Accession Number : AD1027865


Title :   Dynamic Modeling of Cell-Free Biochemical Networks Using Effective Kinetic Models


Descriptive Note : Journal Article - Open Access


Corporate Author : University of California - Santa Barbara Santa Barbara United States


Personal Author(s) : Wayman,Joseph A ; Sagar,Adithya ; Varner,Jeffrey D


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/1027865.pdf


Report Date : 03 Mar 2015


Pagination or Media Count : 26


Abstract : Cell-free systems offer many advantages for the study, manipulation and modeling of metabolism compared to in vivo processes. Many of the challenges confronting genome-scale kinetic modeling can potentially be overcome in a cell-free system. For example, there is no complex transcriptional regulation to consider, transient metabolic measurements are easier to obtain, and we no longer have to consider cell growth. Thus, cell-free operation holds several significant advantages for model development, identification and validation. Theoretically, genome-scale cell-free kinetic models may be possible for industrially important organisms, such as E. coli, if a simple, tractable framework for integrating allosteric regulation with enzyme kinetics can be formulated. Toward this unmet need, we present an effective biochemical network modeling framework for building dynamic cell-free metabolic models. The key innovation of our approach is the integration of simple effective rules encoding complex allosteric regulation with traditional kinetic pathway modeling. We tested our approach by modeling the time evolution of several hypothetical cell-free metabolic networks. We found that simple effective rules, when integrated with traditional enzyme kinetic expressions, captured complex allosteric patterns such as ultra sensitivity or non-competitive inhibition in the absence of mechanistic information. Second, when integrated into network models, these rules captured classic regulatory patterns such as product-induced feedback inhibition. Lastly, we showed, at least for the network architectures considered here, that we could simultaneously estimate kinetic parameters and allosteric connectivity from synthetic data starting from an unbiased collection of possible allosteric structures using particle swarm optimization.


Descriptors :   metabolism , cellfree system , mathematical models , systems biology , optimization , heuristic methods , blo9od coagulation , kinetics


Subject Categories : Biology


Distribution Statement : APPROVED FOR PUBLIC RELEASE