Multi-class and multi-scale models of complex biological phenomena. (June 2016)

• Record Type: Journal Article
• Authors: Yu, Jessica S; Bagheri, Neda
• Is Part Of: Current opinion in biotechnology. Volume 39(2016)
• Page Start: 167
• Page End: 173
• Publication Date: 2016-06
• Subjects: Biotechnology -- Periodicals; 660.6
• DOI: 10.1016/j.copbio.2016.04.002
• Languages: English
• ISSNs: 0958-1669

Abstract

Graphical abstract: Highlights: Choice of modeling framework should account for experimental context and data. Accurate models offer biological insight and explain non-intuitive observations. Multi-scale and multi-class models offer unique advantages for biological systems. Abstract : Computational modeling has significantly impacted our ability to analyze vast (and exponentially increasing) quantities of experimental data for a variety of applications, such as drug discovery and disease forecasting. Single-scale, single-class models persist as the most common group of models, but biological complexity often demands more sophisticated approaches. This review surveys modeling approaches that are multi-class (incorporating multiple model types) and/or multi-scale (accounting for multiple spatial or temporal scales) and describes how these models, and combinations thereof, should be used within the context of the problem statement. We end by highlighting agent-based models as an intuitive, modular, and flexible framework within which multi-scale and multi-class models can be implemented.