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Fast and flexible simulation and parameter estimation for synthetic biology using bioscrape

View ORCID ProfileAnandh Swaminathan, William Poole, View ORCID ProfileVictoria Hsiao, View ORCID ProfileRichard M. Murray
doi: https://doi.org/10.1101/121152
Anandh Swaminathan
1Persephone Biome, San Diego, CA, USA
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  • For correspondence: ananswam@alumni.caltech.edu
William Poole
2Computation and Neural Systems, California Institute of Technology, Pasadena, CA, USA
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Victoria Hsiao
3Amyris, Emeryville, CA, USA
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Richard M. Murray
4Computing and Mathematical Sciences and Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
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Abstract

In systems and synthetic biology, it is common to build chemical reaction network (CRN) models of biochemical circuits and networks. Although automation and other high-throughput techniques have led to an abundance of data enabling data-driven quantitative modeling and parameter estimation, the intense amount of simulation needed for these methods still frequently results in a computational bottleneck. Here we present bioscrape (Bio-circuit Stochastic Single-cell Reaction Analysis and Parameter Estimation) - a Python package for fast and flexible modeling and simulation of highly customizable chemical reaction networks. Specifically, bioscrape supports deterministic and stochastic simulations, which can incorporate delay, cell growth, and cell division. All functionalities - reaction models, simulation algorithms, cell growth models, and partioning models - are implemented as interfaces in an easily extensible and modular object-oriented framework. Models can be constructed via Systems Biology Markup Language (SBML), a simple internal XML language, or specified programmatically via a Python API. Simulation run times obtained with the package are comparable to those obtained using C code - this is particularly advantageous for computationally expensive applications such as Bayesian inference or simulation of cell lineages. We first show the package’s simulation capabilities on a variety of example simulations of stochastic gene expression. We then further demonstrate the package by using it to do parameter inference on a model of integrase enzyme-mediated DNA recombination dynamics with experimental data. The bioscrape package is publicly available online (https://github.com/ananswam/bioscrape) along with more detailed documentation and examples.

Copyright 
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.
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Posted March 25, 2019.
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Fast and flexible simulation and parameter estimation for synthetic biology using bioscrape
Anandh Swaminathan, William Poole, Victoria Hsiao, Richard M. Murray
bioRxiv 121152; doi: https://doi.org/10.1101/121152
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Fast and flexible simulation and parameter estimation for synthetic biology using bioscrape
Anandh Swaminathan, William Poole, Victoria Hsiao, Richard M. Murray
bioRxiv 121152; doi: https://doi.org/10.1101/121152

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