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Processes 2018, 6(8), 132; https://doi.org/10.3390/pr6080132

Dynamic Sequence Specific Constraint-Based Modeling of Cell-Free Protein Synthesis

Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14853, USA
These authors contributed equally to this work.
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Received: 8 June 2018 / Revised: 7 August 2018 / Accepted: 9 August 2018 / Published: 17 August 2018
(This article belongs to the Special Issue Feature Papers for Celebrating the Fifth Founding Year of Processes)
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Abstract

Cell-free protein expression has emerged as an important approach in systems and synthetic biology, and a promising technology for personalized point of care medicine. Cell-free systems derived from crude whole cell extracts have shown remarkable utility as a protein synthesis technology. However, if cell-free platforms for on-demand biomanufacturing are to become a reality, the performance limits of these systems must be defined and optimized. Toward this goal, we modeled E. coli cell-free protein expression using a sequence specific dynamic constraint-based approach in which metabolite measurements were directly incorporated into the flux estimation problem. A cell-free metabolic network was constructed by removing growth associated reactions from the iAF1260 reconstruction of K-12 MG1655 E. coli. Sequence specific descriptions of transcription and translation processes were then added to this metabolic network to describe protein production. A linear programming problem was then solved over short time intervals to estimate metabolic fluxes through the augmented cell-free network, subject to material balances, time rate of change and metabolite measurement constraints. The approach captured the biphasic cell-free production of a model protein, chloramphenicol acetyltransferase. Flux variability analysis suggested that cell-free metabolism was potentially robust; for example, the rate of protein production could be met by flux through the glycolytic, pentose phosphate, or the Entner-Doudoroff pathways. Variation of the metabolite constraints revealed central carbon metabolites, specifically upper glycolysis, tricarboxylic acid (TCA) cycle, and pentose phosphate, to be the most effective at training a predictive model, while energy and amino acid measurements were less effective. Irrespective of the measurement set, the metabolic fluxes (for the most part) remained unidentifiable. These findings suggested dynamic constraint-based modeling could aid in the design of cell-free protein expression experiments for metabolite prediction, but the flux estimation problem remains challenging. Furthermore, while we modeled the cell-free production of only a single protein in this study, the sequence specific dynamic constraint-based modeling approach presented here could be extended to multi-protein synthetic circuits, RNA circuits or even small molecule production. View Full-Text
Keywords: dynamic constraint-based modeling; cell-free protein synthesis; systems biology dynamic constraint-based modeling; cell-free protein synthesis; 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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Dai, D.; Horvath, N.; Varner, J. Dynamic Sequence Specific Constraint-Based Modeling of Cell-Free Protein Synthesis. Processes 2018, 6, 132.

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