1178A, Benedum Hall
My research focuses on the intrinsic and extrinsic variability of embryonic stem cells (ESC). ESC have huge potential to be used in many future therapeutic applications due to their self-renewal and differentiation capacity. However, the variability of stem cells restricts the direct application of deterministic approaches towards drawing mechanistic insight on the system. Our long-term goal is to understand the mechanisms of ESC differentiation to mature, insulin producing β-cells to develop efficient ways in guiding this differentiation for production of functional cells for diabetes treatment. As a step towards this goal, the overall objective of my work is to address and incorporate multi-source uncertainty during the mathematical analysis of stem cell behavior. We do this by employing different modeling approaches to address three sources of variability: (i) internal variability, which is intrinsic to the components of the system, (ii) external variability, which arises from the environment of the system and (iii) process variability, which arises from component interaction,component heterogeneity, and parameter uncertainty.
Using such programming tools as Fortran and Matlab, we employ different computational techniques, including non-linear optimization, Monte Carlo simulations, and robust regression, in order to extract knowledge out of the experimental data and from the system. This data is obtained both during pluripotency as well as differentiation. Directed differentiation of human ESC towards pancreatic lineage is achieved by various chemical cues (growth factors, inhibitors, etc.) and mechanical stimuli (different substrates and matrices). The degree of differentiation is quantified using such assays as quantitative polymerase chain reaction (qPCR) and flow cytometry, the latter being able to capture population behavior and heterogeneity. Different characteristics of the cells are quantified, including gene/protein markers for phenotype commitment and cell cycle dynamics. Specific projects supporting our overall goal include:
· Addressing process variability during endoderm induction by a population-based model
· Gene Regulatory Network identification under high intrinsic uncertainty
· Extrinsic substrate variability and correlating substrate microstructural features with differentiation patterning
· Analysis of cell cycle population dynamics and stochastic effects on cycle transition
· Modeling cycle behavior at the molecular level during differentiation
Teaching assistant, ChE 500: Systems Engineering 1: Dynamics and Modeling , Fall Terms 2010 and 2011
AIChE Annual Conference, 2011. Minneapolis, MN.