Hematopoiesis and its disorders: a systems biology approach

Abstract

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. (Blood. 2010;115:2339-2347) IntroductionDespite advances made in identifying genes, epigenetic modifiers, lipids, proteins, and their posttranslational processing, much remains unknown about the precise roles these components play in health and disease. That biologic processes are complex and dynamic has been clearly established, albeit underappreciated. 1 One obstacle to a more complete understanding is that reductionism dominates biologists' thinking. Reductionism states that a problem can be solved by decomposing it into building blocks and studying them one at a time. 2 Large datasets of genes, lipids, metabolites, and proteins have made it impossible for one to intuit, reinforcing the appeal of reductionism. Yet, by breaking down a system one may lose properties that emerge only by virtue of the system's complexity. Systems biology approaches embrace this complexity, using engineering principles and computational methods to build and validate models using experimental data (Table 1). 3 Using these and other approaches, systems biology seeks to explain and predict the complex properties underlying normal and abnormal physiology.Biologic systems are multiscalar, functioning at the molecular, cellular, tissue, and organismismal levels. To perform their specialized functions, highly differentiated blood cells are continuously produced by stem cells. A combination of more than a dozen growth and stromal factors drive cells to divide asymmetrically, undergo differentiation, and carry out their end-cell functions. More than 10 000 genes are expressed in a B-cell lymphocyte. 4 A simple erythrocyte, enucleated and without mitochondria, contains more than 750 proteins, ignoring posttranslational modifications...

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“…[28][29][30][31][32][33]. They offer the possibility to comprehend processes not yet accessible by experimental measurements (5).…”

Quantitative modeling of chronic myeloid leukemia: insights from radiobiology

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Blood Cell Dynamics: Half of a Century of Modelling

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