Logical modeling of thymus and natural killer lymphocyte differentiation

Thymus (T) and natural killer (NK) lymphocytes are important barriers against diseases. Therefore, it is necessary to understand regulatory mechanisms related to the cell fate decisions involved in the production of these cells. Although some individual information related to T and NK lymphocyte cell fate decisions have been revealed, the related network and its dynamical characteristics still have not been well understood. By integrating individual information and comparing with experimental data, we construct a comprehensive regulatory network and a logical model related to T and NK lymphocyte differentiation. We aim to explore possible mechanisms of how each lineage differentiation is realized by systematically screening individual perturbations. When determining the perturbation strategies, the state transition can be used to identify the roles of specific genes in cell type selection and reprogramming. In agreement with experimental observations, the dynamics of the model correctly restates the cell differentiation processes from common lymphoid progenitors to CD4+ T cells, CD8+ T cells, and NK cells. Our analysis reveals that some specific perturbations can give rise to directional cell differentiation or reprogramming. We test our in silico results by using known experimental observations. The integrated network and the logical model presented here might be a good candidate for providing qualitative mechanisms of cell fate specification involved in T and NK lymphocyte cell fate decisions.Supplementary informationThe online version contains supplementary material available at 10.1007/s10867-021-09563-y.     

Reprogramming cell fates: reconciling rarity with robustness

The stunning possibility of “reprogramming” differentiated somatic cells to express a pluripotent stem cell phenotype (iPS, induced pluripotent stem cell) and the “ground state” character of pluripotency reveal fundamental features of cell fate regulation that lie beyond existing paradigms. The rarity of reprogramming events appears to contradict the robustness with which the unfathomably complex phenotype of stem cells can reliably be generated. This apparent paradox, however, is naturally explained by the rugged “epigenetic landscape” with valleys representing “preprogrammed” attractor states that emerge from the dynamical constraints of the gene regulatory network. This article provides a pedagogical primer to the fundamental principles of gene regulatory networks as integrated dynamic systems and reviews recent insights in gene expression noise and fate determination, thereby offering a formal framework that may help us to understand why cell fate reprogramming events are inherently rare and yet so robust.     

A model of sequential branching in hierarchical cell fate determination

Multipotent stem or progenitor cells undergo a sequential series of binary fate decisions, which ultimately generate the diversity of differentiated cells. Efforts to understand cell fate control have focused on simple gene regulatory circuits that predict the presence of multiple stable states, bifurcations and switch-like transitions. However, existing gene network models do not explain more complex properties of cell fate dynamics such as the hierarchical branching of developmental paths. Here, we construct a generic minimal model of the genetic regulatory network controlling cell fate determination, which exhibits five elementary characteristics of cell differentiation: stability, directionality, branching, exclusivity, and promiscuous expression. We argue that a modular architecture comprising repeated network elements reproduces these features of differentiation by sequentially repressing selected modules and hence restricting the dynamics to lower dimensional subspaces of the high-dimensional state space. We implement our model both with ordinary differential equations (ODEs), to explore the role of bifurcations in producing the one-way character of differentiation, and with stochastic differential equations (SDEs), to demonstrate the effect of noise on the system. We further argue that binary cell fate decisions are prevalent in cell differentiation due to general features of the underlying dynamical system. This minimal model makes testable predictions about the structural basis for directional, discrete and diversifying cell phenotype development and thus can guide the evaluation of real gene regulatory networks that govern differentiation.