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US 11,960,802 B2Granted
Method, processor device and computer program product for designing intervention into the behaviour of a real complex system
AssigneeTurbine Simulated Cell Technologies Limited
InventorsKristóf Zsolt Szalay, Péter Csermely
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Abstract
A computer-implemented method for designing intervention into the behavior of a real complex system of technical or biochemical nature. The real complex system is modeled by a network of objects and relations between the objects. The objects of the system are represented by network points and the relations are represented by edges between the network points. The states of the objects are described by a parameter set and the relations associated with the edges are described by functions of time.
Claims35
A computer-implemented method for identifying biological components in a real complex system of biochemical nature, wherein the biological components are targets for therapeutic intervention by therapeutic agents, the method comprising:
- a) modeling the real complex system by a network of objects and relations between said objects, wherein said objects of the system are represented by network points and relations are represented by edges between the network points, and wherein the states of the objects are described by a parameter set and the relations associated with the edges are described by functions of time, wherein the objects correspond to the biological components;
- b) for each object of the real system, obtaining values for each parameter of said parameter set both for an initial state and a desired target state thereof, wherein the initial state corresponds to a diseased state;
- c) setting the initial values and the desired target values of the parameters of the network points;
- d) using a predetermined metaheuristic algorithm, automatically generating an initial set of test excitations for at least one point of the network, the initial set of test excitations including a predetermined number of test excitations defined by the metaheuristic algorithm;
- e) simulating the behavior of the network by using the set of test excitations;
- f) detecting whether a simulation termination condition in a given simulation step is true and if so, stopping the simulation;
- g) after stopping the simulation, calculating and storing, for each network point, the difference between the parameter values belonging to the desired target state and the parameter values produced by the simulation;
- h) automatically generating a next set of test excitations by using the predetermined metaheuristic algorithm, the next set of test excitations being generated to more closely approach the target state based on the previous test excitations having the smallest difference between the parameter values belonging to the desired target state and the parameter values produced by the simulation, and the number of test excitations in the next set being defined by the metaheuristic algorithm;
- i) iteratively repeating steps e)-h) to reduce the difference between the parameter values produced by the simulations and the parameter values of the target state until a predetermined entire termination condition is satisfied;
- j) from among the stored simulation results, selecting the simulation result best matching the desired target state, the test set of excitations which produced said best matching simulation result is regarded as a final excitation set transferring the network from its initial state to its target state;
- and
- k) outputting the final excitation set as the targets for therapeutic intervention by therapeutic agents in the real complex system of biochemical nature.
9 more elements
A processor device for identifying biological components in a real complex system of biochemical nature, wherein the biological components are targets for therapeutic intervention by therapeutic agents, the device comprising:
- a data carrier storing a model of the real complex system of a biochemical nature, the model comprising a network of objects represented by network points and relations between the objects being represented by edges between the network points, wherein the states of the objects are described by a parameter set and the relations associated with the edges are described by functions of time, and wherein the objects correspond to the biological components;
- a processor device;
- and
- a memory device encoding instructions that, when run on the processor device, cause the processor device to:
- (a) obtain values for an initial state and a desired target state for each parameter of the parameter set for each object, wherein the initial state corresponds to a diseased state;
- (b) set the initial values and the desired target values of the parameters of the network points;
- (c) automatically generate an initial set of test excitations for at least one point of the network, the initial set of test excitations having a number of test excitations selected using a predetermined metaheuristic algorithm;
- (d) simulate behavior of the network using the set of test excitations;
- (e) stop the simulation when a simulation termination condition in a given simulation step is detected;
- (f) after the simulation is stopped, calculate and store the differences between the parameter values belonging to the desired target state and the parameter values produced by the simulation for each network point;
- (g) automatically generate a next set of test excitations selected to more closely approach the target state based on the previous test excitations having the smallest differences by using the predetermined metaheuristic algorithm;
- (h) iteratively repeating steps (d)-(g) until a predetermined termination condition is satisfied;
- (i) from among the stored simulation results, selecting the simulation result best matching the desired target state, the test set of excitations which produced said best matching simulation result is regarded as a final excitation set transferring the network from its initial state to its target state;
- and
- (j) outputting the final excitation set as the targets for therapeutic intervention by therapeutic agents in the real complex system of biochemical nature.
12 more elements
A non-transitory computer program product for identifying biological components in a real complex system of biochemical nature, wherein the biological components are targets for therapeutic intervention by therapeutic agents, the product comprising computer readable instructions which, when run on a computer, cause the computer to:
- a) model the system with a network of objects represented by networks points and relations between the objects represented by edges between the network points, wherein the states of the objects are described by a parameter set and the relations associated with the edges are described by functions of time, wherein the objects are biological components;
- b) for each object of the system, obtain values for each parameter of the parameter set for an initial state and a desired target state thereof, wherein the initial state corresponds to a diseased state;
- c) set the initial values and the desired target values of the parameters of the network points;
- d) automatically generate an initial set of test excitations for at least one point of the network using a predetermined metaheuristic algorithm, the initial set of test excitations including a predetermined number of test excitations defined by the metaheuristic algorithm;
- e) simulating the behavior of the network using the set of test excitations;
- f) detect whether a simulation termination condition in a given stimulation step is true and if so, stop the simulation;
- g) after the simulation is stopped, calculate and store, for each network point, the difference between the parameter values belonging to the desired target state and the parameter values produced by the simulation;
- h) based on the differences and number of test excitations, automatically generate a next set of test excitations using the predetermined metaheuristic algorithm, the next set of test excitations being generated to more closely approach the target state based on the previous test excitations having the smallest difference between the parameter values belonging to the desired target state and the parameter values produced by the simulation, and the number of text excitations in the next set being defined by the metaheuristic algorithm;
- i) iteratively repeat steps e)-h) to reduce the difference between the parameter values produced by the simulations and the parameter values of the target state until a predetermined termination condition is satisfied;
- j) from among the stored simulation results, select the simulation result best matching the desired target state, where the test set of excitations which produced the best matching simulation results is regarded as a final excitation set transferring the network from its initial state to its target state;
- and
- k) outputting the final excitation set as designed targets for therapeutic intervention by therapeutic agents in the system.
9 more elements
Dependencies are inferred from the claim text.