SPEDRE: a web server for estimating rate parameters for cell signaling dynamics in data-rich environments

Cell signaling pathways and metabolic networks are often modeled using ordinary differential equations (ODEs) to represent the production/consumption of molecular species over time. Regardless whether a model is built de novo or adapted from previous models, there is a need to estimate kinetic rate constants based on time-series experimental measurements of molecular abundance. For data-rich cases such as proteomic measurements of all species, spline-based parameter estimation algorithms have been developed to avoid solving all the ODEs explicitly. We report the development of a web server for a spline-based method. Systematic Parameter Estimation for Data-Rich Environments (SPEDRE) estimates reaction rates for biochemical networks. As input, it takes the connectivity of the network and the concentrations of the molecular species at discrete time points. SPEDRE is intended for large sparse networks, such as signaling cascades with many proteins but few reactions per protein. If data are available for all species in the network, it provides global coverage of the parameter space, at low resolution and with approximate accuracy. The output is an optimized value for each reaction rate parameter, accompanied by a range and bin plot. SPEDRE uses tools from COPASI for pre-processing and post-processing. SPEDRE is a free service at http://LTKLab.org/SPEDRE.Singapore-MIT Alliance (IUP R-154-001-348-646

Multiscale Modeling of Neurobiological Systems

The central nervous system (CNS) is one of the most complicated living structures in the universe. A single gene expression, the expression level of a single protein or the concentration of a neurotransmitter could regulate the entire functionality of the CNS. The CNS needs to be investigated as a multiscale system with connections among different levels. The existing technology significantly limits experimental studies, and computational modeling is a useful tool for understanding how parts are connected, regulated, and function together. Ideally, the goal is to develop unified computational methodologies for exploring biological systems at multiple scales ranging from molecular to cellular to tissue level. While rigorous models have been developed at the molecular scale, higher level approaches usually suffer from lack of physical realism and lack of knowledge on model parameters. Molecular level studies can help to define reaction schemes and parameters which could be used in cellular microphysiology models, and image data provide a structural basis for reconstructing the surroundings of the cellular system of interest. This dissertation develops and tests a new multiscale model of dopaminergic signaling and a detailed model of the activation-triggered subunit exchange mechanism of calcium/calmodulin-dependent kinase type II (CaMKII). The goal is to develop and use computational models to understand the molecular mechanisms of neurotransmission, and how disruptions may cause complex disorders and conditions such as drug abuse. The simulations of the dopamine (DA) signaling model show that the addition of the geometry of the environment and localization of individual molecules significantly affect the DA reuptake. Consequently, the formation of DAT clusters reduces the DA clearance rate and increases DA receptor activity. In addition, the effects of the psychostimulants such as cocaine and amphetamine are also investigated. Constructed model and method can potentially serve as an in silico microscope to understand the molecular basis of signaling and regulation events in the CNS. Calibration of CaMKII model shows the limitations of the current parameter estimation methods for large biological models with long simulation times such as hours. The high dimensional parameter space and the limited and noisy data makes the parameter estimation task a challenge

Computational Modeling of Cell Signaling Dynamics: Hypothesis Management and Parameter Estimation Methods Applied to the Akt Pathway

Ph.DDOCTOR OF PHILOSOPH

Early Folding Biases in the Folding Free-Energy Surface of βα-Repeat Proteins: A Dissertation

Early events in folding can determine if a protein is going to fold, misfold, or aggregate. Understanding these deterministic events is paramount for de novo protein engineering, the enhancement of biopharmaceutical stabilities, and understanding neurodegenerative diseases including amyotrophic lateral sclerosis and Alzheimer\u27s disease. However, the physicochemical and structural biases within high energy states of protein biopolymers are poorly understood. A combined experimental and computational study was conducted on the small β/α-repeat protein CheY to determine the structural basis of its submillisecond misfolding reaction to an off-pathway intermediate. Using permutations, we were able to discriminate between the roles of two proposed mechanisms of folding; a nucleation condensation model, and a hydrophobic collapse model driven by the formation of clusters of isoleucine, leucine, and valine (ILV) residues. We found that by altering the ILV cluster connectivity we could bias the early folding events to either favor on or off-pathway intermediates. Structural biases were also experimentally observed in the unfolded state of a de novo designed synthetic β/α-repeat protein, Di-III_14. Although thermodynamically and kinetically 2-state, Di-III_14 has a well structured unfolded state that is only observable under native-favoring conditions. This unfolded state appears to retain native-like structure, consisting of a hydrophobic 7 core (69% ILV) stabilized by solvent exposed polar groups and long range electrostatic interactions. Together, these results suggest that early folding events are largely deterministic in these two systems. Generally, low contact order ILV clusters favor local compaction and, in specific cases, long range electrostatic interactions may have stabilizing effects in higher energy states

Spatiotemporal coordination of signaling at single molecule resolution

Advances in live-cell single-molecule imaging and modeling over the past decade have invited the closer study of biological structure and dynamics at the nanoscale. The higher resolution of these single-molecule experiments results in finely-grained datasets that can feed detailed quantitative models. Likewise, single-molecule models can account for microscopic details such as noise and heterogeneity inherent to diffusional and chemical processes, which are often neglected in models based on bulk concentrations. Examining microscale biological structures at single molecule resolution in living cells has led to new findings, such as the dynamic regulation of nanoscale structure. I cover three topics from the perspective of single molecules. Chapters 1-3 are on modeling the spatiotemporal coordination of both spontaneous and pheromone-guided yeast polarity establishment. Chapter 4 is on computational modeling and analysis for a technique called Binder/Tag, which we applied to study the conformational dynamics of the protein Src kinase in living cells. Chapter 5 is on modeling clustering-mediated activation of immunoreceptors, using the phagocytic receptor FcγRIIA as a prototypical example.Doctor of Philosoph

Comprehensive review of models and methods for inferences in bio-chemical reaction networks

The key processes in biological and chemical systems are described by networks of chemical reactions. From molecular biology to biotechnology applications, computational models of reaction networks are used extensively to elucidate their non-linear dynamics. The model dynamics are crucially dependent on the parameter values which are often estimated from observations. Over the past decade, the interest in parameter and state estimation in models of (bio-) chemical reaction networks (BRNs) grew considerably. The related inference problems are also encountered in many other tasks including model calibration, discrimination, identifiability, and checking, and optimum experiment design, sensitivity analysis, and bifurcation analysis. The aim of this review paper is to examine the developments in literature to understand what BRN models are commonly used, and for what inference tasks and inference methods. The initial collection of about 700 documents concerning estimation problems in BRNs excluding books and textbooks in computational biology and chemistry were screened to select over 270 research papers and 20 graduate research theses. The paper selection was facilitated by text mining scripts to automate the search for relevant keywords and terms. The outcomes are presented in tables revealing the levels of interest in different inference tasks and methods for given models in the literature as well as the research trends are uncovered. Our findings indicate that many combinations of models, tasks and methods are still relatively unexplored, and there are many new research opportunities to explore combinations that have not been considered—perhaps for good reasons. The most common models of BRNs in literature involve differential equations, Markov processes, mass action kinetics, and state space representations whereas the most common tasks are the parameter inference and model identification. The most common methods in literature are Bayesian analysis, Monte Carlo sampling strategies, and model fitting to data using evolutionary algorithms. The new research problems which cannot be directly deduced from the text mining data are also discussed

Nearly reducible finite Markov chains: theory and algorithms

Finite Markov chains are probabilistic network models that are commonly used as representations of dynamical processes in the physical sciences, biological sciences, economics, and elsewhere. Markov chains that appear in realistic modelling tasks are frequently observed to be nearly reducible, incorporating a mixture of fast and slow processes that leads to ill-conditioning of the underlying matrix of probabilities for transitions between states. Hence, the wealth of established theoretical results that makes Markov chains attractive and convenient models often cannot be used straightforwardly in practice, owing to numerical instability associated with the standard computational procedures to evaluate the expressions. This work is concerned with the development of theory, algorithms, and simulation methods for the efficient and numerically stable analysis of finite Markov chains, with a primary focus on exact approaches that are robust and therefore applicable to nearly reducible networks. New methodologies are presented to determine representative paths, identify the dominant transition mechanisms for a particular process of interest, and analyze the local states that have a strong influence on the characteristics of the global dynamics. The novel approaches yield new insights into the behaviour of Markovian networks, addressing and overcoming numerical challenges. The methodology is applied to example models that are relevant to current problems in chemical physics, including Markov chains representing a protein folding transition, and a configurational transition in an atomic cluster. Relevant classical theory of finite Markov chains and a description of existing robust algorithms for their numerical analysis is given in Chapter 1. The remainder of this thesis considers the problem of investigating a transition from an initial set of states in a Markovian network to an absorbing (target) macrostate. A formal approach to determine a finite set of representative transition paths is proposed in Chapter 2, based on exact pathwise decomposition of the total productive flux. This analysis allows for the importance of competing dynamical processes to be rigorously quantified. A robust state reduction algorithm to compute the expectation of any path property for a transition between two endpoint states is also described in Chapter 2. Chapter 3 reports further numerically stable state reduction algorithms to compute quantities that characterize the features of a transition at a statewise level of detail, allowing for identification of the local states that play a key role in modulating the slow dynamics. An expression is derived for the probability that a state is visited on a path that proceeds directly to the absorbing state without revisiting the initial state, which characterizes the dynamical relevance of an individual state to the overall transition process. In Chapter 4, an unsupervised strategy is proposed to utilize a highly efficient simulation algorithm for sampling paths on a Markov chain. The framework employs a scalable community detection algorithm to obtain an initial clustering of the network into metastable sets of states, which is subsequently refined by a variational optimization procedure. The optimized clustering is then used as the basis for simulating trajectory segments that necessarily escape from the metastable macrostates. The thesis is concluded with an overview of recent related advances that are beyond the scope of the current work (Chapter 5), and a discussion of potential applications where the novel methodology reported herein may be applied to perform insightful analyses that were previously intractable.Cambridge Commonwealth, European and International Trust Engineering and Physical Sciences Research Counci

Experimental Modeling of NOx and PM Generation from Combustion of Various Biodiesel Blends for Urban Transport Buses

Biodiesel has diverse sources of feedstock and the amount and composition of its emissions vary significantly depending on combustion conditions. Results of laboratory and field tests reveal that nitrogen oxides (NOx) and particulate matter (PM) emissions from biodiesel are influenced more by combustion conditions than emissions from regular diesel. Therefore, NOx and PM emissions documented through experiments and modeling studies are the primary focus of this investigation. In addition, a comprehensive analysis of the feedstock-related combustion characteristics and pollutants are investigated. Research findings verify that the oxygen contents, the degree of unsaturation, and the size of the fatty acids in biodiesel are the most important factors that determine the amounts and compositions of NOx and PM emissions