Fighting fire with fire: deploying complexity in computational modeling to effectively characterize complex biological systems. (June 2022)

• Record Type: Journal Article
• Authors: Prybutok, Alexis N; Cain, Jason Y; Leonard, Joshua N; Bagheri, Neda
• Is Part Of: Current opinion in biotechnology. Volume 75(2022)
• Publication Date: 2022-06
• Subjects: Biotechnology -- Periodicals; 660.6
• DOI: 10.1016/j.copbio.2022.102704
• Languages: English
• ISSNs: 0958-1669

Abstract

Graphical abstract: Highlights: The complexity of a model is intimately tied to its performance and utility. Each modeling approach has a unique relationship to complexity. Excess detail can result in computationally intractable models. Composite modeling approaches can help to manage complexity. Available data and empirical observations should guide deliberate model design. Abstract : Computational modeling empowers systems biologists to interrogate and understand increasingly complex biological phenomena, and the growing suite of computational approach presents both opportunities and challenges. Choosing the right computational approaches to address a given question requires managing a model's complexity, balancing goals and limitations including interpretability, data resolution, and computational cost. Excess model complexity can diminish the utility for building understanding, while excess simplicity can render the model insufficient for addressing the questions of interest. Using systems immunology as a case study, we review how different model design strategies uniquely manage complexity, ending with a consideration of composite models, which combine the benefits of individual paradigms but present additional challenges arising from added layers of complexity. We anticipate that considering general model design challenges and potential solutions through the lens of complexity will foster enhanced collaboration among computational and experimental researchers.