Abstract
Cells are quintessential examples of out-of-equilibrium systems, but they maintain a homeostatic state over a timescale of hours to days. As a consequence, the statistics of all observables is remarkably consistent. Here, we develop a statistical mechanics framework for living cells by including the homeostatic constraint that exists over the interphase period of the cell cycle. The consequence is the introduction of the concept of a homeostatic ensemble and an associated homeostatic temperature, along with a formalism for the (dynamic) homeostatic equilibrium that intervenes to allow living cells to evade thermodynamic decay. As a first application, the framework is shown to accurately predict the observed effect of the mechanical environment on the in vitro response of smooth muscle cells. This includes predictions that both the mean values and diversity/variability in the measured values of observables such as cell area, shape and tractions decrease with decreasing stiffness of the environment. Thus, we argue that the observed variabilities are inherent to the entropic nature of the homeostatic equilibrium of cells and not a result of in vitro experimental errors.
Highlights
Cells display a fluctuating response in in vitro experiments that results in a diversity of observables in nominally identical tests
The framework introduces the concept of a homeostatic ensemble and an associated homeostatic temperature along with a formalism for the homeostatic equilibrium that intervenes to allow cells to evade thermodynamic decay
We have proposed a statistical mechanics theory for the equilibrium of cells in which the effective temperature emerges as the Lagrange multiplier that enforces the homeostatic constraint for the maximisation of morphological entropy, i.e. the homeostatic temperature is conjugated to the morphological entropy
Summary
Cells display a fluctuating response in in vitro experiments that results in a diversity of observables in nominally identical tests. It is well established that over a large number of observations, the statistics are highly reproducible (Tan et al 2003; Engler et al 2004; Prager-Khoutorsky et al 2011; Saez et al 2007; Discher et al 2005; Chen et al 1997, 2003; Parker et al 2002; Théry et al 2006; Lamers et al 2010). This observed variability is a function of the cell type and a function of the environment.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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 callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
