Kinetic models of hematopoietic differentiation

Abstract

As cell and molecular biology is becoming increasingly quantitative, there is an upsurge of interest in mechanistic modeling at different levels of resolution. Such models mostly concern kinetics and include gene and protein interactions as well as cell population dynamics. The final goal of these models is to provide experimental predictions, which is now taking on. However, even without matured predictions, kinetic models serve the purpose of compressing a plurality of experimental results into something that can empower the data interpretation, and importantly, suggesting new experiments by turning "knobs" in silico. Once formulated, kinetic models can be executed in terms of molecular rate equations for concentrations or by stochastic simulations when only a limited number of copies are involved. Developmental processes, in particular those of stem and progenitor cell commitments, are not only topical but also particularly suitable for kinetic modeling due to the finite number of key genes involved in cellular decisions. Stem and progenitor cell commitment processes have been subject to intense experimental studies over the last decade with some emphasis on embryonic and hematopoietic stem cells. Gene and protein interactions governing these processes can be modeled by binary Boolean rules or by continuous-valued models with interactions set by binding strengths. Conceptual insights along with tested predictions have emerged from such kinetic models. Here we review kinetic modeling efforts applied to stem cell developmental systems with focus on hematopoiesis. We highlight the future challenges including multi-scale models integrating cell dynamical and transcriptional models. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Developmental Biology > Stem Cell Biology and Regeneration.

Multi-scale dynamical modelling of T-cell development from an early thymic progenitor state to lineage commitment

“…On the other hand, with a Boolean approach one can probe larger networks than that modeled here. These issues have been briefly reviewed 34 Importantly, X is a composite function in this model, not a single undefined regulatory gene. As Bcl11b starts out in an epigenetically silent state inherited from hematopoietic stem cells, X measures the resistance to the opening of relevant cisregulatory elements at each allele of the locus.…”

“…Thus, the predicted DN1 clonal heterogeneity in differentiation was validated by experimental data. Whereas other hematopoietic stem cell commitment processes have been subject to mathematical model investigations 34 , few attempts have been made for early Tcells 36,45 . Importantly, our model incorporates the complex molecular mechanisms shown recently to be involved in Bcl11b activation including the four known positive regulators contributing to the logic of Bcl11b activation, and the additional, slow epigenetic step that creates a stochastic delay in Bcl11b activation, long after the positive regulators are present.…”

“…Computational modelling of GRNs represents an important approach for studying mechanisms controlling cell-fate decisions 34 Earlier dynamical models dealt with the flux of T-cell progenitor populations through the DN1 to DN4 stages 35 and the transcriptional network for Bcl11b activation assuming deterministic, direct transcriptional regulation by positive regulators Notch, GATA3, and TCF-1 36 . Both models incorporated experimental evidence, but when juxtaposed, a contradiction emerged.…”

A Quantitative Model of Cellular Decision Making in Direct Neuronal Reprogramming

“…Researchers have engaged in such modelling in closely related fields such as the study of pluripotent cell commitment and reprogramming somatic cells to pluripotency. The resulting models successfully compress many experimental results into a single framework that can be interpreted by researchers, provides further experimental predictions, and can be used to suggest new experiments 12,13,14 . With the goal of understanding direct neural reprogramming, an initial network hypothesis has been published 13 , but its ability to reproduce and explain the behaviour of the cell has not yet been explored in a quantitative way.…”

Modeling binary and graded cone cell fate patterning in the mouse retina

“…Recently, computational work has also focused on developing mechanistic models of cell-fate decisions (Olariu and Peterson, 2019;Rothenberg, 2019;Teles et al, 2013), especially the formation of patterns in time and space (Formosa-Jordan, 2018;Liang et al, 2015). Multiscale approaches that combine stochastic and deterministic models of tissues at the scale of individual cells have shown promise in helping to elucidate the details of tissue patterning (Coulier and Hellander, 2018;Engblom, 2018;Engblom et al, 2018;Folguera-Blasco et al, 2019;Johnston et al, 2011).…”