Gene Network Sciences, Inc.

Cornell University

Iya G Khalil

Colin C Hill

Zachary W Pitluk

Bruce W Church

Thomas A Neyarapally

Paul D McDonagh

The systems and methods described herein utilize a probabilistic modeling framework for reverse engineering an ensemble of causal models, from data and then forward simulating the ensemble of models to analyze and predict the behavior of the network. In certain embodiments, the systems and methods described herein include data-driven techniques for developing causal models for biological networks. Causal network models include computational representations of the causal relationships between independent variables such as a compound of interest and dependent variables such as measured DNA alterations, changes in mRNA, protein, and metabolites to phenotypic readouts of efficacy and toxicity.

Systems and methods for modeling and analyzing networks

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.

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

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Boston, MA, United States of America

Iya G Khalil