title: Tuneable resolution as a systems biology approach for multi-scale, multi-compartment computational models.
abstract: The use of multi-scale mathematical and computational models to study complex biological processes is becoming increasingly productive. Multi-scale models span a range of spatial and/or temporal scales and can encompass multi-compartment (e.g., multi-organ) models. Modeling advances are enabling virtual experiments to explore and answer questions that are problematic to address in the wet-lab. Wet-lab experimental technologies now allow scientists to observe, measure, record, and analyze experiments focusing on different system aspects at a variety of biological scales. We need the technical ability to mirror that same flexibility in virtual experiments using multi-scale models. Here we present a new approach, tuneable resolution, which can begin providing that flexibility. Tuneable resolution involves fine- or coarse-graining existing multi-scale models at the user's discretion, allowing adjustment of the level of resolution specific to a question, an experiment, or a scale of interest. Tuneable resolution expands options for revising and validating mechanistic multi-scale models, can extend the longevity of multi-scale models, and may increase computational efficiency. The tuneable resolution approach can be applied to many model types, including differential equation, agent-based, and hybrid models. We demonstrate our tuneable resolution ideas with examples relevant to infectious disease modeling, illustrating key principles at work.
authors.affiliation: Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, USA.
authors.forename: Denise E
authors.initials: DE
authors.lastname: Kirschner
authors.forename: C Anthony
authors.initials: CA
authors.lastname: Hunt
authors.forename: Simeone
authors.initials: S
authors.lastname: Marino
authors.forename: Mohammad
authors.initials: M
authors.lastname: Fallahi-Sichani
authors.forename: Jennifer J
authors.initials: JJ
authors.lastname: Linderman
mesh_terms: D000465:Algorithms; D000818:Animals; D003141:Communicable Diseases; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D016207:Cytokines / Q000378:metabolism; D006801:Humans; D007107:Immune System / Q000502:physiology; D008954:Models, Biological; D009169:Mycobacterium tuberculosis; D049490:Systems Biology / Q000379:methods*; D014376:Tuberculosis / Q000503:physiopathology
chemical_list: D016207:Cytokines