Abstract
Diseases are believed to arise from dysregulation of biological systems (pathways) perturbed by environmental triggers. Biological systems as a whole are not just the sum of their components, rather ever-changing, complex and dynamic systems over time in response to internal and external perturbation. In the past, biologists have mainly focused on studying either functions of isolated genes or steady-states of small biological pathways. However, it is systems dynamics that play an essential role in giving rise to cellular function/dysfunction which cause diseases, such as growth, differentiation, division and apoptosis. Biological phenomena of the entire organism are not only determined by steady-state characteristics of the biological systems, but also by intrinsic dynamic properties of biological systems, including stability, transient-response, and controllability, which determine how the systems maintain their functions and performance under a broad range of random internal and external perturbations. As a proof of principle, we examine signal transduction pathways and genetic regulatory pathways as biological systems. We employ widely used state-space equations in systems science to model biological systems, and use expectation-maximization (EM) algorithms and Kalman filter to estimate the parameters in the models. We apply the developed state-space models to human fibroblasts obtained from the autoimmune fibrosing disease, scleroderma, and then perform dynamic analysis of partial TGF-β pathway in both normal and scleroderma fibroblasts stimulated by silica. We find that TGF-β pathway under perturbation of silica shows significant differences in dynamic properties between normal and scleroderma fibroblasts. Our findings may open a new avenue in exploring the functions of cells and mechanism operative in disease development.
Highlights
Identifying differentially expressed genes across distinct conditions and clustering co-expressed genes into different functional groups have been general approaches for unraveling molecular mechanisms involved in disease pathogenesis [1]
Kinetic models can be developed for gene regulation [12,27,28], they involve many kinetic parameters that are difficult to be estimated from gene expression data with small number of samples
The state of a dynamic regulatory system is the smallest set of variables which are referred to as state variables such that the current knowledge of these variables together with the current and future knowledge of the input variables will completely determine the behavior of the regulatory system
Summary
Identifying differentially expressed genes across distinct conditions and clustering co-expressed genes into different functional groups have been general approaches for unraveling molecular mechanisms involved in disease pathogenesis [1] These approaches are valuable for looking at isolated events and their correlations, they do not explain the behavior of a bio-system. It is increasingly recognized that biological systems as a whole are not just the sum of their components but, rather, everchanging, complex, interacted and dynamic systems over time in response to internal events and environmental stimuli [2] Cellular functions, such as growth, differentiation, division and apoptosis, and biological phenomena of the entire organisms are determined by steady-state characteristics of the biological systems, and determined by inherent dynamic properties of biological systems. Dynamic properties are correlated with the health status of individuals and are of central importance for comprehensively understanding human biological systems and complex diseases
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