ArticleRoyal Society open science2017
A deterministic method for estimating free energy genetic network landscapes with applications to cell commitment and reprogramming paths.
Article in Royal Society open science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Kernel-DMD for multiome data integration and control.PLoS computational biology · 2026Article
- A neural network-based model framework for cell-fate decisions and development.Communications biology · 2024Article
- CELLoGeNe - An energy landscape framework for logical networks controlling cell decisions.iScience · 2022Article
- Kinetic models of hematopoietic differentiation.Wiley interdisciplinary reviews. Systems biology and medicine · 2019Review
- Transition state characteristics during cell differentiation.PLoS computational biology · 2018Article
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3 authors.
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Abstract
Depicting developmental processes as movements in free energy genetic landscapes is an illustrative tool. However, exploring such landscapes to obtain quantitative or even qualitative predictions is hampered by the lack of free energy functions corresponding to the biochemical Michaelis-Menten or Hill rate equations for the dynamics. Being armed with energy landscapes defined by a network and its interactions would open up the possibility of swiftly identifying cell states and computing optimal paths, including those of cell reprogramming, thereby avoiding exhaustive trial-and-error simulations with rate equations for different parameter sets. It turns out that sigmoidal rate equations do have approximate free energy associations. With this replacement of rate equations, we develop a deterministic method for estimating the free energy surfaces of systems of interacting genes at different noise levels or temperatures. Once such free energy landscape estimates have been established, we adapt a shortest path algorithm to determine optimal routes in the landscapes. We explore the method on three circuits for haematopoiesis and embryonic stem cell development for commitment and reprogramming scenarios and illustrate how the method can be used to determine sequential steps for onsets of external factors, essential for efficient reprogramming.
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