From Flies to Fish: Model-Based Design of Experiments and Multi-Objective Optimization for Understanding Complex Biological Systems
Abstract
Biological systems are complex, consisting of a vast number of components interacting in a nonlinear fashion with processes that vary both temporally and spatially across multiple scales. Along with that, biological data is discrete and sparse, not complete for all unknown components of the system and available in many different types. Systems biology provides a theoretical framework that integrates as much information as possible from all functional levels to understand the system. Mathematical and computational analysis tools are key to formally merge current biological knowledge and hypotheses underlying the mechanism of these systems. The models that encode this knowledge, through mathematical equations and dynamical or spatial rates, are informed by experimental data through a relationship between various system inputs and outputs. One of the biggest challenges of these models is the uncertainty in the precise model structures and parameters. This thesis discusses computational tools developed to acquire and integrate biological information. Model-based design of experiments (MBDOE) capitalizes on the uncertainty in the models to investigate how to perturb the real system to maximize the information obtained from experiments. MBDOE identifies the optimal conditions for stimuli and measurements that yield the most information about the system given practical limitations such as experimental cost and time, feasible inputs and measurements. We use multi-objective optimization (MOO) to investigate how a single model could be used to fit multiple systems by changing parameters. The MOO platform provides a framework to study how a Bone morphogenic protein (BMP) mechanism differs or how it is similar in vastly different organisms, in this case, Drosophila (an arthropod) and zebrafish (a vertebrate). These tools address generalized models used in systems biology, considering both dynamical and spatial distribution of the biological molecules with ordinary and partial differential equations. As a consequence, these methods can be broadly applied to a wide variety of biological applications as demonstrated in this work.
Degree
Ph.D.
Advisors
Buzzard, Purdue University.
Subject Area
Biomedical engineering
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