REMI: Constraint-based method for integrating relative expression and relative metabolite levels into a thermodynamically consistent metabolic model

Flux balance analysis (FBA) allows steady-state flux predictions using optimization principles and often does not result in a unique steady-state flux distribution. Therefore, integration of omics data, such transcriptomics, metabolomics has been employed as additional constraints to reduce the solution space of feasible flux phenotypes. Here, we present a computational method, termed REMI, which integrates relative expression along with relative metabolomics into genome-scale metabolic models (GEMs) to estimate the differential fluxes at GS level. First, we integrated relative expression data into an E.coli GEM using our approach and an existing GX-FBA method (Navid & Almaas, 2012; Orth et al, 2011). The results of our method are more robust and in better agreement with experiments as compared to GX-FBA, because our method facilitates alternative solution enumeration. High frequency solutions analysis between the alternatives may guide in understanding of a biological system physiology. Furthermore, to further reduce the flux space and obtain predictions closer to actual physiological state first we add thermodynamic constraints into models and then employed relative expression as well as relative metabolomics as additional constraints (Henry et al, 2007). The constraint model, resulted in reduced feasible flux space as one can expect, and predicts flux distributions that were in better agreement with experiments. References Henry CS, Broadbelt LJ, Hatzimanikatis V (2007) Thermodynamics-Based Metabolic Flux Analysis. Biophysical Journal 92: 1792-1805 Navid A, Almaas E (2012) Genome-level transcription data of Yersinia pestis analyzed with a New metabolic constraint-based approach. BMC Systems Biology 6: 150 Orth JD, Conrad TM, Na J, Lerman JA, Nam H, Feist AM, Palsson BO (2011) A comprehensive genome-scale reconstruction of Escherichia coli metabolism-2011. Molecular Systems Biology 7:

Context-specific cell signaling analysis using logic framework

The description of signaling networks in textbooks and online resources is usually not context specific, because it is typically based on evidence from multiple experiments, performed for different cell types growing under various conditions. Thus, any analysis of such networks alone will lack all context-specific information that originates from context dependent signaling. Here, we developed a logic-based novel method to perform context-based analysis using signaling networks and context-specific transcriptomics and proteomics data. To understand the interactions of NOTCH1 signaling system in different cancer types we integrated cancer cell line encyclopedia (CCLE; (Barretina et al, 2012) data into the system. In previous studies, MYC was found as expressed in each cancer and it is known to be a key player in many cancers. Additionally, to identify potential cancer biomarkers we integrated CCLE data once again into the atlas of cancer signaling that is large-scale curated signaling map up till now (Kuperstein et al, 2015). Subsequently, we identified many potential cancer biomarkers associated with apoptosis, cell proliferation, and cell survival. Biomarkers, such as FGF3, FGF4, and HIF1A were identified in each cancer types and they are known to be cancer biomarkers (https://www.cancergenomeinterpreter.org/). References: Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, Wilson CJ, Lehar J, Kryukov GV, Sonkin D, Reddy A, Liu M, Murray L, Berger MF, Monahan JE, Morais P, Meltzer J, Korejwa A, Jane-Valbuena J, Mapa FA et al (2012) The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity (vol 483, pg 603, 2012). Nature 492: 290-290 Kuperstein I, Bonnet E, Nguyen HA, Cohen D, Viara E, Grieco L, Fourquet S, Calzone L, Russo C, Kondratova M, Dutreix M, Barillot E, Zinovyev A (2015) Atlas of Cancer Signalling Network: a systems biology resource for integrative analysis of cancer data with Google Maps. Oncogenesis 4: 1

Production of biofuels and biochemicals: in need of an ORACLE

The engineering of cells for the production of fuels and chemicals involves simultaneous optimization of multiple objectives, such as specific productivity, extended substrate range and improved tolerance – all under a great degree of uncertainty. The achievement of these objectives under physiological and process constraints will be impossible without the use of mathematical modeling. However, the limited information and the uncertainty in the available information require new methods for modeling and simulation that will characterize the uncertainty and will quantify, in a statistical sense, the expectations of success of alternative metabolic engineering strategies. We discuss these considerations around the development of a framework for the Optimization and Risk Analysis of Complex Living Entities (ORACLE)

Modeling of uncertainties in biochemical reactions

Mathematical modeling is an indispensable tool for research and development in biotechnology and bioengineering. The formulation of kinetic models of biochemical networks depends on knowledge of the kinetic properties of the enzymes of the individual reactions. However, kinetic data acquired from experimental observations bring along uncertainties due to various experimental conditions and measurement methods. In this contribution, we propose a novel way to model the uncertainty in the enzyme kinetics and to predict quantitatively the responses of metabolic reactions to the changes in enzyme activities under uncertainty. The proposed methodology accounts explicitly for mechanistic properties of enzymes and physico-chemical and thermodynamic constraints, and is based on formalism from systems theory and metabolic control analysis. We achieve this by observing that kinetic responses of metabolic reactions depend: (i) on the distribution of the enzymes among their free form and all reactive states; (ii) on the equilibrium displacements of the overall reaction and that of the individual enzymatic steps; and (iii) on the net fluxes through the enzyme. Relying on this observation, we develop a novel, efficient Monte Carlo sampling procedure to generate all states within a metabolic reaction that satisfy imposed constrains. Thus we derive the statistics of the expected responses of the metabolic reactions to changes in enzyme levels and activities, in the levels of metabolites, and in the values of the kinetic parameters. We present aspects of the proposed framework through an example of the fundamental three-step reversible enzymatic reaction mechanism. We demonstrate that the equilibrium displacements of the individual enzymatic steps have an important influence on kinetic responses of the enzyme. Furthermore, we derive the conditions that must be satisfied by a reversible three-step enzymatic reaction operating far away from the equilibrium in order to respond to changes in metabolite levels according to the irreversible Michelis-Menten kinetics. The efficient sampling procedure allows easy, scalable, implementation of this methodology to modeling of large-scale biochemical networks

Design of novel enzymed-catalyzed reactions linked to protein sequences for finding enzyme engineering targets

A key challenge in metabolic engineering is to find and to improve biosynthetic pathways that lead to the cellular production of a given industrial, pharmaceutical or specialty chemical compound. In many cases, the enzymatic reactions required for bio-production have not been observed in nature and need to be designed from scratch. Computational approaches are essential to predict possible novel biotransformation and to find enzymes that can potentially catalyze the proposed reactions. In this work, we present two computational tools, BNICE.ch and BridgIT, and we demonstrate their concerted action to (i) predict hypothetical biotransformations and (ii) to link these novel reactions with well characterized enzymatic reactions and their associated genes. BNICE.ch reconstructs known reactions and generates novel reactions by applying its integrated, expert curated, generalized enzyme reaction rules on known metabolites. In order to find enzymes that potentially catalyze the biotransformation of these novel reactions, we assume that molecules with a similar reactive site and a similar atomic structure around the reactive site may be recognized and transformed by the same enzyme. Hence, BridgIT compares every predicted novel reaction to all known enzymatic reactions for which a protein sequence is available. Novel and known reactions are compared based on the reactive site of the substrates, the atoms surrounding the reactive site, and the breakage and formation of atomic bonds during the conversion of the substrate to the product. As a result, BridgIT reports a similarity score for each comparison of known reactions to novel reactions, thus giving an estimate of how possible it is that a given enzyme can catalyze a novel reaction. The results are organized in a database of known and hypothetical reactions called the “ATLAS of Biochemistry”1, where every hypothetical reaction is associated with its structurally most similar known enzymatic reactions, thus suggesting a plausible Gene-Protein-Reaction (GPR) association that can be used as a starting point for enzyme engineering. Our database currently spans more than 130’000 biochemically possible reactions between known metabolites from the Kyoto Encyclopedia of Genes and Genomes (KEGG). The ATLAS database and the BridgIT online tool are available on the web (http://lcsb-databases.epfl.ch/) and they can be used to extract potential reactions and pathways and to identify enzyme targets for metabolic and enzymatic engineering purposes. 1Hadadi, N., Hafner, J., Shajkofci, A., Zisaki, A., & Hatzimanikatis, V. (2016). ATLAS of Biochemistry: A repository of all possible biochemical reactions for synthetic biology and metabolic engineering studies. ACS Synthetic Biology, 2016

Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models

The ever-increasing availability of transcriptomic and metabolomic data can be used to deeply analyze and make ever-expanding predictions about biological processes, as changes in the reaction fluxes through genome-wide pathways can now be tracked. Currently, constraint-based metabolic modeling approaches, such as flux balance analysis (FBA), can quantify metabolic fluxes and make steady-state flux predictions on a genome-wide scale using optimization principles. However, relating the differential gene expression or differential metabolite abundances in different physiological states to the differential flux profiles remains a challenge. Here we present a novel method, named REMI (Relative Expression and Metabolomic Integrations), that employs genome-scale metabolic models (GEMs) to translate differential gene expression and metabolite abundance data obtained through genetic or environmental perturbations into differential fluxes to analyze the altered physiology for any given pair of conditions. REMI allows for gene-expression, metabolite abundance, and thermodynamic data to be integrated into a single framework, then uses optimization principles to maximize the consistency between the differential gene-expression levels and metabolite abundance data and the estimated differential fluxes and thermodynamic constraints. We applied REMI to integrate into the Escherichia coli GEM publicly available sets of expression and metabolomic data obtained from two independent studies and under wide-ranging conditions. The differential flux distributions obtained from REMI corresponding to the various perturbations better agreed with the measured fluxomic data, and thus better reflected the different physiological states, than a traditional model. Compared to the similar alternative method that provides one solution from the solution space, REMI was able to enumerate several alternative flux profiles using a mixed-integer linear programming approach. Using this important advantage, we performed a high-frequency analysis of common genes and their associated reactions in the obtained alternative solutions and identified the most commonly regulated genes across any two given conditions. We illustrate that this new implementation provides more robust and biologically relevant results for a better understanding of the system physiology

iSCHRUNK – In Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models of Genome-scale Metabolic Networks

Accurate determination of physiological states of cellular metabolism requires detailed information about metabolic fluxes, metabolite concentrations and distribution of enzyme states. Integration of fluxomics and metabolomics data, and thermodynamics-based metabolic flux analysis contribute to improved understanding of steady-state properties of metabolism. However, knowledge about kinetics and enzyme activities though essential for quantitative understanding of metabolic dynamics remains scarce and involves uncertainty. Here, we present a computational methodology that allow us to determine and quantify the kinetic parameters that correspond to a certain physiology as it is described by a given metabolic flux profile and a given metabolite concentration vector. Though we initially determine kinetic parameters that involve a high degree of uncertainty, through the use of kinetic modeling and machine learning principles we are able to obtain more accurate ranges of kinetic parameters, and hence we are able to reduce the uncertainty in the model analysis. We computed the distribution of kinetic parameters for glucose-fed E. coli producing 1,4-butanediol and we discovered that the observed physiological state corresponds to a narrow range of kinetic parameters of only a few enzymes, whereas the kinetic parameters of other enzymes can vary widely. Furthermore, this analysis suggests which are the enzymes that should be manipulated in order to engineer the reference state of the cell in a desired way. The proposed approach also sets up the foundations of a novel type of approaches for efficient, non-asymptotic, uniform sampling of solution spaces

From network models to network responses: integration of thermodynamic and kinetic properties of yeast genome-scale metabolic networks

Many important problems in cell biology arise from the dense nonlinear interactions between functional modules. The importance of mathematical modelling and computer simulation in understanding cellular processes is now indisputable and widely appreciated. Genome-scale metabolic models have gained much popularity and utility in helping us to understand and test hypotheses about these complex networks. However, there are some caveats that come with the use and interpretation of different types of metabolic models, which we aim to highlight here. We discuss and illustrate how the integration of thermodynamic and kinetic properties of the yeast metabolic networks in network analyses can help in understanding and utilizing this organism more successfully in the areas of metabolic engineering, synthetic biology and disease treatment

A model-based optimization framework for the inference of regulatory interactions using time-course DNA microarray expression data

<p>Abstract</p> <p>Background</p> <p>Proteins are the primary regulatory agents of transcription even though mRNA expression data alone, from systems like DNA microarrays, are widely used. In addition, the regulation process in genetic systems is inherently non-linear in nature, and most studies employ a time-course analysis of mRNA expression. These considerations should be taken into account in the development of methods for the inference of regulatory interactions in genetic networks.</p> <p>Results</p> <p>We use an S-system based model for the transcription and translation process. We propose an optimization-based regulatory network inference approach that uses time-varying data from DNA microarray analysis. Currently, this seems to be the only model-based method that can be used for the analysis of time-course "relative" expressions (expression ratios). We perform an analysis of the dynamic behavior of the system when the number of experimental samples available is varied, when there are different levels of noise in the data and when there are genes that are not considered by the experimenter. Our studies show that the principal factor affecting the ability of a method to infer interactions correctly is the similarity in the time profiles of some or all the genes. The less similar the profiles are to each other the easier it is to infer the interactions. We propose a heuristic method for resolving networks and show that it displays reasonable performance on a synthetic network. Finally, we validate our approach using real experimental data for a chosen subset of genes involved in the sporulation cascade of <it>Bacillus anthracis</it>. We show that the method captures most of the important known interactions between the chosen genes.</p> <p>Conclusion</p> <p>The performance of any inference method for regulatory interactions between genes depends on the noise in the data, the existence of unknown genes affecting the network genes, and the similarity in the time profiles of some or all genes. Though subject to these issues, the inference method proposed in this paper would be useful because of its ability to infer important interactions, the fact that it can be used with time-course DNA microarray data and because it is based on a non-linear model of the process that explicitly accounts for the regulatory role of proteins.</p