Colin C Hill

7415359, Aug 19, 2008

Nov 4, 2002

10/287173

Colin Hill - Ithaca NY, US
Iya Khalil - Ithaca NY, US

Gene Network Sciences, Inc. - Ithaca NY
Cornell Research Foundation, Inc. - Ithaca NY

G01N 33/48

702 19, 702 20, 703 11, 707102

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.

8571803, Oct 29, 2013

Nov 15, 2007

11/985618

Colin C. Hill - Somerville MA, US
Bruce W. Church - Lawrence MA, US
Paul D. McDonagh - Winchester MA, US
Iya G. Khalil - Boston MA, US
Thomas A. Neyarapally - Boston MA, US
Zachary W. Pitluk - New Haven CT, US

Gene Network Sciences, Inc. - Cambridge MA

G06F 7/00

702 19, 702 20, 703 11, 707700

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.

20030144823, Jul 31, 2003

Nov 1, 2002

10/286372

Jeffrey Fox - Ithaca NY, US
Colin Hill - Ithaca NY, US
Vipul Periwal - New City NY, US

G06G007/48
G06G007/58

703/011000

Presently disclosed are methods for inferring a network model of the interactions of biological molecules and systems for practicing such methods. The systems and methods described herein provide a systematic and computationally feasible solution to the problem of rational inference of the architecture of biological networks, based on a combination of rational and statistical evaluations of quantitative and qualitative data. These methods constrain the search space of possible networks and, in some embodiments, allow the online addition of new data into an existing model without any interruption in analysis. Additionally, these methods can provide a quantitative evaluation of the confidence levels associated with the putative network architectures discovered thereby. The methods naturally incorporate latent nodes in the search space of possible architectures; hence, the system is also capable of predicting new interactions and/or substrates for the biological systems being studied.

20040088116, May 6, 2004

May 14, 2003

10/439288

Iya Khalil - Ithaca NY, US
Colin Hill - Ithaca NY, US

Gene Network Sciences, Inc.

G06G007/48
G06G007/58
G06F019/00
G01N033/48
G01N033/50

702/020000, 703/011000

Presented herein are techniques and methodologies for creating large-scale data-driven models of biological systems and exemplary applications thereof including drug discovery and industrial applications. Exemplary embodiments include creating a core skeletal simulation (scaleable to any size) from known biological information, collecting quantitative and qualitative experimental data to constrain the simulation, creating a probable reactions database, integrating the core skeletal simulation, the database of probable reactions, and static and dynamical time course measurements to generate an ensemble of biological network structures and their corresponding molecular concentration profiles and phenotypic outcomes that approximate output of the original biological network used for prediction, and finally experimentally validating and iteratively refining the model. The invention further describes methods of taking conventional small-scale models and extending them to comprehensive large-scale models of biological systems. The methods presented further describe ways to create models of arbitrary size and complexity and various ways to incorporate data to account for missing biological information.

20050267720, Dec 1, 2005

Nov 17, 2004

10/990868

Colin Hill - Ithaca NY, US
Iya Khalil - Ithaca NY, US
Guillermo Calero - Palo Alto CA, US

G06G007/48
G06G007/58

703011000

Systems and methods for modeling the interactions of the several genes, proteins and other components of a cell, employing mathematical techniques to represent the interrelationships between the cell components and the manipulation of the dynamics of the cell to determine which components of a cell may be targets for interaction with therapeutic agents. A first such method is based on a cell simulation approach in which a cellular biochemical network intrinsic to a phenotype of the cell is simulated by specifying its components and their interrelationships. The various interrelationships are represented with one or more mathematical equations which are solved to simulate a first state of the cell. The simulated network is then perturbed by deleting one or more components, changing the concentration of one or more components, or modifying one or more mathematical equations representing the interrelationships between one or more of the components. The equations representing the perturbed network are solved to simulate a second state of the cell which is compared to the first state to identify the effect of the perturbation on the state of the network, thereby identifying one or more components as targets. A second method for identifying components of a cell as targets for interaction with therapeutic agents is 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 to be intrinsic to that phenotype is then specified by identifying its components and their interrelationships and representing those interrelationships in one or more mathematical equations. The network is then perturbed and the equations representing the perturbed network are solved to determine whether the perturbation is likely to cause the transition of the cell from one phenotype to another, thereby identifying one or more components as targets.

20090204374, Aug 13, 2009

Aug 6, 2008

12/221944

Colin Hill - Somerville MA, US
Iya Khalil - Boston MA, US
Guillermo A. Calero - Pittsburgh PA, US

G06F 17/11
G06G 7/58

703 2, 703 11

Systems and methods for modeling the interactions of the several genes, proteins and other components of a cell, employing mathematical techniques to represent the interrelationships between the cell components and the manipulation of the dynamics of the cell to determine which components of a cell may be targets for interaction with therapeutic agents. A first such method is based on a cell simulation approach in which a cellular biochemical network intrinsic to a phenotype of the cell is simulated by specifying its components and their interrelationships. The various interrelationships are represented with one or more mathematical equations which are solved to simulate a first state of the cell. The simulated network is then perturbed by deleting one or more components, changing the concentration of one or more components, or modifying one or more mathematical equations representing the interrelationships between one or more of the components. The equations representing the perturbed network are solved to simulate a second state of the cell which is compared to the first state to identify the effect of the perturbation on the state of the network, thereby identifying one or more components as targets. A second method for identifying components of a cell as targets for interaction with therapeutic agents is 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 to be intrinsic to that phenotype is then specified by identifying its components and their interrelationships and representing those interrelationships in one or more mathematical equations. The network is then perturbed and the equations representing the perturbed network are solved to determine whether the perturbation is likely to cause the transition of the cell from one phenotype to another, thereby identifying one or more components as targets.