Computational Studies on Cellular Metabolism:From Biochemical Pathways to Complex Metabolic Networks

Biotechnology promises the biologically and ecologically sustainable production of commodity chemicals, biofuels, pharmaceuticals and other high-value products using industrial platform microorganisms. Metabolic engineering plays a key role in this process, providing the tools for targeted modifications of microbial metabolism to create efficient microbial cell factories that convert low value substrates to value-added chemicals. Engineering microbes for the bioproduction of chemicals has been practiced through three different approaches: (i) optimization of native pathways of a host organism; (ii) incorporation of heterologous pathways in an amenable organism; and finally (iii) design and introduction of synthetic pathways in an organism. So far, the progress that has been made in the biosynthesis of chemicals was mostly achieved using the first two approaches. Nevertheless, many novel biosynthetic pathways for the production of native and non-native compounds that have potential to provide near-theoretical yields and high specific production rates of chemicals remain yet to be discovered. Therefore, the third approach is crucial for the advancement of bio-based production of value-added chemicals. We need to fully comprehend and analyze the existing knowledge of metabolism in order to generate new hypotheses and design de novo pathways. In this thesis, through development and application of efficient computational methods, we took the research path to expand our understanding of cell metabolism with the aim to discover novel knowledge about metabolic networks. We analyze different aspects of metabolism through five distinct studies. In the first study, we begin with a holistic view of the enzymatic reactions across all the species, and we propose a computational approach for identifying all the theoretically possible enzymatic reactions based on the known biochemistry. We organize our results in a web-based database called âAtlas of biochemistryâ. In the second study, we focus on one of the most structurally diverse and ubiquitous constituents of metabolism, the lipid metabolism. Here we propose a computational framework for integrating lipid species with unknown metabolic/catabolic pathways into metabolic networks. In our next study, we investigate the full metabolic capacity of E. coli. We explore computationally all enzymatic potentials of this organism, and we introduce the âSuper E. coliâ, a new and advanced chassis for metabolic engineering studies. Our next contribution concentrates on the development of a new method for the atom-level description of metabolic networks. We demonstrate the significance of our approach through the reconstruction of atom-level map of the E. coli central metabolism. In the last study, we turn our focus on studying the thermodynamics of metabolism and we present our original approach for estimating the thermodynamic properties of an important class of metabolites. So far, the available thermodynamic properties either from experiments or the computational methods are estimated with respect to the standard conditions, which are different from typical biological conditions. Our workflow paves the way for reliable computing of thermochemical properties of biomolecules at biological conditions of temperature and pressure. Finally, in the conclusion chapter, we discuss the outlook of this work and the potential further applications of the computational methods that were developed in this thesis

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

Design of computational retrobiosynthesis tools for the design of de novo synthetic pathways

Designing putative metabolic pathways is of great interest in synthetic biology. Retrobiosynthesis is a discipline that involves the design, evaluation, and optimization of de novo biosynthetic pathways for the production of high-value compounds and drugs from renewable resources and natural or engineered enzymes. The best candidate pathways are then engineered within a metabolic network of microorganisms that serve as synthetic platforms for synthetic biology. The complexity of biological chemistry and metabolism requires computational approaches to explorethe full possibilities of engineering synthetic pathways towards target compounds. Herein, we discuss recent developments in the design of computational tools for retrosynthetic biochemistry and outline the workflow and design elements for such tools

Exploring chemodiversity in metabolism towards the selective integration of chemistry into biology

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In Silico Atom Labeling for the Reconstruction of Atom-mapped Metabolic Networks

Field of atom mapping of metabolic networks is lacking an automated approach, which accounts for the information of reaction mechanism for atom mapping and is extendable from atom-mapped reactions to atom-mapped reaction networks. We developed “iAM.NICE” (in silico Atom Mapped Network Integrated Computational Explorer), for the atom-level reconstruction of metabolic networks from the in silico labeled substrates to elucidate the mass flow in a biochemical reaction network. “iAM.NICE” transfers the label(s) from a substrate to a product by taking into account the information about the rearrangements of the chemical bonds derived from the “generalized reaction rules” introduced in BNICE.ch [1] . It uses the 582 reaction rules that cover the reconstruction of ~ 90% of known enzymatic reactions (KEGG database). The originality of “iAM.NICE” stems from two aspects: the first automated atom-mapping algorithm that is derived from the underlying enzymatic biotransformation mechanism. its application is not limited to individual reactions and it can be used for the reconstruction of atom-mapped metabolic networks

Feasibility study of the production of ethylbenzene via methylation of toluene

This report reviews recent developments in ethylbenzene (EB) manufacturing technologies and studies in detail the suggested process; Production of Ethylbenzene via Side Alkylation of Toluene by Methanol. The desired amount of ethylbenzene as the product is 303 tonne/day, which finally reduced to 30.21 tonne/day .For this production rate, a 322 tonne/day feed rate containing toluene and methanol was calculated to reach to 99.2% of purity. Chapters 1 through 3 provide an introduction to ethylbenzene and different feasible process for producing of this chemical. A detailed consideration of the process of producing ethylbenzene by toluene and methanol is presented in chapter 4. The use of computer software in process design as well as simulation and calculation parts are described in chapter 5 followed by a discussion on human health, environmental and safety aspects of the main products in chapter 6. The various costs involved in industrial processes, cost estimation, cost accounting and other subjects dealing with economics are covered both qualitatively and quantitatively in chapter 7 and finally, a discussion and conclusion on report writing in chapter 9

In silico tracing of the fate of single carbon atoms in different physiological states of E. coli

Many applications of systems biology such as the simulation of isotope labeling experiments and pathway inference in metabolic engineering rely on atom-level representation of metabolism. Because of its challenging complexity and the difficulty of obtaining correct atom maps of biochemical reactions, the current understanding of the atomic level of metabolism cannot yet fully explain the outcome of stable-isotope labeling experiments. To our knowledge, iAM.NICE is the only computational tool for automatic mapping of single atoms in metabolic reactions, pathways and networks, that ensures the correctness of the mapping based on biochemical reaction mechanisms. iAM.NICE is an extension of BNICE.ch, which reconstructs biochemical reactions using expert curated, generalized biochemical reaction rules. However, inferring differential flux profiles from different atom-mapped metabolic pathways remains a challenge. In this study, we apply iAM.NICE to the core metabolism of E. coli to understand (i) the impact of different physiological states on the distribution of atom labels in the compounds of the central carbon metabolism, and (ii) the consecutive relation of labeled biomass precursor metabolites to the possible labeling distributions in the biomass building blocks (BBB). Our results show that different physiological states of a cell give rise to different distributions of carbon atoms in BBB precursor metabolites, information that is not only crucial for the design and the analysis of isotope labeling experiments, but also deepens our understanding of metabolism at the atomic level. Finally, our method can be easily extended to study in silico the conversion of elements other than carbon, and it can be applied to study organisms other than E. coli. The exhaustive results of our study can be consulted on our website (http://lcsb-databases.epfl.ch/pathways/LabelingList ). Access is freely available for academic purpose upon subscription

Evaluating the similarity of biochemical reactions and its uses for mapping novel reactions to protein sequences

A key challenge in metabolic engineering is to design novel or to improve existing biosynthetic pathways that lead to the cellular production of a given industrial or pharmaceutical compound. In many cases, the required enzymatic reactions for the biosynthesis of the target molecule need to be designed from scratch. BNICE.ch is a method that enables the design of de novo synthetic pathways through the postulation of novel biotransformations. However, finding enzymes that can potentially catalyze the proposed reactions remains a challenge. In this work, we propose a novel method, named BridgIT, to link novel reactions with well characterized enzymatic reactions and their associated genes. 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 similarity of the reactive site of the substrates 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 the likelihood that a given enzyme can catalyze a novel reaction. The candidate proposed enzymes for de novo reactions by BridgIT, are either capable of catalyzing these reactions or they can serve as good initial sequences for the enzyme engineering. BridgIT online tool is freely available on the web (http://lcsb-databases.epfl.ch/) for academia upon subscriptio