Historically, genome-scale analysis has used bottom-up reconstruction of available, biochemical information (from high-throughput datasets and public archives) to provide a snapshot of biochemical relationships in a network. While this approach has been extremely useful, it is an obvious simplification of real, dynamic systems, which continually change in response to perturbations.
UC San Diego inventors have developed an approach for the construction of dynamic genome-scale mathematical models and their governing equations, which can be used to build and analyze dynamic models of biological networks and to predict responses to environmental and genetic perturbations.
The invention identifies over-riding principles and concepts, which are readily translated to models constructed upon different assumptions. Also, while validated for one metabolic pathway, this has direct applicability to other biological processes, including but not limited to signaling networks, small metabolite regulation, transcription, and translation. The approach described for constructing models is scaleable and practical for large networks, given the increasing availability of various high-throughput data, such as metabolomic data.
|United States Of America||Issued Patent||8,301,393||10/30/2012||2008-180|
Additional Patent Pending
dynamic, model, genomic, fluxomic, pathway, process, signaling, network, gradient, thermodynamic, Jacobian, dual, hierarchical, time-scale decomposition, regulation