Unraveling the differential dynamics of developmental fate in central and peripheral nervous systems

Incoherent modulation of bi-stable dynamics orchestrates the Mushroom and Isola bifurcations

In biological networks, steady state dynamics of cell-fate regulatory genes often exhibit Mushroom and Isola kind of bifurcations. How these complex bifurcations emerge for these complex networks, and what are the minimal network structures that can generate these bifurcations, remain elusive. Herein, by employing Waddington's landscape theory and bifurcation analysis, we demonstrate that Mushroom and Isola bifurcations can be realized with four minimal network motifs that are constituted by combining a positive feedback motif with various incoherent feed-forward loops. Our study reveals that the intrinsic bi-stable dynamics originating from the positive feedback motif can be fine-tuned by altering the extent of the incoherence of these minimal networks to produce these complex bifurcations. These modeling insights will be useful in identifying the possible network motifs that may give rise to either Mushroom or Isola bifurcation in other biological systems.

Deciphering the Role of Fluctuation Dependent Intercellular Communication in Neural Stem Cell Development

Neural stem cells (NPCs) efficiently communicate in an intercellular manner to govern specific cell fate decisions during the developmental process despite withstanding the fluctuating cellular environment. How these fluctuations from diverse origins functionally affect the precise cell fate decision making remains elusive. By taking a stochastic mathematical modeling approach, we unravel that the transcriptional variability arising within an NPC population due to intermittent cell cycle events significantly influences the neuron to NPC ratio during development. Our model proficiently quantifies the impact of different sources of heterogeneities in maintaining an exact neuron to NPC ratio and predicts plausible experimental ways to fine-tune the development of NPCs. In the future, these modeling insights may lead to better therapeutic avenues to regenerate neurons from NPCs.

Deciphering the Dynamical Origin of Mixed Population during Neural Stem Cell Development

Neural stem cells (NSCs) often give rise to a mixed population of cells during differentiation. However, the dynamical origin of these mixed states is poorly understood. In this article, our mathematical modeling study demonstrates that the bone morphogenetic protein 2 (BMP2) mediated disparate differentiation dynamics of NSCs in central and peripheral nervous systems essentially function through two distinct bistable switches that are mutually interconnected via a mushroom-like bifurcation. Stochastic simulations of the model reveal that the mixed population originates due to the existence of these bistable switching regulations and that the maintenance of such mixed states depends on the level of stochastic fluctuations of the system. It further demonstrates that due to extrinsic variability, cells in an NSC population can dynamically transit from mushroom to a unique isola kind of bifurcation state, which essentially extends the range of the BMP2-driven mixed population state during differentiation. Importantly, the model predicts that by individually altering the expression level of key regulatory proteins, the NSCs can be converted entirely to a preferred phenotype for BMP2 doses that previously resulted in a mixed population. Our findings show that efficient neuronal regeneration can be achieved by systematically maneuvering the differentiation dynamics.

Alteration in MicroRNA Expression Governs the Nature and Timing of Cellular Fate Commitment

In the central nervous system, the expression level of transcriptional repressor Hes1 (hairy and enhancer of split-1) tightly controls the alternative cell fate commitment during differentiation as well as the time required for such cellular transitions. A microRNA, miR-9, that interacts with Hes1 in a mutually antagonistic manner, influences both the process of lineage specification and timing of differentiation significantly, but the impact of the miR-9 in guiding these events still remains poorly understood. Here, we proposed a stochastic mathematical model of the miR-9/Hes1 double-negative feedback interaction network that at the outset shows how alternative cell fate such as quiescence, progenitor, and neuronal states can be accomplished through fine-tuning the Hes1 dynamics by altering the expression level of miR-9. The model simulations further foretell a correlated variation of the period of oscillation of Hes1, and the time delay observed between Hes1 mRNA and protein as the transcription rate of miR-9 increases during the neural progenitor state attainment. Importantly, the model simulations aided by the systematic sensitivity analysis predict that the timing of differentiation to the neuronal state crucially depends on the negative regulators (miR-9 and Hes6) of the Hes1. Our results indicate that miR-9/Hes1 interaction network can be effectively exploited for an efficient and well-timed neuronal transformation.