US 7,415,359 B2Granted

Methods and systems for the identification of components of mammalian biochemical networks as targets for therapeutic agents

AssigneesGene Network Sciences, Inc., Cornell Research Foundation, Inc.

InventorsColin Hill, Iya Khalil

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Abstract

Systems and methods are presented for cell simulation and cell state prediction. For example, a cellular biochemical network intrinsic to a phenotype of a cell can be simulated by specifying its components and their interrelationships. The various interrelationships can be represented with one or more mathematical equations which can be solved to simulate a first state of the cell. The simulated network can then be perturbed, and the equations representing the perturbed network can be solved to simulate a second state of the cell which can then be compared to the first state, identifying the effect of such perturbation on the network, and thereby identifying one or more components as targets. Alternatively, components of a cell can be identified as targets for interaction with therapeutic agents based upon an analytical approach, in which a stable phenotype of a cell is specified and correlated to the state of the cell and the role of that cellular state to its operation. A cellular biochemical network believed intrinsic to that phenotype can then be specified, mathematically represented, and perturbed, and the equations representing the perturbed network solved, thereby identifying one or more components as targets.

Claims17

Dependent claims

A method of predicting an altered physiological state of a cell comprising:

(a) specifying a biochemical network of a cell;
(b) simulating said network by
(i) specifying the components of said network,
(ii) representing interrelationships between said components in one or more mathematical equations and setting the quantitative parameters of said components, said mathematical equations including nonlinear differential equations where each such nonlinear differential equation includes the time rate of change of one or more gene products that comprise the biochemical network, and including various rate constants associated with said mathematical equations, and
(iii) solving said mathematical equations to produce an initial simulated state of the cell;
(c) optimizing said simulated biochemical network by determining and constraining the values of the parameters of said components by comparing experimental data regarding the value of at least one component with the value of said at least one component in said simulated biochemical network, said optimizing including comparing said simulation to at least one experimentally measured concentration time series and calculating an accuracy cost representing 2 a measure of the overall differences between the experimental data and the simulated time series;
(d) perturbing the optimized simulated network by adding or deleting one or more components thereof, changing the concentration of one or more components thereof or modifying one or more mathematical equations representing interrelationships between one or more of said component, including modifying said rate constants away from their starting values;
(e) solving the equations representing the perturbed network to simulate a modified state of the network, including calculating a new set of rate constants;
(f) comparing said initial and modified simulated states of the network to identify the effect of said perturbation on the state of the network;
(g) repeating said optimizing and said perturbing until the accuracy cost ceases to decrease by a defined increment;
and
(h) outputting the results of said comparison, the results of the perturbation(s) and the predicted state of the cell as perturbed to a user.

9 more elements

A method of predicting the physiological state of a cell, comprising:

(a) specifying a biochemical network of a cell;
(b) simulating said network by
(i) specifying the components of said network, and
(ii) representing interrelationships between said components in one or more mathematical equations, said mathematical equations including nonlinear differential equations where each such nonlinear differential equation includes the time rate of change of one or more gene products that comprise the biochemical network, and setting the quantitative parameters of said components, and including various rate constants associated with said mathematical equations;
(c) iteratively optimizing said simulated biochemical network by determining and constraining the values of the parameters of said components by comparing experimental data regarding the value of at least one component with the value of said at least one component generated by said simulated biochemical network, said optimizing including comparing said simulation to at least one experimentally measured concentration time series and calculating an accuracy cost representing a measure of the overall differences between the experimental data and the simulated time series, said iterative optimizing continuing until said accuracy cost ceases to decrease by a defined increment;
(d) determining the state of said optimized network by solving the mathematical equations and thereby simulating the physiological state of said network;
and
(e) outputting the simulated state of said network and the associated predicted state of the cell to a user.

5 more elements

A method of predicting an altered physiological state of a cell comprising:

(a) specifying a biochemical network of a cell;
(b) simulating said network by
(i) specifying the components of said network,
(ii) representing interrelationships between said components in one or more mathematical equations, said mathematical equations including nonlinear differential equations where each such nonlinear differential equation includes the time rate of change of one or more gene products that comprise the biochemical network, and setting the quantitative parameters of said components, and including various rate constants associated with said mathematical equations, and
(iii) solving said mathematical equations to produce an initial simulated state of the network;
(c) optimizing said simulated biochemical network by determining and constraining the values of the parameters of said components by comparing experimental data regarding at the value of at least one component with the value of said at least one component in said simulated biochemical network, said optimizing including calculating an accuracy cost representing a measure of the overall differences between the exDerimental data and the simulated biochemical network;
(d) perturbing the optimized simulated network by adding or deleting one or more components thereof, changing the concentration of one or more components thereof or modifying one or more mathematical equations representing interrelationships between one or more of said components, including modifying at least one of said rate constants away from their starting values;
(e) solving the equations representing the perturbed network to simulate a modified state of the network, including calculating a new set of rate constants;
(f) optimizing said perturbed simulated biochemical network by determining and constraining the values of the parameters of said components by comparing experimental data regarding at the value of at least one component with the value of said at least one component in said simulated biochemical network and calculating a new accuracy cost;
(g) comparing said initial and modified simulated states of the network to identify the effect of said perturbation on the state of the network, including calculating the change in accuracy cost;
and
(h) outputting the results of said comparison and the associated predicted altered physiological state of the cell to a user.

9 more elements

Dependencies are inferred from the claim text.

Facts

Application: 10/287,173Filed 4 Nov 2002 · us provisional application
Priority: 2 Nov 2001
Published: US 2003/0215786 A120 Nov 2003
Granted: 19 Aug 2008
Term ends: Not estimatedThe source records don’t support an estimate.

Classification9 codes

G16B 5/30
ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic…:…
G16B 5/00
Ict specially adapted for modelling or simulations in systems biology
G01N 2800/52
Detection or diagnosis of diseases: predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in…
G16B 5/10
ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic…:…