GillesPy2

A Biochemical Modeling Framework for Simulation Driven Biological Discovery

Authors

  • Sean Matthew National Environmental Modeling and Analysis Center (NEMAC), University of North Carolina, Asheville, NC 28804 Author
  • Fin Carter Department of Computer Science, University of North Carolina, Asheville, NC 28804 Author
  • Joshua Cooper Department of Computer Science, University of North Carolina, Asheville, NC 28804 Author
  • Matthew Dippel Department of Computer Science, University of North Carolina, Asheville, NC 28804 Author
  • Ethan Green Department of Computer Science, University of North Carolina, Asheville, NC 28804 Author
  • Samuel Hodges Department of Computer Science, North Carolina State University, NC 27695 Author
  • Mason Kidwell Department of Computer Science, University of North Carolina, Asheville, NC 28804 Author
  • Dalton Nickerson Department of Computer Science, University of North Carolina, Asheville, NC 28804 Author
  • Bryan Rumsey National Environmental Modeling and Analysis Center (NEMAC), University of North Carolina, Asheville, NC 28804 Author
  • Jesse Reeve Department of Computer Science, University of North Carolina, Asheville, NC 28804 Author
  • Linda R. Petzold Department of Computer Science and Department of Mechanical Engineering, University of California, Santa Barbara, CA 93106 Author
  • Kevin R. Sanft Department of Computer Science, University of North Carolina, Asheville, NC 28804 Author
  • Brian Drawert National Environmental Modeling and Analysis Center (NEMAC), University of North Carolina, Asheville, NC 28804 Author

DOI:

https://doi.org/10.30707/

Keywords:

Simulation, Modeling, Stochastic, Hybrid

Abstract

Stochastic modeling has become an essential tool for studying biochemical reaction networks. There is a growing need for user-friendly and feature-complete software for model design and simulation. To address this need, we present GillesPy2, an open-source framework for building and simulating mathematical and biochemical models. GillesPy2, a major upgrade from the original GillesPy package, is now a stand-alone Python 3 package. GillesPy2 offers an intuitive interface for robust and reproducible model creation, facilitating rapid and iterative development. In addition to expediting the model creation process, GillesPy2 offers efficient algorithms to simulate stochastic, deterministic, and hybrid stochastic-deterministic models.

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Published

2023-05-26

Issue

Section

Research

How to Cite

GillesPy2: A Biochemical Modeling Framework for Simulation Driven Biological Discovery. (2023). Letters in Biomathematics, 10(1), 87-103. https://doi.org/10.30707/

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