Noise-driven cell differentiation and the emergence of spatiotemporal patterns

Abstract

The emergence of phenotypic diversity in a population of cells and their arrangement in space and time is one of the most fascinating features of living systems. In fact, understanding multicellularity is unthinkable without explaining the proximate and the ultimate causes of cell differentiation in time and space. Simpler forms of cell differentiation can be found in unicellular organisms, such as bacterial biofilm, where reversible cell differentiation results in phenotypically diverse populations. In this manuscript, we attempt to start with the simple case of reversible nongenetic phenotypic to construct a model of differentiation and pattern formation. Our model, which we refer to as noise-driven differentiation (NDD) model, is an attempt to consider the prevalence of noise in biological systems, alongside what is known about genetic switches and signaling, to create a simple model which generates spatiotemporal patterns from bottom-up. Our simulations indicate that the presence of noise in cells can lead to reversible differentiation and the addition of signaling can create spatiotemporal pattern.

A computational model of stem cell molecular mechanism to maintain tissue homeostasis

“…The determinant fluctuations are captured by the Gillespie algorithm [10,40] which is known as the gold standard for simulating models whose stochasticty arises from the small discrete number of reactant molecules [66]. In each time step, two main processes can occur, cell division and the cell determinants interactions.…”

“…The proposed model is defined based on five material principles which has been discussed in [10] to study biofilm formation and they are reconsidered as follows i) stochasticity due to an intrinsic noise is a fundamental part of any living cell [10,17,[41][42][43][44][45][46]. ii) the non-deterministic position of the cell division plane and nonuniform distribution of determinants in the cell imply that the cytoplasmic molecules are distributed randomly among daughter cells during cell division [10,[47][48][49][50][51][52][53]. iii) determination of cell fate by an internal switch upon the completion of cell division [20,54].…”

“…iii) determination of cell fate by an internal switch upon the completion of cell division [20,54]. Cell fate is assumed fixed during cell life cycle [10]. iv) the decision bias in the internal switch is determined by model parameters representing interactions between the switch elements [10].…”

“…Cell fate is assumed fixed during cell life cycle [10]. iv) the decision bias in the internal switch is determined by model parameters representing interactions between the switch elements [10]. v) a switch with more contributing components would be more stable against environmental fluctuations [10,36].…”

“…iv) the decision bias in the internal switch is determined by model parameters representing interactions between the switch elements [10]. v) a switch with more contributing components would be more stable against environmental fluctuations [10,36]. In terms of Waddington landscape, it means that more complex switches lead to deeper valleys from where cells could not easily leave in the presence of stochastic fluctuations.…”