Modeling signaling pathways in biology with MaBoSS: From one single cell to a dynamic population of heterogeneous interacting cells

As a result of the development of experimental technologies and the accumulation of data, biological and molecular processes can be described as complex networks of signaling pathways. These networks are often directed and signed, where nodes represent entities (genes/proteins) and arrows interactions. They are translated into mathematical models by adding a dynamic layer onto them. Such mathematical models help to understand and interpret non-intuitive experimental observations and to anticipate the response to external interventions such as drug effects on phenotypes. Several frameworks for modeling signaling pathways exist. The choice of the appropriate framework is often driven by the experimental context. In this review, we present MaBoSS, a tool based on Boolean modeling using a continuous time approach, which predicts time-dependent probabilities of entities in different biological contexts. MaBoSS was initially built to model the intracellular signaling in non-interacting homogeneous cell pop-ulations. MaBoSS was then adapted to model heterogeneous cell populations (EnsembleMaBoSS) by con-sidering families of models rather than a unique model. To account for more complex questions, MaBoSS was extended to simulate dynamical interacting populations (UPMaBoSS), with a precise spatial distribu-tion (PhysiBoSS). To illustrate all these levels of description, we show how each of these tools can be used with a running example of a simple model of cell fate decisions. Finally, we present practical applications to cancer biology and studies of the immune response. (c) 2022 Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Bio-technology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

Automated summary

Due to experimental technologies and data accumulation, biological and molecular processes can be described as complex networks of signaling pathways. Mathematical models, such as MaBoSS, help to interpret experimental observations and anticipate responses to external interventions. This review presents different frameworks for modeling signaling pathways and introduces MaBoSS and its extensions for modeling cell populations and spatial distributions. Practical applications to cancer biology and immune response studies are also discussed.