Unraveling the Control of Cell Cycle Periods during Intestinal Stem Cell Differentiation

Abstract

During differentiation, intestinal stem cells (ISCs), a prototypical adult stem cell pool, become either secretory transit-amplifying cells, which give rise to all secretory cell types, or absorptive transit-amplifying cells, which give rise to enterocytes. These cells exhibit distinct cell cycle dynamics: ISCs cycle with a period of 24 h and absorptive transit-amplifying cells cycle with a period of $12 h, whereas secretory transit-amplifying cells arrest their cycle. The cell cycle dynamics of ISCs and their progeny are a systems-level property that emerges from interactions between the cell cycle control machinery and multiple regulatory pathways. Although many mathematical models have been developed to study the details of the cell cycle and related regulatory pathways, few models have been constructed to unravel the dynamic consequences of their interactions. To fill this gap, we present a simplified model focusing on the interaction between four key regulatory pathways (STAT, Wnt, Notch, and MAPK) and cell cycle control. After experimentally validating a model prediction, which showed that the Notch pathway can fine-tune the cell cycle period, we perform further model analysis that reveals that the change of cell cycle period accompanying ISC differentiation may be controlled by a design principle that has been well studied in dynamical systems theory-a saddle node on invariant circle bifurcation. Given that the mechanisms that control the cell cycle are conserved in most eukaryotic cell types, this general principle potentially controls the interplay between proliferation and differentiation for a broad range of stem cells.

A Modeling and Machine Learning Pipeline to Rationally Design Treatments to Restore Neuroendocrine Disorders in Heterogeneous Individuals

“… sums effects that origin from all other components not explicitly incorporated in the model, and this parameter can be replaced with additional regulatory terms when the model is expanded. Additional elaboration of this approach and its dynamical properties are available in the literature ( Mjolsness et al, 1991 ; Tyson and Novak, 2010 ) and our previous publication ( Ballweg et al, 2018 ).…”

Understanding virtual patients efficiently and rigorously by combining machine learning with dynamical modelling

“…Clearly, mathematical tools need continuous improvements to meet the ever-growing needs of biomedical and pharmaceutical industries. To deal with a variety of diseases in real patients with different genetic backgrounds and environments, treated by diverse sets of drugs, it will be necessary to improve and refine both our modelling framework and our analytical pipeline [ 12 , 13 , 47 ]. Assiduous improvements of the pipeline will provide the QSP community with powerful analytical tools to understand mechanisms, to make sense of data, and to guide our challenging endeavour to identify and optimize treatments for complex human diseases.…”

“…Our workflow identifies the parameter regions where these behaviors occur. If the behaviors are observed, as in the case of heterogeneous COR levels in patients with stress disorders [12,[18][19][20], our approach can provide strong constraints on the parameter values that should be used to construct representative VPs. On the other hand, if the behaviors are not observed, then model developers can use this knowledge to avoid parameter regions that would construct unacceptable VPs.…”

“…To cope with these practical challenges, we propose an integrated pipeline that combines machine learning (ML) with bifurcation analysis . ML is able to efficiently explore the behaviors of thousands of VPs and quickly discover the parameters that are most significant in determining some specific response of these VPs [ 12 , 13 ]. In this work we show how to supplement ML with bifurcation analysis, in order to provide deeper, mechanistic insights into how the qualitative dynamics of the mathematical model (i.e., the systems-level behavior of the model) emerge from changes of specific parameter values.…”

Treatment of Overweight and Obesity by Suppressing Intestinal Epithelial Renewal in a Mouse Model of Obesity

“…The latter could be inhibitors and antagonists of the Wnt-β-catenin signaling pathway, the EGFR signaling pathway and the Notch signaling pathway. (25)(26)(27)(28)(29)(30)(31).…”