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

Abstract

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.

Description

CROSS REFERENCE TO RELATED APPLICATIONS [0001] This application is a continuation of U.S. patent application Ser. No. 10 / 287,173, filed Nov. 4, 2002, which is hereby incorporated by reference, and claims the benefit of the following U.S. Provisional Patent Applications: “Methods and Systems for the Identification of Components of Mammalian Cells as Targets for Therapeutic Agents”, U.S. Provisional Patent Application Ser. No. 60 / 335,999, filed on Nov. 2, 2001; and “Systems and Methods For Inferring Biological Networks”, Vipul Periwal, Inventor, U.S. Provisional Patent Application Ser. No. 60 / 406,764, filed on Aug. 29, 2002. [0002] This application also claims priority to the following U.S. patent application Ser. No.: “Scale-Free Network Inference Methods”, Jeff Fox, Colin Hill and Vipul Periwal, Inventors, U.S. application Ser. No. ______, filed on Nov. 1, 2002 (serial number to be added by amendment when available).FIELD OF THE INVENTION [0003] The present invention relates to drug...

AI Technical Summary

Benefits of technology

This approach expedites the drug discovery process, reduces costs and labor, and provides insights into the overall dynamics of cells, allowing for the prediction of drug effects on multiple components, thereby minimizing side effects and optimizing drug development.

Problems solved by technology

A difficulty with such methods is that they are basically brute-force empirical methods that reveal little or nothing about the particular phenomena which take place within the cell when it is contacted with the compound identified in the screen.

The actual cellular dynamics may not be understood and this may lead to development of candidate drugs deleteriously, which affect other components in the cell and cause undesirable side effects.

This brute-force screening method is also limited by the speed at which assays can be conducted.

While the art has developed powerful, high throughput screening techniques by which tens of thousands of compounds are routinely screened for their interactive effect with one or more targets, such methodologies are still inherently empirical and leave the researcher with no fundamental information about the mechanisms of interaction of a compound identified by such methods.

Thus the compound so identified may have detrimental interactions with one or more other components of a cell and may cause more harm than good.

These additional tests are extremely time-consuming and expensive.

Images