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A Systems Biology Approach for Identifying Drug Targets
University of California System: University of California, San Diego Technology Transfer
posted on 07/26/2010
Bayesian networks are a popular class of graphical probabilistic models
based on Bayes theorem. Bayesian networks represent a joint probability
distribution over a set of variables. Once known, this joint
distribution may be used to compute the probabilities of any
configuration of the variables. Bayesian networks have been increasingly
applied to various computation applications, such as computational
biology and computer vision. The commonly used approach of modeling
network behavior employs ordinary or partial differential equations (ODE
or PDE), but this approach is limited to analyzing relatively small
networks (10-20 nodes), as ODE or PDE approaches may consider only local
effects in the network. There is need to overcome this limitation and
provide a systematic way, based on biological networks, to evaluate the
effects of inhibiting multiple drug targets on treating a disease.
Detailed Description
Scientists at UC San Diego have developed a method to identify drug
targets using a systems biology approach. Given a network that regulates
a disease, the method can predict the effects of inhibiting a set of
genes on the marker genes of the disease. For example, if two marker
genes are up-regulated in a disease, the method can identify inhibition
of genes that can reduce the expression of the two marker genes to
normal levels. Therefore, the invention provides a systematic way to
evaluate the effects of inhibiting multiple drug targets for treating
diseases. Such effects cannot easily be identified using traditional
molecular biology approaches.
File Number: 21019
| Copyright: | ©2010, The Regents of the University of California |
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This innovation currently is not available for online licensing. Please contact University of California, San Diego Technology Transfer and Intellectual Property Services at University of California System: University of California, San Diego Technology Transfer for more information.
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