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

Abstract

Stem cells, with their capacity to self-renew and to differentiate to more specialized cell types, play a key role to maintain homeostasis in adult tissues. To investigate how, in the dynamic stochastic environment of a tissue, non-genetic diversity and the precise balance between proliferation and differentiation are achieved, it is necessary to understand the molecular mechanisms of the stem cells in decision making process. By focusing on the impact of stochasticity, we proposed a computational model describing the regulatory circuitry as a tri-stable dynamical system to reveal the mechanism which orchestrate this balance. Our model explains how the distribution of noise in genes, linked to the cell regulatory networks, affects cell decision-making to maintain homeostatic state. The noise effect on tissue homeostasis is achieved by regulating the probability of differentiation and self-renewal through symmetric and/or asymmetric cell divisions. Our model reveals, when mutations due to the replication of DNA in stem cell division, are inevitable, how mutations contribute to either aging gradually or the development of cancer in a short period of time. Furthermore, our model sheds some light on the impact of more complex regulatory networks on the system robustness against perturbations.

A computational model of stem cells’ decision-making mechanism to maintain tissue homeostasis and organization in the presence of stochasticity

“…Considering these facts, and inspired by previous studies that revealed the impact of regulatory networks on the stability of biological systems 6 , 10 , 23 , 29 , 35 – 39 , in our model, we introduce multi-stable regulatory networks as the internal decision-makers of dividing cells. Here, a set of ordinary differential equations (ODEs) are defined as the typical mathematical tool to study, and describe the regulatory circuitries 7 , 32 , 40 .…”

“…Considering all the facts discussed before and focusing on the impact of stochasticity, here, we propose a computational model to reveal the mechanism which maintains the proliferation/differentiation balance as well as the spatial pattern in a hypothetical normal adult tissue. The proposed model is defined based on six material principles discussed in 7 , 13 and reconsidered as follows: (1) stochasticity is an inevitable part of any living cell 5 , 7 , 13 , 38 , 45 – 49 . (2) Two major sources of stochasticity, the non-deterministic position of the cell division plane and nonuniform distribution of determinants in the cell lead to the random distribution of the cytoplasmic molecules among daughter cells during cell division 7 , 13 , 50 – 56 .…”

“…The proposed model is defined based on six material principles discussed in 7 , 13 and reconsidered as follows: (1) stochasticity is an inevitable part of any living cell 5 , 7 , 13 , 38 , 45 – 49 . (2) Two major sources of stochasticity, the non-deterministic position of the cell division plane and nonuniform distribution of determinants in the cell lead to the random distribution of the cytoplasmic molecules among daughter cells during cell division 7 , 13 , 50 – 56 . (3) Cell fate is determined based on the number of determinants in the offspring upon the completion of cell division and assumed to be fixed during cell life cycle 7 , 13 .…”

“…(2) Two major sources of stochasticity, the non-deterministic position of the cell division plane and nonuniform distribution of determinants in the cell lead to the random distribution of the cytoplasmic molecules among daughter cells during cell division 7 , 13 , 50 – 56 . (3) Cell fate is determined based on the number of determinants in the offspring upon the completion of cell division and assumed to be fixed during cell life cycle 7 , 13 . (4) Cell determinants interact with each other via an internal switch 16 , 57 .…”

“…(4) Cell determinants interact with each other via an internal switch 16 , 57 . (5) The decision bias in the internal switch is determined by model parameters representing interactions between the switch elements 7 , 13 . (6) The switch parameters could also be affected by the cell location in its environment, and it is the key to the spatial pattern in the population 13 .…”