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Mitochondria are intracellular membrane bound organelles, which are believed to be originated through prokaryotic endosymbiosis. Mitochondria posses their own circular DNA and also they perform a myriad of functions inside a living cell (e.g.) ATP production, TCA cycle, Fatty Acid oxidation, Calcium signaling and Apoptosis. Dysfunction of mitochondria due to mitochondrial DNA mutations has been consistently observed with aging and degenerative diseases. In our lab we are interested in quantitative analysis of formation and accumulation of mitochondrial DNA mutations using stochastic models in different model organisms. Mutagenesis is an inherently stochastic process with different mutation load and spectrum in different cells of same tissue, hence stochastic formulation of chemical kinetics is used along with the knowledge about different aspects of mitochondrial DNA mutations to model the mutation process. more...
A cell can be seen as a microscopic chemical plant, where different cellular components (mRNAs, proteins) are produced, transformed, and consumed (or degraded) to accomplish myriad cellular functions. However, unlike a typical chemical plant, cellular processes, such as gene transcription and protein translation, involve very low concentrations of molecules (on the order of nanomolar). Such low concentration means that these processes can only occur intermittently as discrete and random events. The intrinsic stochastic behavior gives rise to variations in cellular phenotype, even in clonal cell population. Thus, to understand the functioning behavior of a biological network, the intrinsic variations in cellular processes should be explicitly taken into consideration in the system modeling. Here, we are developing a reverse-engineering framework for the identification of biological models that can represent the discrete stochastic nature of processes in a cell. This framework will explicitly consider the stochastic variations in cellular processes and the robust characteristics of cellular systems in the reverse engineering steps. In our preliminary investigation we have implemented a point mutation stochastic framework in unraveling the inherent stochastic nature of aging process. The stochastic model is based on the mitochondrial free radical theory of aging, which implicates the mutations in mitochondrial genome to be largely responsible for the organismâ€™s aging. more...
mtDNA mutation has been implicated as a contributing factor that causes aging. Mitochondria is commonly referred to as the powerhouse of a cell. Far from being a static organelle in the cell, mitochondria continuously moves around the cytoplasm (directed movement) and can undergo fusion and fission with other mitochondria. It could also undergo mitochondrial biogenesis to multiply in numbers and can be removed via mitophagy. How does these dynamics contribute to the maintenance of mtDNA mutation is not known, and the focus of my study is to understand this through modeling of the mitochondria dynamics in silico
Oscillatory behavior is exhibited by many biological systems. Examples these include the circadian rhythm and p53-mdm2 response to DNA damage. Such dynamical behavior is important and integral of a higher biological function, such as sleeping and feeding patterns (circadian rhythm) and DNA repair (p53-mdm2). To study these systems, in-silico models have been developed to aid in the analysis. However, the corresponding model parameters are usually chosen qualitatively such that the system to exhibit the general characteristics of experimental data. The focus of the research here is to facilitate reconciliation of the model with experimental data in a quantitative manner. more...
The inverse modeling of dynamic metabolic pathways is challenging, a model identification cycle is proposed based on the work of many scientists. To make this cycle complete, a streamlined work-flow is needed, consisting of solving the potential problems encountered in the process. These problems include data issues, computational issues, and mathematical issues associated with parameter estimation algorithms and structure identification strategies.This project focuses on parameter estimation and structure identification, tackling those challenges. Specifically, there are three proposals which have been tested or are going to be tested. more...
Recent advances in technology permit high throughput experiments at genomic, transcriptomic, proteomic and metabolomic levels. The information obtained from time-series data, however, is implicit and requires extensive data analysis.The focus of this work is to investigate the identifiability of metabolic network models (particularly BST models), and to suggest model refinement or experimental design that maximizes the number of estimable parameters from data. Two types of identifiability property are considered. First, a priori identifiability analysis yields the identifiable parameters under the assumption of noise-free data. Parametric sensitivities are used as a basis for selecting the a priori identifiable parameters. Secondly, practical identifiability gives the identifiable parameters when the data are contaminated with noise. In other words, this analysis gives the accuracy with which the parameters can be estimated. The practical identifiability analysis methods are based on linear(ized) and nonlinear regression analysis, particularly the statistical inference of confidence interval or region of the parameter estimates. more...
Biological Robustness, an evolved property of biological systems gives rise to cellular functioning and complexity. Hence understanding robustness will enhance our understanding of cellular pathways and underlying mechanisms. The focus of our research is in developing novel robustness measures and analysis methodologies for both deterministic and stochastic models of cellular signalling networks. Of particular interest is the development of an effective sensitivity based methodology named Two-Time Molecular Sensitivity (TTMS) analysis complementing the existing Parametric Sensitivity analysis. We have also successfully developed a novel local TTMS analysis for deterministic models and applied to various cellular signalling pathways. In practice, the analysis can guide model identification and reduction of cellular systems and suggest experimentally testable hypotheses. In addition, the biological knowledge gained can assist drug discovery efforts in the identification of potential drug targets, the understanding of drug efficacy and specificity, and finally the optimization of drug dosing and timing. more...