Large-scale metabolic network models.

Editors

Lee Sweetlove

University of Oxford

Zoran Nikoloski

Max Planck Institute of Molecular Plant Physiology

Impact

Original Research

10 March 2015

Improved evidence-based genome-scale metabolic models for maize leaf, embryo, and endosperm

Samuel M. D. Seaver

, 5 more and

Christopher S. Henry

There is a growing demand for genome-scale metabolic reconstructions for plants, fueled by the need to understand the metabolic basis of crop yield and by progress in genome and transcriptome sequencing. Methods are also required to enable the interpretation of plant transcriptome data to study how cellular metabolic activity varies under different growth conditions or even within different organs, tissues, and developmental stages. Such methods depend extensively on the accuracy with which genes have been mapped to the biochemical reactions in the plant metabolic pathways. Errors in these mappings lead to metabolic reconstructions with an inflated number of reactions and possible generation of unreliable metabolic phenotype predictions. Here we introduce a new evidence-based genome-scale metabolic reconstruction of maize, with significant improvements in the quality of the gene-reaction associations included within our model. We also present a new approach for applying our model to predict active metabolic genes based on transcriptome data. This method includes a minimal set of reactions associated with low expression genes to enable activity of a maximum number of reactions associated with high expression genes. We apply this method to construct an organ-specific model for the maize leaf, and tissue specific models for maize embryo and endosperm cells. We validate our models using fluxomics data for the endosperm and embryo, demonstrating an improved capacity of our models to fit the available fluxomics data. All models are publicly available via the DOE Systems Biology Knowledgebase and PlantSEED, and our new method is generally applicable for analysis transcript profiles from any plant, paving the way for further in silico studies with a wide variety of plant genomes.

7,662 views

53 citations

Changes of optimized metabolic content in Scenario B. The differences (errors) between the optimized metabolic content csim, n (red circle) as well as their lower and upper 95%-confidence limits (error bars) to the measured values cobs, n were scaled by the measured standard deviation by cobs., n* = cobs,n−csim, nσcobs, n. All changes of the values by the optimization can be explained by measurements errors. Note that for the assumed normal distribution of the measurement errors, the 95% confidence intervals corresponds to 2 σcobs., n.

Original Research

02 June 2015

Pool size measurements facilitate the determination of fluxes at branching points in non-stationary metabolic flux analysis: the case of Arabidopsis thaliana

Robert Heise

, 2 more and

Zoran Nikoloski

6,346 views

18 citations

Schematic overview of the described approaches. Depicted are the different levels and methods at which constraint-based approaches integrate metabolite data—starting from the model reconstruction to the validation of experimental observations. MBA, Model Building Algorithm (Jerby et al., 2010); GIM3E, Gene inactivation Moderated by Metabolism, Metabolomics, and Expression (Schmidt et al., 2013); IOMA, Integrative Omics-Metabolic Analysis (Yizhak et al., 2010); InDisMinimzier, Integrative Discrepancy Minimizer, (Recht et al., 2014); TREM-Flux, Time-Resolved Expression and Metabolite-based prediction of flux values; DFBA, Dynamic Flux Balance Analysis (Mahadevan et al., 2002).

Review

17 February 2015

Integration of metabolomics data into metabolic networks

Nadine Töpfer

, 1 more and

Zoran Nikoloski

Metabolite levels together with their corresponding metabolic fluxes are integrative outcomes of biochemical transformations and regulatory processes and they can be used to characterize the response of biological systems to genetic and/or environmental changes. However, while changes in transcript or to some extent protein levels can usually be traced back to one or several responsible genes, changes in fluxes and particularly changes in metabolite levels do not follow such rationale and are often the outcome of complex interactions of several components. The increasing quality and coverage of metabolomics technologies have fostered the development of computational approaches for integrating metabolic read-outs with large-scale models to predict the physiological state of a system. Constraint-based approaches, relying on the stoichiometry of the considered reactions, provide a modeling framework amenable to analyses of large-scale systems and to the integration of high-throughput data. Here we review the existing approaches that integrate metabolomics data in variants of constrained-based approaches to refine model reconstructions, to constrain flux predictions in metabolic models, and to relate network structural properties to metabolite levels. Finally, we discuss the challenges and perspectives in the developments of constraint-based modeling approaches driven by metabolomics data.

11,976 views

91 citations

Original Research

22 January 2015

A multi-tissue genome-scale metabolic modeling framework for the analysis of whole plant systems

Cristiana Gomes de Oliveira Dal'Molin

, 2 more and

Lars K. Nielsen

Genome scale metabolic modeling has traditionally been used to explore metabolism of individual cells or tissues. In higher organisms, the metabolism of individual tissues and organs is coordinated for the overall growth and well-being of the organism. Understanding the dependencies and rationale for multicellular metabolism is far from trivial. Here, we have advanced the use of AraGEM (a genome-scale reconstruction of Arabidopsis metabolism) in a multi-tissue context to understand how plants grow utilizing their leaf, stem and root systems across the day-night (diurnal) cycle. Six tissue compartments were created, each with their own distinct set of metabolic capabilities, and hence a reliance on other compartments for support. We used the multi-tissue framework to explore differences in the “division-of-labor” between the sources and sink tissues in response to: (a) the energy demand for the translocation of C and N species in between tissues; and (b) the use of two distinct nitrogen sources (NO⁻₃ or NH⁺₄). The “division-of-labor” between compartments was investigated using a minimum energy (photon) objective function. Random sampling of the solution space was used to explore the flux distributions under different scenarios as well as to identify highly coupled reaction sets in different tissues and organelles. Efficient identification of these sets was achieved by casting this problem as a maximum clique enumeration problem. The framework also enabled assessing the impact of energetic constraints in resource (redox and ATP) allocation between leaf, stem, and root tissues required for efficient carbon and nitrogen assimilation, including the diurnal cycle constraint forcing the plant to set aside resources during the day and defer metabolic processes that are more efficiently performed at night. This study is a first step toward autonomous modeling of whole plant metabolism.

11,874 views

113 citations

Review

19 September 2014

Generalized framework for context-specific metabolic model extraction methods

Semidán Robaina Estévez

and

Zoran Nikoloski

Optimal choice of methodologies when tackling a context-specific reconstruction problem. The choice can be made by answering a few questions, in a flowchart manner, related to: demand of model extraction and flux prediction, knowledge on a required metabolic functionality, the type of experimental data available or the computational platform.

Genome-scale metabolic models (GEMs) are increasingly applied to investigate the physiology not only of simple prokaryotes, but also eukaryotes, such as plants, characterized with compartmentalized cells of multiple types. While genome-scale models aim at including the entirety of known metabolic reactions, mounting evidence has indicated that only a subset of these reactions is active in a given context, including: developmental stage, cell type, or environment. As a result, several methods have been proposed to reconstruct context-specific models from existing genome-scale models by integrating various types of high-throughput data. Here we present a mathematical framework that puts all existing methods under one umbrella and provides the means to better understand their functioning, highlight similarities and differences, and to help users in selecting a most suitable method for an application.

10,779 views

86 citations