model-parameters are integrated; and allowing a simulation module to receive the simulation-input information to output information on one or more among a single drug sensitivity, a multi-drug sensitivity, an optimal drug, and an optimal drug combination.

Description

An optimal anti-cancer drug identification platform for patient-tailored therapies,

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an information providing system using a computer, and more particularly, to a computer platform technology for providing a technique for selecting an optimal target cancer therapy for a patient-customized treatment.

Of the various anticancer drugs, studies are being conducted in the medical and biomedical fields to find drugs that are effective in the treatment of cancer patients through minimal trial and error. The technology related to anticancer drug prescription service is disclosed in "Korean Patent Application No. 10-2013-0058994 (2013.05.24)" Method and system for prescribing anticancer drug based on electronic medical record system and providing anti-cancer record management service ".

In the present invention, a service for determining an optimal target cancer therapy for a patient-customized treatment is provided using a remote-server computing platform. The technology related to the network-based medical service system is disclosed in Korean Patent Application No. 10- "Network Medical Service System and Method", 1999-0047870 (1999.11.01), and "Medical Diagnosis Service Providing System and Method" of Korean Patent Application No. 10-2009-0073250 (Aug. 10, 2009).

In the present invention, for the determination of an anticancer drug, the simulation includes a process of processing computerized data without experimenting with various anti-cancer drugs in the human body or tissues. In this regard, it can be predicted by using a network model which can simulate the state of cancer cells to be killed, disrupted, and resting, using a computer. The network model may be presented as a model having a degree of expression of various proteins and genes contained in cancer cells as a node and a link regulation relation between the mutual expression levels of the proteins and the genes. Examples of the related technologies are Korean Patent Application No. 10-2012-0098296 (Sep. 5, 2012) entitled " Method for analyzing network characteristics and storage medium and apparatus therefor ", and Korean Patent Application No. 10-2013-0033844 (Mar. &Quot; Biomedical Signal Transduction Network Analysis Method "

The foregoing description of the related art and other technical contents for facilitating understanding of the present invention has been made. However, the above technical contents are not necessarily recognized as the prior art.

The present invention aims to provide a technique for quickly finding the optimal drug or drug combination through computer simulation even if the various anti-cancer drugs are not actually tested in the human body or tissues.

An information processing method provided in accordance with an aspect of the present invention is characterized in that the network selection unit of the computing system determines a reference network corresponding to information on cancer of a patient or a cell line among a plurality of cancer- ; Wherein the drug-target mapper portion of the computing system determines one or more target-nodes targeted by each drug included in the information about the one or more drugs entered by the user among the nodes included in the reference network ; Wherein the network node value limiter of the computing system is configured to provide the reference-network with information about the change in the molecular level so as to reflect information on a change in the level of a molecule occurring in the gene of the patient or cell line, - transforming into model-parameters; Generating data-integration portions of the computing system, simulation-input information incorporating the reference network, the one or more target-nodes, and the one or more specific-model-parameters; And a simulation module of the computing system receiving the simulation-input information and outputting information relating to at least one of a single drug sensitivity, multiple drug sensitivity, optimal drug, and optimal drug combination.

Wherein the simulation module includes: converting the reference network into a private network by reflecting the one or more specific-model-parameters to the reference network; And selecting one or more of the one or more target-nodes included in the singular-network to determine the single drug sensitivity, the multiple drug sensitivity, the optimal drug, and the optimal drug combination: And repeatedly executing the process of controlling the value of the selected target-nodes.

At this time, the simulation module may be any one of a signal flow analysis module (SFA analysis module), a state attractor analysis module, and a machine learning analysis module.

An information processing method provided according to another aspect of the present invention includes: preparing a reference network corresponding to a cancer provided in advance; Determining one or more target-nodes each targeted by one or more of the drugs included in the reference network; Transforming the information about the change in the level of the molecule into one or more specific-model-parameters so that the reference-network can reflect information on a change in the level of the molecule occurring in the gene of the cancer; And a simulation module of the computing system receives simulation-input information incorporating the reference network, the one or more target-nodes, and the one or more specific-model-parameters to determine a single drug sensitivity, Sensitivity, sensitivity, optimal drug, and optimal drug combination.

According to another aspect of the present invention, there is provided a computing system comprising: a network selection unit that determines a reference network corresponding to information on cancer of a patient or a cell line among a plurality of cancer-related networks related to a plurality of cancer; Wherein the drug-target mapper of the computing system determines the one or more target-nodes targeted by each drug included in the information about the one or more drugs entered by the user among the nodes included in the reference network, A selection unit; A network node that converts information about the change in the level of the molecule to one or more specific-model-parameters so that the reference-network can reflect information about a change in the level of a molecule occurring in the gene of the patient or cell line, Value limiting unit; A data integration unit for generating simulation-input information incorporating the reference network, the one or more target-nodes, and the one or more specific-model-parameters; And a simulation module that receives the simulation-input information and outputs information about one or more of a single drug sensitivity, multiple drug sensitivity, optimal drug, and optimal drug combination.

A computer readable non-transitory recording medium provided in accordance with yet another aspect of the present invention provides a computing system including one or more computing devices with a reference network corresponding to a pre- ; Determining one or more target-nodes each targeted by one or more of the drugs included in the reference network; Transforming the information about the change in the level of the molecule into one or more specific-model-parameters so that the reference-network can reflect information on a change in the level of the molecule occurring in the gene of the cancer; And a simulation module of the computing system receives simulation-input information incorporating the reference network, the one or more target-nodes, and the one or more specific-model-parameters to determine a single drug sensitivity, Sensitivity, sensitivity, optimal drug, and optimal drug combination for the patient.

An information processing method provided according to another aspect of the present invention includes (1) a reference network corresponding to a specific cancer; (2) one or more target-nodes, each of which is targeted by one or more of the nodes included in the reference network; And (3) one or more specific-model-parameters generated from information on the change in the level of the molecule so that information on a change in the level of a molecule occurring in a gene of the cancer cell can be reflected in the reference- Generating simulation-input information including the input information; And generating information on the efficacy of a possible combination of the one or more drugs using the simulation-input information.

A computing system provided in accordance with yet another aspect of the present invention includes (1) a reference network corresponding to a particular cancer; (2) one or more target-nodes, each of which is targeted by one or more of the nodes included in the reference network; And (3) one or more specific-model-parameters generated from information on the change in the level of the molecule so that information on a change in the level of a molecule occurring in a gene of the cancer cell can be reflected in the reference- A data integration unit for generating simulation-input information, And a simulation module that receives the simulation-input information and generates information about the efficacy of the possible combination of the one or more drugs.

A computer readable non-transitory recording medium provided in accordance with yet another aspect of the present invention allows a computing system, including one or more computing devices, to perform: (1) a reference network ; (2) one or more target-nodes, each of which is targeted by one or more of the nodes included in the reference network; And (3) one or more specific genes (or genes) derived from the information on the change in the level of the molecule, so that the information on the change in the level of the molecule occurring in the patient or the cancer cell line gene having the specific cancer can be reflected in the reference- Generating simulation-input information comprising model-parameters; And generating information on the efficacy of the possible combination of the one or more drugs using the simulation-input information.

Wherein the information about the potency of the possible combination of one or more drugs may include information about one or more of a single drug sensitivity, multiple drug sensitivity, optimal drug, and optimal drug combination.

Also, information about the efficacy of a possible combination of the one or more drugs, the synergistic efficacy of all possible combinations of the one or more drugs, i.e., information on synergistic efficacy.

The simulation module may execute a method of calculating a drug efficacy to generate information about one or more of the optimal drug and the optimal drug combination.

One embodiment of the method for calculating drug efficacy comprises the steps of: generating a first specific network by mapping gene mutation information of a first cancer cell to a nominal network; Applying a first perturbation corresponding to a first drug to the first specific network to create a first specific perturbation network; Generating information regarding a first perturbation state transition diagram that is a state transition diagram of the first specific perturbation network; And calculating a score for the utility of the first drug based on the baseline size of the first perturbation state transition diagram.

At this time, the first drug may be a combination of two or more different drugs.

Wherein the method for calculating drug efficacy comprises applying a second perturbation corresponding to a second drug or a second drug combination to the first specific network to generate a second specific perturbation network, Generating information on a second perturbation state transition diagram that is a state transition diagram of the second specific perturbation network; Applying a third perturbation corresponding to a combination of the first drug and the second drug to the first specific network to generate a third specific perturbation network; And generating a third perturbation state transition diagram that is a state transition diagram of the third specific perturbation network.

Wherein the score for efficacy may comprise a synergistic value indicative of a synergistic effect of the combination of the first drug and the second drug.

At this time, the method for calculating the synergy value may further comprise calculating, based on the size of the bases showing cell death that can be obtained from the information on the first perturbation state transition diagram, the first D ratio which is the probability value at which the first cancer cells die, (D A ); Calculating a second D ratio (D B ), which is a probability value at which the first cancer cell is to be killed, based on a size of bases indicating cell apoptosis that can be obtained from the information on the second perturbation state transition diagram; Calculating a third D-ratio (D AB ), which is a probability value at which the first cancer cells are to be killed, based on the size of bases indicating cell apoptosis that can be obtained from the information on the third perturbation state transition diagram; And calculating the synergy value (S score) using the first D ratio, the second D ratio, and the third D ratio.

At this time, the synergy value can be calculated using the following equation.

[Formula] S score = D AB - {1- (1-D A ) (1-D B )}

At this time, the score for the utility may be a first state transition diagram of the first spe- cific network, wherein the first propagation diagram is a first propagation diagram that is proportional to the size of the bases representing cell proliferation, cell cycle arrest, probability (P P_before), the first cycle stop probability (P A_before), and the first kill probability (P D_before) to the following equation P P, P a, the first R score (R before being output to each assigned to P D ), And a second proliferation probability (P P_after ), a two-cycle outage probability (P A_after ), and a second proliferation probability (P A_after ) that are proportional to the sizes of bases indicating cell proliferation, cell cycle arrest , (D score) calculated using the second R score (R after ) calculated by substituting the second extinction probability (P D_after ) into P P , P A and P D in the following expressions .

[Equation]

R score = W P * P P + W A * P A + W D * P D

W P , W A, and W D is a predetermined constant

At this time, the efficacy value (D score) can be calculated using the following equation.

[Equation]

D score = (R after -R before ) / (R max -R before )

Here, R max is the maximum value that the R score in Equation 1 can have

At this time, the first drug or the second drug may be a combination of two or more different drugs.

The step of mapping the gene mutation information of the first cancer cell to the nominal network to generate the first specific network may further include the steps of: receiving the nucleotide network; acquiring gene mutation information of the first cancer cell; ; Finding a first node in the nominal network corresponding to a non-sense mutated gene of the first cancer cell or a gene replicating the HOMDEL replica, and changing the nominal network so that the first node always has a deactivated state step; A second node corresponding to a gene having a functional impact score greater than a predetermined first value among the mismatch mutated genes of the first cancer cell is found in the nominal network, and when the mismatch mutated gene is an Oncogene, Changing the nominal network such that the second node always has an active state and changing the nominal network such that the second node is always inactive if the missense mutated gene is a tumors suppressor; A third node corresponding to the z-score of the mRNA expression in the LOSS replicated water-stranded gene of the first cancer cell is smaller than a predetermined second value in the nominal network, and the third node is always in a deactivated state Varying the nominal network to have the nominal network; And a fourth node corresponding to a GAIN replicated watery gene or an AMP replicated watery gene of the first cancer cell corresponds to a z-score of mRNA expression greater than a predetermined third value in the nominal network, And changing the nominal network so that the four nodes always have an active state.

The method for calculating drug efficacy provided according to another embodiment is a method for calculating the drug efficacy using a specific perturbation network generated by mapping perturbation corresponding to a specific drug to a specific network generated by mapping gene mutation information of cancer cells to a nominal network Generating a state transition diagram of the perturbation state transition diagram; And calculating a score for the effectiveness of the drug based on the baseline size of the perturbation state transition diagram.

The method for calculating the drug efficacy can be performed by a computing device according to a program code recorded in a computer readable non-transactional recording medium.

According to the present invention, it is possible to provide a technique of quickly finding the optimal drug or drug combination through computer simulation even if the various anti-cancer drugs are not actually tested in the human body or tissues.

BRIEF DESCRIPTION OF THE DRAWINGS Fig. 1 shows an example of the configuration of an information processing system for providing an information processing method for providing information for selection of an optimal target cancer therapy for patient-customized treatment, which is provided according to an embodiment of the present invention.
2 illustrates a configuration of a computing system provided in accordance with an embodiment of the present invention.
Figure 3 is a flow chart comprising steps for an information processing method for providing information for selection of an optimal target chemotherapeutic agent for a patient-customized treatment, provided in accordance with an embodiment of the present invention.
Figure 4 illustrates how a simulation module in accordance with an embodiment of the invention generates information about one or more of a single drug sensitivity, multiple drug sensitivity, optimal drug, and optimal drug combination using the simulation-input information ≪ / RTI >
FIG. 5 is an exemplary network presented to help understand the structure of the cancer-network described above.
FIG. 6A is a state transition diagram showing a total of 16 states that the Boolean network shown in FIG. 5A can have. FIG.
6 (b) shows two point attractors (= attractors) and one cyclic attractor included in the state transition diagram shown in FIG. 6 (a) separately.
7 (a) shows a biomolecule network modeled for an actual cancer cell.
FIG. 7 (b) is a state transition diagram showing a total of 2 ^ 16 states that the Boolean network shown in FIG. 7 (a) can have.
FIG. 8 is a diagram illustrating a procedure of a drug prediction method through an attractor dynamics analysis of a cancer-network (cancer cell network) provided according to an embodiment of the present invention.
FIG. 9 is intended to illustrate a method of calculating the efficacy value of a drug according to an embodiment of the present invention.
Figure 10 is intended to illustrate a method of calculating the synergy value of a drug combination according to one embodiment of the present invention.
11 is a flowchart for a method for calculating drug efficacy according to an embodiment of the present invention.
12 is a flowchart showing a method of calculating the synergy value shown in FIG.
13 is a flowchart showing a specific method for generating a special network by mapping genetic mutation information of cancer cells to the nominal network shown in FIG.

Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. However, the present invention is not limited to the embodiments described herein, but may be implemented in various other forms. The terminology used herein is for the purpose of understanding the embodiments and is not intended to limit the scope of the present invention. Also, the singular forms as used below include plural forms unless the phrases expressly have the opposite meaning.

1, 2, 3, and 4 will be described below together.

According to an embodiment of the present invention, it is possible to provide an information processing method for providing information for selecting an optimal target cancer therapy for a patient-customized treatment.

The information processing method may include the following steps.

In step S10, the computing system 100 searches for a reference network corresponding to cancer of one patient or one cell out of a plurality of cancer-networks associated with a plurality of arms stored in the network DB 4, (5) can be determined.

In the present invention, cancer-network means a network based on the causal interaction of important genes involved in the development, growth, and metastasis of cancer. Different networks can be constructed depending on the type of cancer and the biological context. For example, there is a network describing the proliferation of malignant colorectal cancer, a network involved in the evolution of benign colon cancer to malignancy, and a network describing metastatic breast cancer. If the patient is thought to have 'benign colorectal cancer', a benign colorectal cancer network can be considered as a reference.

The computing system 100 may comprise a computing system 100 having a single power source, or a plurality of discrete computing systems 100 having different power sources may be interconnected via a network.

The plurality of arms may refer to a collection of different types of arms. Breast cancer and liver cancer, for example, are different types of cancer.

Each of the arm-networks may be defined to include a plurality of nodes and a plurality of links connecting the plurality of nodes. Each of the nodes may have a predetermined range of values, and the influence of the value of the first node on the value of the second node may be expressed using the link. For example, each of the links may indicate a causal relationship indicating a relationship as to whether the value of the second node increases or decreases by an increase or decrease in the value of the first node connected by the link.

In one embodiment, each node may be defined to have only a binary value of '0' or '1'. At this time, the binary value may indicate 'activation' or 'deactivation'.

In one embodiment of the present invention, each of the nodes may represent an expression amount of a specific gene or an expression amount of a specific protein.

Cancer-networks for different types of cancer may be different. For example, the cancer-network for breast cancer may be different from the cancer-network for liver cancer.

The computing system 100 may receive information 102 about the type of cancer from the user through the user interface unit 101 regarding the one patient or the one cell line.

The computing system 100 may obtain information 42 about the plurality of arm-networks from the network DB 4. [

The network DB 4 is a DB that the computing system 100 can access. The network DB 4 may be installed in an apparatus included in the computing system 100 or installed in an apparatus provided outside the computing system 100.

The network DB 4 may include a table including identification information for identifying each of the plurality of arm-networks and a range of parameters of the nodes and links constituting the respective arm-networks.

The reference network 5 may refer to a cancer-network corresponding to the type of cancer for the patient or cell line among the plurality of cancer-networks. Therefore, the reference network 5 may be defined to include the plurality of nodes and the plurality of links.

The computing system 100 may include a network selector 41 including matching information indicating which of the plurality of arms each of the plurality of arms is matched with. The computing system 100 can use the network selector 41 to determine a cancer-network matching information 102 about the type of cancer for the patient or cell line input via the user interface 101 have.

The network selector 41 may be a software module running on a particular computing device within the computing system 100 or a software module running on another computing device not included in the computing system 100. [

In step S20, the computing system 100 determines one or more target-nodes 7 targeted by each of the one or more drugs designated by the user among the nodes included in the determined reference network 5 You can decide.

To this end, the computing system 100 may receive information (103) about one or more drugs from a user via the user interface (101). The user may enter information about the first drug into the computing system 100 when it is desired to obtain information about how much the first drug has an effect on the type of cancer of the input type.

In addition, the computing system 100 may access the drug database 6 to obtain information 62 about a plurality of drugs. The drug DB 6 may be provided in a particular computing device within the computing system 100 as a DB accessible by the computing system 100 or may be provided by other computing devices not included in the computing system 100 .

The drug DB 6 includes a plurality of drugs among the nodes constituting each of the cancer networks and a drug-target (target) having information about the target-node 7 each of which is targeted by the plurality of drugs And may include a node table. For example, when the first drug is involved in the expression of a first gene or a first protein, the drug-target node table associates the first drug with a node corresponding to the first gene or a node corresponding to the first protein Information to be transmitted. A node associated with the first drug entered by the user may be referred to as the target-node 7 that the particular drug targets. Thus, the amount of expression of the gene or protein corresponding to the target-node of the first drug can be regulated by administration of the first drug.

The computing system 100 may include information obtained from the drug DB 6 for the target-node 7 that matches each of the one or more drugs included in the information 103 for one or more drugs entered by the user, And a drug-target mapper unit 61 for determining the drug-target mapper unit 61 using the drug-target mapper unit 61.

The computing system 100 may use the drug-target mapper unit 61 to determine one or more target-nodes 7 targeted by one or more drugs entered by the user.

Target-mapper portion 61 may be a software module running on a particular computing device contained within computing system 100 or a software module running on another computing device not included in computing system 100. [

In step S30, the computing system 100 generates information on the change in the level of the molecule so that the information on the change in the level of the molecule occurring in the gene of the patient or cell line can be reflected in the reference- To one or more specific-model-parameters (8).

To this end, the computing system 100 may receive information 104 from the user via the user interface 101 about changes in molecular level occurring in the genes of the patient or cell line. For example, the information on the change in the molecular level may be a gene expression, a copy number variation (CNV), or information on a mutation.

Computing system 100 may also include other computing devices that are accessible to or may be provided by a particular computing device included in computing system 100 or other computing devices that are not included in computing system 100. [ The rule DB 9 provided by the user can be used.

The rule DB 9 may include identification information for distinguishing among the mutations that are distinguished from each other among the changes included in the information (104) about the change in the molecular level occurring in the genes of the patient or cell line. Also, the rule DB 9 may include information about the nodes of the arm-network, whose values should be limited by the respective changes. Hereinafter, a node whose value is limited in this specification may be referred to as a pinned node. In addition, the rule DB 9 may include information on a limited range of values that the pin-node can have.

For example, in certain patients or cell lines, genes such as APC may have a loss of function. At this time, each node of the reference network 5 may have only binary values of '0' or '1'. Now, in order to reflect the loss of the function in the reference network 5, the reference network 5 can be changed by setting the node corresponding to the APC to have a value of '0'. The converted reference network 5 may be referred to as a special network (a specific network). The special network may be generated in the simulation module 11 described later.

The computing system 100 is configured to perform the steps of: (1) using information (104) about a change in a molecular level generated in a gene of the patient or cell line inputted by a user, (2) And a network node value limiter 91 for determining the pin-node among the nodes included in the reference network 5 and determining the limit range of the value possessed by the pin-node.

Whereby the computing system 100 is able to determine the limit value of the value of the pin-node and the pin-node corresponding to the information (104) about the change in the level of the molecule occurring in the gene of the patient or cell line input by the user Can be obtained. One or more of the pin-nodes may be selected according to a specific example of the information 104 about the change in the molecular level occurring in the gene of the patient or cell line.

The specific-model-parameter 8 may refer to parameters including information about the pin-node and a limit range of values of the pin-node.

The network node value limiter 91 may be a software module running on a particular computing device included in the computing system 100 or a software module running on another computing device not included in the computing system 100. [

 In an embodiment of the present invention, the value of one or more of the nodes comprising any of the sub-networks may be set to a fixed value or to have only a specific range of values.

At step S40, the computing system 100 is connected to a simulation-input (or simulation) system incorporating a reference network 5, one or more target-nodes 7, and one or more specific- Information 88 can be generated.

To that end, the computing system 100 includes a data integration unit 10, which is a software module that runs on a particular computing device included in the computing system 100 or runs on another computing device that is not included in the computing system 100, Can be used.

The data integration unit 10 uses the one or more specific-model-parameters 8 to convert information for changing the characteristics of some of the nodes constituting the reference network 5, (11). For example, among the plurality of nodes constituting the reference network 5, the value of the node corresponding to the pin-node included in the specific-model-parameters 8 is the value of the value of the pin- It is possible to change the reference network 5 so that it can change only within a limited range. The reference network 5, in which the characteristics of some nodes are changed by the one or more specific-model-parameters 8 as described above, may be hereinafter referred to as a specific network.

In addition, the data integration unit 10 may include information on the one or more target-nodes 7 targeted by a drug input by a user among the nodes constituting the private network, The simulation module 11 can be provided with information about the values of the target-nodes 7 modified by the drug.

In step S50, the computing system 100 may enter the simulation-input information 88 into the simulation module 11. The simulation model 11 can generate information 16 about one or more of a single drug sensitivity, multiple drug sensitivity, optimal drug, and optimal drug combination by calculating the simulation-input information 88. [

For this purpose, the computing system 100 may utilize the simulation module 11. The simulation module 11 may be a software module running on a particular computing device included in the computing system 100 or running on another computing device not included in the computing system 100.

The simulation module 11 provides the simulation-input information generated by integrating the reference network 5, one or more target-nodes 7, and one or more specific-model-parameters 8 Can receive. The reference network 5 and the one or more specific-model-parameters 8 may be used to create the special network.

At this time, the simulation module 11 may receive information about a plurality of target-nodes, e.g., the first target-node targeted by the first drug and the second target-node targeted by the second drug.

In step S51, the simulation module 11 may generate the special network using the reference network 5, and one or more specific-model-parameters 8.

In step S52, the simulation module 11 may simulate the state of administration of the first drug to the patient or cell line by controlling the value of the first target-node in the special network. For example, the effect of modulating the expression level of a gene or protein corresponding to the first target-node by administration of the first drug may be simulated by fixing the value of the first target-node to a specific range of values or a specific value can do.

In step S53, the simulation module 11 may simulate the state of administration of the second drug to the patient or cell line by controlling the value of the second target-node of the special network. For example, by fixing the value of the second target-node to a specific range of values or a specific value, the effect of modulating the expression level of the gene or protein corresponding to the second target-node by the administration of the second drug is simulated can do.

In step S54, the simulation module 11 concurrently controls the values of the first target-node and the second target-node of the special network to determine whether the first drug and the second Simultaneous administration of the drug can be achieved.

As such, the simulation module 11 may be configured to control only one of the plurality of target-node values corresponding to a plurality of drugs, or to control the values of two or more target-nodes together, A plurality of combined drugs can be simulated.

The simulation module 11 may output a single drug sensitivity 16 that is a sensitivity to a single drug corresponding to the target-node by controlling the value of one target-node. In addition, by controlling the values of two or more target-nodes, it is possible to output multiple drug sensitivities 16, which are the sensitivities to combinations of drugs corresponding to the two or more target-nodes. Also, information (16) about the optimal drug and / or the optimal combination drug from the single drug sensitivities for each of a plurality of different single drugs and the multiple drug sensitivities for each of the different combinations of drugs Can be output.

The sensitivity described above can be defined as the degree of response of the cell to the dose of the administered drug or the degree of response of the patient.

The simulation module 11 may be any one of a signal flow analysis module (SFA analysis module), a state attractor analysis module, and a machine learning analysis module.

 The method for calculating the sensitivity using the state-relief analysis module may include the following steps S511 and S512.

In step S511, we observe how the baseline profile of the state attractor of the buoyant network changes before and after administration of the drug. For example, before administration of the drug, 10% of the baseline of the state transducer A, 10% of the baseline of the state transducer B, 80% of the baseline of the state transducer C, 50% of the baseline of the state transducer A, B may have a base of 20%, and a state attractor C may have a base of 30%.

In step S512, it can be assumed that the type of the state attractor related to the sensitivity is the state attractor A. [ At this time, the basic profile of the state attractor A changed from 10% to 50%. Using this, the sensitivity is 50% ?? 10% = 40%, or 50% / 10% = 4. Or there may be a different third calculation method.

The computing system 100 may be a system in which a plurality of computing devices operating by different power supplies are connected through a network, or a system composed of one computing device.

Hereinafter, with reference to FIGS. 5 to 13, an example of a method for generating the optimal drug and / or optimal combination drug information 16 using the above-described state-relief analysis module will be described.

FIG. 5 is an exemplary network presented to help understand the structure of the cancer-network described above.

5 (a) is a network of biomolecule networks modeled by four nodes (x1 to x4) and links showing the mutual relationship therebetween. Each node may represent the expression amount of biomolecules. The expression level may simply have a binary value of 0 or 1, wherein the network represented by Figure 5 (a) may be a Boolean network.

The arrow in Fig. 5 (a) may indicate the relationship in which the expression amount of the node arranged at the end point of the arrow (the head part of the arrow) increases and the opposite relationship when the expression amount of the node arranged at the start point of the arrow increases .

In FIG. 5 (a), the nail shape shows a relationship in which the expression amount of the node disposed at the nail-shaped end point (nail head portion) decreases when the expression amount of the node disposed at the nail-shaped starting point increases and vice versa .

FIG. 5 (b) shows a truth table that the values of the four nodes shown in FIG. 5 (a) can have.

FIG. 6A is a state transition diagram showing a total of 16 states that the Boolean network shown in FIG. 5A can have. FIG.

6 (b) shows two point attractors (= attractors) and one cyclic attractor included in the state transition diagram shown in FIG. 6 (a) separately.

The state transition diagram shown in FIG. 6A includes a first set of states converging to '0010' as a first point attractor, a set of states converging to '0000' as a second point attractor And a third basin, which is a set of states hunting for the first cyclic attractor ['0001', '1000', '0100'].

7 (a) shows a biomolecule network modeled for an actual cancer cell. Each of the alphabetical identifiers shown in (a) of FIG. 7 represents each of the above-described nodes, and can represent a gene or a protein. An arrow or nail shape connecting the nodes indicates a link indicating interaction between the nodes.

In the biomolecule network shown in FIG. 7A, 16 nodes are shown. When each node is set to have a binary value, the biomolecule network becomes a Boolean network as a result. At this time, the total number of states of the Boolean network is 2 ^ 16.

FIG. 7 (b) is a state transition diagram showing a total of 2 ^ 16 states that the Boolean network shown in FIG. 7 (a) can have. In the state transition diagram shown in Fig. 7 (b), there are five point attractors and one cyclic aurator, and there are six bases in total. In the state transition diagram shown in FIG. 7 (b), each state can be represented by a fine point.

FIG. 8 is a diagram illustrating a procedure of a drug prediction method through an attractor dynamics analysis of a cancer-network (cancer cell network) provided according to an embodiment of the present invention.

Figure 8 (a) shows the steps of combining the cancer genomic data obtained from the patient groups observed in one or more organs and the cancer cell main databases obtained by raising the cancer cells in the laboratory into the nominal network 100 .

The nominal network 100 is an example of the above-described biomolecule network modeled so that each node can have a binary value of 0 or 1.

For the present invention, different nominal networks may be provided, wherein the nominal network 100 shown in FIG. 8 represents one of these.

The database generated from the patient group observed in the one or more organs may include, for example, TCGA and COSMIC. The TCGA may be a database generated from the first patient group observed in the first organ, and the COSMIC may be a database generated from the second patient group observed in the second organ.

In addition, CCLE is an example of a cancer cell main database prepared by raising cancer cells in the above-mentioned laboratory.

The cancer genomic data may include a plurality of different cancer cells.

FIG. 8 (b) shows a plurality of different specific networks obtained by mapping gene mutation information of different cancer cells to the nominal network 100.

For example, the first specific network 111 is obtained by mapping the gene mutation information of the first cancer cell to the nominal network 100, and the k.sup.th special network 112 is obtained by mapping k And the Nth special network 113 is obtained by mapping the gene mutation information of the N cancer cell to the nominal network 100. [

Gray nodes included in each of the special networks shown in FIG. 8 (b) represent nodes in a steady state that can have all values of 0 or 1, white nodes represent 0 and 1 indicates a first type constraint node limited to have only a value indicating inactivation, and black nodes indicates a second type constraint node limited to have only a value indicating 0 or 1 among values of activation.

Which node among the nodes included in the nominal network 100 becomes the first type restriction node or the second type restriction node is determined according to the gene mutation information of the cancer cells mapped to the nominal network 100 .

FIG. 8 (c) shows a step of applying a first perturbation to each of the private networks to change the corresponding private network. That is, each of the networks shown in FIG. 8C may be referred to as a 'special perturbation network'.

Here, the first perturbation may mean the effect that the first drug or the first drug combination acts on cancer cells. According to the first perturbation, one or more specific nodes may be removed from the biomolecule network, or the value of one or more specific nodes in the biomolecule network may be limited to have a certain range of values.

For example, the first specific perturbation network 121 may be configured to detect a first perturbation that is indicative of the effect of the first drug or first drug combination on cancer cells, .

Likewise, the k-th spective perturbation network 122 is adapted to determine whether the first perturbation, which is indicative of the effect of the first drug or first drug combination on cancer cells, .

Similarly, the Nth preferential perturbation network 123 may be configured to determine whether the first perturbation, which is indicative of the effect of the first drug or first drug combination on the cancer cells, is applied to the Nth preferential network 113, .

Each of the above specific perturbation networks can be regarded as a defined network used to generate state transition diagrams.

FIG. 8 (d) shows perturbation state transition diagrams, which are state transition diagrams calculated by the respective specific perturbation networks.

For example, the first perturbation state transition diagram 131 may be a state transition diagram of the first specific perturbation network 121 modeled when the first drug or the first drug combination is administered to the first cancer cells.

And the kth perturbation state transition diagram 132 may be a state transition diagram of the kth special perturbation network 122 modeled when the first drug or first drug combination is administered to a second cancer cell.

And the Nth perturbation state transition diagram 131 may be a state transition diagram of the Nth special perturbation network 123 modeled when the first drug or the first drug combination is administered to the Nth cancer cell.

In the example of FIG. 8 (d), for the first perturbation state transition diagram 131, the k th perturbation state transition diagram 132, and the N th perturbation state transition diagram 133, The phenotypes are cell proliferation (P), cell cycle (A), and apoptosis (D), respectively. That is, when the first drug or the first drug combination, which is the same drug, is administered to the first cancer cell, the k th cancer cell, and the N th cancer cell, the first cancer cell is still proliferated, Is stopped, and the N-th cancer cell is killed. That is, the effect of a specific drug, such as the first drug or the first drug combination, may be different depending on the type of cancer cells and can be determined through computer simulation.

8 (c) and 8 (d) show the respective specific networks depending on the first drug or the second drug combination which is a specific drug. Alternatively, a second perturbation different from the first perturbation may be applied to each of the specific networks to change the pertinent private network. In this way, each of the specific perturbation networks shown in FIGS. 8 (c) and 8 (d) can be different from the perturbation state transition diagram.

That is, different perturbations representing different drugs may be applied to each specific network to obtain information about the reaction results of different cancer cells for each drug.

Now, a method of calculating the utility of a drug or drug combination using the perturbation state transition diagrams obtained in (d) of FIG. 8 will be described. Here, efficiency may be an upper concept including an efficacy score and a synergy score.

FIG. 9 is intended to illustrate a method of calculating the efficacy value of a drug according to an embodiment of the present invention.

9, the second specific perturbation network 502 may be obtained by applying a first perturbation showing the effect of the first drug to the first specific network 501 to which the gene mutation information of the first cancer cell is mapped have.

At this time, a first state transition diagram 511 may be generated from the first special network 501 and a second perturbation state transition diagram 512 may be generated from the second specific perturbation network 502.

The first state transition diagram 511 and the second perturbation state transition diagram 512 can then be analyzed to calculate the efficacy value of the first drug.

The first state transition diagram 511 provides a probability value of the phenotype of the first cancer cell when the first drug is not administered. That is, when the first drug is not administered, the probability that the phenotype of the first cancer cell will become proliferation (P), cycle stop (A), and death (D) is 72%, 20%, and 7%, respectively .

The second perturbation state transition diagram 512 provides a probability value of the phenotype that the first cancer cell shows when the first drug is administered. That is, when the first drug is administered, the probability that the phenotype of the first cancer cell will become proliferation (P), cycle stop (A), and death (D) is 15%, 10%, and 75%, respectively.

The efficacy value of the first drug can now be calculated using the method shown in table 520.

The first to fourth columns of table 520 indicate the presence or absence of inhibitory treatment, major cellular phenotype, response phenotype score, and drug efficacy score, respectively, .

The first row of the table 520 indicates before the inhibitor treatment is applied, that is, before the first drug is administered to the first cancer cell, and the second row shows that after the inhibitor treatment is applied, After administration to the first cancer cell.

Since the probability of cell proliferation of the first cancer cell is 72% before the inhibitor treatment is applied in Table 520, the main cell phenotype of the first cancer cell is 'proliferation', but after the treatment with the inhibitory drug is applied, The primary cell phenotype of the first cancer cell is changed to 'death' because the probability of death is 75%.

The response phenotypic values shown in the third column of the table 520, i.e., the R scores, represent the probability proportional to the size of each basin according to each state transition diagram, i.e. the proliferation probability (P P ), the cycle halt probability (P A ) probability (P D) after each pre-multiplied by a predetermined weight W P, W a, W and D to each other may be defined as a plus value. That is, the R score can be defined as Equation 1 below.

[Equation 1]

R score = W P * P P + W A * P A + W D * P D

In the equation (1), for example, W P <W A <W D may be set, but the present invention is not limited thereto. In the example of FIG. 5, W P = 1, W A = 2, and W D = 4 are set.

The R scores can be calculated for different state transition diagrams, respectively.

9, the first R score, which is the R score for the first state transition diagram 511 is 1.4, and the second R score, which is the R score for the second perturbation state transition diagram 512, is 3.35 It can be understood that it is calculated.

Now, in one embodiment of the present invention, the D score, which is the efficacy value for the first drug described above, may be defined as: &quot; (2) &quot;

[Equation 2]

D score = (R after -R before ) / (R max -R before )

here,

R after is the R score of the cancer cells after the inhibitory drug treatment

R before is the R score of the cancer cells before the inhibitor treatment

R max is the maximum value of the R score that a cancer cell can have

In one preferred embodiment, R max of formula 2 may be the same value W and D.

In the example of FIG. 9, it can be understood that the D score of the first drug for the first cancer cell is calculated to be 0.75.

The D score in the case of administration of the second drug or the first drug combination different from the first drug to the first cancer cells may be calculated again.

When calculating the D score for a plurality of drugs or drug combinations for any cancer cell, the drug or drug combination with the highest D score may be evaluated as the most effective drug or drug combination in leading to the cancer death have.

The method for calculating the D score of the first drug or the first drug for the first cancer cell when the first drug or the first drug combination is administered to the first cancer cell according to an embodiment of the present invention The following steps may be included.

In step S51, the first specific network 501 is generated by mapping the gene mutation information of the first cancer cell to the previously provided nominal network.

In step S52, a first perturbation indicative of the effect of the first drug on the special network is applied to generate a second percipient network 502.

In step S53, a first state transition diagram 511 and a second perturbation state transition diagram 512 are generated from the first special network 501 and the second specific perturbation network 502, respectively .

In step S54, based on the sizes of the basins obtained in the first state transition diagram 511, the first propagation probability P P_before , the first cycle stop probability P A_before , and the first extinction probability (P D_before ) and based on the size of the basins obtained in the second perturbation state transition diagram 512, the second propagation probability (P P_after ), the second cycle stop probability (P A_after ) 2 extinction probability (P D_after ).

In step S55, the first R score (R before ), which is the R score for the first state transition diagram 511, is calculated using Equation 1 and the R score for the second perturbation state transition diagram 512 is calculated And a second R score (R after ). Then, the maximum value ( Rmax ) of the R score according to Equation (1) is calculated.

In this case, to calculate the first R scores, each of the first multiplication probability in the formula 1 P P, P A, P D (P P_before), the first cycle stop probability (P A_before), and the first The probability of extinction (P D_before ) can be substituted.

And wherein to calculate the 1 R Scores, Formula 1 of P P, P A, each of the second growth probability P D (P P_after), probability of stopping the second cycle (P A_after), and the second kill probability (P D_after ) can be substituted.

In step S56, the first R score (R before ), the second R score (R after ), and the maximum value (R max ) of the R score are substituted into the equation (2) The D score indicating the efficacy value for the first cancer cell is calculated.

Figure 10 is intended to illustrate a method of calculating the synergy value of a drug combination according to one embodiment of the present invention. This will be described below with reference to FIG.

The first specific perturbation network 601 may be obtained by applying a first perturbation indicating the effect of the drug A to the first specific network to which the gene mutation information of the first cancer cell is mapped.

The second specific perturbation network 602 may be obtained by applying a second perturbation indicative of the effect of drug B to the first specific network.

The third specific perturbation network 603 may be obtained by applying a third perturbation that together exhibits the effect of the drug A and the effect of the drug B on the first specific network.

Generating a first perturbation state transition diagram 611 from the first specific perturbation network 601, generating a second perturbation state transition diagram 612 from the second perturbation network 602, and A third perturbation state transition diagram 613 may be generated from the third specific perturbation network 603. [

At this time, the drug combination synergistic value according to an embodiment of the present invention can be calculated using the following steps.

In step S61, the gene network mutation information of the first cancer cell is mapped to the previously provided nominal network to generate a special network.

In step S62, the first perturbation, the second perturbation, and the third perturbations, which exhibit the effect of the combination of Drug A, Drug B, and Drug A and Drug B on the specific network, respectively, Generates a first specific perturbation network 601, a second specific perturbation network 602, and a third specific perturbation network 603.

At step S63 the first perceptual state transition diagram 601, the second perceptual perturbation network 602 and the third perturbation perturbation network 603 are respectively derived from the first perturbation state transition diagram 601, ), A second perturbation state transition diagram 612, and a third perturbation state transition diagram 613.

In step S64, based on the size of the bases indicating cell death that can be obtained in the first perturbation state transition diagram 611, the second perturbation state transition diagram 612, and the third perturbation state transition diagram 613, (D ratio), which is a probability value at which the first cancer cells will die, respectively.

In step S65, a predicted D ratio is calculated using equation (3).

[Equation 3]

Prediction D ratio = 1 - (1-D A ) (1-D B )

here,

D A is due to, drug A showing a first Spanish during bay god% size that is the cell death in the Peek perturbation network obtained by applying a first perturbation showing the effect of drug A in Spain during pick-network of the first cancer cell D ratio

D B is the percentage of bases indicative of cell death in a second specific perturbation network obtained by applying a second perturbation indicative of the effect of drug B to the primary cancer cell's specific network, ratio

In step S66, the synergy value (S score) of the drug combination is calculated using equation (4).

[Equation 4]

S score = D AB - { predicted D ratio } = D AB - {1 - (1 - D A ) (1 - D B )}

Here, D AB represents the percentage of bases representing apoptosis in a third specific perturbation network obtained by applying a third perturbation showing the effect of drug A and drug B being administered together in the specific network of the first cancer cells D ratio by combination of Drug A and Drug B

In Steps S61 to S66 described above, although Drug A and Drug B are each assumed to be a single drug, any one of Drug A and Drug B may mean a combination drug in which two or more drugs are already combined . That is, the synergy value depending on the combination of three or more drugs can be calculated.

The graph 620 in FIG. 10 is intended to illustrate the synergy level when drug A and drug B are administered together. The D ratio of drug A, D A = 0.2, and the D ratio of drug B, D B = 0.15. At this time, when Drug A and Drug B are administered together, The expected predicted D ratio, 1- (1-0.2) (1-0.15) = 0.32. At this time, since the D ratio, D AB = 0.64 when the drug A and the drug B are administered together, is larger than the predicted D ratio of 0.32. Therefore, the S score has 0.32 which is a value obtained by subtracting 0.32 from the predicted D ratio at D AB = 0.64.

If the S score is greater than 0, it can be judged that there is synergy between Drug A and Drug B (synergistic effect), and if the S score is less than 0, it can be judged that there is a correlation between Drug A and Drug B (Resistance effect), and when the S score is 0, it can be judged that there is a merge effect of the drug A and the drug B merely.

11 is a flowchart for a method of calculating drug efficacy according to an embodiment of the present invention.

In step S71, the first specific network may be generated by mapping the gene mutation information of the first cancer cell to the nominal network.

In step S72, a first perturbation corresponding to the first drug may be applied to the first specific network to create a first specific perturbation network.

In step S73, information on the first perturbation state transition diagram, which is the state transition diagram of the first specific perturbation network, can be generated.

In step S74, a second perturbation corresponding to the second drug or second drug combination may be applied to the first specific network to create a second specific perturbation network.

In step S75, information on the second perturbation state transition diagram, which is the state transition diagram of the second specific perturbation network, can be generated.

In step S76, a third perturbation corresponding to the combination of the first drug and the second drug may be applied to the first specific network to generate a third specific perturbation network.

In step S77, information on the third perturbation state transition diagram, which is the state transition diagram of the third specific perturbation network, can be generated.

In step S78, a score for the utility of the first drug may be calculated based on the size of the bases of the first perturbation state transition diagram.

At this time, the score for the utility may include an efficacy score (D score) calculated using the following method.

Wherein the efficacy figure (D score) is a first state transition diagram of the first spe- cific network, wherein the efficacy figure (D score) is a first state transition diagram, (R (R)) calculated by substituting P P , P A , and P D of the equation (1) into the first probability of occurrence (P P_before ), the first cycle stop probability (P A_before ), and the first survival probability before), and the first perturbation state transition diagrams each cell proliferation, cell stop cycle of, and a second multiplication probability in proportion to the bay of the gods size that is the cell death (P P_after), 2 cycles stop probability (P A_after), (R after ) calculated by substituting the second extinction probability ( PD_after ) and the second extinction probability (P D_after ) into P P , P A , and P D in the above equation (1), respectively.

Wherein the score for the efficacy may also include a synergistic value indicative of a synergistic effect of the combination of the first drug and the second drug.

12 is a flowchart showing a method of calculating the synergy value shown in FIG.

In step S81, based on the size of bases indicating cell death that can be obtained from the information on the first perturbation state transition diagram, the first D ratio D A , which is the probability value at which the first cancer cells are killed, Can be calculated.

In step S82, a second D-ratio (D B ), which is a probability value at which the first cancer cells are to be killed, is calculated based on the size of bases indicating cell death that can be obtained from the information on the second perturbation state transition diagram, Can be calculated.

In step S83, a third D ratio (D AB ), which is a probability value at which the first cancer cells are to be killed, is calculated based on the size of bases indicating cell death that can be obtained from the information on the third perturbation state transition diagram, Can be calculated.

In step S84, the synergy value (S score) may be calculated using the first D ratio, the second D ratio, and the third D ratio.

13 is a flowchart showing a specific method for generating a special network by mapping genetic mutation information of cancer cells to the nominal network shown in FIG.

In one embodiment of the present invention, observable gene mutation (alteration) is selected from the group consisting of Nonsense mutation, Missense mutation, HOMDEL replication watershed, LOSS replication watershed, GAIN replication watershed, and AMP replication watershed Can be classified as belonging to a group.

The non-sense mutation may be a change corresponding to the disappearance of a part of the gene sequence.

The missense mutation may be a change corresponding to a case in which some of the gene sequences have erroneous information when compared with a normal state.

The HOMDEL replicated water surface may be used to define the LOSS replicated water surface, the GAIN replica water surface, and the AMP replica water surface, and the gene cloning level L3 for normal cell division.

The LOSS replicase may be a change corresponding to a case where the gene replication level L2 at the time of cell division is smaller than the L3, regardless of the information on the gene sequence.

The HOMDEL replicase may be a change corresponding to a case where the level of gene replication L1 at the time of cell division is smaller than L2, regardless of the information about the gene sequence.

The GAIN copying water phase may be a change corresponding to a case where the gene cloning level L4 at the time of cell division is larger than the L3 at the time of cell division regardless of the information on the gene sequence.

The AMP replication watershed may be a change corresponding to a case where the gene replication level L5 at the time of cell division is greater than the L4 regardless of the information about the gene sequence.

That is, when CN (genetic replication watershed type) is defined as a gene replication level,

CN (HOMDEL replica) = L1

&Lt; CN (LOSS replica) = L2

&Lt; CN (normal gene) = L3

<CN (GAIN replica water level) = L4

&Lt; CN (AMP replica) = L5

Can be established.

According to an embodiment of the present invention, a specific method of mapping a genetic mutation information of a cancer cell to a nominal network to generate a special network may include the following steps executed by the computing device.

Even if the above-mentioned gene mutation occurs, it may affect the function of the gene and may not affect the function of the gene.

In step S90, the computing device is provided with a previously prepared nominal network. The nominal network may comprise a plurality of nodes and links representing the interrelationship between the nodes. At least some of the plurality of nodes may represent genes. Each node of the nominal network may have probabilistically active and inactive states.

In step S91, gene mutation information of a specific cancer cell is obtained. The gene mutation information may include at least one of the Nonsense mutation, the mismatch mutation, the HOMDEL duplication watershed, the LOSS duplication watershed, the GAIN duplication watershed, and the AMP duplication watershed.

In step S92, a first node corresponding to the Nonsense mutated gene or the HOMDEL replicated watery gene of the specific cancer cell is found in the nominal network, and the first node is always inactivated, You can change the null network.

In the nominal network, a second node corresponding to a gene having a functional impact score greater than a predetermined value among mismatch mutated genes of the specific cancer cells is found in the nominal network, and the mismatch mutated gene is oncogene The method further comprises changing the nominal network so that the second node always has an active state, and when the missense mutated gene is a tumor suppressor, changing the nominal network so that the second node always has an inactive state .

In step S94, a third node is found in the nominal network corresponding to a z-score of the mRNA expression in the LOSS replicated water-shifted gene of the particular cancer cell is smaller than a predetermined value (e.g., ?? 2) The nominal network can be changed so that the third node always has a disabled state.

In step S95, a fourth node corresponding to a GAIN replicated watery gene or the AMP replicated watery gene of the specific cancer cell corresponds to a z-score of mRNA expression greater than a predetermined value (for example, +2) And change the nominal network so that the fourth node is always in an active state.

The process of performing the steps S92 to S95 may be regarded as a process of mapping the gene mutation information of the specific cancer cell to the nominal network.

When the steps S92 to S95 are performed, the special network may be generated by mapping the genetic mutation information of the specific cancer cells to the nominal network.

Steps S92 to S95 may be performed freely mutually.

The information 16 about the optimal drug and / or the optimal combination drug using the above-described state-release analyzing module can be generated using information on the efficacy and synergistic values for each drug and drug combination . For example, the drug with the highest efficacy value may be the optimal drug, and the drug with the highest synergistic value may be determined among the combination of the drugs having the highest synergistic value.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the essential characteristics thereof. The contents of each claim in the claims may be combined with other claims without departing from the scope of the claims.

5: Reference Network
7: target-nodes
8: Specific-Model-Parameters
10: Data integration unit
11: Simulation module
41: Network selector
61: drug-target mapper section
88: Simulation - Input information
91: Network node value limit unit
100: Computing System
102: Cancer information about a patient or cell line
103: Information about one or more drugs entered by the user
104: Information on changes in the molecular level of genes in patients or cell lines

Claims (10)

The network selector of the computing system determining a reference network corresponding to information about the cancer of the patient or cell line, of the plurality of cancer-networks associated with the plurality of cancers;
Wherein the drug-target mapper portion of the computing system determines one or more target-nodes targeted by each drug included in the information about the one or more drugs entered by the user among the nodes included in the reference network ;
Wherein the network node value limiter of the computing system is configured to provide the reference-network with information about the change in the molecular level so as to reflect information on a change in the level of a molecule occurring in the gene of the patient or cell line, - transforming into model-parameters;
Generating data-integration portions of the computing system, simulation-input information incorporating the reference network, the one or more target-nodes, and the one or more specific-model-parameters; And
Wherein the simulation module of the computing system receives the simulation-input information and outputs information about one or more of a single drug sensitivity, multiple drug sensitivity, optimal drug, and optimal drug combination;
/ RTI &gt;
Information processing method. The method according to claim 1,
The simulation module includes:
Converting the reference network into a private network by reflecting the one or more specific-model-parameters to the reference network; And
Selecting one or more of the one or more target-nodes included in the singular-network to determine the single drug sensitivity, the multi-drug sensitivity, the optimal drug, and the optimal drug combination; Repeatedly executing a process of controlling values of selected target-nodes;
Lt; / RTI &gt;
Information processing method. 2. The method of claim 1, wherein the simulation module is one of an SFA analysis module, an attractor analysis module, and a machine learning analysis module, . Preparing a reference network corresponding to the cancer provided in advance;
Determining one or more target-nodes each targeted by one or more of the drugs included in the reference network;
Transforming the information about the change in the level of the molecule into one or more specific-model-parameters so that the reference-network can reflect information on a change in the level of the molecule occurring in the gene of the cancer; And
Wherein the simulation module of the computing system receives simulation-input information incorporating the reference network, the one or more target-nodes, and the one or more specific-model-parameters to determine a single drug sensitivity, , The optimal drug, and the optimal drug combination;
/ RTI &gt;
Information processing method. A network selection unit that determines a reference network corresponding to cancer information on a patient or a cell line among a plurality of cancer-networks related to the plurality of cancer;
Wherein the drug-target mapper of the computing system determines the one or more target-nodes targeted by each drug included in the information about the one or more drugs entered by the user among the nodes included in the reference network, A selection unit;
A network node that converts information about the change in the level of the molecule to one or more specific-model-parameters so that the reference-network can reflect information about a change in the level of a molecule occurring in the gene of the patient or cell line, Value limiting unit;
A data integration unit for generating simulation-input information incorporating the reference network, the one or more target-nodes, and the one or more specific-model-parameters; And
A simulation module that receives the simulation-input information and outputs information about one or more of a single drug sensitivity, multiple drug sensitivity, optimal drug, and optimal drug combination;
/ RTI &gt;
Computing system. A computing system, including one or more computing devices,
Preparing a reference network corresponding to the cancer provided in advance;
Determining one or more target-nodes each targeted by one or more of the drugs included in the reference network;
Transforming the information about the change in the level of the molecule into one or more specific-model-parameters so that the reference-network can reflect information on a change in the level of the molecule occurring in the gene of the cancer; And
Wherein the simulation module of the computing system receives simulation-input information incorporating the reference network, the one or more target-nodes, and the one or more specific-model-parameters, Outputting information on the efficacy of the combination;
Is recorded,
A computer readable non-transitory recording medium. (1) a reference network corresponding to a particular cancer; (2) one or more target-nodes, each of which is targeted by one or more of the nodes included in the reference network; And (3) one or more specific-model-parameters generated from information on the change in the level of the molecule so that information on a change in the level of a molecule occurring in a gene of the cancer cell can be reflected in the reference- Generating simulation-input information including the input information; And
Using the simulation-input information to generate information on the efficacy of a possible combination of the one or more drugs;
/ RTI &gt;
Information processing method. 8. The method of claim 7,
Wherein the generating comprises:
Converting the reference network into a private network by reflecting the one or more specific-model-parameters to the reference network; And
Selecting one or more of the one or more target-nodes included in the singular-network to determine the single drug sensitivity, the multi-drug sensitivity, the optimal drug, and the optimal drug combination; Repeatedly executing a process of controlling values of selected target-nodes;
/ RTI &gt;
Information processing method. (1) a reference network corresponding to a particular cancer; (2) one or more target-nodes, each of which is targeted by one or more of the nodes included in the reference network; And (3) one or more specific genes (or genes) derived from the information on the change in the level of the molecule, so that the information on the change in the level of the molecule occurring in the patient or the cancer cell line gene having the specific cancer can be reflected in the reference- Simulator including model-parameters; a data integrator for generating input information; And
A simulation module that receives the simulation-input information and generates information about the efficacy of the possible combination of the one or more drugs;
/ RTI &gt;
Computing system. A computing system, including one or more computing devices,
(1) a reference network corresponding to a particular cancer; (2) one or more target-nodes, each of which is targeted by one or more of the nodes included in the reference network; And (3) one or more specific-model-parameters generated from information on the change in the level of the molecule so that information on a change in the level of a molecule occurring in a gene of the cancer cell can be reflected in the reference- Generating simulation-input information including the input information; And
Using the simulation-input information to generate information on the efficacy of a possible combination of the one or more drugs;
Is recorded,
A computer readable non-transitory recording medium.
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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20200067481A (en) * 2018-12-04 2020-06-12 한국과학기술원 Method for identifying drug target molecules based on influence analysis of molecules in signaling networks
KR20210020675A (en) * 2019-08-16 2021-02-24 한국과학기술원 An optimized method of searching a boundary state of a Boolean network with minimal complexity of distance calculation using structural information and basin information of the Boolean network
KR20210021790A (en) * 2019-08-19 2021-03-02 한국과학기술원 A control method for driving any state of a Boolean network to a boundary state of the basin of a desired attractor by using a minimum temporary perturbation
KR20210098104A (en) * 2020-01-31 2021-08-10 한국과학기술원 A method for analyzing a resistance of targeted anti-cancer therapy and identifying a combination target to overcome the resistance using network simulation
KR102508252B1 (en) * 2022-01-11 2023-03-09 주식회사 볼츠만바이오 Method for training a bio signal transfer network model and device for the same

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20140032070A (en) * 2012-09-05 2014-03-14 한국과학기술원 Method for analyzing network characteristic and computer-readable medium and appratus for the same

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20140032070A (en) * 2012-09-05 2014-03-14 한국과학기술원 Method for analyzing network characteristic and computer-readable medium and appratus for the same

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
B. L. Punlya 외, "Systems Perturbation Analysis of a Large-Scale Signal Transduction Model (후략)", Frontiers in Bioengineering and Biotechnology, 4권, 10호, 2016.02.* *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20200067481A (en) * 2018-12-04 2020-06-12 한국과학기술원 Method for identifying drug target molecules based on influence analysis of molecules in signaling networks
KR20210020675A (en) * 2019-08-16 2021-02-24 한국과학기술원 An optimized method of searching a boundary state of a Boolean network with minimal complexity of distance calculation using structural information and basin information of the Boolean network
KR20210021790A (en) * 2019-08-19 2021-03-02 한국과학기술원 A control method for driving any state of a Boolean network to a boundary state of the basin of a desired attractor by using a minimum temporary perturbation
KR20210098104A (en) * 2020-01-31 2021-08-10 한국과학기술원 A method for analyzing a resistance of targeted anti-cancer therapy and identifying a combination target to overcome the resistance using network simulation
KR102508252B1 (en) * 2022-01-11 2023-03-09 주식회사 볼츠만바이오 Method for training a bio signal transfer network model and device for the same

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