UCSF

Biomedical science can be characterized as the pursuit of deeper, more useful mechanistic insight into biological phenomena to facilitate advancing health. In silico modeling and simulation can accelerate this process when models are designed for particular use cases aimed at challenging explanatory, mechanistic hypotheses. Modeling and simulation is ubiquitous in biology. Yet, a mental gap exists between computational versus wet-lab models in terms of their perceived scientific usefulness. Bridging this gap requires focusing on computational models as scientific tools that can explore and challenge explanatory, mechanistic hypotheses.

I begin by exploring the roles of computational, mechanistic models in science and their relationship to traditional wet-lab science. I analyze biological modeling and simulation from a philosophy of science perspective, culminating in a requirements list for computational models that are to be scientifically useful. To fulfill these requirements, I present the synthetic analog approach, a developing simulation methodology for simulating complex, multi-scale biological systems, and discuss agent-based modeling as an appropriate modeling formalism. I then extend this approach to include the development and execution of virtual biomedical experiments, which are simulations of wet-lab or clinical biomedical experiments. Executing virtual biomedical experiments allows one to mimic all relevant aspects of the wet-lab scientific process—from hypothesis formation to data analysis, and key concepts in between. I envision virtual experimentation not as a supplement to traditional wet-lab experimentation, but rather as an essential part of the scientific method itself.

I demonstrate the above methodology and virtual experiment vision by developing software analogs that quantitatively mimic wet-lab experiments related to drug metabolism, drug-induced toxicity, and immune system interactions with the liver. I use these models to explore several mechanistic hypotheses, reaching several validation milestones and falsifying several mechanistic explanations along the way. I highlight the importance of modularity in achieving the above vision, and develop a modularization approach to facilitate model reuse and repurposing. Finally, I discuss important technical issues in agent-based modeling and offer practical solutions. Taken together, this dissertation aims to demonstrate and lay the preliminary groundwork for engineering scientifically useful virtual biomedical experiments.