Differential dynamic properties of scleroderma fibroblasts in response to perturbation of environmental stimuli.

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-beta pathway in both normal and scleroderma fibroblasts stimulated by silica. We find that TGF-beta 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.

Differential dynamic properties of scleroderma fibroblasts in response to perturbation of environmental stimuli.

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-beta pathway in both normal and scleroderma fibroblasts stimulated by silica. We find that TGF-beta 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.

The differential expression of the $\beta\sb2$-adrenergic receptor modifies agonist-dependent adenylylcyclase activation

There have been multiple reports which indicate that variations in $\beta$AR expression affect the V$\sb{\rm max}$ observed for the agonist-dependent activation of adenylylcyclase. This observation has been ignored by most researchers when V$\sb{\rm max}$ values obtained for wild type and mutant receptors are compared. Such an imprecise analysis may lead to erroneous conclusions concerning the ability of a receptor to activate adenylylcyclase. Equations were derived from the Cassel-Selinger model of GTPase activity and Tolkovsky and Levitzki's Collision Coupling model which predict that the EC$\sb{50}$ and V$\sb{\rm max}$ for the activation of adenylylcyclase are a function of receptor number. Experimental results for L cell clones in which either hamster or human $\beta$AR were transfected at varying levels showed that EC$\sb{50}$ decreases and V$\sb{\rm max}$ increases as receptor number increases. Comparison of these results with simulations obtained from the equations describing EC$\sb{50}$ and V$\sb{\rm max}$ showed a close correlation. This documents that the kinetic parameters of adenylylcyclase activation change with the level of receptor expression and relates this phenomenon to a theoretical framework concerning the mechanisms involved in $\beta$AR signal transduction.^ One of the terms used in the equations which expressed the EC$\sb{50}$ and V$\sb{\rm max}$ as a function of receptor number is coupling efficiency, defined as $\rm k\sb1/k\sb{-1}$. Calculation of $\rm k\sb1/k\sb{-1}$ can be accomplished for wild type receptors with the easily measured experimental values of agonist K$\sb{\rm d}$, EC$\sb{50}$ and receptor number. This was demonstrated for hamster $\beta$AR which yielded a coupling efficiency of 0.15 $\pm$ 0.003 and human $\beta$AR which yielded a coupling efficiency of 0.90 $\pm$ 0.031. $\rm k\sb1/k\sb{-1}$ replaces the traditional qualitative evaluation of the ability to activate adenylylcyclase, which utilizes V$\sb{\rm max}$ without correction for variation in receptor number, with a quantitative definition that more accurately describes the ability of $\beta$AR to couple to G$\sb{\rm s}$.^ The equations which express the EC$\sb{50}$ and V$\sb{\rm max}$ for adenylylcyclase activation as a function of receptor number and coupling efficiency were tested to determine whether they could accurately simulate the changes seen in these parameters during desensitization. Data from original desensitization experiments and data from the literature (24,25,52,54,83) were compared to simulated changes in EC$\sb{50}$ and V$\sb{\rm max}$. In a variety of systems the predictions of the equations were consistent with the changes observed in EC$\sb{50}$ and V$\sb{\rm max}$. In addition reductions in the calculated value of $\rm k\sb1/k\sb{-1}$ was shown to correlate well with $\beta$AR phosphorylation and to be minimally affected by sequestration and down-regulation. ^

Pathway Semantics: An Algebraic Data Driven Algorithm to Generate Hypotheses about Molecular Patterns Underlying Disease Progression

The overarching goal of the Pathway Semantics Algorithm (PSA) is to improve the in silico identification of clinically useful hypotheses about molecular patterns in disease progression. By framing biomedical questions within a variety of matrix representations, PSA has the flexibility to analyze combined quantitative and qualitative data over a wide range of stratifications. The resulting hypothetical answers can then move to in vitro and in vivo verification, research assay optimization, clinical validation, and commercialization. Herein PSA is shown to generate novel hypotheses about the significant biological pathways in two disease domains: shock / trauma and hemophilia A, and validated experimentally in the latter. The PSA matrix algebra approach identified differential molecular patterns in biological networks over time and outcome that would not be easily found through direct assays, literature or database searches. In this dissertation, Chapter 1 provides a broad overview of the background and motivation for the study, followed by Chapter 2 with a literature review of relevant computational methods. Chapters 3 and 4 describe PSA for node and edge analysis respectively, and apply the method to disease progression in shock / trauma. Chapter 5 demonstrates the application of PSA to hemophilia A and the validation with experimental results. The work is summarized in Chapter 6, followed by extensive references and an Appendix with additional material.