TIGER: Toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks

<p>Abstract</p> <p>Background</p> <p>Several methods have been developed for analyzing genome-scale models of metabolism and transcriptional regulation. Many of these methods, such as Flux Balance Analysis, use constrained optimization to predict relationships between metabolic flux and the genes that encode and regulate enzyme activity. Recently, mixed integer programming has been used to encode these gene-protein-reaction (GPR) relationships into a single optimization problem, but these techniques are often of limited generality and lack a tool for automating the conversion of rules to a coupled regulatory/metabolic model.</p> <p>Results</p> <p>We present TIGER, a Toolbox for Integrating Genome-scale Metabolism, Expression, and Regulation. TIGER converts a series of generalized, Boolean or multilevel rules into a set of mixed integer inequalities. The package also includes implementations of existing algorithms to integrate high-throughput expression data with genome-scale models of metabolism and transcriptional regulation. We demonstrate how TIGER automates the coupling of a genome-scale metabolic model with GPR logic and models of transcriptional regulation, thereby serving as a platform for algorithm development and large-scale metabolic analysis. Additionally, we demonstrate how TIGER's algorithms can be used to identify inconsistencies and improve existing models of transcriptional regulation with examples from the reconstructed transcriptional regulatory network of <it>Saccharomyces cerevisiae</it>.</p> <p>Conclusion</p> <p>The TIGER package provides a consistent platform for algorithm development and extending existing genome-scale metabolic models with regulatory networks and high-throughput data.</p

merlin v4: an updated platform for reconstructing genome-scale metabolic models

The Metabolic Models Reconstruction Using Genome-Scale Information (merlin) software is an open source user-friendly Java application developed for Windows and Unix, aimed towards the reconstruction of genome-scale metabolic models. The development of merlin follows a design philosophy of automating time-consuming steps in the reconstruction of genome-scale metabolic models, while allowing users to control the parameters of operations and manually curate the results. All major steps involved in the reconstruction of a metabolic model are implemented in merlin, including genome retrieval and its functional annotation, construction of the reactions set and associated entities, model compartmentalization and conversion to standard SBML formats. The fourth iteration of merlin includes a major overhaul of the user interface, implementation of new features, improvements to existing features, and most notably, the implementation of the object-relational mapping framework Hibernate. The graphical layout has been significantly streamlined, while supporting the latest version of AiBench, providing users with an intuitive and responsive interface. Development was also focused at new quality of life improvements, aimed mainly towards importing, exporting and duplicating merlin user projects. The development of the latest version of merlin followed a modular approach, culminating in the implementation of a plugin manager which simplifies and hastens the process of updating and debugging the various features of merlin. In addition, TranSyT, a state-of-the-art genome-wide transmembrane transport system annotation tool has been implemented to overcome the limitations of the previously available TRIAGE module. Finally, it is noteworthy to mention the implementation of BioISO, a tool aimed at evaluating a genome-scale metabolic network or biomass formulation, based on the previously available COBRA and FBA frameworks.info:eu-repo/semantics/publishedVersio

Recon 2.2: from reconstruction to model of human metabolism.

IntroductionThe human genome-scale metabolic reconstruction details all known metabolic reactions occurring in humans, and thereby holds substantial promise for studying complex diseases and phenotypes. Capturing the whole human metabolic reconstruction is an on-going task and since the last community effort generated a consensus reconstruction, several updates have been developed.ObjectivesWe report a new consensus version, Recon 2.2, which integrates various alternative versions with significant additional updates. In addition to re-establishing a consensus reconstruction, further key objectives included providing more comprehensive annotation of metabolites and genes, ensuring full mass and charge balance in all reactions, and developing a model that correctly predicts ATP production on a range of carbon sources.MethodsRecon 2.2 has been developed through a combination of manual curation and automated error checking. Specific and significant manual updates include a respecification of fatty acid metabolism, oxidative phosphorylation and a coupling of the electron transport chain to ATP synthase activity. All metabolites have definitive chemical formulae and charges specified, and these are used to ensure full mass and charge reaction balancing through an automated linear programming approach. Additionally, improved integration with transcriptomics and proteomics data has been facilitated with the updated curation of relationships between genes, proteins and reactions.ResultsRecon 2.2 now represents the most predictive model of human metabolism to date as demonstrated here. Extensive manual curation has increased the reconstruction size to 5324 metabolites, 7785 reactions and 1675 associated genes, which now are mapped to a single standard. The focus upon mass and charge balancing of all reactions, along with better representation of energy generation, has produced a flux model that correctly predicts ATP yield on different carbon sources.ConclusionThrough these updates we have achieved the most complete and best annotated consensus human metabolic reconstruction available, thereby increasing the ability of this resource to provide novel insights into normal and disease states in human. The model is freely available from the Biomodels database (http://identifiers.org/biomodels.db/MODEL1603150001)

Standardization Framework for Sustainability from Circular Economy 4.0

The circular economy (CE) is widely known as a way to implement and achieve sustainability, mainly due to its contribution towards the separation of biological and technical nutrients under cyclic industrial metabolism. The incorporation of the principles of the CE in the links of the value chain of the various sectors of the economy strives to ensure circularity, safety, and efficiency. The framework proposed is aligned with the goals of the 2030 Agenda for Sustainable Development regarding the orientation towards the mitigation and regeneration of the metabolic rift by considering a double perspective. Firstly, it strives to conceptualize the CE as a paradigm of sustainability. Its principles are established, and its techniques and tools are organized into two frameworks oriented towards causes (cradle to cradle) and effects (life cycle assessment), and these are structured under the three pillars of sustainability, for their projection within the proposed framework. Secondly, a framework is established to facilitate the implementation of the CE with the use of standards, which constitute the requirements, tools, and indicators to control each life cycle phase, and of key enabling technologies (KETs) that add circular value 4.0 to the socio-ecological transition

Path2Models: large-scale generation of computational models from biochemical pathway maps

Background: Systems biology projects and omics technologies have led to a growing number of biochemical pathway models and reconstructions. However, the majority of these models are still created de novo, based on literature mining and the manual processing of pathway data. Results: To increase the efficiency of model creation, the Path2Models project has automatically generated mathematical models from pathway representations using a suite of freely available software. Data sources include KEGG, BioCarta, MetaCyc and SABIO-RK. Depending on the source data, three types of models are provided: kinetic, logical and constraint-based. Models from over 2 600 organisms are encoded consistently in SBML, and are made freely available through BioModels Database at http://www.ebi.ac.uk/biomodels-main/path2models. Each model contains the list of participants, their interactions, the relevant mathematical constructs, and initial parameter values. Most models are also available as easy-to-understand graphical SBGN maps. Conclusions: To date, the project has resulted in more than 140 000 freely available models. Such a resource can tremendously accelerate the development of mathematical models by providing initial starting models for simulation and analysis, which can be subsequently curated and further parameterized