#49 Most Permissive Boolean Networks with Loïc Paulevé

<p>In systems biology, Boolean networks are a way to model interactions such as gene regulation or cell signaling. The standard interpretations of Boolean networks are the synchronous, asynchronous, and fully asynchronous semantics.</p> <p>In this episode, <a href="https://loicpauleve.name/">Loïc Paulevé</a> explains how the same Boolean networks can be interpreted in a new, “most permissive” way. Loïc proved mathematically that his semantics can reproduce all behaviors achievable by a compatible quantitative model, whereas the traditional interpretations in general cannot. Furthermore, it turns out that deciding whether a certain state in a Boolean network is reachable can be done much more efficiently in MPBNs than in the traditional interpretations.</p> <p><img src="https://bioinformatics.chat/img/boolean-networks-2.png" alt="Transitions between states in a Most Permissive Boolean Network" /></p> <p>Links:</p> <ul> <li><a href="https://www.biorxiv.org/content/10.1101/2020.03.22.998377v2">Reconciling Qualitative, Abstract, and Scalable Modeling of Biological Networks</a> (Loïc Paulevé, Juraj Kolčák, Thomas Chatain, Stefan Haar)</li> <li><a href="https://github.com/pauleve/mpbn">mpbn on GitHub</a>: an implementation of reachability and attractor analysis in Most Permissive Boolean Networks</li> <li><a href="https://github.com/bioasp/bonesis">BoNesis on GitHub</a>: synthesis of Most Permissive Boolean Networks from network architecture and dynamical properties</li> </ul>