A least microenvironmental uncertainty principle (LEUP) as a generative model of collective cell migration mechanisms

Abstract

Collective migration is commonly observed in groups of migrating cells, in the form of swarms or aggregates. Mechanistic models have proven very useful in understanding collective cell migration. Such models, either explicitly consider the forces involved in the interaction and movement of individuals or phenomenologically define rules which mimic the observed behavior of cells. However, mechanisms leading to collective migration are varied and specific to the type of cells involved. Additionally, the precise and complete dynamics of many important chemomechanical factors influencing cell movement, from signalling pathways to substrate sensing, are typically either too complex or largely unknown. The question is how to make quantitative/qualitative predictions of collective behavior without exact mechanistic knowledge. Here we propose the least microenvironmental uncertainty principle (LEUP) that may serve as a generative model of collective migration without precise incorporation of full mechanistic details. Using statistical physics tools, we show that the famous Vicsek model is a special case of LEUP. Finally, to test the biological applicability of our theory, we apply LEUP to construct a model of the collective behavior of spherical Serratia marcescens bacteria, where the underlying migration mechanisms remain elusive.

Close to Optimal Cell Sensing Ensures the Robustness of Tissue Differentiation Process: The Avian Photoreceptor Mosaic Case

“…In this paper, we have posed the question (Q1) on how cells coordinate intrinsic and extrinsic variables to determine cell decisions that eventually lead to organized and stable tissues. To tackle this problem, we have employed the Least microEnvironmental Uncertainty Principle (LEUP), which has been recently proposed to understand cell decision-making in multicellular systems, and so far it has been applied to cell migration force distribution [ 25 ], collective cell migration [ 26 ], and binary phenotypic plasticity [ 27 ]. In the context of the LEUP, we regard differentiation as a sort of Bayesian decision-making, where cells update their intrinsic variables by encoding microenvironmental information and producing relevant responses.…”

“…In order to answer (Q1), in this paper, we employ the recently proposed the Least microEnvironmental Uncertainty Principle (LEUP)—which is essentially a statistical mechanical theory for cell decision-making [ 25 , 26 , 27 ]—and apply it to the problem of cell differentiation. The LEUP is inspired by the theories of Bayesian brain hypothesis [ 28 ], the free-energy principle [ 29 ], and other dynamic Bayesian inference theories that try to explain human brain cognitive dynamics.…”

Close to optimal cell sensing ensures the robustness of tissue differentiation process: the avian photoreceptor mosaic case

“…The Least microEnvironmental Uncertainty Principle (LEUP) has been proposed recently to understand cell decision-making in multicellular systems and so far it has been applied to cell migration force distribution [14], collective cell migration [15], and phenotypic plasticity [16]. In this paper, we have used this formalism as a starting point and have developed a thermodynamic-like theory for a general cell and tissue differentiation process.…”

“…In order to tackle the aforementioned questions and problems, in this paper, we apply the Least microEnvironmental Uncertainty Principle (LEUP) which is essentially a statistical mechanical theory for cell decision-making [14][15][16] to the problem of cell differentiation. Cells decide and change their fates (or, phenotypes) according to the LEUP, in order to minimize their local microenvironmental entropy.…”

“…In order to answer (Q1), in this paper, we employ the recently proposed the Least microEnvironmental Uncertainty Principle (LEUP) -which is essentially a statistical mechanical theory for cell decision-making [25][26][27] -and apply it to the problem of cell differentiation. The LEUP is inspired by the theories of Bayesian brain hypothesis [28], the free-energy principle [29], and other dynamic Bayesian inference theories that try to explain human brain cognitive dynamics.…”