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Processes 2015, 3(1), 138-160; doi:10.3390/pr3010138

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

School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14853, USA
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Academic Editor: Doraiswami Ramkrishna
Received: 8 September 2014 / Revised: 16 February 2015 / Accepted: 17 February 2015 / Published: 3 March 2015
(This article belongs to the Special Issue Dynamic Approaches to Metabolic Modeling and Metabolic Engineering)
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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 ultrasensitivity 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. However, when starting with an initial population that was heavily enriched with incorrect structures, our particle swarm approach could converge to an incorrect structure. While only an initial proof-of-concept, the framework presented here could be an important first step toward genome-scale cell-free kinetic modeling of the biosynthetic capacity of industrially important organisms. View Full-Text
Keywords: allosteric regulation; cell-free metabolism; heuristic optimization; mathematical modeling; parameter identification; systems biology allosteric regulation; cell-free metabolism; heuristic optimization; mathematical modeling; parameter identification; systems biology
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Wayman, J.A.; Sagar, A.; Varner, J.D. Dynamic Modeling of Cell-Free Biochemical Networks Using Effective Kinetic Models. Processes 2015, 3, 138-160.

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