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<title>Rule-based modeling</title>

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<span id="openzim-page-title" class="mw-page-title-main"><span class="mw-page-title-main">Rule-based modeling</span></span>

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<p><b>Rule-based modeling</b> is a <a href="Conceptual_model" title="Conceptual model">modeling</a> approach that uses a set of rules that indirectly specifies a mathematical model. The rule-set can either be translated into a model such as <a href="Markov_chains" class="mw-redirect" title="Markov chains">Markov chains</a> or differential equations, or be treated using tools that directly work on the rule-set in place of a translated model, as the latter is typically much bigger. Rule-based modeling is especially effective in cases where the rule-set is significantly simpler than the model it implies, meaning that the model is a repeated manifestation of a limited number of patterns. An important domain where this is often the case is biochemical models of living organisms. Groups of mutually corresponding substances are subject to mutually corresponding interactions.

</p><p>BioNetGen<sup id="cite_ref-1" class="reference"><a href="#cite_note-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup> is a suite of software tools used to generate mathematical models consisting of <a href="Ordinary_differential_equation" title="Ordinary differential equation">ordinary differential equations</a> without generating the equations directly. For example below is an example rule in the BioNetGen format:

</p><p><span class="mwe-math-element mwe-math-element-inline"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle A(a,a)+B(b)->A(a!1).B(b!1)}">

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<annotation encoding="application/x-tex">{\displaystyle A(a,a)+B(b)->A(a!1).B(b!1)}</annotation>

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</math></span><img src="./bfcae4870badcf7fe5ce769f0e830147ccd92ea3.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:33.369ex; height:2.843ex;" alt="{\displaystyle A(a,a)+B(b)->A(a!1).B(b!1)}" loading="lazy"></span>

</p><p>Where:

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<ol><li>A(a,a): Represents a model species A with two free binding sites a</li>

<li>B(b): Represents a model species B with one free binding site</li>

<li>A(a!1).B(b!1): Represents model species where at least one binding site of A is bound to the binding site of B</li></ol>

<p>With the above line of code, BioNetGen will automatically create an ODE for each model species with the correct mass balance. Additionally, an additional species will be created because the rule above implies that two B molecules can bind to a single A molecule since there are two binding sites. Therefore, the following species will be generated:

</p><p>4. A(a!1,a!2).B(b!1).B(b!2): Molecule A with both binding sites occupied by two different B molecules.

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<div class="mw-heading mw-heading2"><h2 id="For_biochemical_systems">For biochemical systems</h2></div>

<p>Early efforts to use rule-based modeling in simulation of biochemical systems include the stochastic simulation systems StochSim<sup id="cite_ref-2" class="reference"><a href="#cite_note-2"><span class="cite-bracket">[</span>2<span class="cite-bracket">]</span></a></sup>

</p><p>A widely used tool for rule-based modeling of biochemical networks is BioNetGen <sup id="cite_ref-3" class="reference"><a href="#cite_note-3"><span class="cite-bracket">[</span>3<span class="cite-bracket">]</span></a></sup> It is released under the <a href="GNU_GPL" class="mw-redirect" title="GNU GPL">GNU GPL</a>, version 3. BioNetGen includes a language to describe chemical substances, including the states they can assume and the bindings they can undergo. These rules can be used to create a reaction network model or to perform <a href="Computer_simulation" title="Computer simulation">computer simulations</a> directly on the rule set. The biochemical modeling framework <a href="Virtual_Cell" title="Virtual Cell">Virtual Cell</a> includes a BioNetGen interpreter.

</p><p>A close alternative is the Kappa language.<sup id="cite_ref-4" class="reference"><a href="#cite_note-4"><span class="cite-bracket">[</span>4<span class="cite-bracket">]</span></a></sup> Another alternative is BioChemical Space language.<sup id="cite_ref-5" class="reference"><a href="#cite_note-5"><span class="cite-bracket">[</span>5<span class="cite-bracket">]</span></a></sup>

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<div class="mw-heading mw-heading2"><h2 id="References">References</h2></div>

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</style><cite id="CITEREFFaederBlinovHlavacek2009" class="citation cs2">Faeder, James R.; Blinov, Michael L.; Hlavacek, William S. (2009), "Rule-Based Modeling of Biochemical Systems with BioNetGen", <a rel="nofollow" class="external text" href="https://dx.doi.org/10.1007/978-1-59745-525-1_5"><i>Systems Biology</i></a>, Methods in Molecular Biology, vol. 500, Totowa, NJ: Humana Press, pp. <span class="nowrap">113–</span>167, <a href="CiteSeerX_(identifier)" class="mw-redirect" title="CiteSeerX (identifier)">CiteSeerX</a> <span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.323.9577">10.1.1.323.9577</a></span>, <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1007%2F978-1-59745-525-1_5">10.1007/978-1-59745-525-1_5</a>, <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>978-1-934115-64-0</bdi>, <a href="PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/19399430">19399430</a><span class="reference-accessdate">, retrieved <span class="nowrap">2020-12-14</span></span></cite></span>

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<li id="cite_note-2"><span class="mw-cite-backlink"><b><a href="#cite_ref-2">^</a></b></span> <span class="reference-text">Morton-Firth CJ, Bray D (1998) <a rel="nofollow" class="external text" href="https://www.sciencedirect.com/science/article/pii/S0022519397906512">Predicting temporal fluctuations in an intracellular signalling pathway</a>. J Theor Biol. 1998 192(1):117-28.</span>

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<li id="cite_note-3"><span class="mw-cite-backlink"><b><a href="#cite_ref-3">^</a></b></span> <span class="reference-text"><a rel="nofollow" class="external text" href="http://bionetgen.org">BioNetGen</a></span>

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<li id="cite_note-4"><span class="mw-cite-backlink"><b><a href="#cite_ref-4">^</a></b></span> <span class="reference-text"><a rel="nofollow" class="external text" href="http://www.kappalanguage.org/">Kappa</a></span>

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<li id="cite_note-5"><span class="mw-cite-backlink"><b><a href="#cite_ref-5">^</a></b></span> <span class="reference-text">Děd et al. (2016) <a rel="nofollow" class="external text" href="http://www.sciencedirect.com/science/article/pii/S1571066116300615">Formal Biochemical Space with Semantics in Kappa and BNGL</a> ENTCS 326:27-49.</span>

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