Plant Metabolic Modeling: Achieving New Insight into Metabolism and Metabolic Engineering

Models are used to represent aspects of the real world for specific purposes, and mathematical models have opened up new approaches in studying the behavior and complexity of biological systems. However, modeling is often time-consuming and requires significant computational resources for data development, data analysis, and simulation. Computational modeling has been successfully applied as an aid for metabolic engineering in microorganisms. But such model-based approaches have only recently been extended to plant metabolic engineering, mainly due to greater pathway complexity in plants and their highly compartmentalized cellular structure. Recent progress in plant systems biology and bioinformatics has begun to disentangle this complexity and facilitate the creation of efficient plant metabolic models. This review highlights several aspects of plant metabolic modeling in the context of understanding, predicting and modifying complex plant metabolism. We discuss opportunities for engineering photosynthetic carbon metabolism, sucrose synthesis, and the tricarboxylic acid cycle in leaves and oil synthesis in seeds and the application of metabolic modeling to the study of plant acclimation to the environment. The aim of the review is to offer a current perspective for plant biologists without requiring specialized knowledge of bioinformatics or systems biology.     

Modeling and simulation of biological systems with stochasticity

Mathematical modeling is a powerful approach for understanding the complexity of biological systems. Recently, several successful attempts have been made for simulating complex biological processes like metabolic pathways, gene regulatory networks and cell signaling pathways. The pathway models have not only generated experimentally verifiable hypothesis but have also provided valuable insights into the behavior of complex biological systems. Many recent studies have confirmed the phenotypic variability of organisms to an inherent stochasticity that operates at a basal level of gene expression. Due to this reason, development of novel mathematical representations and simulations algorithms are critical for successful modeling efforts in biological systems. The key is to find a biologically relevant representation for each representation. Although mathematically rigorous and physically consistent, stochastic algorithms are computationally expensive, they have been successfully used to model probabilistic events in the cell. This paper offers an overview of various mathematical and computational approaches for modeling stochastic phenomena in cellular systems.     

The biophysical limitations in physiological transport and exchange in plants grown in microgravity

This paper describes how changes in the physical behavior of fluids and gases in microgravity can limit the physiological transport and exchange in higher plants. These types of effects are termed indirect effects of microgravity because they are not due to gravity interacting with the mass of the plant body itself. The impact of limiting gravity-dependent transport phenomena has been analyzed by the use of mathematical modeling to simulate and compare biophysical transport in the 1g and spaceflight environments. These data clearly show that the microgravity environment induces significant limitations on basic physiological and biochemical processes within the aerial and rootzone portions of the plant. Furthermore, this mathematical model provides a solid foundation for explaining the physiological effects that have been noted in past spaceflight experiments.     

Translating Biomaterial Properties to Intracellular Signaling

Bioactive materials present important micro-environmental cues that induce specific intracellular signaling responses which ultimately determine cell behavior. For example, vascular endothelial cells on a normal vessel wall resist inflammation and thrombosis, but the same cells seeded on an artificial vascular graft or stent do not. What makes these cells behave so differently when they are adhered to different materials? Intracellular signaling from integrins and other cell-surface receptors is an important part of the answer, but these signaling responses constitute a highly-branched, interconnected network of molecules. In order to perform rational design of biomaterials, one must understand how altering the properties of the material (micro-environment) causes changes in cell behavior, and this in turn requires understanding the complex signaling response. Systems biology and mathematical modeling aid analysis of the connectivity of this network. This review summarizes applicable systems biology and mathematical modeling techniques including ordinary differential equations-based models, principal component analysis, and Bayesian networks. Next covered is biomaterials research which studies the intracellular signaling responses generated by variation of biomaterial properties. Finally, the review details ways in which modeling has been or could be applied to better understand the link between biomaterial properties and intracellular signaling.     

Unraveling gene function in agricultural species using gene co-expression networks

Co-expression networks have been shown to be a powerful tool for inferring a gene's function when little is known about it. With the advent of next generation sequencing technologies, the construction and analysis of co-expression networks is now possible in non-model species, including those with agricultural importance. Here, we review fundamental concepts in the construction and application of co-expression networks with a focus on agricultural crops. We survey past and current applications of co-expression network analysis in several agricultural species and provide perspective on important considerations that arise when analyzing network relationships. We conclude with a perspective on future directions and potential challenges of utilizing this powerful approach in crops. This article is part of a Special Issue entitled: Plant Gene Regulatory Mechanisms and Networks, edited by Dr. Erich Grotewold and Dr. Nathan Springer.