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