Integrating extrinsic and intrinsic cues into a minimal model of lineage commitment for hematopoietic progenitors

Cell Decision Making through the Lens of Bayesian Learning

Cell decision making refers to the process by which cells gather information from their local microenvironment and regulate their internal states to create appropriate responses. Microenvironmental cell sensing plays a key role in this process. Our hypothesis is that cell decision-making regulation is dictated by Bayesian learning. In this article, we explore the implications of this hypothesis for internal state temporal evolution. By using a timescale separation between internal and external variables on the mesoscopic scale, we derive a hierarchical Fokker-Planck equation for cell-microenvironment dynamics. By combining this with the Bayesian learning hypothesis, we find that changes in microenvironmental entropy dominate the cell state probability distribution. Finally, we use these ideas to understand how cell sensing impacts cell decision making. Notably, our formalism allows us to understand cell state dynamics even without exact biochemical information about cell sensing processes by considering a few key parameters.

Trajectory-based energy landscapes of gene regulatory networks

Multistability and natural biological variability can result in significant heterogeneity within a cell population, leading to challenges in understanding and modulating cell behavior. Energy landscapes can offer qualitatively intuitive visualizations of cell phenotype and facilitate a more quantitative understanding of cellular dynamics, but current methods for landscape generation are mathematically involved and often require specific system properties (e.g., ergodicity or independent gene/protein probability distributions) that do not always hold. Here, we present a simple kinetic Monte Carlo-based method for landscape generation from a system of ordinary differential equations using only simulation trajectories initialized throughout the phase space of interest. The resulting landscape produces three quantitative features relevant to understanding cell behavior: stability (reflected by the depth or potential of landscape valleys), velocity (representing average directional movement on the landscape), and variance in velocity (indicative of landscape positions with heterogeneous movements). We applied this method to a genetic toggle switch, a core decision-making network in binary cellular responses, to elucidate effects of biologically relevant intrinsic and extrinsic cues. Intrinsic noise, such as stochasticity in transcription-translation and differences in cell cycle position, manifests through changes in valley width and position, reflecting increased population heterogeneity and more probabilistic cell fate transitions. The landscapes also capture the effect of an external inducer, revealing a quantitative correlation between the rate of cell fate transition and the energy barrier above a threshold inducer concentration determined by the permissivity of the valley. Further, in tracking dynamically changing landscapes under time-varying external cues, we unexpectedly found that an oscillatory inducer input can modulate cell fate heterogeneity and lead to periodic cell fate transitions entrained to the input frequency, depending on the intrinsic degradation rate of the switch. The landscape generation approach outlined herein is generalizable to other network topologies and may provide new quantitative insights into their dynamics.

Variable cellular decision-making behavior in a constant synthetic network topology

Background

Modules of interacting components arranged in specific network topologies have evolved to perform a diverse array of cellular functions. For a network with a constant topological structure, its function within a cell may still be tuned by changing the number of instances of a particular component (e.g., gene copy number) or by modulating the intrinsic biochemical properties of a component (e.g., binding strength or catalytic efficiency). How such perturbations affect cellular response dynamics remains poorly understood. Here, we explored these effects in a common decision-making motif, cross-antagonism with autoregulation, by synthetically constructing this network in yeast.

Results

We employed the engineering design strategy of reuse to build this topology with a single protein building block, TetR, creating necessary components through TetR mutations and fusion partners. We then studied the impact of several topology-preserving perturbations – strength of cross-antagonism, number of operator sites in a promoter, and gene dosage – on decision-making behavior. We found that reducing TetR repression strength, which hinders cross-antagonism, resulted in a loss of mutually exclusive cell responses. Unexpectedly, increasing the number of operator sites also impeded decision-making exclusivity, which may be a consequence of the averaging effect that arises when multiple transcriptional activators and repressors are accommodated at a given locus. Stochastic simulations of this topology revealed that, even for networks with high TetR repression strength and a low number of operator sites, increasing gene dosage can reduce exclusivity in response dynamics. We further demonstrated this result experimentally by quantifying gene copy numbers in selected yeast clones with differing phenotypic responses.

Conclusions

Our study illustrates how parameters that do not change the topological structure of a decision-making network can nonetheless exert significant influence on its response dynamics. These findings should further inform the study of native motifs, including the effects of topology-preserving mutations, and the robust engineering of synthetic networks.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2866-6) contains supplementary material, which is available to authorized users.

Engineering of a synthetic quadrastable gene network to approach Waddington landscape and cell fate determination

The process of cell fate determination has been depicted intuitively as cells travelling and resting on a rugged landscape, which has been probed by various theoretical studies. However, few studies have experimentally demonstrated how underlying gene regulatory networks shape the landscape and hence orchestrate cellular decision-making in the presence of both signal and noise. Here we tested different topologies and verified a synthetic gene circuit with mutual inhibition and auto-activations to be quadrastable, which enables direct study of quadruple cell fate determination on an engineered landscape. We show that cells indeed gravitate towards local minima and signal inductions dictate cell fates through modulating the shape of the multistable landscape. Experiments, guided by model predictions, reveal that sequential inductions generate distinct cell fates by changing landscape in sequence and hence navigating cells to different final states. This work provides a synthetic biology framework to approach cell fate determination and suggests a landscape-based explanation of fixed induction sequences for targeted differentiation.

DOI:http://dx.doi.org/10.7554/eLife.23702.001

Cells in animals use a process called differentiation to specialize into specific cell types such as skin cells and liver cells. Proteins called transcription factors drive particular steps in differentiation by controlling the activity of specific genes. Many transcription factors interact with each other to form complex networks that regulate gene activity to determine the fate of a cell and control the whole differentiation process. Some individual gene networks can program cells to become any one of several different cell fates, a feature known as multistability.

In the 1950s, a scientist called Conrad Waddington proposed the concept of an “epigenetic landscape” to describe how the fate of a cell is decided as an animal develops. The cell, depicted as a ball, rolls down a rugged landscape and has the option of taking several different routes. Each route will eventually lead to a distinct cell fate. As the ball moves down the hill, the choice of routes and final destinations becomes more limited. Theoretical approaches have been used to understand how gene regulatory networks shape the epigenetic landscape of an animal. However, few studies have experimentally tested the findings of the theoretical approaches and it is not clear how environmental inputs help to determine which path a cell will take.

Although bacteria cells do not generally specialize into particular cell types, bacteria cells can use multistability in transcription factor networks to switch between different behaviors or “states” in response to cues from the environment. Wu et al. used a bacterium called E. coli as a model to investigate how a gene network called MINPA from mammals, which is involved in differentiation and is believed to show multistability, can guide cells to adopt different states. The work combined experimental and mathematical approaches to design, construct and test an artificial version of the MINPA gene network in E. coli.

The experiments showed that MINPA could direct the cells to adopt four different stable states in which the cells produced fluorescent proteins of different colors. With the help of mathematical modeling, Wu et al. charted how the landscape of cell states changed when external chemical cues were applied. Exposing the cells to several cues in particular orders guided the cells to different final states.

The findings of Wu et al. shed new light on how the fate of a cell is determined and provide a theoretical framework for understanding the complex networks that control cell differentiation. This could help develop new ways of directing cell fate that could ultimately be used to generate cells to treat human diseases.

DOI:http://dx.doi.org/10.7554/eLife.23702.002

The homeobox gene DLX4 regulates erythro-megakaryocytic differentiation by stimulating IL-1β and NF-κB signaling

Megakaryocyte and erythroid development are tightly controlled by a repertoire of cytokines, but it is not clear how cytokine-activated signaling pathways are controlled during development of these two lineages. Here, we identify that expression of DLX4, a transcription factor encoded by a homeobox gene, increases during megakaryopoiesis but decreases during erythropoiesis. Enforced expression of DLX4 in CD34(+) stem and progenitor cells and in bipotent K562 cells induced lineage markers and morphologic features of megakaryocytes and repressed erythroid marker expression and hemoglobin levels. Converse results were obtained when DLX4 was knocked down. Gene Ontology and Gene Set Enrichment Analyses of genome-wide changes in gene expression revealed that DLX4 induces a megakaryocytic transcriptional program and inhibits an erythroid transcriptional program. DLX4 also induced gene signatures that are associated with nuclear factor κB (NF-κB) signaling. The ability of DLX4 to promote megakaryocyte development at the expense of erythroid generation was diminished by blocking NF-κB activity or by repressing IL1B, a transcriptional target of DLX4. Collectively, our findings indicate that DLX4 exerts opposing effects on the megakaryocytic and erythroid lineages in part by inducing IL-1β and NF-κB signaling.

Robust hematopoietic progenitor cell commitment in the presence of a conflicting cue

Hematopoietic lineage commitment is regulated by cytokines and master transcription factors, but it remains unclear how a progenitor cell chooses a lineage in the face of conflicting cues. Through transcript counting in megakaryocyte-erythroid progenitors undergoing erythropoiesis, we show that the expression levels of the pro-erythropoiesis transcription factor EKLF (also known as KLF1) and receptor EpoR are inversely correlated with their pro-megakaryopoiesis counterparts, FLI-1 and TpoR (also known as MPL). Notably, as progenitors commit to the erythrocyte lineage, EpoR is upregulated and TpoR is strongly downregulated, thus boosting the potency of the pro-erythropoiesis cue erythropoietin and effectively eliminating the activity of the pro-megakaryopoiesis cue thrombopoietin. Based on these findings, we propose a new model for exclusive decision making that explicitly incorporates signals from extrinsic cues, and we experimentally confirm a model prediction of temporal changes in transcript noise levels in committing progenitors. Our study suggests that lineage-specific receptor levels can modulate potencies of cues to achieve robust commitment decisions.

Cell commitment motif composed of progenitor-specific transcription factors and mutual-inhibition regulation

Simple mutual-inhibition networks are frequently occurring motifs in transcriptional regulatory networks for cell lineage commitment. Stable attractors represent cell commitment states. However, how progenitor-specific transcription factors stabilise progenitor cells and commit them to different cell fates remains unexplained. In this study, the authors represent the cell commitment motifs composed of mutual-inhibition regulation and progenitor-specific transcription factors, and develop associated mathematical model to understand how specific cell fate decisions are made. Bifurcation analysis and numerical simulation show that the model could exhibit multiple stable steady states corresponding to progenitor and committed cell states. The transitions between different cell states correspond to different commitment processes. Furthermore, the authors demonstrate that different commitment patterns, for example, haematopoietic and neural fate decisions fall within the scope of proposed framework.

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

Background

Hematopoiesis is a highly orchestrated developmental process that comprises various developmental stages of the hematopoietic stem cells (HSCs). During development, the decision to leave the self-renewing state and selection of a differentiation pathway is regulated by a number of transcription factors. Among them, genes GATA-1 and PU.1 form a core negative feedback module to regulate the genetic switching between the cell fate choices of HSCs. Although extensive experimental studies have revealed the mechanisms to regulate the expression of these two genes, it is still unclear how this simple module regulates the genetic switching.

Methods

In this work we proposed a mathematical model to study the mechanisms of the GATA-PU.1 gene network in the determination of HSC differentiation pathways. We incorporated the mechanisms of GATA switch into the module, and developed a mathematical model that comprises three genes GATA-1, GATA-2 and PU.1. In addition, a novel multiple-objective optimization method was designed to infer unknown parameters in the proposed model by realizing different experimental observations. A stochastic model was also designed to describe the critical function of noise, due to the small copy numbers of molecular species, in determining the differentiation pathways.

Results

The proposed deterministic model has successfully realized three stable steady states representing the priming and different progenitor cells as well as genetic switching between the genetic states under various experimental conditions. Using different values of GATA-1 synthesis rate for the GATA-1 protein availability in the chromatin sites during the time period of GATA switch, stochastic simulations for the first time have realized different proportions of cells leading to different developmental pathways under various experimental conditions.

Conclusions

Mathematical models provide testable predictions regarding the mechanisms and conditions for realizing different differentiation pathways of hematopoietic stem cells. This work represents the first attempt at using a discrete stochastic model to realize the decision of HSC differentiation pathways showing a multimodal distribution.

Systems hematology: an introduction

Hematologists have traditionally studied blood and its components by simplifying it into its components and functions. A variety of new techniques have generated large and complex datasets. Coupled to an appreciation of blood as a dynamic system, a new approach in systems hematology is needed. Systems hematology embraces the multi-scale complexity with a combination of mathematical, engineering, and computational tools for constructing and validating models of biological phenomena. The validity of mathematical modeling in hematopoiesis was established early by the pioneering work of Till and McCulloch. This volume seeks to introduce to the various scientists and physicians to the multi-faceted field of hematology by highlighting recent works in systems biology. Deterministic, stochastic, statistical, and network-based models have been used to better understand a range of topics in hematopoiesis, including blood cell production, the periodicity of cyclical neutropenia, stem cell production in response to cytokine administration, and the emergence of drug resistance. Future advances require technological improvements in computing power, imaging, and proteomics as well as greater collaboration between experimentalists and modelers. Altogether, systems hematology will improve our understanding of normal and abnormal hematopoiesis, better define stem cells and their daughter cells, and potentially lead to more effective therapies.

Asymmetry in erythroid-myeloid differentiation switch and the role of timing in a binary cell-fate decision

GATA1-PU.1 genetic switch is a paradigmatic genetic switch that governs the differentiation of progenitor cells into two different fates, erythroid and myeloid fates. In terms of dynamical model representation of these fates or lineages corresponds to stable attractor and choosing between the attractors. Small asymmetries and stochasticity intrinsically present in all genetic switches lead to the effect of delayed bifurcation which will change the differentiation result according to the timing of the process and affect the proportion of erythroid versus myeloid cells. We consider the differentiation bifurcation scenario in which there is a symmetry-breaking in the bifurcation diagrams as a result of asymmetry in external signaling. We show that the decision between two alternative cell fates in this structurally symmetric decision circuit can be biased depending on the speed at which the system is forced to go through the decision point. The parameter sweeping speed can also reduce the effect of asymmetry and produce symmetric choice between attractors, or convert the favorable attractor. This conversion may have important contributions to the immune system when the bias is in favor of the attractor which gives rise to non-immune cells.

Stochastic cytokine expression induces mixed T helper cell States

During early differentiation of T helper cells, stochastic cytokine expression triggers the co-expression of antagonistic transcription factors at high levels, buffered by the interplay between extracellular and intracellular signaling components.

During eukaryotic development, the induction of a lineage-specific transcription factor typically drives differentiation of multipotent progenitor cells, while repressing that of alternative lineages. This process is often mediated by some extracellular signaling molecules, such as cytokines that can bind to cell surface receptors, leading to activation and/or repression of transcription factors. We explored the early differentiation of naive CD4 T helper (Th) cells into Th1 versus Th2 states by counting single transcripts and quantifying immunofluorescence in individual cells. Contrary to mutually exclusive expression of antagonistic transcription factors, we observed their ubiquitous co-expression in individual cells at high levels that are distinct from basal-level co-expression during lineage priming. We observed that cytokines are expressed only in a small subpopulation of cells, independent from the expression of transcription factors in these single cells. This cell-to-cell variation in the cytokine expression during the early phase of T helper cell differentiation is significantly larger than in the fully differentiated state. Upon inhibition of cytokine signaling, we observed the classic mutual exclusion of antagonistic transcription factors, thus revealing a weak intracellular network otherwise overruled by the strong signals that emanate from extracellular cytokines. These results suggest that during the early differentiation process CD4 T cells acquire a mixed Th1/Th2 state, instructed by extracellular cytokines. The interplay between extracellular and intracellular signaling components unveiled in Th1/Th2 differentiation may be a common strategy for mammalian cells to buffer against noisy cytokine expression.

During the development of a multicellular organism, the progenitor cells, which have the potential to become any of several different cell lineages with specialized functions, commit and differentiate into one particular lineage. This differentiation of progenitors is driven by the induction of lineage-specific transcription factors, molecules that regulate gene expression. This process is often mediated by extracellular signaling molecules, including a class of molecules called cytokines that can bind to cell surface receptors, activating and/or repressing transcription factors. Here we explored the early differentiation of naive T helper (Th) cells, an important class of T lymphocytes that help effector immune cells to defend the body against various pathogens. We measured both mRNA and protein levels of cytokines and transcription factors in individual cells. In particular, mRNA levels were measured with single-molecule resolution. Contrary to the expression of only one set of lineage-specific transcription factors, we observed ubiquitous high-level co-expression of antagonistic transcription factors in individual cells. We found that cytokines are expressed only in a small subpopulation of cells, independent from the expression of transcription factors in individual cells. When cytokine signaling is inhibited, each cell expressed only one of the antagonistic transcription factors at high levels. This reveals a weak intracellular network that is otherwise overruled by the strong signals that emanate from extracellular cytokines. These results suggest that during the early differentiation process T helper cells acquire a mixed Th1/Th2 state, instructed by extracellular cytokines. The interplay between extracellular and intracellular signaling components unveiled in Th1/Th2 differentiation may be a common strategy for mammalian cells to buffer against noisy cytokine expression.

An in silico erythropoiesis model rationalizing synergism between stem cell factor and erythropoietin

Stem cell factor (SCF) and erythropoietin (EPO) are two most recognized growth factors that play in concert to control in vitro erythropoiesis. However, exact mechanisms underlying the interplay of these growth factors in vitro remain unclear. We developed a mathematical model to study co-signaling effects of SCF and EPO utilizing the ERK1/2 and GATA-1 pathways (activated by SCF and EPO) that drive the proliferation and differentiation of erythroid progenitors. The model was simplified and formulated based on three key features: synergistic contribution of SCF and EPO on ERK1/2 activation, positive feedback effects on proliferation and differentiation, and cross-inhibition effects of activated ERK1/2 and GATA-1. The model characteristics were developed to correspond with biological observations made known thus far. Our simulation suggested that activated GATA-1 has a more dominant cross-inhibition effect and stronger positive feedback response on differentiation than the proliferation pathway, while SCF contributed more to the activation of ERK1/2 than EPO. A sensitivity analysis performed to gauge the dynamics of the system was able to identify the most sensitive model parameters and illustrated a contribution of transient activity in EPO ligand to growth factor synergism. Based on theoretical arguments, we have successfully developed a model that can simulate growth factor synergism observed in vitro for erythropoiesis. This hypothesized model can be applied to further computational studies in biological systems where synergistic effects of two ligands are seen.

Transient noise amplification and gene expression synchronization in a bistable mammalian cell-fate switch

Progenitor cells within a clonal population show variable proclivity toward lineage commitment and differentiation. This cell-to-cell variability has been attributed to transcriptome-wide gene expression noise generated by fluctuations in the amount of cellular machinery and stochasticity in the biochemical reactions involved in protein synthesis. It therefore remains unclear how a signaling network, in the presence of such noise, can execute unequivocal cell-fate decisions from external cues. Here, we use mathematical modeling and model-guided experiments to reveal functional interplay between instructive signaling and noise in erythropoiesis. We present evidence that positive transcriptional feedback loops in a lineage-specific receptor signaling pathway can generate ligand-induced memory to engender robust, switch-like responses. These same feedback loops can also transiently amplify gene expression noise in the signaling network, suggesting that external cues can actually bias seemingly stochastic decisions during cell-fate specification. Gene expression levels among key effectors in the signaling pathway are uncorrelated in the initial population of progenitor cells but become synchronized after addition of ligand, which activates the transcriptional feedback loops. Finally, we show that this transient noise amplification and gene expression synchronization induced by ligand can directly influence cell survival and differentiation kinetics within the population.

Mathematical modelling of stem cell differentiation: the PU.1-GATA-1 interaction

The transcription factors PU.1 and GATA-1 are known to be important in the development of blood progenitor cells. Specifically they are thought to regulate the differentiation of progenitor cells into the granulocyte/macrophage lineage and the erythrocyte/megakaryocite lineage. While several mathematical models have been proposed to investigate the interaction between the transcription factors in recent years, there is still debate about the nature of the progenitor state in the dynamical system, and whether the existing models adequately capture new knowledge about the interactions gleaned from experimental data. Further, the models utilise different formalisms to represent the genetic regulation, and it appears that the resulting dynamical system depends upon which formalism is adopted. In this paper we analyse the four existing models, and propose an alternative model which is shown to demonstrate a rich variety of dynamical systems behaviours found across the existing models, including both bistability and tristability required for modelling the undifferentiated progenitors.

Cell differentiation modeled via a coupled two-switch regulatory network

Mesenchymal stem cells can give rise to bone and other tissue cells, but their differentiation still escapes full control. In this paper we address this issue by mathematical modeling. We present a model for a genetic switch determining the cell fate of progenitor cells which can differentiate into osteoblasts (bone cells) or chondrocytes (cartilage cells). The model consists of two switch mechanisms and reproduces the experimentally observed three stable equilibrium states: a progenitor, an osteogenic, and a chondrogenic state. Conventionally, the loss of an intermediate (progenitor) state and the entailed attraction to one of two opposite (differentiated) states is modeled as a result of changing parameters. In our model in contrast, we achieve this by distributing the differentiation process to two functional switch parts acting in concert: one triggering differentiation and the other determining cell fate. Via stability and bifurcation analysis, we investigate the effects of biochemical stimuli associated with different system inputs. We employ our model to generate differentiation scenarios on the single cell as well as on the cell population level. The single cell scenarios allow to reconstruct the switching upon extrinsic signals, whereas the cell population scenarios provide a framework to identify the impact of intrinsic properties and the limiting factors for successful differentiation.

Erythropoietin receptor response circuits

PURPOSE OF REVIEW

In 1985-1989, erythropoietin (EPO), its receptor (EPOR), and janus kinase 2 were cloned; established to be essential for definitive erythropoiesis; and initially intensely studied. Recently, new impetus, tools, and model systems have emerged to re-examine EPO/EPOR actions, and are addressed in this review. Impetus includes indications that EPO affects significantly more than standard erythroblast survival pathways, the development of novel erythropoiesis-stimulating agents, increasing evidence for EPO/EPOR cytoprotection of ischemically injured tissues, and potential EPO-mediated worsening of tumorigenesis.

RECENT FINDINGS

New findings are reviewed in four functional contexts: (pro)erythroblast survival mechanisms, new candidate EPO/EPOR effects on erythroid cell development and new EPOR responses, EPOR downmodulation and trafficking, and novel erythropoiesis-stimulating agents.

SUMMARY

As Current Opinion, this monograph seeks to summarize, and provoke, new EPO/EPOR action concepts. Specific problems addressed include: beyond (and before) BCL-XL, what key survival factors are deployed in early-stage proerythroblasts? Are distinct EPO/EPOR signals transduced in stage-selective fashions? Is erythroblast proliferation also modulated by EPO/EPOR signals? What functions are subserved by new noncanonical EPO/EPOR response factors (e.g. podocalyxin like-1, tribbles 3, reactive oxygen species, and nuclear factor kappa B)? What key regulators mediate EPOR inhibition and trafficking? And for emerging erythropoiesis-stimulating agents, to what extent do activities parallel EPOs (or differ in advantageous, potentially complicating ways, or both)?

Temperature control of fimbriation circuit switch in uropathogenic Escherichia coli: quantitative analysis via automated model abstraction

Uropathogenic Escherichia coli (UPEC) represent the predominant cause of urinary tract infections (UTIs). A key UPEC molecular virulence mechanism is type 1 fimbriae, whose expression is controlled by the orientation of an invertible chromosomal DNA element-the fim switch. Temperature has been shown to act as a major regulator of fim switching behavior and is overall an important indicator as well as functional feature of many urologic diseases, including UPEC host-pathogen interaction dynamics. Given this panoptic physiological role of temperature during UTI progression and notable empirical challenges to its direct in vivo studies, in silico modeling of corresponding biochemical and biophysical mechanisms essential to UPEC pathogenicity may significantly aid our understanding of the underlying disease processes. However, rigorous computational analysis of biological systems, such as fim switch temperature control circuit, has hereto presented a notoriously demanding problem due to both the substantial complexity of the gene regulatory networks involved as well as their often characteristically discrete and stochastic dynamics. To address these issues, we have developed an approach that enables automated multiscale abstraction of biological system descriptions based on reaction kinetics. Implemented as a computational tool, this method has allowed us to efficiently analyze the modular organization and behavior of the E. coli fimbriation switch circuit at different temperature settings, thus facilitating new insights into this mode of UPEC molecular virulence regulation. In particular, our results suggest that, with respect to its role in shutting down fimbriae expression, the primary function of FimB recombinase may be to effect a controlled down-regulation (rather than increase) of the ON-to-OFF fim switching rate via temperature-dependent suppression of competing dynamics mediated by recombinase FimE. Our computational analysis further implies that this down-regulation mechanism could be particularly significant inside the host environment, thus potentially contributing further understanding toward the development of novel therapeutic approaches to UPEC-caused UTIs.

Hematopoiesis and its disorders: a systems biology approach

Scientists have traditionally studied complex biologic systems by reducing them to simple building blocks. Genome sequencing, high-throughput screening, and proteomics have, however, generated large datasets, revealing a high level of complexity in components and interactions. Systems biology embraces this complexity with a combination of mathematical, engineering, and computational tools for constructing and validating models of biologic phenomena. The validity of mathematical modeling in hematopoiesis was established early by the pioneering work of Till and McCulloch. In reviewing more recent papers, we highlight deterministic, stochastic, statistical, and network-based models that have been used to better understand a range of topics in hematopoiesis, including blood cell production, the periodicity of cyclical neutropenia, stem cell production in response to cytokine administration, and the emergence of imatinib resistance in chronic myeloid leukemia. Future advances require technologic improvements in computing power, imaging, and proteomics as well as greater collaboration between experimentalists and modelers. Altogether, systems biology will improve our understanding of normal and abnormal hematopoiesis, better define stem cells and their daughter cells, and potentially lead to more effective therapies.