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Featured Defense - February 2014

 

Samiur Arif
MS, Old Dominion University

 


Bio

Samiur received his B.S. in computer science from the Department of Computer Science and engineering, BRAC University, Bangladesh in 2008. Then for a brief period of time he worked at Goodrich Aerostructures Service Center, Singapore.

In fall 2008, Samiur joined Old Dominion University and started his Phd in Computer Science. Initially he started working under Dr. Zubair. After successfully implementing Asian Option pricing in IBM CELL parallel architecture he focused his research on Stochastic Epidemic Modeling under Dr. Olariu from May, 2010. Initially research was primarily on compartmental disease epidemic models such as SI, SIS and SIR. He found out numerous interesting applications of epidemics model in the field of computer ccience, finance, marketing, population biology.

In the fall of 2011, Samiur started looking at stem cell growth models which is vital in regenerative medicine. He successfully came up with a stochastic growth model in stem cell growth which closely mimics the predicted growth pattern in vitro. The resulting paper "A Versatile Model for Stem Cell Growth" was selected for cisiting scholar award for excellence in the natural & computational sciences at The College of William and Mary's 11th Annual graduate research symposium. He has also published his results in peer reviewed journals.

In August of 2012, Samiur successfully passed the PhD candidacy examination with thesis proposal topic titled "Modeling Tissue Cell Dynamics".

Abstract


Stochastic nature of biological system mathematical and computational modeling approaches have become more accepted by experimentalists and clinicians in recent years as contributing to new understandings of complicated cell mechanisms and tissue physiology. Indeed, even a single cell or small tissue samples are complex dynamical systems that adapt to environmental challenges in space and time which renders them suitable to modeling. Mathematical models and computer simulations can explain and uncover some still unknown aspects of cell behavior and tissue function. Models based on key biological mechanisms can give interesting insights and formulate predictions that cannot be derived from specific experiments or statistical data alone. Therefore, novel research approaches should incorporate interdisciplinary dialogs between biology, mathematical modeling and computational simulations to validate experimental data and non-intuitive scenarios such as the stem cell hypothesis. The tissue of a higher organism such as human beings can be described as a set of a large number of cells concerning the functions and morphology. However, most of the mature cells are deprived of the potential to replenish themselves. Such imperfection of mature cells is compensated by the presence of population of stem cells which possesses a capability to self-renew and to differentiate into various cell lineages. This process of continual cell replacement critical for the maintenance of adult tissues, is called tissue homeostasis, and is maintained through the presence of different control mechanisms. The homeostatic replacement of cells varies substantially among different tissues. Unquestionably, the most important ability of stem cells to maintain the homeostasis is supplying tissues with the specialized cells. The decision for individual stem cells to either renew or differentiate is also a stochastic process. Several research programs supported by hospitals, health institutes are trying to understand underlying mechanism how stem cell proliferate, differentiates and maintains equilibrium with or without feedback. At this stage researchers are not able to answer key questions, for example the rate of proliferation, stem cell homeostasis and feedback that plays a crucial role in tissue equilibrium. The main goal of this thesis is to visualize some of these key issues using stochastic modeling and computer simulations.