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Kinetic models of hematopoietic differentiation

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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
(a) Boolean model states for a progenitor (MP) differentiating to B‐cells and macrophages, respectively. Model simulations starts from the unstable MP state in the absence of cytokine (upper) and after the addition of CSF1 and IL7 (lower left and lower right). The trajectory from MP to B‐cells goes through 9 and 19 states in the two compartments. Similarly, one has 8 and 36 states to pass for the macrophages. (b) Averaged stochastic simulations from random update ordering showing the evolution over time, before and after cytokine exposition, of the fractions of cells expressing specific macrophage factors (top), B‐cell factors (middle), and cell‐type signatures (bottom). The x and y axes represent time (in arbitrary units) and fractions of positive cells, respectively. (Reprinted with permission from Collombet et al. (). Copyright 2017 National Academy of Sciences)
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(a) Schematic representation of the transcriptional bursting model. For a given gene, the promoter can be in two different states, active or repressed, with the average time spent in each state being controlled by the average times for activation (τON) and repression (τOFF). When in the active promoter state, the gene is transcribed and produces mRNA molecules after an average production time. Finally, mRNA molecules are degraded after an average time, τRNA, irrespective of promoter states. (b) Best parameter sets for each gene allow for the reconstitution of the experimentally observed distributions (top) within our model simulations (bottom). (c) The parameters suggest different modes of stochastic expression for the different genes, with highly variable burst frequencies and duration (gray bars) as well as mRNA dynamics (colored lines). (Reprinted with permission from Teles et al. (). Copyright 2013 PLOS Journals)
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The GATA2–GATA1–PU.1 network and the corresponding free energy and the shortest paths between the attractors. (a) The GATA2–GATA1–PU.1 transcription factor (TF) circuit with the well‐established GATA1–PU.1 mutual inhibition and positive auto‐regulations, GATA1 repressing GATA2 while GATA2 induces GATA1. The circuit includes the interactions of GATA2–PU.1 mutual inhibition and GATA2 negative self‐interaction determined by combinatorial searches using rate equations (May et al., ). (b) The free energy exhibiting two attractors: (1) GATA2, GATA1‐high and PU.1‐low; and (2) PU.1‐high and GATA2, GATA1‐low. (c) Variation of the free energy and gene expressions along the shortest path between (2) and (1). (d) The corresponding variation along the shortest path between (1) and (2). (Reprinted with permission from Olariu et al. (). Copyright 2017 Royal Society Publishing)
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The triad interaction circuit depicting the most likely architecture from fitting to gene expression data. (a) The core network for the mutual and self‐regulatory interactions between GATA1, PU.1, and GATA2. Red interaction indicates increase in time of the binding strength, blue corresponds to decreasing, and black shows constant binding strengths (as shown in the panel below the network diagram). The external signal Epo is represented in gray. (b) Expression time series of GATA1 (red), PU.1 (green), and GATA2 (blue) from the network model (solid lines) with the best set of estimated parameters. Experimental data are represented by circles. Expression levels are normalized by expression at t = 0 (multipotent state). (Reprinted with permission from May et al. (). Copyright 2013 Cell Press)
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Workflow when using computational models to deduce relevant components and interactions
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Switch architecture with two mutually repressing genes A and B, which are both positively self‐interacting along with corresponding energy landscape, bifurcation diagram, and attractor space
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Models of Systems Properties and Processes > Mechanistic Models
Developmental Biology > Stem Cell Biology and Regeneration

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