2019
DOI: 10.1101/692285
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Free-energy-based framework for early forecasting of stem cell differentiation

Abstract: Commitment of stem cells to different lineages is inherently stochastic but regulated by a range of environmental bio/chemo/mechanical cues. Here we develop an integrated stochastic modelling framework for predicting the differentiation of hMSCs in response to a range of environmental cues including sizes of adhesive islands, stiffness of substrates and treatment with ROCK inhibitors in both growth and mixed media. The statistical framework analyses the fluctuations of cell morphologies over around a 24-hour p… Show more

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Cited by 3 publications
(4 citation statements)
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“…It is certainly clear that there are bounds on the * c.dunlop@surrey.ac.uk energy budget of the cell [15], with a link between cell contractility and energy consumption demonstrated [16]. Theoretically models have explored this energy budget using energy constraints as drivers of differentiation [17] or cell shape control [18].…”
mentioning
confidence: 99%
“…It is certainly clear that there are bounds on the * c.dunlop@surrey.ac.uk energy budget of the cell [15], with a link between cell contractility and energy consumption demonstrated [16]. Theoretically models have explored this energy budget using energy constraints as drivers of differentiation [17] or cell shape control [18].…”
mentioning
confidence: 99%
“…However, clear trends emerge when the statistics of these observables are analyzed. The homeostatic ensemble ( 26 ) has been shown to successfully predict these statistics for cells in a range of environments when no external loads are imposed ( 6 , 8 , 27 ). This motivates us to extend the framework to predict the response of cells on substrates subjected to cyclic loading.…”
Section: Dynamic Equilibrium Under Cyclic Strainmentioning
confidence: 99%
“…( 26 ) proposed a statistical framework, called the homeostatic ensemble, that captures the interplay between the cytoskeletal structure and cell morphology. The approach has been shown to accurately predict the distribution of observed shapes of cells, in absence of cyclic loads, in numerous environments ( 6 , 8 , 27 ). Here, we extend this framework to cyclic loading conditions.…”
Section: Introductionmentioning
confidence: 99%
“…The homeostatic statistical mechanics description for cells [22] has already been shown to successfully capture a range of observations for smooth muscle cells seeded on elastic substrates [22,23] and for myofibroblasts seeded on substrates micropatterned with stripes of fibronectin [24,25] as well as for the differentiation of hMSCs in response to a range of environmental cues including stiffness of substrates and sizes of adhesive islands [26]. These give us confidence in utilizing the homeostatic mechanics framework to investigate the response of cells on a dense array of micro-posts.…”
Section: Introductionmentioning
confidence: 99%