CELLoGeNe - an Energy Landscape Framework for Logical Networks Controlling Cell Decisions

Abstract

AbstractExperimental and computational efforts are constantly made to elucidate mechanisms controlling cell fate decisions during development and cell reprogramming. One powerful method is to consider cell commitment and reprogramming as movements in an energy landscape. Here, we develop CELLoGeNe (Computation of Energy Landscapes of Logical Gene Networks), which maps Boolean implementation of gene regulatory networks (GRNs) into energy landscapes. CELLoGeNe removes inadvertent symmetries in the energy landscapes normally arising from standard Boolean operators. Furthermore, CELLoGeNe provides a tool for visualising multi-dimensional energy landscapes and a platform to stochastically probe and analyse the shapes of the computed landscapes corresponding to the epigenetic landscapes for development and reprogramming. We demonstrate CELLoGeNe on a GRN governing maintenance and self-renewal of pluripotency, identifying attractors experimentally validated. We also apply CELLoGeNe on a network controlling reprogramming from mouse embryonic fibroblast (MEF) to induced pluripotent stem cells (iPSCs) where we identify potential roadblocks as attractors. CELLoGeNe is a general framework that can be applied to various biological systems offering a broad picture of intracellular dynamics otherwise inaccessible with existing methods.