Data Repository for Automated Metabolomics Administration (DRAMA)

DRAMA framework was primarily developed with the purpose of facilitating data integration into genome scale models (GEM) of cellular metabolism. The existence of differently annotated genome scale models and metabolomics datasets that are provided without generic metabolite identifiers (such as KEGG, SEED, or CHEBI ids) requires a community effort in order to come up with standardize ways for model and metabolomics automated annotation. Three such efforts that aim to uniquely identify metabolites and reactions through databases have been published [1-3]. Besides the problems regarding standardization of model annotation, the current methodologies available for the analysis of metabolic models require that a lot of manual work is performed in order to match the metabolomics data to the corresponding metabolites in the models when integrating this data into the model. This work can become easily cumbersome when one is manipulating many different datasets from different sources, where metabolite nomenclatures seldom match between data sources & GEM and identifiers are from different databases or not provided. DRAMA is built as a toolbox that aims to combine standardization of data & GEM annotation along with an integrative analysis framework which is independent of model curation.

Metabolic control analysis of the central carbon pathway in optimally grown E. coli

Stefano Andreozzi, Vassily Hatzimanikatis, Ljubisa Miskovic, Keng Cher Soh

The engineering of the metabolism requires the building of reliable kinetic models of the metabolic pathways through the integration of the information from heterogeneous data sources. Unfortunately, the quantitative knowledge about fluxes, mechanisms or kinetic parameters of the constituent reactions of the metabolic network is most of the times uncertain or missing, due to limitations in the measurement techniques of the relevant biological properties of the cell and variability in experimental conditions. In this contribution, we employ ORACLE (Optimization and Risk Analysis of Complex Living Entities), the computational framework for the analysis and optimization of metabolic networks that addresses these issues. ORACLE allows us to generate populations of kinetic models that account explicitly for the physico-chemical and thermodynamic features and constraints of metabolic networks. We show how ORACLE incorporates fluxomics and metabolomics data and constraints into the central carbon core model of E. coli to uncover its possible steady state behaviors, when the cells are cultivated under the physiological condition of optimal growth on different carbon sources, both anaerobically and aerobically. Alternative cases of possible intracellular flux and thermodynamic states, corresponding to the given experimental data, are identified and subsequently the kinetic analysis is performed to evaluate how the control over fluxes and concentrations is distributed over the metabolic network. We demonstrate that ORACLE methodology provides guidance for metabolic engineering in spite of uncertain/missing information in the local/global properties of the metabolic network, integrating a statistical account on the semi-quantitative available data with consistent thermodynamic information and plausible hypotheses.

2012

Computational Studies on Cellular Bioenergetics

Keng Cher Soh

Genome-scale metabolic reconstructions have become indispensable tools for understanding and investigation of the metabolism of any organism in this post-genomic era. However, there are still knowledge gaps in our understanding of the metabolism of even the most well-studied organism, hence a gapfilling algorithm based on novel reaction generating framework was implemented and shown to be able to refine the reconstruction as well as improve our knowledge of the network. The analysis of these large-scale models has their own set of challenges and difficulties that limit their full utility in practical application areas and higher usage by industry and experimentalists. Constraint-based analyses of metabolic models are highly popular methods, as it only requires knowledge of stoichiometry and key input/output fluxes of the system to infer the underlying intracellular fluxes. However due to the underdetermined nature of most large metabolic networks, additional constraints have to be incorporated to ascertain the actual state of the network. Thermodynamic constraints eliminate thermodynamically infeasible solutions and also provide another layer of information onto metabolic networks. Using Thermodynamics-based Flux Balance Analysis (TFBA) of metabolic models and taking into account pH and ionic strength differences, we study how these factors can affect overall cellular energetics. TFBA also enables us to integrate metabolomics data to reduce the solution space of the problem. As genome-scale networks have too many degrees of freedom for systematic analysis and conceptually too difficult to manage, a consistent approach of reducing genome-scale models to core metabolic models that retain most of the network characteristics of the original model is proposed. Such core metabolic models are valuable resources for simplifying analysis that can be extrapolated to the original model. A novel framework for characterizing the flux and thermodynamic state of metabolic networks is proposed in order to systematically analyze the possible intracellular states given the set of known parameters. The characterized states can be further analyzed using metabolic control analysis to provide insights as to how the control of the network is distributed and also provide a bridge to the formulation of large-scale kinetic models.

EPFL2013

Identifying patterns in kinetic parameters that determine the impact of rate-limiting enzymes

Vassily Hatzimanikatis, Ljubisa Miskovic, Michaël Roger Germain Moret

Kinetic models are essential for studying complex behavior and properties of metabolism. However, a persistent hurdle for constructing these models is uncertainty in the kinetic properties of enzymes. Currently available methods for building kinetic models cope in an indirect way with uncertainties by integrating data from different biological levels and origins into models. In this context, we have recently proposed iSCHRUNK1 a new approach for characterization and reduction of uncertainty that makes use of the ORACLE2 (Optimization and Risk Analysis of Complex Living Entities) framework and machine learning classification techniques. ORACLE allows us to exploit synergies between different data sources and generate population of nonlinear kinetic models that are consistent with a reference physiological states and the available data. On the other hand, with the machine learning techniques we are able to data mine the integrated datasets together with the outputs of ORACLE in order to discern complex interactions and correlations between the parameters of kinetic models and available partial data such as metabolite concentrations, metabolic fluxes, thermodynamic data and information about kinetic properties of enzymes. The proposed approach is versatile and can be used to infer more accurate ranges of kinetic parameters of all enzymes in large-scale and genome-scale metabolic networks that are consistent with the observed properties of the metabolic network. In this work, we studied which enzymes determine the sign (positive or negative values) of important control coefficients, and then we identified what should be the configuration of the kinetic parameters of identified enzymes that will determine its role as a rate-limiting enzyme. Based on this analysis we categorized the enzymes in the network in groups that act on studied properties: (i) in a concerted fashion; (ii) antagonistically; or (iii) in a mutually independent way. This work will allow us to investigate how to engineer the enzymes in order to achieve a pre-specified metabolic behavior and address the inverse metabolic engineering problems in a new way.