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Published May 17, 2019 | Submitted
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An automated model reduction tool to guide the design and analysis of synthetic biological circuits

Abstract

We present an automated model reduction algorithm that uses quasi-steady state approximation to minimize the error between the desired outputs. Additionally, the algorithm minimizes the sensitivity of the error with respect to parameters to ensure robust performance of the reduced model in the presence of parametric uncertainties. We develop the theory for this model reduction algorithm and present the implementation of the algorithm that can be used to perform model reduction of given SBML models. To demonstrate the utility of this algorithm, we consider the design of a synthetic biological circuit to control the population density and composition of a consortium consisting of two different cell strains. We show how the model reduction algorithm can be used to guide the design and analysis of this circuit.

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-ND 4.0 International license. Version 1 - May 17, 2019; Version 2 - April 28, 2022. We would like to thank Chelsea Hu, Reed McCardell, and Shailja for insightful discussions. We would also like to thank Samuel Clamons for helping with sensitivity analysis computations. The author A.P. is supported by the Defense Advanced Research Projects Agency (Agreement HR0011-17- 2-0008). 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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August 19, 2023
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