Extended kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks.

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks.

Heterotrimeric G protein -mediated signal transduction in Neurospora crassa

Heterotrimeric G protein-mediated signal transduction is one of numerous means that cells utilize to respond to external stimuli. G proteins consist of α, β andγ subunits. Extracellular ligands bind to seven-transmembrane helix receptors, triggering conformational changes. This is followed by activation of coupled G proteins through the exchange of GDP for GTP on the Gα subunit. Once activated, Gα-GTP dissociates from the βγ dimer. Both of these two moieties can interact with downstream effectors, such as adenylyl cyclase, phospholipase C, phosphodiesterases, or ion channels, leading to a series of changes in cellular metabolism and physiology. ^ Neurospora crassa is a eukaryotic multicellular filamentous fungus, with asexual/vegetative and sexual phases to its life cycle. Three Gα (GNA-1, GNA-2, GNA-3) and one Gβ (GNB-1) proteins have been identified in this organism. This dissertation investigates GNA-1 and GNB-1 mediated signaling pathways in N. crassa. ^ GNA-1 was the first identified microbial Gα that belongs to a mammalian superfamily (Gαi). Deletion of GNA-1 leads to multiple defects in N. crassa. During the asexual cycle, Δgna-1 strains display a slower growth rate and delayed conidiation on solid medium. In the sexual cycle, the Δgna-1 mutant is male-fertile but female-sterile. Biochemical studies have shown that Δ gna-1 strains have lower adenosine 3′–5 ′ cyclic monophosphate (cAMP) levels than wild type under conditions where phenotypic defects are observed. In this thesis work, strains containing one of two GTPase-deficient gna-1 alleles (gna-1 R178C, gna-1Q204L) leading to constitutive activation of GNA-1 have been constructed and characterized. Activation of GNA-1 causes uncontrolled aerial hyphae proliferation, elevated sensitivity to heat and oxidative stresses, and lower carotenoid synthesis. To further study the function of GNA-1, constructs to enable expression of mammalian Gαi superfamily members were transformed into a Δ gna-1 strain, and complementation of Δgna-1 defects investigated. Gαs, which is not a member of Gα i superfamily was used as a control. These mammalian Gα genes were able to rescue the vegetative growth rate defect of the Δ gna-1 strain in the following order: Gαz > Gα o > Gαs > Gαt > Gαi. In contrast, only Gαo was able to complement the sexual defect of a Δgna-1 strain. With regard to the thermotolerance phenotype, none of the mammalian Gα genes restored the sensitivity to a wild type level. These results suggest that GNA-1 regulates two independent pathways during the vegetative and sexual cycles in N. crassa. ^ GNB-1, a G protein β subunit from N. crassa, was identified and its functions investigated in this thesis work. The sequence of the gnb-1 gene predicts a polypeptide of 358 residues with a molecular mass of 39.7 kDa. GNB-1 exhibits 91% identity to Cryphonectria parasitica CPGB-1, and also displays significant homology with human and Dictyostelium Gβ genes (∼66%). A Δ gnb-1 strain was constructed and shown to exhibit defects in asexual spore germination, vacuole number and size, mass accumulation and female fertility. A novel role for GNB-1 in regulation of GNA-1 and GNA-2 protein levels was also demonstrated. ^

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 INTERACTION BETWEEN INTERNAL AND EXTERNAL INFORMATION ON RELATIONAL DATA SEARCH

People often use tools to search for information. In order to improve the quality of an information search, it is important to understand how internal information, which is stored in user’s mind, and external information, represented by the interface of tools interact with each other. How information is distributed between internal and external representations significantly affects information search performance. However, few studies have examined the relationship between types of interface and types of search task in the context of information search. For a distributed information search task, how data are distributed, represented, and formatted significantly affects the user search performance in terms of response time and accuracy. Guided by UFuRT (User, Function, Representation, Task), a human-centered process, I propose a search model, task taxonomy. The model defines its relationship with other existing information models. The taxonomy clarifies the legitimate operations for each type of search task of relation data. Based on the model and taxonomy, I have also developed prototypes of interface for the search tasks of relational data. These prototypes were used for experiments. The experiments described in this study are of a within-subject design with a sample of 24 participants recruited from the graduate schools located in the Texas Medical Center. Participants performed one-dimensional nominal search tasks over nominal, ordinal, and ratio displays, and searched one-dimensional nominal, ordinal, interval, and ratio tasks over table and graph displays. Participants also performed the same task and display combination for twodimensional searches. Distributed cognition theory has been adopted as a theoretical framework for analyzing and predicting the search performance of relational data. It has been shown that the representation dimensions and data scales, as well as the search task types, are main factors in determining search efficiency and effectiveness. In particular, the more external representations used, the better search task performance, and the results suggest the ideal search performance occurs when the question type and corresponding data scale representation match. The implications of the study lie in contributing to the effective design of search interface for relational data, especially laboratory results, which are often used in healthcare activities.