Although the great advances in experimental biology have fueled our ability to explore the behavior of natural and synthetic biological systems, key challenges still exist. A major shortcoming is that, unlike other research areas, biological systems are significantly non-linear with unknown molecular components. In addition, the inherent stochasticity of biological systems forces identical cells to behave dissimilarly even when exposed to the same environmental conditions. These challenges limit in-depth understanding of biological systems using solely experimental techniques.
The current research is focused on the joint frontier of mathematical modeling and experimental work in biology. Guided by experimental observations, quantitative modeling analysis of two natural and two synthetic biological systems was carried out. These systems are all gene regulatory networks and range from the single cell level to the population level. The objective of this research is three-fold: 1) The development of detailed mathematical models that capture the relevant biomolecular interactions of the systems of interest. Experimental data are used to inform and validate these models. 2) The use of the models as a means for understanding the complexity underlying biological systems. This allows for explaining the behavior of biological systems by quantifying the molecular interactions involved. 3) The simulation of the behavior of biological systems and the associated molecular parts. This helps to quickly and inexpensively predict the behavior of these systems under various conditions and motivates new sets of experiments.
University of Minnesota Ph.D. dissertation. June 2013. Major:Chemical Engineering. Advisors: Yiannis N. Kaznessis, Prodromos Daoutidis. 1 computer file (PDF); x, 130 pages, appendices A-B.
Quantitative analysis of gene regulatory networks: from single cells to cell communities.
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