Abstract
Statistical physics provides a useful perspective for the analysis of many complex systems; it allows us to relate microscopic fluctuations to macroscopic observations. Developmental biology, but also cell biology more generally, are examples where apparently robust behaviour emerges from highly complex and stochastic sub-cellular processes. Here we attempt to make connections between different theoretical perspectives to gain qualitative insights into the types of cell-fate decision making processes that are at the heart of stem cell and developmental biology. We discuss both dynamical systems as well as statistical mechanics perspectives on the classical Waddington or epigenetic landscape. We find that non-equilibrium approaches are required to overcome some of the shortcomings of classical equilibrium statistical thermodynamics or statistical mechanics in order to shed light on biological processes, which, almost by definition, are typically far from equilibrium.
Highlights
Cells are often seen as the fundamental level from which to start investigating biological systems [1]: drill down in detail, or increase resolution, and we end up with intricate molecular processes and arrangements that shape the cells physiology and behaviour
If we identify cell fates with the stationary points, x∗, of a dynamical system, even if we do not know the structure and form of the dynamical system, i.e., we do not know the mathematical form of f (x) in Eq (1) or Eq (2), we can still make some general qualitative statements
One problem related to the difficulty in developing a statistical mechanics for stem cell biology, comes from the fact that much of the appeal of statistical mechanics lies in the fact that entropic arguments can be used to determine system states
Summary
Cells are often seen as the fundamental level from which to start investigating biological systems [1]: drill down in detail, or increase resolution, and we end up with intricate molecular processes and arrangements that shape the cells physiology and behaviour. Analysis of data is largely descriptive, but even from these descriptions we can learn or distill some important lessons that could inform mechanistic modelling in the future [14] Three such examples include molecular noise, the dynamics of gene regulation, and the time it takes for cell-fate decisions to take place. There is clear evidence that the regulation at the gene expression level is highly dynamic and shaped by factors at the epigenetic, transcriptomic, proteomic, and post-translational modification levels [18,19,20,21] We cannot describe this in terms of static gene regulatory networks, and instead need to develop explicitly dynamical descriptions; even we need to take into account the uncertainty in these networks [22]. In the following we will outline a set of qualitative frameworks for the analysis of cell differentiation dynamics, developing their connections, and follow one of these, non-equilibrium statistical mechanics, further in order to characterise transitions between states or cell fates
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
