Tuneable resolution as a systems biology approach for multi‐scale, multi‐compartment computational models. (9th May 2014)

• Record Type: Journal Article
• Authors: Kirschner, Denise E.; Hunt, C. Anthony; Marino, Simeone; Fallahi‐Sichani, Mohammad; Linderman, Jennifer J.
• Is Part Of: Wiley interdisciplinary reviews. Volume 6:Number 4(2014)
• Page Start: 225
• Page End: 245
• Publication Date: 2014-05-09
• Subjects: Systems biology -- Periodicals; Medicine -- Periodicals; 610
• DOI: 10.1002/wsbm.1270
• Languages: English
• ISSNs: 1939-5094

Abstract

<abstract abstract-type="main" id="wsbm1270-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p id="wsbm1270-para-0001">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. <italic>WIREs Syst Biol Med</italic> 2014, 6:225–245. doi: 10.1002/wsbm.1270</p> <p>For further resources related to this article, please visit the <ext-link ext-link-type="uri" xlink:href="http://wires.wiley.com/remdoi.cgi?doi=10.1002/wsbm.1270" xlink:type="simple" xmlns:xlink="http://www.w3.org/1999/xlink">WIREs website</ext-link>.</p> <p id="wsbm1270-para-0002">Conflict of interest: The authors have declared no conflicts of interest for this article.</p> </abstract>