At the Biological Modeling and Simulation Frontier

We provide a rationale for and describe examples of synthetic modeling and simulation (M&S) of biological systems. We explain how synthetic methods are distinct from familiar inductive methods. Synthetic M&S is a means to better understand the mechanisms that generate normal and disease-related phenomena observed in research, and how compounds of interest interact with them to alter phenomena. An objective is to build better, working hypotheses of plausible mechanisms. A synthetic model is an extant hypothesis: execution produces an observable mechanism and phenomena. Mobile objects representing compounds carry information enabling components to distinguish between them and react accordingly when different compounds are studied simultaneously. We argue that the familiar inductive approaches contribute to the general inefficiencies being experienced by pharmaceutical R&D, and that use of synthetic approaches accelerates and improves R&D decision-making and thus the drug development process. A reason is that synthetic models encourage and facilitate abductive scientific reasoning, a primary means of knowledge creation and creative cognition. When synthetic models are executed, we observe different aspects of knowledge in action from different perspectives. These models can be tuned to reflect differences in experimental conditions and individuals, making translational research more concrete while moving us closer to personalized medicine

Modeling and Simulation of Complex Biological Systems: Agent-Based Modeling of Hepatic Xenobiotic Elimination

Hepatocytes interact with new compounds: metabolism is a frequent consequence. Hepatocyte micromechanisms have evolved to adjust in real time to interact with any never-before-seen compound. To demonstrate understanding of those mechanisms, nextgeneration, pharmacologically useful hepatocyte models must strive to do the same: their micromechanisms recognize a compound as not previously seen and respond based on prior experience. How might that be accomplished when uncertainty is large and detailed data are chronically limited? My first goal was to answer that question: engineer a new class of hepatocyte models that draw on their own past experiences and compound physicochemical properties (molecular weight, pKa, logP, etc.) to respond uniquely to a new compound: the events that emerge during model execution provide a useful prediction of that compound's metabolic clearance. I used the new synthetic (combining elements to form a whole) method of modeling and simulation. The method involves building extant--actually existing and observable--working biomimetic micromechanisms. The micromechanisms within my successful in silico hepatocytes are comprised of autonomous, interactive objects and agents that map to six key components: extracellular media, cells, transporters, metabolic enzymes, cytosolic binding factors, and compounds. In silico clearance emerges from their interactions. Within livers, hepatocytes are spatially organized into functional units called lobules. Those hepatocytes exhibit location dependent, possibly cooperative, properties, including gene expression and metabolic clearance. These properties frequently adapt to changes in compound type and exposure. Drug-induced hepatocyte damage can also be location dependent. Such spatially heterogeneous phenomena are referred to as hepatic zonation. Next generation, pharmacologically useful, liver models must be capable of exhibiting similar phenomena under analogous conditions. My second goal was to extend my new class of models to the level of hepatic lobules. I organized autonomous agents, which map to small units of lobular function, into structures called Zonally Responsive Lobular Analogues (ZoRLA). The micromechanisms within each that enabled achieving a degree of validation were given two tasks: protect hypothetical external "tissues" by eliminating simulated toxins and minimize resource consumption. The zonation patterns that emerged are striking similarities to reported patterns. ZoRLA are designed to become components of future, virtual organisms