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

Swaminathan, Anandh and Poole, William and Hsiao, Victoria and Murray, Richard M. (2017) Fast and flexible simulation and parameter estimation for synthetic biology using bioscrape. . (Unpublished)

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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 ( along with more detailed documentation and examples.

Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription Paper
Swaminathan, Anandh0000-0001-9935-6530
Hsiao, Victoria0000-0001-9297-1522
Murray, Richard M.0000-0002-5785-7481
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. bioRxiv preprint first posted online Mar. 27, 2017. AS was supported by AFOSR grant FA9550-14-1-0060. AS and VH were 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. WP was supported by an NSF Graduate Research Fellowship (No.2017246618). The authors acknowledge members of the Murray lab at Caltech for assistance with experiments and helpful feedback and also acknowledge all the members of the scientific community at large who have used and provided feedback on bioscrape.
Funding AgencyGrant Number
Air Force Office of Scientific Research (AFOSR)FA9550-14-1-0060
Defense Advanced Research Projects Agency (DARPA)HR0011-17-2-0008
NSF Graduate Research Fellowship2017246618
Record Number:CaltechAUTHORS:20190325-142752006
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Official Citation:Fast and flexible simulation and parameter estimation for synthetic biology using bioscrape. Anandh Swaminathan, William Poole, Victoria Hsiao, Richard M Murray. bioRxiv 121152; doi:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:94128
Deposited By: Tony Diaz
Deposited On:25 Mar 2019 21:48
Last Modified:03 Oct 2019 21:01

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