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.

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.

Studies of MEKK2 activation mechanisms and its biological role in LPS/TLR4 signaling

MEKK2 is an evolutionarily conserved mitogen-activated protein kinase (MAPK) kinase kinase (MAP3K) that controls the MAPK and IKK-NF-κB pathways. The MAPK and IKK pathways are intracellular signaling networks that are crucial for the Toll-like receptor (TLR) mediated innate immunity, cellular stress and many other physiological responses. Members of the MAP3K family are central to the activation of these processes. However, the molecular mechanisms underlying stimuli-mediated MAP3K activation remain largely unknown. In this study, we identified a key phosphoserine residue, Ser-519 in MEKK2, and its equivalent site Ser-526 in MEKK3 within their activation loop whose phosphorylation are essential for their optimal activation. Mutation of this regulatory serine to an alanine severely impaired MEKK2 activation and MEKK2 signaling to its downstream targets. To demonstrate that physiological stimuli induce this serine phosphorylation, we generated an antibody that specifically recognizes the phosphorylated serine residue. We found that many, but not all, of the MAPK agonists, including the TLR ligands, growth factors, cytokines and cellular stresses, induced this regulatory serine phosphorylation in MEKK2, suggesting an involvement of MEKK2 in the activation of the MAPK cascade leading to different cellular responses. We further investigated the specific role of MEKK2 in LPS/TLR4 signaling by using MEKK2−/− mice. We found that MEKK2 was selectively required for LPS-induced ERK1/2 activation, but not JNK, p38 or NF-κB activation. We also found that MEKK2 was involved in TLR4 dependent induction of proinflammatory cytokines and LPS-induced septic shock. In conclusion, we identified a key regulatory serine residue in the activation loop of MEKK2 whose phosphorylation is a key sensor of receptor- and cellular stress-mediated signals. We also demonstrated that MEKK2 is crucial for TLR4-mediated innate immunity. ^