Systems Biology Approaches to Development beyond Bioinformatics: Nonlinear Mechanistic Models Using Plant Systems. (6th April 2016)

• Record Type: Journal Article
• Authors: Álvarez-Buylla, Elena R.; Dávila-Velderrain, Jose; Martínez-García, Juan Carlos
• Is Part Of: BioScience. Volume 66:Number 5(2016:May)
• Page Start: 371
• Page End: 383
• Publication Date: 2016-04-06
• Subjects: gene regulatory networks -- genotype–phenotype map -- epigenetic landscape -- systems developmental biology -- nonlinear stochastic modeling; Life sciences -- Periodicals; Life sciences -- Research -- Periodicals; Life sciences -- United States -- Periodicals; Life sciences -- Government policy -- United States -- Periodicals; Biology -- Periodicals; Biotechnology industries -- Periodicals; 570
• DOI: 10.1093/biosci/biw027
• Languages: English
• ISSNs: 0006-3568

Abstract

Abstract: Biological systems are complex, stochastic, and nonlinear; therefore, understanding how genes map to phenotypes remains a challenge. A complex-systems mechanistic approach, emphasizing relations over associations, is required for understanding the emergence of cell differentiation and morphogenesis during development. An increasing number of contemporary studies that integrate biological data into dynamic, nonlinear, and stochastic models are providing novel explanations for development. Unfortunately, the adaptation of the biological research tradition to such quantitative and interdisciplinary approaches is not straightforward. In an attempt to contribute to this necessary transition, drawing mainly on our own studies as examples, we present here a nontechnical overview article of how such models are helping unravel the emergence of cell differentiation, pattern formation, and morphogenesis. The studies reviewed here suggest that we need to reevaluate how biological causal and functional roles are interpreted.