Mathematical modeling of GATA-switching for regulating the differentiation of hematopoietic stem cell

Tian, Tianhai; Smith‐Miles, Kate

doi:10.1186/1752-0509-8-s1-s8

Cited by 31 publications

(23 citation statements)

References 53 publications

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“…The situation a = 0, including self-promotion in the regulatory motif, describes more architectures such as B cells promoting an antibody class switch 71 , gene regulatory networks with slow promoter kinetics 72 and cell-fate development and differentiation in eukaryotic cells 50,52,[73][74][75][76] . A famous example is the GATA-1-PU.1 system controlling hematopoietic stem cell differentiation 43,46,72,[77][78][79][80] . We predict that ATP variability will influence fate decisions across this broad variety of systems, organisms and branches of life.…”

Section: Discussionmentioning

confidence: 99%

Intracellular Energy Variability Modulates Cellular Decision-Making Capacity

Kerr

Jabbari

Johnston

2019

Preprint

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Cells are able to generate phenotypic diversity both during development and in response to stressful and changing environments, aiding survival. The biologically and medically vital process of a cell assuming a functionally important fate from a range of phenotypic possibilities can be thought of as a cell decision. To make these decisions, a cell relies on energy dependent pathways of signalling and expression. However, energy availability is often overlooked as a modulator of cellular decisionmaking. As cells can vary dramatically in energy availability, this limits our knowledge of how this key biological axis affects cell behaviour. Here, we consider the energy dependence of a highly generalisable decision-making regulatory network, and show that energy variability changes the sets of decisions a cell can make and the ease with which they can be made. Increasing intracellular energy levels can increase the number of stable phenotypes it can generate, corresponding to increased decision-making capacity. For this decision-making architecture, a cell with intracellular energy below a threshold is limited to a singular phenotype, potentially forcing the adoption of a specific cell fate. We suggest that common energetic differences between cells may explain some of the observed variability in cellular decision-making, and demonstrate the importance of considering energy levels in several diverse biological decision-making phenomena. endospores is a survival mechanism used by diverse genera including Bacillus and Clostridium 25 , contributing to foodborne disease and food spoilage 26,27 . The decision to become a persister cell (a phenotype more tolerant to external stress, including antibiotics), is made in several bacterial species 28-31 and can have a dramatic impact on the efficacy of treatments [32][33][34][35] .Many of the mechanisms behind cellular decision-making in eukaryotes and prokaryotes remain poorly understood. Regulatory networks, representing the interactions between genes that govern these decisions, are often used to summarise our knowledge 36 . Typically, edges in a schematic network illustrate processes such as transcription and translation and nodes represent genes. However, this coarse-graining can often omit a substantial amount of important detail. In particular, the fact that the processes represented by these edges are energy dependent (Figure 1) is rarely considered. Transcription and translation require a substantial ATP budget 37, 38 , so there exists a core energy dependence in the dynamics of gene regulatory networks, potentially affecting the decisions supported by a given cell.This energy dependence is important because different cells, particularly in microbiology, can have substantially different levels of available energy. Energy variability has been observed within genetically-identical Escherichia coli bacteria cells in a population, where absolute concentrations of intracellular ATP were spread over at least half an order of magnitude, in a skewed distribution around 1.54 ± 1...

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Section: Discussionmentioning

confidence: 99%

Intracellular Energy Variability Modulates Cellular Decision-Making Capacity

Kerr

Jabbari

Johnston

2019

Preprint

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“…Differing interpretations and emphases on the specifics of the PU.1 and GATA-1 example have led to a variety of alternative mathematical models, a number of which are reviewed in (Duff et al, 2012); see also (Tian and Smith-Miles, 2014;Alsaedi et al, 2014) for models incorporating the GATA-2 factor.…”

Section: Modelling Assumptionsmentioning

confidence: 99%

Limit-cycle oscillatory coexpression of cross-inhibitory transcription factors: a model mechanism for lineage promiscuity

Bokes

King

2018

Mathematical Medicine and Biology: A Journal of the IMA

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Lineage switches are genetic regulatory motifs that govern and maintain the commitment of a developing cell to a particular cell fate. A canonical example of a lineage switch is the pair of transcription factors PU.1 and GATA-1, of which the former is affiliated with the myeloid and the latter with the erythroid lineage within the hematopoietic system. On a molecular level, PU.1 and GATA-1 positively regulate themselves and antagonise each other via direct protein-protein interactions. Here we use mathematical modelling to identify a novel type of dynamic behaviour that can be supported by such a regulatory architecture. Guided by the specifics of the PU.1-GATA-1 interaction, we formulate, using the law of mass action, a system of differential equations for the key molecular concentrations. After a series of systematic approximations, the system is reduced to a simpler one, which is tractable to phase-plane and linearisation methods. The reduced system formally resembles, and generalises, a well-known model for competitive species from mathematical ecology. However, in addition to the qualitative regimes exhibited by a pair of competitive species (exclusivity, bistable exclusivity, stable-node coexpression) it also allows for oscillatory limit-cycle coexpression. A key outcome of the model is that, in the context of cell-fate choice, such oscillations could be harnessed by a differentiating cell to prime alternately for opposite outcomes; a bifurcation-theory approach is adopted to characterise this possibility.

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“…Ordinary differential equation (ODE) is a method for modelling the complex biological systems based on the known biochemical kinetics. It has been successfully applied for numerous processes in living organisms [18,21]. CellDesigher is a process diagram editor for biochemical networks as well as an ODE-based simulator [9].…”

Section: Celldesigner Simulationmentioning

confidence: 99%

Data-driven prediction of cancer cell fates with a nonlinear model of signaling pathways

Zhang

Kwoh

et al. 2014

Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

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Signals from the environment of a cell are captured and transmitted by signaling proteins inside the cell. The cell will respond to these signal inputs by leading to one of several possible phenotypic outputs, e.g., cell survival or cell death. However, the underlying mechanisms of processing molecular information are unknown, thus data-driven models are needed to bridge signaling data with phenotypic measurements of cell fates. The traditional linear model has its limitations because it assumes that one cell fate is proportional to the activity of every signaling protein, which is unlikely to be true in the complex biological systems. Therefore, we propose a nonlinear model to predict the probability of cell fates based on activity levels of signaling proteins.Cross validation is used to estimate the performance of our model. We compared our nonlinear model with the linear model, demonstrating that the present nonlinear model has superior performance on cell fates prediction. Moreover, by in silico examination of gene knock-down, the proposed model is able to reveal the drug effects which can provide great assistance to traditional approaches such as binding affinity analysis.

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Mathematical modeling of GATA-switching for regulating the differentiation of hematopoietic stem cell

Cited by 31 publications

References 53 publications

Intracellular Energy Variability Modulates Cellular Decision-Making Capacity

Intracellular Energy Variability Modulates Cellular Decision-Making Capacity

Limit-cycle oscillatory coexpression of cross-inhibitory transcription factors: a model mechanism for lineage promiscuity

Data-driven prediction of cancer cell fates with a nonlinear model of signaling pathways

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