Toward modular biological models: defining analog modules based on referent physiological mechanisms

Abstract

BackgroundCurrently, most biomedical models exist in isolation. It is often difficult to reuse or integrate models or their components, in part because they are not modular. Modular components allow the modeler to think more deeply about the role of the model and to more completely address a modeling project’s requirements. In particular, modularity facilitates component reuse and model integration for models with different use cases, including the ability to exchange modules during or between simulations. The heterogeneous nature of biology and vast range of wet-lab experimental platforms call for modular models designed to satisfy a variety of use cases. We argue that software analogs of biological mechanisms are reasonable candidates for modularization. Biomimetic software mechanisms comprised of physiomimetic mechanism modules offer benefits that are unique or especially important to multi-scale, biomedical modeling and simulation.ResultsWe present a general, scientific method of modularizing mechanisms into reusable software components that we call physiomimetic mechanism modules (PMMs). PMMs utilize parametric containers that partition and expose state information into physiologically meaningful groupings. To demonstrate, we modularize four pharmacodynamic response mechanisms adapted from an in silico liver (ISL). We verified the modularization process by showing that drug clearance results from in silico experiments are identical before and after modularization. The modularized ISL achieves validation targets drawn from propranolol outflow profile data. In addition, an in silico hepatocyte culture (ISHC) is created. The ISHC uses the same PMMs and required no refactoring. The ISHC achieves validation targets drawn from propranolol intrinsic clearance data exhibiting considerable between-lab variability. The data used as validation targets for PMMs originate from both in vitro to in vivo experiments exhibiting large fold differences in time scale.ConclusionsThis report demonstrates the feasibility of PMMs and their usefulness across multiple model use cases. The pharmacodynamic response module developed here is robust to changes in model context and flexible in its ability to achieve validation targets in the face of considerable experimental uncertainty. Adopting the modularization methods presented here is expected to facilitate model reuse and integration, thereby accelerating the pace of biomedical research.

Competing Mechanistic Hypotheses of Acetaminophen-Induced Hepatotoxicity Challenged by Virtual Experiments

“…(E) Cells in Endothelial space control APAP entry and exit and contain a probability-specified number of Binders; for this work, we only required that they bind and release APAP. Hepatocytes use three previously validated event management modules [13], which control 1) material entry and removal, 2) binding and object transformations, and 3) up- and down-regulation of events such as Metabolism (not used for this work).…”

Evaluating computational models of cholesterol metabolism

“…Lipid metabolism could thus profit from more attention in the field of mathematical modeling. Though the prevailing view is that a model should be 'fit to purpose' [85], it would be helpful if models were designed to allow application to more conditions than just the one considered during its inception, so it could be reused in a different form within a modular system [29]. Unfortunately, models that were evaluated in the present study often could not be reproduced from information provided in the article and supplemental files.…”

“…Though the same or a similar test was applied whenever possible, our test may be outside the scope the authors of the model had originally envisioned, putting these models at a disadvantage in passing this test. On the other hand, there is increasing interest in modular modeling, where models can be reused within the hierarchy of a larger model [29,30]. This would require that models have a more general applicability, thus also beyond their original scope.…”

STON: exploring biological pathways using the SBGN standard and graph databases

“…The semantic interpretation of links between identified subnetworks and the judgment how feasible these links are in a biological sense remain two open research questions (e. g., [31]). STON does not provide solutions to these questions but it is already capable of extracting meaningful and self-contained subgraphs from an existing network.…”