Multiscale plant modeling: from genome to phenome and beyond

Abstract

Plants are complex organisms that adapt to changes in their environment using an array of regulatory mechanisms that span across multiple levels of biological organization. Due to this complexity, it is difficult to predict emergent properties using conventional approaches that focus on single levels of biology such as the genome, transcriptome, or metabolome. Mathematical models of biological systems have emerged as useful tools for exploring pathways and identifying gaps in our current knowledge of biological processes. Identification of emergent properties, however, requires their vertical integration across biological scales through multiscale modeling. Multiscale models that capture and predict these emergent properties will allow us to predict how plants will respond to a changing climate and explore strategies for plant engineering. In this review, we (1) summarize the recent developments in plant multiscale modeling; (2) examine multiscale models of microbial systems that offer insight to potential future directions for the modeling of plant systems; (3) discuss computational tools and resources for developing multiscale models; and (4) examine future directions of the field.

Exploring the metabolic landscape of pancreatic ductal adenocarcinoma cells using genome-scale metabolic modeling

“…These models provide a systems-level approach to studying the metabolism of tumor cells based on conservation of mass under pseudo-steady state conditions. Because genome-scale metabolic models are capable of efficient mapping of the genotype to the phenotype ( Bernstein et al., 2021 ; Cardoso et al., 2015 ; Castillo, Patil and Jouhten, 2019 ; Lewis et al., 2012 ; Matthews and Marshall-Colón, 2021 ; O'Brien et al., 2015 ), integrating multi-level omics data with these models enhances their predictive power and allows for a systems-level study of the metabolic reprogramming happening in living organisms under various genetic and environmental perturbations or diseases. Applications of the genome-scale metabolic modeling to cancer includes network comparison between healthy and cancerous cells, gene essentiality and robustness studies, integrative analysis of omics data, and identifying reporter pathways and reporter metabolites ( Zhang et al., 2019 ; Turanli et al., 2019 ; Nilsson and Nielsen, 2017 ; Ghaffari et al., 2015 ; Jerby and Ruppin, 2012 ).…”

Biological Parts for Plant Biodesign to Enhance Land-Based Carbon Dioxide Removal

“…Multiscale plant modeling, with partial-or fullintegration of transcriptomics, proteomics, metabolomics, and phenomics data, has a great potential for identifying candidate genes for plant engineering [17,168,169] and should be considered as a key approach for identifying new biological parts relevant to CDR engineering. Multiscale modeling has been successfully used for informing genetic engineering in plants [167].…”

Exploring the metabolic landscape of pancreatic ductal adenocarcinoma cells using genome-scale metabolic modeling

“…These models provide a systems-level approach to studying the metabolism of tumor cells based on conservation of mass under pseudo-steady state condition. Since genome-scale metabolic models are capable of efficient mapping of the genotype to the phenotype [30][31][32][33][34][35], integrating multi-level omics data with these models enhances their predictive power and allows for a systems-level study of the metabolic reprogramming happening in living organisms under various genetic and environmental perturbations or diseases. Applications of the genome-scale metabolic modeling to cancer includes network comparison between healthy and cancerous cells, gene essentiality and robustness studies, integrative analysis of omics data, and identifying reporter pathways and reporter metabolites [36][37][38][39][40].…”