Mathematical Models of Pluripotent Stem Cells: At the Dawn of Predictive Regenerative Medicine

Abstract

Regenerative medicine, ranging from stem cell therapy to organ regeneration, is promising to revolutionize treatments of diseases and aging. These approaches require a perfect understanding of cell reprogramming and differentiation. Predictive modeling of cellular systems has the potential to provide insights about the dynamics of cellular processes, and guide their control. Moreover in many cases, it provides alternative to experimental tests, difficult to perform for practical or ethical reasons. The variety and accuracy of biological processes represented in mathematical models grew in-line with the discovery of underlying molecular mechanisms. High-throughput data generation led to the development of models based on data analysis, as an alternative to more established modeling based on prior mechanistic knowledge. In this chapter, we give an overview of existing mathematical models of pluripotency and cell fate, to illustrate the variety of methods and questions. We conclude that current approaches are yet to overcome a number of limitations: Most of the computational models have so far focused solely on understanding the regulation of pluripotency, and the differentiation of selected cell lineages. In addition, models generally interrogate only a few biological processes. However, a better understanding of the reprogramming process leading to ESCs and iPSCs is required to improve stem-cell therapies. One also needs to understand the links between signaling, metabolism, regulation of gene expression, and the epigenetics machinery.

Predicting pattern formation in embryonic stem cells using a minimalist, agent-based probabilistic model

“…Different modelling methods have been applied to study stem cells at a population level to reproduce the population dynamics by generating minimal models 32 . For example, Libby and colleagues have applied cellular Pott models to human pluripotent stem cells, enabling a machine learning optimisation approach to predict experimental conditions that yield targeted multicellular patterns 33 .…”

The recent advances in the mathematical modelling of human pluripotent stem cells

“…More recently, PTFs have been modelled through branching processes [ 109 ]. A thorough review of the models of pluripotency is available [ 18 ], along with a review of computational modelling of the fate control of mouse embryonic stem cells, with many models transferable to hPSCs [ 105 ].…”

“…Similarly, mathematical models are a powerful tool to further our understanding of hPSC behaviours and optimise crucial experiments. The first mathematical model of stem cells, a stochastic model of cell fate decisions [17], has since been extended to include many other aspects of cell behaviour [18,19,20,21,22]. In particular, when such mathematical models are rigorously underpinned and validated on experimental observations, the reciprocal benefit for experimentation can be profound: an example is the development of an experimentally-rained model of hiPSC programming, which led in turn to strategies for marked improvements in reprogramming efficiency [23].…”

Computational Modeling and Reverse Engineering to Reveal Dominant Regulatory Interactions Controlling Osteochondral Differentiation: Potential for Regenerative Medicine

“…The various formalisms described above have been applied frequently for prediction of pluripotent and stem cell fate decision with application in regenerative medicine (Pir and Le Novère, 2016 ). This section gives an overview of different published models, focusing on (stem) cell fate decision in general and on chondrocyte differentiation and growth plate dynamics in particular.…”