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[[Systems biology]] relies heavily on building [[mathematical models]] to help understand and make predictions of biological processes. Specialized software to assist in building models has been developed since the arrival of the first digital computers.<ref>{{cite journal |last1=Chance |first1=Britton |last2=Garfinkel |first2=David |last3=Higgins |first3=Joseph |last4=Hess |first4=Benno |last5=Chance |first5=E.M. |title=Metabolic Control Mechanisms |journal=Journal of Biological Chemistry |date=August 1960 |volume=235 |issue=8 |pages=2426–2439 |doi=10.1016/S0021-9258(18)64638-1|doi-access=free }}</ref><ref>{{cite book |last1=Chance |first1=Britton |last2=Higgins |first2=Joseph |last3=Garfinkel |first3=David |title=Analogue and digital computer representations of biochemical processes |date=1962 |publisher=Federation Proceedings, Vol 12. No. 1-2 |location=Federation of American Societies for Experimental Biology.. |pages=75}}</ref><ref>{{cite journal |last1=Burns |first1=Jim |title=Metabolic Control Analysis |journal=Thesis |date=1 March 1973 |doi=10.5281/zenodo.7240738}}</ref><ref>{{cite journal |last1=Garfinkel |first1=David |title=A machine-independent language for the simulation of complex chemical and biochemical systems |journal=Computers and Biomedical Research |date=August 1968 |volume=2 |issue=1 |pages=31–44 |doi=10.1016/0010-4809(68)90006-2|pmid=5743538 }}</ref> The following list gives the currently supported software applications available to researchers.
[[Systems biology]] relies heavily on building [[mathematical models]] to help understand and make predictions of biological processes. Specialized software to assist in building models has been developed since the arrival of the first digital computers.<ref>{{cite journal |last1=Chance |first1=Britton |last2=Garfinkel |first2=David |last3=Higgins |first3=Joseph |last4=Hess |first4=Benno |last5=Chance |first5=E.M. |title=Metabolic Control Mechanisms |journal=Journal of Biological Chemistry |date=August 1960 |volume=235 |issue=8 |pages=2426–2439 |doi=10.1016/S0021-9258(18)64638-1|doi-access=free }}</ref><ref>{{cite book |last1=Chance |first1=Britton |last2=Higgins |first2=Joseph |last3=Garfinkel |first3=David |title=Analogue and digital computer representations of biochemical processes |date=1962 |publisher=Federation Proceedings, Vol 12. No. 1-2 |location=Federation of American Societies for Experimental Biology.. |pages=75}}</ref><ref>{{cite thesis |last1=Burns |first1=Jim |title=Metabolic Control Analysis |journal=Thesis |date=1 March 1973 |doi=10.5281/zenodo.7240738}}</ref><ref>{{cite journal |last1=Garfinkel |first1=David |title=A machine-independent language for the simulation of complex chemical and biochemical systems |journal=Computers and Biomedical Research |date=August 1968 |volume=2 |issue=1 |pages=31–44 |doi=10.1016/0010-4809(68)90006-2|pmid=5743538 }}</ref> The following list gives the currently supported software applications available to researchers.


The vast majority of modern systems biology modeling software support [[SBML]], which is the de facto standard for exchanging models of biological cellular processes. Some tools also support [[CellML]], a standard used for representing physiological processes. The advantage of using standard formats is that even though a particular software application may eventually become unsupported and even unusable, the models developed by that application can be easily transferred to more modern equivalents. This allows scientific research to be [[reproducible]] long after the original publication of the work.
The vast majority of modern systems biology modeling software support [[SBML]], which is the de facto standard for exchanging models of biological cellular processes. Some tools also support [[CellML]], a standard used for representing physiological processes. The advantage of using standard formats is that even though a particular software application may eventually become unsupported and even unusable, the models developed by that application can be easily transferred to more modern equivalents. This allows scientific research to be [[reproducible]] long after the original publication of the work.
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| [https://pubmed.ncbi.nlm.nih.gov/30818351/ ReaDDy]
| [https://pubmed.ncbi.nlm.nih.gov/30818351/ ReaDDy]
| Particle-based spatial simulator with intermolecular potentials<ref>{{cite journal |last1=Schöneberg |first1=J. |last2=Ullrich |first2=A. |last3=Noé |first3=F. |date=2014 |title=Simulation tools for particle-based reaction-diffusion dynamics in continuous space |journal=BMC Biophys. |volume=7 |pages=1}}</ref> || Linux and Mac || Custom || [https://readdy.github.io/index.html]|| Not applicable
| Particle-based spatial simulator with intermolecular potentials<ref>{{cite journal |last1=Schöneberg |first1=J. |last2=Ullrich |first2=A. |last3=Noé |first3=F. |date=2014 |title=Simulation tools for particle-based reaction-diffusion dynamics in continuous space |journal=BMC Biophys. |volume=7 |pages=1|doi=10.1186/s13628-014-0011-5 |pmid=25737778 |pmc=4347613 }}</ref> || Linux and Mac || Custom || [https://readdy.github.io/index.html]|| Not applicable
|-
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| [https://pubmed.ncbi.nlm.nih.gov/34554191/ SBSCL]
| [https://pubmed.ncbi.nlm.nih.gov/34554191/ SBSCL]
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| [https://pubmed.ncbi.nlm.nih.gov/14683609/ SBW (software)]
| [https://pubmed.ncbi.nlm.nih.gov/14683609/ SBW (software)]
| A distributed workbench<ref>{{cite journal |last1=Hucka |first1=M. |last2=Finney |first2=A. |last3=Sauro |first3=H. M. |last4=Bolouri |first4=H. |last5=Doyle |first5=J. |last6=Kitano |first6=H. |title=The Erato Systems Biology Workbench: Enabling Interaction and Exchange Between Software Tools for Computational Biology |journal=Biocomputing 2002 |date=December 2001 |pages=450–461 |doi=10.1142/9789812799623_0042|pmid=11928498 |isbn=978-981-02-4777-5 }}</ref><ref>{{cite journal |last1=Kawasaki |first1=Regiane |last2=Baraúna |first2=Rafael A. |last3=Silva |first3=Artur |last4=Carepo |first4=Marta S. P. |last5=Oliveira |first5=Rui |last6=Marques |first6=Rodolfo |last7=Ramos |first7=Rommel T. J. |last8=Schneider |first8=Maria P. C. |title=Reconstruction of the Fatty Acid Biosynthetic Pathway of Exiguobacterium antarcticum B7 Based on Genomic and Bibliomic Data |journal=BioMed Research International |date=2016 |volume=2016 |pages=1–9 |doi=10.1155/2016/7863706|pmid=27595107 |pmc=4993939 |doi-access=free }}</ref> that includes many modeling tools
| A distributed workbench<ref>{{cite journal |last1=Hucka |first1=M. |last2=Finney |first2=A. |last3=Sauro |first3=H. M. |last4=Bolouri |first4=H. |last5=Doyle |first5=J. |last6=Kitano |first6=H. |title=The Erato Systems Biology Workbench: Enabling Interaction and Exchange Between Software Tools for Computational Biology |journal=Biocomputing 2002 |date=December 2001 |pages=450–461 |doi=10.1142/9789812799623_0042|pmid=11928498 |isbn=978-981-02-4777-5 |url=https://resolver.caltech.edu/CaltechAUTHORS:20130108-142104885 }}</ref><ref>{{cite journal |last1=Kawasaki |first1=Regiane |last2=Baraúna |first2=Rafael A. |last3=Silva |first3=Artur |last4=Carepo |first4=Marta S. P. |last5=Oliveira |first5=Rui |last6=Marques |first6=Rodolfo |last7=Ramos |first7=Rommel T. J. |last8=Schneider |first8=Maria P. C. |title=Reconstruction of the Fatty Acid Biosynthetic Pathway of Exiguobacterium antarcticum B7 Based on Genomic and Bibliomic Data |journal=BioMed Research International |date=2016 |volume=2016 |pages=1–9 |doi=10.1155/2016/7863706|pmid=27595107 |pmc=4993939 |doi-access=free }}</ref> that includes many modeling tools
||multiplatform (C/C++)||[[BSD-3]]||[https://sbw.sourceforge.net/]|| Yes
||multiplatform (C/C++)||[[BSD-3]]||[https://sbw.sourceforge.net/]|| Yes
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| [https://pubmed.ncbi.nlm.nih.gov/34293100/ Smoldyn]
| [https://pubmed.ncbi.nlm.nih.gov/34293100/ Smoldyn]
| Particle-based simulator for spatial stochastic simulations with individual molecules<ref>{{cite journal |last1=Andrews |first1=Steven S. |last2=Bray |first2=Dennis |title=Stochastic simulation of chemical reactions with spatial resolution and single molecule detail |journal=Physical Biology |date=2004 |volume=1 |issue=3–4 |pages=137–151|doi=10.1088/1478-3967/1/3/001 |pmid=16204833 |bibcode=2004PhBio...1..137A |s2cid=16394428 }}</ref><ref>{{cite journal |last1=Andrews |first1=Steven S. |last2=Addy |first2= Nathan J. |last3= Brent |first3=Roger |last4=Arkin |first4=Adam P. |title=Detailed simulations of cell biology with Smoldyn 2.1 |journal=PLOS Comput. Biol. |date=2010 |volume=6 |issue=3 |pages=e1000705|doi=10.1371/journal.pcbi.1000705 |pmid=20300644 |pmc=2837389 |bibcode=2010PLSCB...6E0705A }}</ref><ref>{{cite journal |last1=Andrews |first1=Steven S. |title=Smoldyn: particle-based simulation with rule-based modeling, improved molecular interaction, and a library interface |journal=Bioinformatics |date=2017 |volume=33 |issue=5 |pages=710–717|doi=10.1093/bioinformatics/btw700 |pmid=28365760 }}</ref><ref>{{cite journal |last1=Singh |first1 =Dilawar |last2=Andrews |first2=Steven S. |title=Python interfaces for the Smoldyn simulator |journal=Bioinformatics |date=2022 |volume=38 |pages=291–293|doi =10.1093/bioinformatics/btab530 |pmid =34293100 }}</ref>
| Particle-based simulator for spatial stochastic simulations with individual molecules<ref>{{cite journal |last1=Andrews |first1=Steven S. |last2=Bray |first2=Dennis |title=Stochastic simulation of chemical reactions with spatial resolution and single molecule detail |journal=Physical Biology |date=2004 |volume=1 |issue=3–4 |pages=137–151|doi=10.1088/1478-3967/1/3/001 |pmid=16204833 |bibcode=2004PhBio...1..137A |s2cid=16394428 }}</ref><ref>{{cite journal |last1=Andrews |first1=Steven S. |last2=Addy |first2= Nathan J. |last3= Brent |first3=Roger |last4=Arkin |first4=Adam P. |title=Detailed simulations of cell biology with Smoldyn 2.1 |journal=PLOS Comput. Biol. |date=2010 |volume=6 |issue=3 |pages=e1000705|doi=10.1371/journal.pcbi.1000705 |pmid=20300644 |pmc=2837389 |bibcode=2010PLSCB...6E0705A }}</ref><ref>{{cite journal |last1=Andrews |first1=Steven S. |title=Smoldyn: particle-based simulation with rule-based modeling, improved molecular interaction, and a library interface |journal=Bioinformatics |date=2017 |volume=33 |issue=5 |pages=710–717|doi=10.1093/bioinformatics/btw700 |pmid=28365760 }}</ref><ref>{{cite journal |last1=Singh |first1 =Dilawar |last2=Andrews |first2=Steven S. |title=Python interfaces for the Smoldyn simulator |journal=Bioinformatics |date=2022 |volume=38 |issue =1 |pages=291–293|doi =10.1093/bioinformatics/btab530 |pmid =34293100 }}</ref>
|| multiplatform (C/C++/Python)||[[LGPL]]||[https://www.smoldyn.org/]||Not applicable
|| multiplatform (C/C++/Python)||[[LGPL]]||[https://www.smoldyn.org/]||Not applicable
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| [https://pubmed.ncbi.nlm.nih.gov/22574658/ STEPS]
| [https://pubmed.ncbi.nlm.nih.gov/22574658/ STEPS]
|Stochastic reaction-diffusion and membrane potential solver on distributed meshes<ref>{{cite journal |last1=Hepburn |first1=Iain |last2=Chen |first2=Weiliang |last3=Wils |first3=Stefan |last4=De Schutter |first4=Erik |title=STEPS: efficient simulation of stochastic reaction–diffusion models in realistic morphologies |journal=BMC Systems Biology |date=May 2012 |volume=7 |issue=1 |pages=36 |doi=10.1186/1752-0509-6-36|pmid=22574658 |s2cid=9165862 }}</ref><ref>{{cite journal |last1=Chen |first1=Weiliang |last2=De Schutter |first2=Erik |title=Parallel STEPS: Large Scale Stochastic Spatial Reaction-Diffusion Simulation with High Performance Computers |journal=Frontiers in Neuroinformatics |date=February 2017 |volume=11 |issue=1 |pages=13 |doi=10.3389/fninf.2017.00013|pmid=28239346 |pmc=5301017 |doi-access=free }}</ref><ref>{{cite journal |last1=Hepburn |first1=Iain |last2=Chen |first2=Weiliang |last3=De Schutter |first3=Erik |title=Accurate reaction-diffusion operator splitting on tetrahedral meshes for parallel stochastic molecular simulations |journal=The Journal of Chemical Physics |date=August 2016 |volume=145 |issue=5 |pages=054118 |doi=10.1063/1.4960034|pmid=27497550 |arxiv=1512.03126 |bibcode=2016JChPh.145e4118H |s2cid=17356298 }}</ref><ref>{{cite journal |last1=Chen |first1=Weiliang |last2=Carel |first2=Tristan |last3=Awile |first3=Omar |last4=Cantarutti |first4=Nicola |last5=Castiglioni |first5=Giacomo |last6=Cattabiani |first6=Alessandro |last7=Del Marmol |first7=Baudouin |last8=Hepburn |first8=Iain |last9=King |first9=James G. |last10=Kotsalos |first10=Christos |last11=Kumbhar |first11=Pramod |last12=Lallouette |first12=Jules |last13=Melchior |first13=Samuel |last14=Schürmann |first14=Felix |last15=De Schutter |first15=Erik |title=STEPS 4.0: Fast and memory-efficient molecular simulations of neurons at the nanoscale |journal=Frontiers in Neuroinformatics |date=October 2022 |volume=16 |doi=10.3389/fninf.2022.883742 |issn=1662-5196|doi-access=free }}</ref>
|Stochastic reaction-diffusion and membrane potential solver on distributed meshes<ref>{{cite journal |last1=Hepburn |first1=Iain |last2=Chen |first2=Weiliang |last3=Wils |first3=Stefan |last4=De Schutter |first4=Erik |title=STEPS: efficient simulation of stochastic reaction–diffusion models in realistic morphologies |journal=BMC Systems Biology |date=May 2012 |volume=7 |issue=1 |pages=36 |doi=10.1186/1752-0509-6-36|pmid=22574658 |pmc=3472240 |s2cid=9165862 }}</ref><ref>{{cite journal |last1=Chen |first1=Weiliang |last2=De Schutter |first2=Erik |title=Parallel STEPS: Large Scale Stochastic Spatial Reaction-Diffusion Simulation with High Performance Computers |journal=Frontiers in Neuroinformatics |date=February 2017 |volume=11 |issue=1 |pages=13 |doi=10.3389/fninf.2017.00013|pmid=28239346 |pmc=5301017 |doi-access=free }}</ref><ref>{{cite journal |last1=Hepburn |first1=Iain |last2=Chen |first2=Weiliang |last3=De Schutter |first3=Erik |title=Accurate reaction-diffusion operator splitting on tetrahedral meshes for parallel stochastic molecular simulations |journal=The Journal of Chemical Physics |date=August 2016 |volume=145 |issue=5 |pages=054118 |doi=10.1063/1.4960034|pmid=27497550 |arxiv=1512.03126 |bibcode=2016JChPh.145e4118H |s2cid=17356298 }}</ref><ref>{{cite journal |last1=Chen |first1=Weiliang |last2=Carel |first2=Tristan |last3=Awile |first3=Omar |last4=Cantarutti |first4=Nicola |last5=Castiglioni |first5=Giacomo |last6=Cattabiani |first6=Alessandro |last7=Del Marmol |first7=Baudouin |last8=Hepburn |first8=Iain |last9=King |first9=James G. |last10=Kotsalos |first10=Christos |last11=Kumbhar |first11=Pramod |last12=Lallouette |first12=Jules |last13=Melchior |first13=Samuel |last14=Schürmann |first14=Felix |last15=De Schutter |first15=Erik |title=STEPS 4.0: Fast and memory-efficient molecular simulations of neurons at the nanoscale |journal=Frontiers in Neuroinformatics |date=October 2022 |volume=16 |page=883742 |doi=10.3389/fninf.2022.883742 |pmid=36387588 |pmc=9645802 |issn=1662-5196|doi-access=free }}</ref>
|multiplatform (C++/Python)
|multiplatform (C++/Python)
|[[GPLv2]]
|[[GPLv2]]
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| [[VCell]]
| [[VCell]]
| Comprehensive modeling platform<ref>{{cite journal |last1=Schaff |first1=J. |last2=Fink |first2=C.C. |last3=Slepchenko |first3=B. |last4=Carson |first4=J.H. |last5=Loew |first5=L.M. |title=A general computational framework for modeling cellular structure and function |journal=Biophysical Journal |date=September 1997 |volume=73 |issue=3 |pages=1135–1146 |doi=10.1016/S0006-3495(97)78146-3|bibcode=1997BpJ....73.1135S |s2cid=39818739 }}</ref><ref>{{cite journal |last1=Cowan |first1=Ann E. |last2=Moraru |first2=Ion I. |last3=Schaff |first3=James C. |last4=Slepchenko |first4=Boris M. |last5=Loew |first5=Leslie M. |title=Spatial Modeling of Cell Signaling Networks |journal=Methods in Cell Biology |date=2012 |volume=110 |pages=195–221 |doi=10.1016/B978-0-12-388403-9.00008-4|pmid=22482950 |pmc=3519356 |isbn=9780123884039 }}</ref> for non-spatial, spatial, deterministic and stochastic simulations, including both reaction networks and reaction rules.
| Comprehensive modeling platform<ref>{{cite journal |last1=Schaff |first1=J. |last2=Fink |first2=C.C. |last3=Slepchenko |first3=B. |last4=Carson |first4=J.H. |last5=Loew |first5=L.M. |title=A general computational framework for modeling cellular structure and function |journal=Biophysical Journal |date=September 1997 |volume=73 |issue=3 |pages=1135–1146 |doi=10.1016/S0006-3495(97)78146-3|pmid=9284281 |pmc=1181013 |bibcode=1997BpJ....73.1135S |s2cid=39818739 }}</ref><ref>{{cite journal |last1=Cowan |first1=Ann E. |last2=Moraru |first2=Ion I. |last3=Schaff |first3=James C. |last4=Slepchenko |first4=Boris M. |last5=Loew |first5=Leslie M. |title=Spatial Modeling of Cell Signaling Networks |journal=Methods in Cell Biology |date=2012 |volume=110 |pages=195–221 |doi=10.1016/B978-0-12-388403-9.00008-4|pmid=22482950 |pmc=3519356 |isbn=9780123884039 }}</ref> for non-spatial, spatial, deterministic and stochastic simulations, including both reaction networks and reaction rules.
||multiplatform (Java)||MIT||[https://vcell.org]|| Yes
||multiplatform (Java)||MIT||[https://vcell.org]|| Yes
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== Particle-based simulators ==
== Particle-based simulators ==


Particle based simulators treat each molecule of interest as an individual particle in continuous space, simulating molecular diffusion, molecule-membrane interactions and chemical reactions.<ref>{{cite journal |last1=Schöneberg |first1=J |last2=Ullrich |first2=A |last3=Noé |first3=F |date=2014 |title=Simulation tools for particle-based reaction-diffusion dynamics in continuous space |journal=BMC Biophys |volume=7 |issue=1 |pages=1}}</ref>
Particle based simulators treat each molecule of interest as an individual particle in continuous space, simulating molecular diffusion, molecule-membrane interactions and chemical reactions.<ref>{{cite journal |last1=Schöneberg |first1=J |last2=Ullrich |first2=A |last3=Noé |first3=F |date=2014 |title=Simulation tools for particle-based reaction-diffusion dynamics in continuous space |journal=BMC Biophys |volume=7 |issue=1 |page=11 |doi=10.1186/s13628-014-0011-5 |pmid=25737778 |pmc=4347613 }}</ref>


=== Comparison of particle-based simulators ===
=== Comparison of particle-based simulators ===


The following list compares the features for several particle-based simulators. This table is edited from a version that was originally published in the Encyclopedia of Computational Neuroscience.<ref>{{cite journal |last1=Andrews |first1=Steven S. |title=Particle-based stochastic simulators |journal=Encyclopedia of Computational Neuroscience |date=2018 |volume=10 |pages=978-1 |doi=10.1007/978-1-4614-7320-6_191-2}}</ref> System boundaries codes: R = reflecting, A = absorbing, T = transmitting, P = periodic, and I = interacting. * Algorithm is exact but software produced incorrect results at the time of original table compilation. † These benchmark run times are not comparable with others due to differing levels of detail.
The following list compares the features for several particle-based simulators. This table is edited from a version that was originally published in the Encyclopedia of Computational Neuroscience.<ref>{{cite journal |last1=Andrews |first1=Steven S. |title=Particle-based stochastic simulators |journal=Encyclopedia of Computational Neuroscience |date=2018 |volume=10 |pages=978–1 |doi=10.1007/978-1-4614-7320-6_191-2|isbn=978-1-4614-7320-6 }}</ref> System boundaries codes: R = reflecting, A = absorbing, T = transmitting, P = periodic, and I = interacting. * Algorithm is exact but software produced incorrect results at the time of original table compilation. † These benchmark run times are not comparable with others due to differing levels of detail.


{| class="wikitable"
{| class="wikitable"
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| References ||
| References ||
<ref>{{cite journal |last1=Stiles |first1=JR |last2=Bartol |first2=TM |date=2001 |title=Chapter 4, Monte Carlo methods for simulating realistic synaptic microphysiology using MCell |series=In: Computational neuroscience, realistic modeling for experimentalists, De Schutter, E (ed.) |publisher=CRC Press |publication-place=Boca Raton |pages=87–127}}</ref><ref>{{cite journal |last1=Stefan |first1=MI |last2=Bartol |first2=TM |last3=Sejnowski |first3=TJ |last4=Kennedy |first4=MB |date=2014 |title=Multi-state modeling of biomolecules |journal=PLoS Comput Biol |volume=10 |issue=9 |pages=e1003844}}</ref><ref>{{cite journal |last1=Stiles |first1=JR |last2=Van Helden |first2=D |last3=Bartol |first3=TM |last4=Salpeter |first4=EE |last5=Salpeter |first5=MM |date=1996 |title=Miniature endplate current rise times less than 100 microseconds from improved dual recordings can be modeled with passive acetylcholine diffusion from a synaptic vesicle |journal=Proceedings of the National Academy of Sciences, USA |volume=93 |issue=12 |pages=5747–5752}}</ref>
<ref>{{cite journal |last1=Stiles |first1=JR |last2=Bartol |first2=TM |date=2001 |title=Chapter 4, Monte Carlo methods for simulating realistic synaptic microphysiology using MCell |series=In: Computational neuroscience, realistic modeling for experimentalists, De Schutter, E (ed.) |publisher=CRC Press |publication-place=Boca Raton |pages=87–127}}</ref><ref>{{cite journal |last1=Stefan |first1=MI |last2=Bartol |first2=TM |last3=Sejnowski |first3=TJ |last4=Kennedy |first4=MB |date=2014 |title=Multi-state modeling of biomolecules |journal=PLOS Comput Biol |volume=10 |issue=9 |pages=e1003844|doi=10.1371/journal.pcbi.1003844 |pmid=25254957 |pmc=4201162 |bibcode=2014PLSCB..10E3844S }}</ref><ref>{{cite journal |last1=Stiles |first1=JR |last2=Van Helden |first2=D |last3=Bartol |first3=TM |last4=Salpeter |first4=EE |last5=Salpeter |first5=MM |date=1996 |title=Miniature endplate current rise times less than 100 microseconds from improved dual recordings can be modeled with passive acetylcholine diffusion from a synaptic vesicle |journal=Proceedings of the National Academy of Sciences, USA |volume=93 |issue=12 |pages=5747–5752|doi=10.1073/pnas.93.12.5747 |pmid=8650164 |pmc=39132 |bibcode=1996PNAS...93.5747S |doi-access=free }}</ref>
|| <ref>{{cite journal |last1=Andrews |first1=SS |date=2017 |title=Smoldyn: particle-based simulation with rule-based modeling, improved molecular interaction and a library interface |journal=Bioinformatics |volume=33 |issue=5 |pages=710–717}}</ref><ref>{{cite journal |last1=Andrews |first1=SS |last2=Addy |first2=NJ |last3=Brent |first3=R |last4=Arkin |first4=AP |date=2010 |title=Detailed simulations of cell biology with Smoldyn 2.1 |journal=PLoS Comput Biol |volume=6 |pages=e1000705}}</ref>
|| <ref>{{cite journal |last1=Andrews |first1=SS |date=2017 |title=Smoldyn: particle-based simulation with rule-based modeling, improved molecular interaction and a library interface |journal=Bioinformatics |volume=33 |issue=5 |pages=710–717|doi=10.1093/bioinformatics/btw700 |pmid=28365760 }}</ref><ref>{{cite journal |last1=Andrews |first1=SS |last2=Addy |first2=NJ |last3=Brent |first3=R |last4=Arkin |first4=AP |date=2010 |title=Detailed simulations of cell biology with Smoldyn 2.1 |journal=PLOS Comput Biol |volume=6 |issue=3 |pages=e1000705|doi=10.1371/journal.pcbi.1000705 |pmid=20300644 |bibcode=2010PLSCB...6E0705A |s2cid=2945597 }}</ref>
|| <ref>{{cite arXiv |last1=Sokolowski |first1=TR |last2=ten Wolde |first2=PR |date=2017 |title=Spatial-stochastic simulation of reaction-diffusion systems |arxiv=1705.08669}}</ref><ref>{{cite journal |last1=Takahashi |first1=K |last2=Tănase-Nicola |first2=S |last3=Ten Wolde |first3=PR |date=2010 |title=Spatio-temporal correlations can drastically change the response of a MAPK pathway |journal=Proc Natl Acad Sci |volume=107 |issue=6 |pages=2473–2478}}</ref><ref>{{cite journal |last1=Tomita |first1=M |last2=Hashimoto |first2=K |last3=Takahashi |first3=K |last4=Shimizu |first4=TS |display-authors=et al. |date=1999 |title=E-cell: software environment for whole-cell simulation |journal=Bioinformatics |volume=15 |issue=1 |pages=72–84}}</ref>
|| <ref>{{cite arXiv |last1=Sokolowski |first1=TR |last2=ten Wolde |first2=PR |date=2017 |title=Spatial-stochastic simulation of reaction-diffusion systems |class=q-bio.MN |eprint=1705.08669}}</ref><ref>{{cite journal |last1=Takahashi |first1=K |last2=Tănase-Nicola |first2=S |last3=Ten Wolde |first3=PR |date=2010 |title=Spatio-temporal correlations can drastically change the response of a MAPK pathway |journal=Proc Natl Acad Sci |volume=107 |issue=6 |pages=2473–2478|doi=10.1073/pnas.0906885107 |pmid=20133748 |pmc=2811204 |arxiv=0907.0514 |bibcode=2010PNAS..107.2473T |doi-access=free }}</ref><ref>{{cite journal |last1=Tomita |first1=M |last2=Hashimoto |first2=K |last3=Takahashi |first3=K |last4=Shimizu |first4=TS |display-authors=et al. |date=1999 |title=E-cell: software environment for whole-cell simulation |journal=Bioinformatics |volume=15 |issue=1 |pages=72–84|doi=10.1093/bioinformatics/15.1.72 |pmid=10068694 }}</ref>
|| <ref>{{cite journal |last1=Michalski |first1=PJ |last2=Loew |first2=LM |date=2016 |title=SpringSaLaD: a spatial, particle-based biochemical simulation platform with excluded volume |journal=Biophys J |volume=110 |issue=3 |pages=523–529}}</ref>
|| <ref>{{cite journal |last1=Michalski |first1=PJ |last2=Loew |first2=LM |date=2016 |title=SpringSaLaD: a spatial, particle-based biochemical simulation platform with excluded volume |journal=Biophys J |volume=110 |issue=3 |pages=523–529|doi=10.1016/j.bpj.2015.12.026 |pmid=26840718 |pmc=4744174 |bibcode=2016BpJ...110..523M }}</ref>
|| <ref>{{cite journal |last1=Schöneberg |first1=J |last2=Noé |first2=F |date=2013 |title=ReaDDy-a software for particle-based reaction-diffusion dynamics in crowded cellular environments |journal=PLoS One |volume=8 |issue=9 |pages=e74261}}</ref>
|| <ref>{{cite journal |last1=Schöneberg |first1=J |last2=Noé |first2=F |date=2013 |title=ReaDDy-a software for particle-based reaction-diffusion dynamics in crowded cellular environments |journal=PLOS ONE |volume=8 |issue=9 |pages=e74261|doi=10.1371/journal.pone.0074261 |pmid=24040218 |pmc=3770580 |bibcode=2013PLoSO...874261S |doi-access=free }}</ref>
|}
|}


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| BIOSIM<ref>{{cite journal |last1=Garfinkel |first1=David |title=A machine-independent language for the simulation of complex chemical and biochemical systems |journal=Computers and Biomedical Research |date=August 1968 |volume=2 |issue=1 |pages=31–44 |doi=10.1016/0010-4809(68)90006-2}}</ref> || The first ever recorded digital simulator of biochemical networks (by David Garfinkel) || FORTRAN IV || 1968
| BIOSIM<ref>{{cite journal |last1=Garfinkel |first1=David |title=A machine-independent language for the simulation of complex chemical and biochemical systems |journal=Computers and Biomedical Research |date=August 1968 |volume=2 |issue=1 |pages=31–44 |doi=10.1016/0010-4809(68)90006-2|pmid=5743538 }}</ref> || The first ever recorded digital simulator of biochemical networks (by David Garfinkel) || FORTRAN IV || 1968
|-
|-
| [[English Electric KDF9|KDF 9]]<ref>{{cite journal |last1=Burns |first1=Jim |title=Metabolic Control Anlaysis |date=1 March 1973 |doi=10.5281/zenodo.7240738}}</ref>
| [[English Electric KDF9|KDF 9]]<ref>{{cite thesis |last1=Burns |first1=Jim |title=Metabolic Control Anlaysis |date=1 March 1973 |doi=10.5281/zenodo.7240738}}</ref>
|| First simulator to support [[Metabolic control analysis|MCA]]. Developed by the late Jim Burns in Edinburgh|| Early form of FORTRAN || 1968
|| First simulator to support [[Metabolic control analysis|MCA]]. Developed by the late Jim Burns in Edinburgh|| Early form of FORTRAN || 1968
|-
|-
| METASIM<ref>{{cite journal |last1=Park |first1=D.J.M. |last2=Wright |first2=B.E. |title=Metasim, a general purpose metabolic simulator for studying cellular transformations |journal=Computer Programs in Biomedicine |date=March 1973 |volume=3 |issue=1 |pages=10–26 |doi=10.1016/0010-468X(73)90010-X}}</ref>
| METASIM<ref>{{cite journal |last1=Park |first1=D.J.M. |last2=Wright |first2=B.E. |title=Metasim, a general purpose metabolic simulator for studying cellular transformations |journal=Computer Programs in Biomedicine |date=March 1973 |volume=3 |issue=1 |pages=10–26 |doi=10.1016/0010-468X(73)90010-X|pmid=4735157 }}</ref>
|| Early simulator by Park and Wright || PL/1 || 1973
|| Early simulator by Park and Wright || PL/1 || 1973
|-
|-
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|| First Java-based systems biology modeling platform || Java || Yes || 2003
|| First Java-based systems biology modeling platform || Java || Yes || 2003
|-
|-
| MetaMod<ref>{{cite journal |last1=Hofmeyr |first1=J. H. S. |last2=Merwe |first2=K. J. van der |title=METAMOD: software for steady-state modelling and control analysis of metabolic pathways on the BBC microcomputer |journal=Bioinformatics |date=1986 |volume=2 |issue=4 |pages=243–249 |doi=10.1093/bioinformatics/2.4.243}}</ref>
| MetaMod<ref>{{cite journal |last1=Hofmeyr |first1=J. H. S. |last2=Merwe |first2=K. J. van der |title=METAMOD: software for steady-state modelling and control analysis of metabolic pathways on the BBC microcomputer |journal=Bioinformatics |date=1986 |volume=2 |issue=4 |pages=243–249 |doi=10.1093/bioinformatics/2.4.243|pmid=3450367 }}</ref>
|| One of the first PC-based systems biology simulators || BBC Micro|| No || 1986
|| One of the first PC-based systems biology simulators || BBC Micro|| No || 1986
|-
|-

Revision as of 16:34, 18 February 2023

Systems biology relies heavily on building mathematical models to help understand and make predictions of biological processes. Specialized software to assist in building models has been developed since the arrival of the first digital computers.[1][2][3][4] The following list gives the currently supported software applications available to researchers.

The vast majority of modern systems biology modeling software support SBML, which is the de facto standard for exchanging models of biological cellular processes. Some tools also support CellML, a standard used for representing physiological processes. The advantage of using standard formats is that even though a particular software application may eventually become unsupported and even unusable, the models developed by that application can be easily transferred to more modern equivalents. This allows scientific research to be reproducible long after the original publication of the work.

To obtain more information about a particular tool, click on the name of the tool. This will direct you either to a peer-reviewed publication or, in some rare cases, to a dedicated Wikipedia page.

Actively supported open-source software applications

General information

Name Description/Notability OS License Site SBML Support
iBioSim iBioSim[5][6] is a computer-aided design (CAD) tool for the modeling, analysis, and design of genetic circuits. multiplatform (Java/C++) Apache [1] Yes
CompuCell3D GUI/Scripting tool[7] for building and simulating multicellular models. multiplatform (C++/Python) MIT [2] Yes, but only for reactions.
COPASI GUI tool[8][9] for analyzing and simulating SBML models. multiplatform (C++) Artistic License [3] Yes
Cytosim Spatial simulator for flexible cytoskeletal filaments and motor proteins[10] Mac, Linux, Cygwin (C++) GPL3 [4] Not applicable
libroadrunner High-performance software library for simulation and analysis of SBML models[11][12] multiplatform (C/C++) Apache License [5] Yes
massPy Simulation tool [13][14] that can work with COBRApy[15] multiplatform (Python) MIT [6] Yes
MCell GUI tool for particle-based spatial stochastic simulation with individual molecules[16][17][18] multiplatform MIT and GPLv2 [7] Not applicable
OpenCOR A cross-platform modelling environment, which is aimed at organizing, editing, simulating, and analysing CellML files on Windows, Linux and macOS. multiplatform (C++/Python) GPLv3 [8] Uses CellML
PhysiBoSS A specialized form of the PhysiCell agent-based modeling platform that directly integrates Boolean signaling networks into cell Agents[19] multiplatform (C++) BSD-3 [9] Yes, but only for reactions
PhysiCell A agent-based[20] modeling framework for multicellular systems biology. multiplatform (C++) BSD-3 [10] Yes, but only for reactions
PySCeS Python tool for modeling and analyzing SBML models[21][22][23] multiplatform (Python) BSD-3 [11] Yes
pySB Python-based[24] platform with specialization in rule-based models. multiplatform (Python) BSD-3 [12] Partial
ReaDDy Particle-based spatial simulator with intermolecular potentials[25] Linux and Mac Custom [13] Not applicable
SBSCL Java library[26][27] with efficient and exhaustive support for SBML multiplatform (Java) LGPL [14] Yes
SBW (software) A distributed workbench[28][29] that includes many modeling tools multiplatform (C/C++) BSD-3 [15] Yes
Smoldyn Particle-based simulator for spatial stochastic simulations with individual molecules[30][31][32][33] multiplatform (C/C++/Python) LGPL [16] Not applicable
Spatiocyte Spatial modeling software that uses a fine lattice with up to one molecule per site[34][35] multiplatform Unknown [17] Not applicable
SpringSaLaD Particle-based spatial simulator in which molecules are spheres that are linked by springs[36] multiplatform Unknown [18] Not applicable
STEPS Stochastic reaction-diffusion and membrane potential solver on distributed meshes[37][38][39][40] multiplatform (C++/Python) GPLv2 [19] Partial [20]
Tellurium (software) Simulation environment,[41][42] that packages multiple libraries into one platform. multiplatform (Python) Apache License [21] Yes
URDME Stochastic reaction-diffusion simulation on unstructured meshes[43] MatLab on Mac, Linux GPL3 [22] Not applicable
VCell Comprehensive modeling platform[44][45] for non-spatial, spatial, deterministic and stochastic simulations, including both reaction networks and reaction rules. multiplatform (Java) MIT [23] Yes


Feature Tables

Supported modeling paradigms

Name ODE Constraint based Stochastic Logical Agent based Spatial (particle) Spatial (continuous)
iBioSim Yes No Yes No Limited No No
CompuCell3D Yes No No No Yes No Yes
COPASI Yes No Yes No No No No
Cytosim No No Yes No ? Yes ?
libroadrunner Yes No Yes No No No No
massPy Uses libroadrunner Uses COBRApy No No No No
MCell No No ?\No No No Yes No
OpenCOR Yes No No No No No No
PhysiBoSS
PhysiCell Uses libroadrunner No No No Yes ? Yes
PySCeS Yes No ? No No No No
pySB Yes No No No No No No
ReaDDy
SBSCL Yes ? ? No No No No
SBW Yes No Yes No No No No
Smoldyn No No No No No No Yes
Spatiocyte
SpringSaLaD
STEPS
Tellurium (software) Uses libroadrunner
URDME
VCell Yes No ? No No No Single Cell

Differential equation specific features

Name Non-stiff solver Stiff solver Steady-state solver Steady-state sensitivities Time-dependent sensitivities Bifurcation Analysis
iBioSim Yes Yes No No ? No
CompuCell3D Uses libroadrunner NA
COPASI Yes Yes Yes Yes ? Limited
libroadrunner Yes Yes Yes Yes Yes via plugin
masspy Uses libroadrunner
OpenCOR Yes Yes ? ? ? No
PhysiBoSS
PhysiCell Uses libroadrunner
PySCeS Yes Yes Yes Yes ? Limited+
pySB Yes No No No No No
SBSCL
SBW Uses C# edition of roadrunner Yes
Tellurium (software) Uses libroadrunner
VCell Yes Yes No No No No

File format support and interface type

Name Import Export Primary Interface Network visualization (editing)
iBioSim SBML SBML GUI Yes (Yes)
CompuCell3D Native XML specification format and SBML Native XML GUI/Python scripting No
COPASI Native XML specification format and SBML Native XML and SBML GUI Yes (No)
libroadrunner SBML SBML Python scripting No
masspy SBML SBML Python scripting No

Advanced features (where applicable)

Name Stoichiometry matrix Reduced stoich matrix Conserved moiety analysis Jacobian MCA
COPASI Yes Yes Yes Yes Yes
libroadrunner Yes Yes Yes Yes Yes
masspy via libroadrunner
PySCeS Yes Yes Yes Yes Yes
VCell ? ? ? ? Limited

Other features

Name Parameter Estimation DAE support Units support
iBioSim No ? ?
ComputeCell3D NA NA ?
COPASI Yes No Yes
libroadrunner via Python packages Limited Yes
masspy via Python packages Limited Yes

Particle-based simulators

Particle based simulators treat each molecule of interest as an individual particle in continuous space, simulating molecular diffusion, molecule-membrane interactions and chemical reactions.[46]

Comparison of particle-based simulators

The following list compares the features for several particle-based simulators. This table is edited from a version that was originally published in the Encyclopedia of Computational Neuroscience.[47] System boundaries codes: R = reflecting, A = absorbing, T = transmitting, P = periodic, and I = interacting. * Algorithm is exact but software produced incorrect results at the time of original table compilation. † These benchmark run times are not comparable with others due to differing levels of detail.

Feature MCell Smoldyn eGFRD SpringSaLaD ReaDDy
Time steps ~1 us ns to ms event-based ~10 ns ~0.1 ns to us
Molecules points points, spheres spheres multi-spheres multi-spheres
Dimensions 2,3 1,2,3 3 3 3
System boundaries R,A,P,T R,A,P,T P R P,I
Surfaces triangle mesh many primitives - 1 flat surface plane, sphere
Surface molecules 1/tile, 2 states unlimited, 4 states - unlimited, 3 states -
Excluded volume - excellent exact good excellent
Multimers states only rule-based model - explicit explicit
Allostery - yes - yes -
Reaction accuracy very good excellent exact* excellent excellent
Dissociation products stochastic fixed separation adjacent adjacent adjacent
Molecule-surface interactions good excellent - to sites only potentials
Long-range interactions - yes - - yes
Benchmark run time 67 s 22 s 13 days† 9.1 months† 13 minutes
Distribution executable executable self-compile Java file self-compile
User interface GUI, text text text GUI Python script
Graphical output excellent good partial support partial support good
Library interface Python C/C++, Python - - Python
References

[48][49][50]

[51][52] [53][54][55] [56] [57]

Legacy open-source software applications

The following list some very early software for modeling biochemical systems that were developed pre-1980s There are listed for historical interest.

Name Description/Notability Language Terminus ante quem[58]
BIOSIM[59] The first ever recorded digital simulator of biochemical networks (by David Garfinkel) FORTRAN IV 1968
KDF 9[60] First simulator to support MCA. Developed by the late Jim Burns in Edinburgh Early form of FORTRAN 1968
METASIM[61] Early simulator by Park and Wright PL/1 1973

The following list shows some of the software modeling applications that were developed in the 1980s and 1990s. There are listed for historical interest.

Name Description/Notability Language SBML Support Terminus ante quem[62]
COR[63] First public CellML-based environment. Object Pascal Uses CellML 2010
DBsolve[64] Early GUI based simulation platform. C/C++ No 1999
E-Cell[65] One of the earliest attempts at a whole-cell modeling platform. C/C++ No 1999
Gepasi[66] First GUI application that supported metabolic control analysis and parameter estimation. C/C++ Yes 1993
Jarnac[67] First GUI based application to support scripting in systems biology modeling. Object Pascal Yes 2000
JSim[68] First Java-based systems biology modeling platform Java Yes 2003
MetaMod[69] One of the first PC-based systems biology simulators BBC Micro No 1986
MetaModel[70] Early PC-based systems biology simulator Turbo Pascal 5.0 No 1991
MIST[71] GUI based simulator Borland Pascal 7.0 No 1995
SCAMP[72] First application to support metabolic control analysis and simulation on a PC Pascal, later in C No 1985 (Thesis)


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