Cell-free protein synthesis (CFPS) is a widely used research tool in systems and synthetic biology. However, if CFPS is to become a mainstream technology for applications such as point of care manufacturing, we must understand the performance limits and costs of these systems. Toward this question, we used sequence specific constraint based modeling to evaluate the performance of E. coli cell-free protein synthesis. A core E. coli metabolic network, describing glycolysis, the pentose phosphate pathway, energy metabolism, amino acid biosynthesis, and degradation was augmented with sequence specific descriptions of transcription and translation and effective models of promoter function. Model parameters were largely taken from literature; thus the constraint based approach coupled the transcription and translation of the protein product, and the regulation of gene expression, with the availability of metabolic resources using only a limited number of adjustable model parameters. We tested this approach by simulating the expression of two model proteins: chloramphenicol acetyltransferase and dual emission green fluorescent protein, for which we have data sets; we then expanded the simulations to a range of additional proteins. Protein expression simulations were consistent with measurements for a variety of cases. The constraint based simulations confirmed that oxidative phosphorylation was active in the CAT cell-free extract, as without it there was no feasible solution within the experimental constraints of the system. We then compared the metabolism of theoretically optimal and experimentally constrained CFPS reactions, and developed parameter free correlations which could be used to estimate productivity as a function of carbon number and promoter type. Lastly, global sensitivity analysis identified the key metabolic processes that controlled CFPS productivity and energy efficiency. In summary, sequence specific constraint based modeling of CFPS offered a novel means to a priori estimate the performance of a cell-free system, using only a limited number of adjustable parameters. While we modeled the production of a single protein in this study, the approach could easily be extended to multiprotein synthetic circuits, RNA circuits, or the cell-free production of small molecule products.
Cell-free protein synthesis (CFPS) has become a widely used research tool in systems and synthetic biology. In this study, we used sequence specific constraint based modeling to evaluate the performance of an E. coli cell-free protein synthesis system. A core E. coli metabolic model, describing glycolysis, the pentose phosphate pathway, energy metabolism, amino acid biosynthesis and degradation was augmented with sequence specific descriptions of transcription and translation with effective models of promoter function. Thus, sequence specific constraint based modeling explicitly couples transcription and translation processes and the regulation of gene expression with the availability of metabolic resources. We tested this approach by simulating the expression of two model proteins: chloramphenicol acetyltransferase and dual emission green fluorescent protein, for which we have training data sets; we then 1 . CC-BY-NC-ND4.0 International license peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/139774 doi: bioRxiv preprint first posted online May. 23, 2017; expanded the simulations to a range of therapeutically relevant proteins. Protein expression simulations were consistent with measurements for a variety of cases. We then compared optimal and experimentally constrained CFPS reactions, which suggested the experimental system over-consumed glucose and had suboptimal oxidative phosphorylation activity. Lastly, global sensitivity analysis identified the key metabolic processes that controlled the CFPS productivity, energy efficiency, and carbon yield.In summary, sequence specific constraint based modeling of CFPS offered a novel means to a priori estimate the performance of a cell-free system, using only a limited number of of adjustable parameters. In this study we modeled the production of a single protein, however this approach could be extended to multi-protein synthetic circuits, RNA circuits or small molecule production.
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