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Published March 28, 2023 | Submitted
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Characterization of Integrase and Excisionase Activity in Cell-free Protein Expression System Using a Modeling and Analysis Pipeline

Abstract

We present a full-stack modeling, analysis, and parameter identification pipeline to guide the modeling and design of biological systems starting from specifications to circuit implementations and parameterizations. We demonstrate this pipeline by characterizing the integrase and excisionase activity in cell-free protein expression system. We build on existing Python tools — BioCRNpyler, AutoReduce, and Bioscrape — to create this pipeline. For enzyme-mediated DNA recombination in cell-free system, we create detailed chemical reaction network models from simple high-level descriptions of the biological circuits and their context using BioCRNpyler. We use Bioscrape to show that the output of the detailed model is sensitive to many parameters. However, parameter identification is infeasible for this high-dimensional model, hence, we use AutoReduce to automatically obtain reduced models that have fewer parameters. This results in a hierarchy of reduced models under different assumptions to finally arrive at a minimal ODE model for each circuit. Then, we run sensitivity analysis-guided Bayesian inference using Bioscrape for each circuit to identify the model parameters. This process allows us to quantify integrase and excisionase activity in cell extracts enabling complex-circuit designs that depend on accurate control over protein expression levels through DNA recombination. The automated pipeline presented in this paper opens up a new approach to complex circuit design, modeling, reduction, and parameterization.

Additional Information

The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license. The authors thank the many users of BioCRNpyler, AutoReduce, and Bioscrape who have provided invaluable feedback on the software tools demonstrated in this paper. In particular, the authors acknowledge Victoria Hsiao for providing the integrase plasmid used in this paper, Anandh Swaminathan for working on a preliminary version of the integrase model, Andrey Shur and Zoltan Tuza for their feedback on the analysis, and Zoila Jurado and Alex Johnson for their insightful comments on the manuscript. A.P. is currently supported by the AFOSR MURI grant FA9550-22-1-0316 and was previously funded in part by the NSF grant CBET-1903477 and DARPA grant HR0011-17-2-0008. W.P. was partly supported by the NSF grant MCB-2152267 and Army Research Office grant W911NF-19-D-0001. The content of the information does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred. The authors have declared no competing interest.

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Additional details

Created:
August 20, 2023
Modified:
December 13, 2023