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77 changes: 77 additions & 0 deletions joss.00674/10.21105.joss.00674.crossref.xml
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<title>MicroBenthos: a modeling framework for microbial benthic ecology</title>
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<meta name="citation_title" content="MicroBenthos: a modeling framework for microbial benthic ecology">
<meta name="citation_author" content="Chennu, Arjun">
<meta name="citation_publication_date" content="2018//">
<meta name="citation_journal_title" content="The Journal of Open Source Software">
<meta name="citation_pdf_url" content="http://www.theoj.org/joss-papers/joss.00674/10.21105.joss.00674.pdf">
<meta name="citation_doi" content="10.21105/joss.00674">
<meta name="citation_issn" content="2475-9066">
<div class="accepted-paper">
<h1>MicroBenthos: a modeling framework for microbial benthic ecology</h1>
<div class="columns links">
<div class="column four-fifths" style="padding-bottom: 10px;">
<strong>Authors</strong>
<ul class="author-list">
<li><a href="http://orcid.org/0000-0002-0389-5589" target="_blank">Arjun Chennu</a></li>
</ul>
</div>
<div class="one-third column">
<span class="repo">Repository:<br /><a href="https://github.com/achennu/microbenthos">Repository link &raquo;</a></span>
</div>
<div class="one-third column">
<span class="paper">Paper:<br /><a href="http://www.theoj.org/joss-papers/joss.00674/10.21105.joss.00674.pdf">PDF link &raquo;</a></span>
</div>
<div class="one-third column">
<span class="paper">Review:<br /><a href="https://github.com/openjournals/joss-reviews/issues/674">View review issue &raquo;</a></span>
</div>

<div class="one-third column" style="padding-top: 20px;">
<span class="repo">DOI:<br /><a href="https://doi.org/10.21105/joss.00674">https://doi.org/10.21105/joss.00674</a></span>
</div>
<div class="one-third column" style="padding-top: 20px;">
<span class="paper">Status badge:<br /><img src="http://joss.theoj.org/papers/10.21105/joss.00674/status.svg"></span>
</div>
<div class="one-third column" style="padding-top: 20px;">
<span class="paper">
Submitted: 04 April 2018 <br />
Published: 04 May 2018
</span>

</div>
<div class="two-thirds column" style="padding-top: 20px;">
<span class="paper">Citation:<br />
<small>Chennu, (2018). MicroBenthos: a modeling framework for microbial benthic ecology. <em>Journal of Open Source Software</em>, 3(25), 674. https://doi.org/10.21105/joss.00674</small>
</div>
</div>
<div class="paper-body">
<h1 id="summary">Summary</h1>
<p>Microbial benthic habitats, such as microbial mats and sediments, exhibit extremely steep gradients in the physical, chemical and biotic parameters within the space of a few millimeters. These micro-environments drive the localization and exploitation of physico-chemical niches by a variety of microbial groups, such as cyanobacteria, sulfur-oxidizing bacteria, etc <span class="citation" data-cites="VanGemerden-1993">(Van Gemerden 1993)</span>. Studies of biogeochemistry and microbial ecology in these systems use various sensors to profile micro-environments and infer the local budgets and productivities of the microbial groups and metabolisms <span class="citation" data-cites="Revsbech-1983">(Revsbech et al. 1983)</span>. Microbenthic habitats are typically modeled as diffusive-reactive systems <span class="citation" data-cites="deWit-1995">(Wit, Ende, and Gemerden 1995)</span>, i.e.&#160;the dominant mass transport mode is physical diffusion of solutes within the porespaces of the sediment matrix. The &#8220;reactive&#8221; aspect refers to the presence of a large number of local sources and sinks within the mat system.</p>
<p>MicroBenthos is a modeling framework created to study <em>in silico</em> microbenthic habitats. The main perspective is to recognize that while modeling physical diffusion is straightforward, the larger challenge is to have a flexible way to define, compose and study various microbial metabolisms under dynamic conditions. MicroBenthos enables this by providing a high-level abstraction to compose and simulate microbenthic systems in terms of solar irradiance, chemical solutes, microbial groups and chemical or metabolic processes. While the software is written in python, with a modular structure for ease of extensibility, it can be used without programming through a (YAML) structured text file as the interface. This allows the user to focus on specifying the constitutive relations between environmental parameters and processes as a simple mathematical formula, which is then symbolically cast (using sympy <span class="citation" data-cites="Meurer-2017">(Meurer et al. 2017)</span>) into a set of coupled partial differential equations for the full model. Using a simple command, the equations can be numerically solved (using fipy <span class="citation" data-cites="Guyer-2009">(Guyer, Wheeler, and Warren 2009)</span>) to study the evolution of the various model variables.</p>
<p>MicroBenthos provides several useful features:</p>
<ul>
<li>Modular and extensible abstractions to create microbenthic systems</li>
<li>Non-programming interface to define processes and model structure</li>
<li>On-line visualization of running simulations and video export</li>
<li>Stateful simulations that can be interrupted and resumed</li>
<li>Export of detailed model data in open archival format</li>
<li>Open-source software (MIT license): https://github.com/achennu/microbenthos</li>
<li>Detailed documentation and tutorials: https://microbenthos.readthedocs.io</li>
</ul>
<h1 id="references" class="unnumbered">References</h1>
<div id="refs" class="references">
<div id="ref-Guyer-2009">
<p>Guyer, Jonathan E., Daniel Wheeler, and James A. Warren. 2009. &#8220;FiPy: Partial Differential Equations with Python.&#8221; <em>Computing in Science &amp; Engineering</em> 11 (3). Institute of Electrical; Electronics Engineers (IEEE):6&#8211;15. <a href="https://doi.org/10.1109/mcse.2009.52" class="uri">https://doi.org/10.1109/mcse.2009.52</a>.</p>
</div>
<div id="ref-Meurer-2017">
<p>Meurer, Aaron, Christopher P. Smith, Mateusz Paprocki, Ond&#345;ej &#268;ert&#237;k, Sergey B. Kirpichev, Matthew Rocklin, AMiT Kumar, et al. 2017. &#8220;SymPy: Symbolic Computing in Python.&#8221; <em>PeerJ Computer Science</em> 3 (January). PeerJ:e103. <a href="https://doi.org/10.7717/peerj-cs.103" class="uri">https://doi.org/10.7717/peerj-cs.103</a>.</p>
</div>
<div id="ref-Revsbech-1983">
<p>Revsbech, Niels Peter, Bo Barker J&#248;rgensen, T Henry Blackburn, and Yehuda Cohen. 1983. &#8220;Microelectrode Studies of the Photosynthesis and O2, H2S, and pH Profiles of a Microbial Mat.&#8221; <em>Limnology and Oceanography</em> 28 (6):1062&#8211;74. <a href="https://doi.org/10.4319/lo.1983.28.6.1062" class="uri">https://doi.org/10.4319/lo.1983.28.6.1062</a>.</p>
</div>
<div id="ref-VanGemerden-1993">
<p>Van Gemerden, Hans. 1993. &#8220;Microbial Mats: A Joint Venture.&#8221; <em>Marine Geology</em> 113 (1). Elsevier:3&#8211;25. <a href="https://doi.org/10.1016/0025-3227(93)90146-M" class="uri">https://doi.org/10.1016/0025-3227(93)90146-M</a>.</p>
</div>
<div id="ref-deWit-1995">
<p>Wit, Rutger de, Frank P. van den Ende, and Hans van Gemerden. 1995. &#8220;Mathematical Simulation of the Interactions Among Cyanobacteria, Purple Sulfur Bacteria and Chemotrophic Sulfur Bacteria in Microbial Mat Communities.&#8221; <em>FEMS Microbiology Ecology</em> 17 (2). Oxford University Press (OUP):117&#8211;36. <a href="https://doi.org/10.1111/j.1574-6941.1995.tb00136.x" class="uri">https://doi.org/10.1111/j.1574-6941.1995.tb00136.x</a>.</p>
</div>
</div>
</div>
</div>
EOF
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65 changes: 65 additions & 0 deletions joss.00674/10.21105.joss.00674.xml
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<title>MicroBenthos: a modeling framework for microbial benthic ecology</title>
<authors>
<author>
<name>Arjun Chennu</name>
<orcid>0000-0002-0389-5589</orcid>
<affiliation>
<orgname>
1
</orgname>
</affiliation>
</author>
</authors>
<tags>
<tag>marine biology</tag>
<tag>biogeochemistry</tag>
<tag>microbial ecology</tag>
<tag>microbial mats</tag>
<tag>sediments</tag>
<tag>modeling</tag>
<tag>simulation</tag>
<tag>microbenthic habitat</tag>
</tags>
<date>4 April 2018</date>
<paper_doi>10.21105/joss.00674</paper_doi>
<software_repository>https://github.com/achennu/microbenthos</software_repository>
<software_archive>http://dx.doi.org/10.5281/zenodo.1240940</software_archive>
<paper_url>http://www.theoj.org/joss-papers/joss.00674/10.21105.joss.00674.pdf</paper_url>
</articleinfo>
<body>
<h1 id="summary">Summary</h1>
<p>Microbial benthic habitats, such as microbial mats and sediments, exhibit extremely steep gradients in the physical, chemical and biotic parameters within the space of a few millimeters. These micro-environments drive the localization and exploitation of physico-chemical niches by a variety of microbial groups, such as cyanobacteria, sulfur-oxidizing bacteria, etc <span class="citation" data-cites="VanGemerden-1993">(Van Gemerden 1993)</span>. Studies of biogeochemistry and microbial ecology in these systems use various sensors to profile micro-environments and infer the local budgets and productivities of the microbial groups and metabolisms <span class="citation" data-cites="Revsbech-1983">(Revsbech et al. 1983)</span>. Microbenthic habitats are typically modeled as diffusive-reactive systems <span class="citation" data-cites="deWit-1995">(Wit, Ende, and Gemerden 1995)</span>, i.e. the dominant mass transport mode is physical diffusion of solutes within the porespaces of the sediment matrix. The “reactive” aspect refers to the presence of a large number of local sources and sinks within the mat system.</p>
<p>MicroBenthos is a modeling framework created to study <em>in silico</em> microbenthic habitats. The main perspective is to recognize that while modeling physical diffusion is straightforward, the larger challenge is to have a flexible way to define, compose and study various microbial metabolisms under dynamic conditions. MicroBenthos enables this by providing a high-level abstraction to compose and simulate microbenthic systems in terms of solar irradiance, chemical solutes, microbial groups and chemical or metabolic processes. While the software is written in python, with a modular structure for ease of extensibility, it can be used without programming through a (YAML) structured text file as the interface. This allows the user to focus on specifying the constitutive relations between environmental parameters and processes as a simple mathematical formula, which is then symbolically cast (using sympy <span class="citation" data-cites="Meurer-2017">(Meurer et al. 2017)</span>) into a set of coupled partial differential equations for the full model. Using a simple command, the equations can be numerically solved (using fipy <span class="citation" data-cites="Guyer-2009">(Guyer, Wheeler, and Warren 2009)</span>) to study the evolution of the various model variables.</p>
<p>MicroBenthos provides several useful features:</p>
<ul>
<li>Modular and extensible abstractions to create microbenthic systems</li>
<li>Non-programming interface to define processes and model structure</li>
<li>On-line visualization of running simulations and video export</li>
<li>Stateful simulations that can be interrupted and resumed</li>
<li>Export of detailed model data in open archival format</li>
<li>Open-source software (MIT license): https://github.com/achennu/microbenthos</li>
<li>Detailed documentation and tutorials: https://microbenthos.readthedocs.io</li>
</ul>
<h1 id="references" class="unnumbered">References</h1>
<div id="refs" class="references">
<div id="ref-Guyer-2009">
<p>Guyer, Jonathan E., Daniel Wheeler, and James A. Warren. 2009. “FiPy: Partial Differential Equations with Python.” <em>Computing in Science &amp; Engineering</em> 11 (3). Institute of Electrical; Electronics Engineers (IEEE):6–15. <a href="https://doi.org/10.1109/mcse.2009.52" class="uri">https://doi.org/10.1109/mcse.2009.52</a>.</p>
</div>
<div id="ref-Meurer-2017">
<p>Meurer, Aaron, Christopher P. Smith, Mateusz Paprocki, Ondřej Čertík, Sergey B. Kirpichev, Matthew Rocklin, AMiT Kumar, et al. 2017. “SymPy: Symbolic Computing in Python.” <em>PeerJ Computer Science</em> 3 (January). PeerJ:e103. <a href="https://doi.org/10.7717/peerj-cs.103" class="uri">https://doi.org/10.7717/peerj-cs.103</a>.</p>
</div>
<div id="ref-Revsbech-1983">
<p>Revsbech, Niels Peter, Bo Barker Jørgensen, T Henry Blackburn, and Yehuda Cohen. 1983. “Microelectrode Studies of the Photosynthesis and O2, H2S, and pH Profiles of a Microbial Mat.” <em>Limnology and Oceanography</em> 28 (6):1062–74. <a href="https://doi.org/10.4319/lo.1983.28.6.1062" class="uri">https://doi.org/10.4319/lo.1983.28.6.1062</a>.</p>
</div>
<div id="ref-VanGemerden-1993">
<p>Van Gemerden, Hans. 1993. “Microbial Mats: A Joint Venture.” <em>Marine Geology</em> 113 (1). Elsevier:3–25. <a href="https://doi.org/10.1016/0025-3227(93)90146-M" class="uri">https://doi.org/10.1016/0025-3227(93)90146-M</a>.</p>
</div>
<div id="ref-deWit-1995">
<p>Wit, Rutger de, Frank P. van den Ende, and Hans van Gemerden. 1995. “Mathematical Simulation of the Interactions Among Cyanobacteria, Purple Sulfur Bacteria and Chemotrophic Sulfur Bacteria in Microbial Mat Communities.” <em>FEMS Microbiology Ecology</em> 17 (2). Oxford University Press (OUP):117–36. <a href="https://doi.org/10.1111/j.1574-6941.1995.tb00136.x" class="uri">https://doi.org/10.1111/j.1574-6941.1995.tb00136.x</a>.</p>
</div>
</div>
</body>
</article>

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