Hematopoiesis and its disorders: a systems biology approach

Dynamically adjusted cell fate decisions and resilience to mutant invasion during steady-state hematopoiesis revealed by an experimentally parameterized mathematical model

A major next step in hematopoietic stem cell (HSC) biology is to enhance our quantitative understanding of cellular and evolutionary dynamics involved in undisturbed hematopoiesis. Mathematical models have been and continue to be key in this respect, and are most powerful when parameterized experimentally and containing sufficient biological complexity. In this paper, we use data from label propagation experiments in mice to parameterize a mathematical model of hematopoiesis that includes homeostatic control mechanisms as well as clonal evolution. We find that nonlinear feedback control can drastically change the interpretation of kinetic estimates at homeostasis. This suggests that short-term HSC and multipotent progenitors can dynamically adjust to sustain themselves temporarily in the absence of long-term HSCs, even if they differentiate more often than they self-renew in undisturbed homeostasis. Additionally, the presence of feedback control in the model renders the system resilient against mutant invasion. Invasion barriers, however, can be overcome by a combination of age-related changes in stem cell differentiation and evolutionary niche construction dynamics based on a mutant-associated inflammatory environment. This helps us understand the evolution of e.g., TET2 or DNMT3A mutants, and how to potentially reduce mutant burden.

Predictive nonlinear modeling of malignant myelopoiesis and tyrosine kinase inhibitor therapy

Chronic myeloid leukemia (CML) is a blood cancer characterized by dysregulated production of maturing myeloid cells driven by the product of the Philadelphia chromosome, the BCR-ABL1 tyrosine kinase. Tyrosine kinase inhibitors (TKIs) have proved effective in treating CML, but there is still a cohort of patients who do not respond to TKI therapy even in the absence of mutations in the BCR-ABL1 kinase domain that mediate drug resistance. To discover novel strategies to improve TKI therapy in CML, we developed a nonlinear mathematical model of CML hematopoiesis that incorporates feedback control and lineage branching. Cell-cell interactions were constrained using an automated model selection method together with previous observations and new in vivo data from a chimeric BCR-ABL1 transgenic mouse model of CML. The resulting quantitative model captures the dynamics of normal and CML cells at various stages of the disease and exhibits variable responses to TKI treatment, consistent with those of CML patients. The model predicts that an increase in the proportion of CML stem cells in the bone marrow would decrease the tendency of the disease to respond to TKI therapy, in concordance with clinical data and confirmed experimentally in mice. The model further suggests that, under our assumed similarities between normal and leukemic cells, a key predictor of refractory response to TKI treatment is an increased maximum probability of self-renewal of normal hematopoietic stem cells. We use these insights to develop a clinical prognostic criterion to predict the efficacy of TKI treatment and design strategies to improve treatment response. The model predicts that stimulating the differentiation of leukemic stem cells while applying TKI therapy can significantly improve treatment outcomes.

Identification of Altered Primary Immunodeficiency-Associated Genes and Their Implications in Pediatric Cancers

BACKGROUND

Cancer is the leading cause of disease-related mortality in children and malignancies are more frequently observed in individuals with primary immunodeficiencies (PIDs). This study aimed to identify and highlight the molecular mechanisms, such as oncogenesis and immune evasion, by which PID-related genes may lead to the development of pediatric cancers.

METHOD

We implemented a novel bioinformatics framework using patient data from the TARGET database and performed a comparative transcriptome analysis of PID-related genes in pediatric cancers between normal and cancer tissues, gene ontology enrichment, and protein-protein interaction analyses, and determined the prognostic impacts of commonly mutated and differentially expressed PID-related genes.

RESULTS

From the Fulgent Genetics Comprehensive Primary Immunodeficiency panel of 472 PID-related genes, 89 genes were significantly differentially expressed between normal and cancer tissues, and 20 genes were mutated in two or more patients. Enrichment analysis highlighted many immune system processes as well as additional pathways in the mutated PID-related genes related to oncogenesis. Survival outcomes for patients with altered PID-related genes were significantly different for 75 of the 89 DEGs, often resulting in a poorer prognosis.

CONCLUSIONS

Overall, multiple PID-related genes demonstrated the connection between PIDs and cancer development and should be studied further, with hopes of identifying new therapeutic targets.

Why are cell populations maintained via multiple compartments?

We consider the maintenance of 'product' cell populations from 'progenitor' cells via a sequence of one or more cell types, or compartments, where each cell's fate is chosen stochastically. If there is only one compartment then large amplification, that is, a large ratio of product cells to progenitors comes with disadvantages. The product cell population is dominated by large families (cells descended from the same progenitor) and many generations separate, on average, product cells from progenitors. These disadvantages are avoided using suitably constructed sequences of compartments: the amplification factor of a sequence is the product of the amplification factors of each compartment, while the average number of generations is a sum over contributions from each compartment. Passing through multiple compartments is, in fact, an efficient way to maintain a product cell population from a small flux of progenitors, avoiding excessive clonality and minimizing the number of rounds of division en route. We use division, exit and death rates, estimated from measurements of single-positive thymocytes, to choose illustrative parameter values in the single-compartment case. We also consider a five-compartment model of thymocyte differentiation, from double-negative precursors to single-positive product cells.

Single Cell Transcriptomics to Understand HSC Heterogeneity and Its Evolution upon Aging

Single-cell transcriptomic technologies enable the uncovering and characterization of cellular heterogeneity and pave the way for studies aiming at understanding the origin and consequences of it. The hematopoietic system is in essence a very well adapted model system to benefit from this technological advance because it is characterized by different cellular states. Each cellular state, and its interconnection, may be defined by a specific location in the global transcriptional landscape sustained by a complex regulatory network. This transcriptomic signature is not fixed and evolved over time to give rise to less efficient hematopoietic stem cells (HSC), leading to a well-documented hematopoietic aging. Here, we review the advance of single-cell transcriptomic approaches for the understanding of HSC heterogeneity to grasp HSC deregulations upon aging. We also discuss the new bioinformatics tools developed for the analysis of the resulting large and complex datasets. Finally, since hematopoiesis is driven by fine-tuned and complex networks that must be interconnected to each other, we highlight how mathematical modeling is beneficial for doing such interconnection between multilayered information and to predict how HSC behave while aging.

Predicting Time to Relapse in Acute Myeloid Leukemia through Stochastic Modeling of Minimal Residual Disease Based on Clonality Data

Event-free and overall survival remain poor for patients with acute myeloid leukemia. Chemoresistant clones contributing to relapse arise from minimal residual disease (MRD) or newly-acquired mutations. However, the dynamics of clones comprising MRD is poorly understood. We developed a predictive stochastic model, based on a multitype age-dependent Markov branching process, to describe how random events in MRD contribute to the heterogeneity in treatment response. We employed training and validation sets of patients who underwent whole genome sequencing and for whom mutant clone frequencies at diagnosis and relapse were available. The disease evolution and treatment outcome are subject to stochastic fluctuations. Estimates of malignant clone growth rates, obtained by model fitting, are consistent with published data. Using the estimates from the training set, we developed a function linking MRD and time of relapse, with MRD inferred from the model fits to clone frequencies and other data. An independent validation set confirmed our model. In a third data set, we fitted the model to data at diagnosis and remission and predicted the time to relapse. As a conclusion, given bone marrow genome at diagnosis and MRD at or past remission, the model can predict time to relapse, and help guide treatment decisions to mitigate relapse.

Discovery of N-(4-(3-isopropyl-2-methyl-2H-indazol-5-yl)pyrimidin-2-yl)-4-(4-methylpiperazin-1-yl)quinazolin-7-amine as a Novel, Potent, and Oral Cyclin-Dependent Kinase Inhibitor against Haematological Malignancies

Hematologic malignancies (HM) start in blood forming tissue or in the cells of the immune system. Cyclin-dependent kinases (CDKs) regulate cell cycle progression, and some of them control cellular transcription. CDK inhibition can trigger apoptosis and could be particularly useful in hematological malignancies. Herein, we describe our efforts toward the discovery of a novel series of quinazoline derivatives as CDK inhibitors. Intensive structural modifications lead to the identification of compound 37d as the most active inhibitors of CDKs 1, 2, 4, 8 and 9 with balancing potency and selectivity against CDKs. Further biological studies revealed that compound 37d can arrest the cell cycle and induce apoptosis via activating PARP and caspase 3. More importantly, compound 37d showed good antitumor efficacy in multiple HM mice xenograft models with no obvious toxicity. These results indicated that CDK 1, 2, 4, 8, and 9 inhibitors could be potentially used to treat certain hematologic malignancies.

Computational Reconstruction of Clonal Hierarchies From Bulk Sequencing Data of Acute Myeloid Leukemia Samples

Acute myeloid leukemia is an aggressive cancer of the blood forming system. The malignant cell population is composed of multiple clones that evolve over time. Clonal data reflect the mechanisms governing treatment response and relapse. Single cell sequencing provides most direct insights into the clonal composition of the leukemic cells, however it is still not routinely available in clinical practice. In this work we develop a computational algorithm that allows identifying all clonal hierarchies that are compatible with bulk variant allele frequencies measured in a patient sample. The clonal hierarchies represent descendance relations between the different clones and reveal the order in which mutations have been acquired. The proposed computational approach is tested using single cell sequencing data that allow comparing the outcome of the algorithm with the true structure of the clonal hierarchy. We investigate which problems occur during reconstruction of clonal hierarchies from bulk sequencing data. Our results suggest that in many cases only a small number of possible hierarchies fits the bulk data. This implies that bulk sequencing data can be used to obtain insights in clonal evolution.

Negative Regulation of Erythroid Differentiation via the CBX8-TRIM28 Axis

Although the mechanism of chronic myeloid leukemia (CML) initiation through BCR/ABL oncogene has been well characterized, CML cell differentiation into erythroid lineage cells remains poorly understood. Using CRISPR-Cas9 screening, we identify Chromobox 8 (CBX8) as a negative regulator of K562 cell differentiation into erythrocytes. CBX8 is degraded via proteasomal pathway during K562 cell differentiation, which activates the expression of erythroid differentiation-related genes that are repressed by CBX8 in the complex of PRC1. During the differentiation process, the serine/threonine-protein kinase PIM1 phosphorylates serine 196 on CBX8, which contributes to CBX8 reduction. When CD235A expression levels are analyzed, the result reveals that the knockdown of PIM1 inhibits K562 cell differentiation. We also identify TRIM28 as another interaction partner of CBX8 by proteomic analysis. Intriguingly, TRIM28 maintains protein stability of CBX8 and TRIM28 loss significantly induces proteasomal degradation of CBX8, resulting in an acceleration of erythroid differentiation. Here, we demonstrate the involvement of the CBX8-TRIM28 axis during CML cell differentiation, suggesting that CBX8 and TRIM28 are promising novel targets for CML research.

Mathematical Modeling Provides Evidence for Niche Competition in Human AML and Serves as a Tool to Improve Risk Stratification

Acute myeloid leukemia (AML) is a stem cell-driven malignant disease. There is evidence that leukemic stem cells (LSC) interact with stem cell niches and outcompete hematopoietic stem cells (HSC). The impact of this interaction on the clinical course of the disease remains poorly understood. We developed and validated a mathematical model of stem cell competition in the human HSC niche. Model simulations predicted how processes in the stem cell niche affect the speed of disease progression. Combining the mathematical model with data of individual patients, we quantified the selective pressure LSCs exert on HSCs and demonstrated the model's prognostic significance. A novel model-based risk-stratification approach allowed extraction of prognostic information from counts of healthy and malignant cells at the time of diagnosis. This model's feasibility was demonstrable based on a cohort of patients with ALDH-rare AML and shows that the model-based risk stratification is an independent predictor of disease-free and overall survival. This proof-of-concept study shows how model-based interpretation of patient data can improve prognostic scoring and contribute to personalized medicine. SIGNIFICANCE: Combining a novel mathematical model of the human hematopoietic stem cell niche with individual patient data enables quantification of properties of leukemic stem cells and improves risk stratification in acute myeloid leukemia.

Network Pharmacology-Based Investigation of the System-Level Molecular Mechanisms of the Hematopoietic Activity of Samul-Tang, a Traditional Korean Herbal Formula

Hematopoiesis is a dynamic process of the continuous production of diverse blood cell types to meet the body's physiological demands and involves complex regulation of multiple cellular mechanisms in hematopoietic stem cells, including proliferation, self-renewal, differentiation, and apoptosis. Disruption of the hematopoietic system is known to cause various hematological disorders such as myelosuppression. There is growing evidence on the beneficial effects of herbal medicines on hematopoiesis; however, their mechanism of action remains unclear. In this study, we conducted a network pharmacological-based investigation of the system-level mechanisms underlying the hematopoietic activity of Samul-tang, which is an herbal formula consisting of four herbal medicines, including Angelicae Gigantis Radix, Rehmanniae Radix Preparata, Paeoniae Radix Alba, and Cnidii Rhizoma. In silico analysis of the absorption-distribution-metabolism-excretion model identified 16 active phytochemical compounds contained in Samul-tang that may target 158 genes/proteins associated with myelosuppression to exert pharmacological effects. Functional enrichment analysis suggested that the targets of Samul-tang were significantly enriched in multiple pathways closely related to the hematopoiesis and myelosuppression development, including the PI3K-Akt, MAPK, IL-17, TNF, FoxO, HIF-1, NF-kappa B, and p53 signaling pathways. Our study provides novel evidence regarding the system-level mechanisms underlying the hematopoiesis-promoting effect of herbal medicines for hematological disorder treatment.

Quantifying Drug-Induced Bone Marrow Toxicity Using a Novel Haematopoiesis Systems Pharmacology Model

Haematological toxicity associated with cancer therapeutics is monitored by changes in blood cell count, and their primary effect is on proliferative progenitors in the bone marrow. Using observations in rat bone marrow and blood, we characterize a mathematical model that comprises cell proliferation and differentiation of the full haematopoietic phylogeny, with interacting feedback loops between lineages in homeostasis as well as following carboplatin exposure. We accurately predicted the temporal dynamics of several mature cell types related to carboplatin-induced bone marrow toxicity and identified novel insights into haematopoiesis. Our model confirms a significant degree of plasticity within bone marrow cells, with the number and type of both early progenitors and circulating cells affecting cell balance, via feedback mechanisms, through fate decisions of the multipotent progenitors. We also demonstrated cross-species translation of our predictions to patients, applying the same core model structure and considering differences in drug-dependent and physiology-dependent parameters.

Mutation, drift and selection in single-driver hematologic malignancy: Example of secondary myelodysplastic syndrome following treatment of inherited neutropenia

Cancer development is driven by series of events involving mutations, which may become fixed in a tumor via genetic drift and selection. This process usually includes a limited number of driver (advantageous) mutations and a greater number of passenger (neutral or mildly deleterious) mutations. We focus on a real-world leukemia model evolving on the background of a germline mutation. Severe congenital neutropenia (SCN) evolves to secondary myelodysplastic syndrome (sMDS) and/or secondary acute myeloid leukemia (sAML) in 30–40%. The majority of SCN cases are due to a germline ELANE mutation. Acquired mutations in CSF3R occur in >70% sMDS/sAML associated with SCN. Hypotheses underlying our model are: an ELANE mutation causes SCN; CSF3R mutations occur spontaneously at a low rate; in fetal life, hematopoietic stem and progenitor cells expands quickly, resulting in a high probability of several tens to several hundreds of cells with CSF3R truncation mutations; therapeutic granulocyte colony-stimulating factor (G-CSF) administration early in life exerts a strong selective pressure, providing mutants with a growth advantage. Applying population genetics theory, we propose a novel two-phase model of disease development from SCN to sMDS. In Phase 1, hematopoietic tissues expand and produce tens to hundreds of stem cells with the CSF3R truncation mutation. Phase 2 occurs postnatally through adult stages with bone marrow production of granulocyte precursors and positive selection of mutants due to chronic G-CSF therapy to reverse the severe neutropenia. We predict the existence of the pool of cells with the mutated truncated receptor before G-CSF treatment begins. The model does not require increase in mutation rate under G-CSF treatment and agrees with age distribution of sMDS onset and clinical sequencing data.

Cancer develops by multistep acquisition of mutations in a progenitor cell and its daughter cells. Severe congenital neutropenia (SCN) manifests itself through an inability to produce enough granulocytes to prevent infections. SCN commonly results from a germline ELANE mutation. Large doses of the blood growth factor granulocyte colony-stimulating factor (G-CSF) rescue granulocyte production. However, SCN frequently transforms to a myeloid malignancy, commonly associated with a somatic mutation in CSF3R, the gene encoding the G-CSF Receptor. We built a mathematical model of evolution for CSF3R mutation starting with bone marrow expansion at the fetal development stage and continuing with postnatal competition between normal and malignant bone marrow cells. We employ tools of probability theory such as multitype branching processes and Moran models modified to account for expansion of hematopoiesis during human development. With realistic coefficients, we obtain agreement with the age range at which malignancy arises in patients. In addition, our model predicts the existence of a pool of cells with mutated CSF3R before G-CSF treatment begins. Our findings may be clinically applied to intervene more effectively and selectively in SCN patients.

Understanding Hematological Toxicities Using Mathematical Modeling

Balancing antitumor efficacy with toxicity is a significant challenge, and drug-induced myelosuppression is a common dose-limiting toxicity of cancer treatments. Mathematical modeling has proven to be a powerful ally in this field, scaling results from animal models to humans, and designing optimized treatment regimens. Here we outline existing mathematical approaches for studying bone marrow toxicity, identify gaps in current understanding, and make future recommendations to advance this vital field of safety research further.

Kinetic models of hematopoietic differentiation

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.

Reduced hematopoietic stem cell frequency predicts outcome in acute myeloid leukemia

In patients with acute myeloid leukemia and low percentages of aldehyde-dehydrogenase-positive cells, non-leukemic hematopoietic stem cells can be separated from leukemic cells. By relating hematopoietic stem cell frequencies to outcome we detected poor overall- and disease-free survival of patients with low hematopoietic stem cell frequencies. Serial analysis of matched diagnostic and follow-up samples further demonstrated that hematopoietic stem cells increased after chemotherapy in patients who achieved durable remissions. However, in patients who eventually relapsed, hematopoietic stem cell numbers decreased dramatically at the time of molecular relapse demonstrating that hematopoietic stem cell levels represent an indirect marker of minimal residual disease, which heralds leukemic relapse. Upon transplantation in immune-deficient mice cases with low percentages of hematopoietic stem cells of our cohort gave rise to leukemic or no engraftment, whereas cases with normal hematopoietic stem cell levels mostly resulted in multi-lineage engraftment. Based on our experimental data, we propose that leukemic stem cells have increased niche affinity in cases with low percentages of hematopoietic stem cells. To validate this hypothesis, we developed new mathematical models describing the dynamics of healthy and leukemic cells under different regulatory scenarios. These models suggest that the mechanism leading to decreases in hematopoietic stem cell frequencies before leukemic relapse must be based on expansion of leukemic stem cells with high niche affinity and the ability to dislodge hematopoietic stem cells. Thus, our data suggest that decreasing numbers of hematopoietic stem cells indicate leukemic stem cell persistence and the emergence of leukemic relapse.

Cell division patterns in acute myeloid leukemia stem-like cells determine clinical course: a model to predict patient survival

Acute myeloid leukemia (AML) is a heterogeneous disease in which a variety of distinct genetic alterations might occur. Recent attempts to identify the leukemia stem-like cells (LSC) have also indicated heterogeneity of these cells. On the basis of mathematical modeling and computer simulations, we have provided evidence that proliferation and self-renewal rates of the LSC population have greater impact on the course of disease than proliferation and self-renewal rates of leukemia blast populations, that is, leukemia progenitor cells. The modeling approach has enabled us to estimate the LSC properties of 31 individuals with relapsed AML and to link them to patient survival. On the basis of the estimated LSC properties, the patients can be divided into two prognostic groups that differ significantly with respect to overall survival after first relapse. The results suggest that high LSC self-renewal and proliferation rates are indicators of poor prognosis. Nevertheless, high LSC self-renewal rate may partially compensate for slow LSC proliferation and vice versa. Thus, model-based interpretation of clinical data allows estimation of prognostic factors that cannot be measured directly. This may have clinical implications for designing treatment strategies.

Mathematical Modelling as a Tool to Understand Cell Self-renewal and Differentiation

Mathematical modeling is a powerful technique to address key questions and paradigms in a variety of complex biological systems and can provide quantitative insights into cell kinetics, fate determination and development of cell populations. The chapter is devoted to a review of modeling of the dynamics of stem cell-initiated systems using mathematical methods of ordinary differential equations. Some basic concepts and tools for cell population dynamics are summarized and presented as a gentle introduction to non-mathematicians. The models take into account different plausible mechanisms regulating homeostasis. Two mathematical frameworks are proposed reflecting, respectively, a discrete (punctuated by division events) and a continuous character of transitions between differentiation stages. Advantages and constraints of the mathematical approaches are presented on examples of models of blood systems and compared to patients data on healthy hematopoiesis.

Targeted Therapy of FLT3 in Treatment of AML-Current Status and Future Directions

Internal tandem duplications (ITDs) of the gene encoding the Fms-Like Tyrosine kinase-3 (FLT3) receptor are present in approximately 25% of patients with acute myeloid leukemia (AML). The mutation is associated with poor prognosis, and the aberrant protein product has been hypothesized as an attractive therapeutic target. Various tyrosine kinase inhibitors (TKIs) have been developed targeting FLT3, but in spite of initial optimism the first generation TKIs tested in clinical studies generally induce only partial and transient hematological responses. The limited treatment efficacy generally observed may be explained by numerous factors; extensively pretreated and high risk cohorts, suboptimal pharmacodynamic and pharmacokinetic properties of the compounds, acquired TKI resistance, or the possible fact that inhibition of mutated FLT3 alone is not sufficient to avoid disease progression. The second-generation agent quizartinb is showing promising outcomes and seems better tolerated and with less toxic effects than traditional chemotherapeutic agents. Therefore, new generations of TKIs might be feasible for use in combination therapy or in a salvage setting in selected patients. Here, we sum up experiences so far, and we discuss the future outlook of targeting dysregulated FLT3 signaling in the treatment of AML.

Etiology and treatment of hematological neoplasms: stochastic mathematical models

Leukemias are driven by stemlike cancer cells (SLCC), whose initiation, growth, response to treatment, and posttreatment behavior are often "stochastic", i.e., differ substantially even among very similar patients for reasons not observable with present techniques. We review the probabilistic mathematical methods used to analyze stochastics and give two specific examples. The first example concerns a treatment protocol, e.g., for acute myeloid leukemia (AML), where intermittent cytotoxic drug dosing (e.g., once each weekday) is used with intent to cure. We argue mathematically that, if independent SLCC are growing stochastically during prolonged treatment, then, other things being equal, front-loading doses are more effective for tumor eradication than back loading. We also argue that the interacting SLCC dynamics during treatment is often best modeled by considering SLCC in microenvironmental niches, with SLCC-SLCC interactions occurring only among SLCC within the same niche, and we present a stochastic dynamics formalism, involving "Poissonization," applicable in such situations. Interactions at a distance due to partial control of total cell numbers are also considered. The second half of this chapter concerns chromosomal aberrations, lesions known to cause some leukemias. A specific example is the induction of a Philadelphia chromosome by ionizing radiation, subsequent development of chronic myeloid leukemia (CML), CML treatment, and treatment outcome. This time evolution involves a coordinated sequence of > 10 steps, each stochastic in its own way, at the subatomic, molecular, macromolecular, cellular, tissue, and population scales, with corresponding time scales ranging from picoseconds to decades. We discuss models of these steps and progress in integrating models across scales.

A model to explain specific cellular communications and cellular harmony:- a hypothesis of coupled cells and interactive coupling molecules

BACKGROUND

The various cell types and their relative numbers in multicellular organisms are controlled by growth factors and related extracellular molecules which affect genetic expression pathways. However, these substances may have both/either inhibitory and/or stimulatory effects on cell division and cell differentiation depending on the cellular environment. It is not known how cells respond to these substances in such an ambiguous way. Many cellular effects have been investigated and reported using cell culture from cancer cell lines in an effort to define normal cellular behaviour using these abnormal cells.A model is offered to explain the harmony of cellular life in multicellular organisms involving interacting extracellular substances.

METHODS

A basic model was proposed based on asymmetric cell division and evidence to support the hypothetical model was accumulated from the literature. In particular, relevant evidence was selected for the Insulin-Like Growth Factor system from the published data, especially from certain cell lines, to support the model. The evidence has been selective in an attempt to provide a picture of normal cellular responses, derived from the cell lines.

RESULTS

The formation of a pair of coupled cells by asymmetric cell division is an integral part of the model as is the interaction of couplet molecules derived from these cells. Each couplet cell will have a receptor to measure the amount of the couplet molecule produced by the other cell; each cell will be receptor-positive or receptor-negative for the respective receptors. The couplet molecules will form a binary complex whose level is also measured by the cell. The hypothesis is heavily supported by selective collection of circumstantial evidence and by some direct evidence. The basic model can be expanded to other cellular interactions.

CONCLUSIONS

These couplet cells and interacting couplet molecules can be viewed as a mechanism that provides a controlled and balanced division-of-labour between the two progeny cells, and, in turn, their progeny. The presence or absence of a particular receptor for a couplet molecule will define a cell type and the presence or absence of many such receptors will define the cell types of the progeny within cell lineages.

Can systems biology approach help in finding more effective treatment for acute myeloid leukemia?

Acute myeloid leukemia (AML) is a hematological cancer comprising of cancer stem cells (CSCs) that are responsible for the disease progression, drug resistance and post treatment relapses. Advances in genomic technologies have identified AML as a genetically heterogenous disease with dysregulated gene expression networks. Furthermore, observation of intracellular signaling in individual CSCs by mass cytometry has demonstrated the dysregulation of the mitogen associated protein kinase (MAPK) pathways. It has been envisaged that the future treatment for AML would entail upon formulating individualized treatment plans leading to decreased drug related toxicities for patients. However the emerging role of signaling pathways as dynamic molecular switches influencing the cell cycle process, thereby leading to varying stages of cell differentiation, is making community rethink about the current strategies used for the treatment of AML. This commentary will focus on discovering novel biomarkers and identifying new therapeutic targets, to analyze and treat AML, on a platform enabled by systems biology approach.

Computational promoter modeling identifies the modes of transcriptional regulation in hematopoietic stem cells

Extrinsic and intrinsic regulators are responsible for the tight control of hematopoietic stem cells (HSCs), which differentiate into all blood cell lineages. To understand the fundamental basis of HSC biology, we focused on differentially expressed genes (DEGs) in long-term and short-term HSCs, which are closely related in terms of cell development but substantially differ in their stem cell capacity. To analyze the transcriptional regulation of the DEGs identified in the novel transcriptome profiles obtained by our RNA-seq analysis, we developed a computational method to model the linear relationship between gene expression and the features of putative regulatory elements. The transcriptional regulation modes characterized here suggest the importance of transcription factors (TFs) that are expressed at steady state or at low levels. Remarkably, we found that 24 differentially expressed TFs targeting 21 putative TF-binding sites contributed significantly to transcriptional regulation. These TFs tended to be modulated by other nondifferentially expressed TFs, suggesting that HSCs can achieve flexible and rapid responses via the control of nondifferentially expressed TFs through a highly complex regulatory network. Our novel transcriptome profiles and new method are powerful tools for studying the mechanistic basis of cell fate decisions.

Clonal selection and therapy resistance in acute leukaemias: mathematical modelling explains different proliferation patterns at diagnosis and relapse

Recent experimental evidence suggests that acute myeloid leukaemias may originate from multiple clones of malignant cells. Nevertheless, it is not known how the observed clones may differ with respect to cell properties, such as proliferation and self-renewal. There are scarcely any data on how these cell properties change due to chemotherapy and relapse. We propose a new mathematical model to investigate the impact of cell properties on the multi-clonal composition of leukaemias. Model results imply that enhanced self-renewal may be a key mechanism in the clonal selection process. Simulations suggest that fast proliferating and highly self-renewing cells dominate at primary diagnosis, while relapse following therapy-induced remission is triggered mostly by highly self-renewing but slowly proliferating cells. Comparison of simulation results to patient data demonstrates that the proposed model is consistent with clinically observed dynamics based on a clonal selection process.

Mathematical modelling of adult hippocampal neurogenesis: effects of altered stem cell dynamics on cell counts and bromodeoxyuridine-labelled cells

In the adult hippocampus, neurogenesis—the process of generating mature granule cells from adult neural stem cells—occurs throughout the entire lifetime. In order to investigate the involved regulatory mechanisms, knockout (KO) experiments, which modify the dynamic behaviour of this process, were conducted in the past. Evaluating these KOs is a non-trivial task owing to the complicated nature of the hippocampal neurogenic niche. In this study, we model neurogenesis as a multicompartmental system of ordinary differential equations based on experimental data. To analyse the results of KO experiments, we investigate how changes of cell properties, reflected by model parameters, influence the dynamics of cell counts and of the experimentally observed counts of cells labelled by the cell division marker bromodeoxyuridine (BrdU). We find that changing cell proliferation rates or the fraction of self-renewal, reflecting the balance between symmetric and asymmetric cell divisions, may result in multiple time phases in the response of the system, such as an initial increase in cell counts followed by a decrease. Furthermore, these phases may be qualitatively different in cells at different differentiation stages and even between mitotically labelled cells and all cells existing in the system.

The holistic effects of acupuncture treatment

Traditional Chinese Medicine (TCM), as a complex medical science which reflects philosophical principles and embodies large dialectical thought, is used to place the human body into a large system for observation. Acupuncture as a vital part of TCM, has been practiced to treat various diseases and symptoms. However, acupuncture is also facing severe challenges resulted from insufficient modern scientific research. Nowadays, the holistic effects of acupuncture can be researched by some modern approaches, such as the systems biology and fMRI technique. It is believed that having a better understand will greatly promote acupuncture research and be beneficial to scientization and modernization of acupuncture.

Stochasticity and determinism in models of hematopoiesis

This chapter represents a novel view of modeling in hematopoiesis, synthesizing both deterministic and stochastic approaches. Whereas the stochastic models work in situations where chance dominates, for example when the number of cells is small, or under random mutations, the deterministic models are more important for large-scale, normal hematopoiesis. New types of models are on the horizon. These models attempt to account for distributed environments such as hematopoietic niches and their impact on dynamics. Mixed effects of such structures and chance events are largely unknown and constitute both a challenge and promise for modeling. Our discussion is presented under the separate headings of deterministic and stochastic modeling; however, the connections between both are frequently mentioned. Four case studies are included to elucidate important examples. We also include a primer of deterministic and stochastic dynamics for the reader's use.

Clinical Applications of Omics Technologies on ZHENG Differentiation Research in Traditional Chinese Medicine

Traditional Chinese medicine (TCM) ZHENG is the basic concept of TCM theory. The effectiveness of TCM treatment depends on the accuracy of ZHENG differentiation. ZHENG differentiation, using the "four diagnostic methods," has the drawbacks of subjectivity and variability. Following development of omics technologies, which study the functional activities of human body from a system-wide perspective, it has been more and more applied in study of objectivity differentiating TCM ZHENG and understanding its biological mechanisms. This paper reviewed the literatures of clinical TCM ZHENG differentiation researches, underlying omics technologies, and indicated the increased trends of related articles with four kinds of omics technologies, including genomics, transcriptomics, proteomics and metabolomics, and the correlations between ZHENG differentiation and findings in omics studies. Moreover, the paper summarized the typical omics application in common studied diseases and TCM ZHENGs and discussed the main problems and countermeasure of ZHENG differentiation researches. The work here may provide a reference for further research of TCM ZHENG differentiation using omics technologies.

Stochastic Hypothesis of Transition from Inborn Neutropenia to AML: Interactions of Cell Population Dynamics and Population Genetics

We present a stochastic model of driver mutations in the transition from severe congenital neutropenia to myelodysplastic syndrome to acute myeloid leukemia (AML). The model has the form of a multitype branching process. We derive equations for the distributions of the times to consecutive driver mutations and set up simulations involving a range of hypotheses regarding acceleration of the mutation rates in successive mutant clones. Our model reproduces the clinical distribution of times at diagnosis of secondary AML. Surprisingly, within the framework of our assumptions, stochasticity of the mutation process is incapable of explaining the spread of times at diagnosis of AML in this case; it is necessary to additionally assume a wide spread of proliferative parameters among disease cases. This finding is unexpected but generally consistent with the wide heterogeneity of characteristics of human cancers.

Systems biology of meridians, acupoints, and chinese herbs in disease

Meridians, acupoints, and Chinese herbs are important components of traditional Chinese medicine (TCM). They have been used for disease treatment and prevention and as alternative and complementary therapies. Systems biology integrates omics data, such as transcriptional, proteomic, and metabolomics data, in order to obtain a more global and complete picture of biological activity. To further understand the existence and functions of the three components above, we reviewed relevant research in the systems biology literature and found many recent studies that indicate the value of acupuncture and Chinese herbs. Acupuncture is useful in pain moderation and relieves various symptoms arising from acute spinal cord injury and acute ischemic stroke. Moreover, Chinese herbal extracts have been linked to wound repair, the alleviation of postmenopausal osteoporosis severity, and anti-tumor effects, among others. Different acupoints, variations in treatment duration, and herbal extracts can be used to alleviate various symptoms and conditions and to regulate biological pathways by altering gene and protein expression. Our paper demonstrates how systems biology has helped to establish a platform for investigating the efficacy of TCM in treating different diseases and improving treatment strategies.

Quantitative modeling of chronic myeloid leukemia: insights from radiobiology

Mathematical models of chronic myeloid leukemia (CML) cell population dynamics are being developed to improve CML understanding and treatment. We review such models in light of relevant findings from radiobiology, emphasizing 3 points. First, the CML models almost all assert that the latency time, from CML initiation to diagnosis, is at most ∼10 years. Meanwhile, current radiobiologic estimates, based on Japanese atomic bomb survivor data, indicate a substantially higher maximum, suggesting longer-term relapses and extra resistance mutations. Second, different CML models assume different numbers, between 400 and 10(6), of normal HSCs. Radiobiologic estimates favor values>10(6) for the number of normal cells (often assumed to be the HSCs) that are at risk for a CML-initiating BCR-ABL translocation. Moreover, there is some evidence for an HSC dead-band hypothesis, consistent with HSC numbers being very different across different healthy adults. Third, radiobiologists have found that sporadic (background, age-driven) chromosome translocation incidence increases with age during adulthood. BCR-ABL translocation incidence increasing with age would provide a hitherto underanalyzed contribution to observed background adult-onset CML incidence acceleration with age, and would cast some doubt on stage-number inferences from multistage carcinogenesis models in general.

Differentially regulated splice variants and systems biology analysis of Kaposi's sarcoma-associated herpesvirus-infected lymphatic endothelial cells

Alternative RNA splicing greatly increases proteome diversity, and the possibility of studying genome-wide alternative splicing (AS) events becomes available with the advent of high-throughput genomics tools devoted to this issue. Kaposi's sarcoma associated herpesvirus (KSHV) is the etiological agent of KS, a tumor of lymphatic endothelial cell (LEC) lineage, but little is known about the AS variations induced by KSHV. We analyzed KSHV-controlled AS using high-density microarrays capable of detecting all exons in the human genome. Splicing variants and altered exon–intron usage in infected LEC were found, and these correlated with protein domain modification. The different 3′-UTR used in new transcripts also help isoforms to escape microRNA-mediated surveillance. Exome-level analysis further revealed information that cannot be disclosed using classical gene-level profiling: a significant exon usage difference existed between LEC and CD34⁺ precursor cells, and KSHV infection resulted in LEC-to-precursor, dedifferentiation-like exon level reprogramming. Our results demonstrate the application of exon arrays in systems biology research, and suggest the regulatory effects of AS in endothelial cells are far more complex than previously observed. This extra layer of molecular diversity helps to account for various aspects of endothelial biology, KSHV life cycle and disease pathogenesis that until now have been unexplored.

A multicellular basis for the origination of blast crisis in chronic myeloid leukemia

Chronic myeloid leukemia (CML) is characterized by a specific chromosome translocation, and its pathobiology is considered comparatively well understood. Thus, quantitative analysis of CML and its progression to blast crisis may help elucidate general mechanisms of carcinogenesis and cancer progression. Hitherto, it has been widely postulated that CML blast crisis originates mainly via cell-autonomous mechanisms such as secondary mutations or genomic instability. However, recent results suggest that carcinogenic transformation may be an inherently multicellular event, in departure from the classic unicellular paradigm. We investigate this possibility in the case of blast crisis origination in CML. A quantitative, mechanistic cell population dynamics model was employed. This model used recent data on imatinib-treated CML; it also used earlier clinical data, not previously incorporated into current mathematical CML/imatinib models. With the pre-imatinib data, which include results on many more blast crises, we obtained evidence that the driving mechanism for blast crisis origination is a cooperation between specific cell types. Assuming leukemic-normal interactions resulted in a statistically significant improvement over assuming either cell-autonomous mechanisms or interactions between leukemic cells. This conclusion was robust with regard to changes in the model's adjustable parameters. Application of the results to patients treated with imatinib suggests that imatinib may act not only on malignant blast precursors, but also, to a limited degree, on the malignant blasts themselves.

Long-term single-cell imaging of mammalian stem cells

Continuous long-term single-cell observation provides insight into the molecular control of cell fate. This is particularly important for rare and heterogeneous populations of cells, such as mammalian stem cells. The current lack of usable off-the-shelf hardware and software for such experiments makes their implementation technically challenging. Here I discuss the need for continuous single-cell quantification to understand molecular cell fate control as well as organizational and technical solutions for long-term imaging and tracking of stem cells.

Endothelial and cardiac progenitors: boosting, conditioning and (re)programming for cardiovascular repair

Preclinical studies performed in cell culture and animal systems have shown the outstanding ability of stem cells to repair ischemic heart and lower limbs by promoting the formation of new blood vessels and new myocytes. In contrast, clinical studies of stem cell administration in patients with myocardial ischemia have revealed only modest, although promising, results. Basic investigations have shown the feasibility of adult cells reprogramming into pluripotent cells by defined factors, thus opening the way to the devise of protocols to ex vivo derive virtually unexhausted cellular pools. In contrast, cellular and molecular studies have indicated that risk factors limit adult-derived stem cell survival, proliferation and engraftment in ischemic tissues. The use of fully reprogrammed cells raises safety concerns; therefore, adult cells remain a primary option for clinicians interested in therapeutic cardiovascular repair. Pharmacologic approaches have been devised to restore the cardiovascular repair ability of failing progenitors from patients at risk. In the present contribution, the most advanced pharmacologic approaches to (re)program, boost, and condition endothelial and cardiac progenitor cells to enhance cardiovascular regeneration are discussed.