Alfonso Landeros1, Timothy Stutz2, Kevin L Keys3, Alexander Alekseyenko4, Janet S Sinsheimer5, Kenneth Lange6, Mary E Sehl7. 1. Department of Biomathematics, David Geffen School of Medicine at UCLA, USA. Electronic address: alanderos@ucla.edu. 2. Department of Biomathematics, David Geffen School of Medicine at UCLA, USA. Electronic address: stutztim@ucla.edu. 3. Department of Medicine, University of California, San Francisco, CA, USA. Electronic address: kevin.keys@ucsf.edu. 4. Department of Public Health Sciences, Medical University of South Carolina, USA. Electronic address: alekseye@musc.edu. 5. Department of Human Genetics, David Geffen School of Medicine at UCLA, USA. Electronic address: JanetS@mednet.ucla.edu. 6. Department of Biomathematics, David Geffen School of Medicine at UCLA, USA. Electronic address: klange@ucla.edu. 7. Department of Biomathematics, David Geffen School of Medicine at UCLA, USA. Electronic address: msehl@mednet.ucla.edu.
Abstract
BACKGROUND AND OBJECTIVES: Biological systems with intertwined feedback loops pose a challenge to mathematical modeling efforts. Moreover, rare events, such as mutation and extinction, complicate system dynamics. Stochastic simulation algorithms are useful in generating time-evolution trajectories for these systems because they can adequately capture the influence of random fluctuations and quantify rare events. We present a simple and flexible package, BioSimulator.jl, for implementing the Gillespie algorithm, τ-leaping, and related stochastic simulation algorithms. The objective of this work is to provide scientists across domains with fast, user-friendly simulation tools. METHODS: We used the high-performance programming language Julia because of its emphasis on scientific computing. Our software package implements a suite of stochastic simulation algorithms based on Markov chain theory. We provide the ability to (a) diagram Petri Nets describing interactions, (b) plot average trajectories and attached standard deviations of each participating species over time, and (c) generate frequency distributions of each species at a specified time. RESULTS: BioSimulator.jl's interface allows users to build models programmatically within Julia. A model is then passed to the simulate routine to generate simulation data. The built-in tools allow one to visualize results and compute summary statistics. Our examples highlight the broad applicability of our software to systems of varying complexity from ecology, systems biology, chemistry, and genetics. CONCLUSION: The user-friendly nature of BioSimulator.jl encourages the use of stochastic simulation, minimizes tedious programming efforts, and reduces errors during model specification.
BACKGROUND AND OBJECTIVES: Biological systems with intertwined feedback loops pose a challenge to mathematical modeling efforts. Moreover, rare events, such as mutation and extinction, complicate system dynamics. Stochastic simulation algorithms are useful in generating time-evolution trajectories for these systems because they can adequately capture the influence of random fluctuations and quantify rare events. We present a simple and flexible package, BioSimulator.jl, for implementing the Gillespie algorithm, τ-leaping, and related stochastic simulation algorithms. The objective of this work is to provide scientists across domains with fast, user-friendly simulation tools. METHODS: We used the high-performance programming language Julia because of its emphasis on scientific computing. Our software package implements a suite of stochastic simulation algorithms based on Markov chain theory. We provide the ability to (a) diagram Petri Nets describing interactions, (b) plot average trajectories and attached standard deviations of each participating species over time, and (c) generate frequency distributions of each species at a specified time. RESULTS: BioSimulator.jl's interface allows users to build models programmatically within Julia. A model is then passed to the simulate routine to generate simulation data. The built-in tools allow one to visualize results and compute summary statistics. Our examples highlight the broad applicability of our software to systems of varying complexity from ecology, systems biology, chemistry, and genetics. CONCLUSION: The user-friendly nature of BioSimulator.jl encourages the use of stochastic simulation, minimizes tedious programming efforts, and reduces errors during model specification.
Authors: Urban Bezeljak; Hrushikesh Loya; Beata Kaczmarek; Timothy E Saunders; Martin Loose Journal: Proc Natl Acad Sci U S A Date: 2020-03-11 Impact factor: 11.205