Gunawan:Research

Chemical and Biological Systems Engineering Laboratory

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Reverse-engineering of Stochastic Biological Systems
Investigator : Suresh Kumar Poovathingal
Collaborators : Jan Gruber & Barry Halliwell

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 organism’s aging

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Synergistic Analysis of Apoptosis Network

Apoptosis is the machinery of programmed cell death (cell suicide), whose failure has been associated with many diseases including cancer. Indeed, reinstating apoptosis in has become a major strategy in new cancer drug development, some of which currently under clinical trials. Though promising, many of these drugs have limited effectiveness when used individually. Nevertheless, scientists have found that cocktails of drugs can have synergistic activities beyond the cumulative efficacy of each drug. The identification of such cocktails however is still done in an ad-hoc manner. The goal of this research is to develop system analysis tools that can assist the identification of synergistic drugs combination, and can further suggest scheduling and (relative) dosing of these drugs, through mathematical modeling. Conversely, the same analysis tool can also be used to identify synergistic toxicity of different cancer drugs when used at the same time. Using models of cancerous and healthy cells, such analysis can also characterize drug(s) that will induce apoptosis with a specific efficacy in tumor cells.

Quorum Sensing of P. aeruginosa Bacterial Infection

In the natural environment, cells do not live independently, but rather they interact with each other. In a bacterial colony, for example, the survival of a single bacterium should become secondary to that of the whole population. One of such interactions is the cell-to-cell signalling, known as quorum sensing, which is used to sense population density and to synchronize gene expression. Some of these bacteria have been associated with infections in human and animals, such as P. aeruginosa which causes infections in immunocompromised patients. The goals of the research are to establish an accurate dynamical representation of QS population using a population balance model, to understand the implications of QS on the sensitivity and robustness properties of a single cell and the colony through the application of system analysis, and finally to identify the treatment options and procedures for infection caused by QS bacteria through model-based optimization. The specific application of the research is on P. aeruginosa virulence. The framework for representing cell population dynamics in this work will have general applicability to other multicellular biological systems.

Parameter Estimation of Oscillatory Systems
Investigator: Ang Kok Siong

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.