Publications by authors named "Vassily Hatzimanikatis"

100 Publications

Finding metabolic pathways in large networks through atom-conserving substrate-product pairs.

Bioinformatics 2021 May 18. Epub 2021 May 18.

Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, Lausanne, Switzerland.

Motivation: Finding biosynthetic pathways is essential for metabolic engineering of organisms to produce chemicals, biodegradation prediction of pollutants and drugs, and for the elucidation of bioproduction pathways of secondary metabolites. A key step in biosynthetic pathway design is the extraction of novel metabolic pathways from big networks that integrate known biological, as well as novel, predicted biotransformations. However, the efficient analysis and the navigation of big biochemical networks remain a challenge.

Results: Here, we propose the construction of searchable graph representations of metabolic networks. Each reaction is decomposed into pairs of reactants and products, and each pair is assigned a weight, which is calculated from the number of conserved atoms between the reactant and the product molecule. We test our method on a biochemical network that spans 6,546 known enzymatic reactions to show how our approach elegantly extracts biologically relevant metabolic pathways from biochemical networks, and how the proposed network structure enables the application of efficient graph search algorithms that improve navigation and pathway identification in big metabolic networks. The weighted reactant-product pairs of an example network and the corresponding graph search algorithm are available online. The proposed method extracts metabolic pathways fast and reliably from big biochemical networks, which is inherently important for all applications involving the engineering of metabolic networks.

Availability: https://github.com/EPFL-LCSB/nicepath.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btab368DOI Listing
May 2021

Constraint-based metabolic control analysis for rational strain engineering.

Metab Eng 2021 Jul 22;66:191-203. Epub 2021 Apr 22.

Laboratory of Computational Systems Biology (LCSB), EPFL, CH-1015, Lausanne, Switzerland. Electronic address:

The advancements in genome editing techniques over the past years have rekindled interest in rational metabolic engineering strategies. While Metabolic Control Analysis (MCA) is a well-established method for quantifying the effects of metabolic engineering interventions on flows in metabolic networks and metabolite concentrations, it does not consider the physiological limitations of the cellular environment and metabolic engineering design constraints. We report here a constraint-based framework, Network Response Analysis (NRA), for rational genetic strain design. NRA is cast as a Mixed-Integer Linear Programming problem that integrates MCA, Thermodynamically-based Flux Analysis (TFA), biologically relevant constraints, as well as genome editing restrictions into a comprehensive platform for identifying metabolic engineering targets. We show that the NRA formulation and its core constraints are equivalent to the ones of Flux Balance Analysis (FBA) and TFA, which allows it to be used for a wide range of optimization criteria and with various physiological constraints. We also show how the parametrization and introduction of biological constraints enhance the NRA formulation compared to the classical MCA approach, and we demonstrate its features and its ability to generate multiple alternative optimal strategies given several user-defined boundaries and objectives. In summary, NRA is a sophisticated alternative to classical MCA for rational metabolic engineering that accommodates the incorporation of physiological data at metabolic flux, metabolite concentration, and enzyme expression levels.
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http://dx.doi.org/10.1016/j.ymben.2021.03.003DOI Listing
July 2021

The effects of model complexity and size on metabolic flux distribution and control: case study in Escherichia coli.

BMC Bioinformatics 2021 Mar 20;22(1):134. Epub 2021 Mar 20.

Laboratory of Computational Systems Biotechnology (LCSB), Swiss Federal Institute of Technology (EPFL), 1015, Lausanne, Switzerland.

Background: Significant efforts have been made in building large-scale kinetic models of cellular metabolism in the past two decades. However, most kinetic models published to date, remain focused around central carbon pathways or are built around ad hoc reduced models without clear justification on their derivation and usage. Systematic algorithms exist for reducing genome-scale metabolic reconstructions to build thermodynamically feasible and consistently reduced stoichiometric models. However, it is important to study how network complexity affects conclusions derived from large-scale kinetic models built around consistently reduced models before we can apply them to study biological systems.

Results: We reduced the iJO1366 Escherichia Coli genome-scale metabolic reconstruction systematically to build three stoichiometric models of different size. Since the reduced models are expansions around the core subsystems for which the reduction was performed, the models are nested. We present a method for scaling up the flux profile and the concentration vector reference steady-states from the smallest model to the larger ones, whilst preserving maximum equivalency. Populations of kinetic models, preserving similarity in kinetic parameters, were built around the reference steady-states and their metabolic sensitivity coefficients (MSCs) were computed. The MSCs were sensitive to the model complexity. We proposed a metric for measuring the sensitivity of MSCs to these structural changes.

Conclusions: We proposed for the first time a workflow for scaling up the size of kinetic models while preserving equivalency between the kinetic models. Using this workflow, we demonstrate that model complexity in terms of networks size has significant impact on sensitivity characteristics of kinetic models. Therefore, it is essential to account for the effects of network complexity when constructing kinetic models. The presented metric for measuring MSC sensitivity to structural changes can guide modelers and experimentalists in improving model quality and guide synthetic biology and metabolic engineering. Our proposed workflow enables the testing of the suitability of a kinetic model for answering certain study-specific questions. We argue that the model-based metabolic design targets that are common across models of different size are of higher confidence, while those that are different could be the objective of investigations for model improvement.
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http://dx.doi.org/10.1186/s12859-021-04066-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7981984PMC
March 2021

A computational workflow for the expansion of heterologous biosynthetic pathways to natural product derivatives.

Nat Commun 2021 03 19;12(1):1760. Epub 2021 Mar 19.

Department of Bioengineering, Stanford University, Stanford, CA, USA.

Plant natural products (PNPs) and their derivatives are important but underexplored sources of pharmaceutical molecules. To access this untapped potential, the reconstitution of heterologous PNP biosynthesis pathways in engineered microbes provides a valuable starting point to explore and produce novel PNP derivatives. Here, we introduce a computational workflow to systematically screen the biochemical vicinity of a biosynthetic pathway for pharmaceutical compounds that could be produced by derivatizing pathway intermediates. We apply our workflow to the biosynthetic pathway of noscapine, a benzylisoquinoline alkaloid (BIA) with a long history of medicinal use. Our workflow identifies pathways and enzyme candidates for the production of (S)-tetrahydropalmatine, a known analgesic and anxiolytic, and three additional derivatives. We then construct pathways for these compounds in yeast, resulting in platforms for de novo biosynthesis of BIA derivatives and demonstrating the value of cheminformatic tools to predict reactions, pathways, and enzymes in synthetic biology and metabolic engineering.
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http://dx.doi.org/10.1038/s41467-021-22022-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7979880PMC
March 2021

Emergence of diauxie as an optimal growth strategy under resource allocation constraints in cellular metabolism.

Proc Natl Acad Sci U S A 2021 Feb;118(8)

Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland

Diauxie, or the sequential consumption of carbohydrates in bacteria such as , has been hypothesized to be an evolutionary strategy which allows the organism to maximize its instantaneous specific growth-giving the bacterium a competitive advantage. Currently, the computational techniques used in industrial biotechnology fall short of explaining the intracellular dynamics underlying diauxic behavior. In particular, the understanding of the proteome dynamics in diauxie can be improved. We developed a robust iterative dynamic method based on expression- and thermodynamically enabled flux models to simulate the kinetic evolution of carbohydrate consumption and cellular growth. With minimal modeling assumptions, we couple kinetic uptakes, gene expression, and metabolic networks, at the genome scale, to produce dynamic simulations of cell cultures. The method successfully predicts the preferential uptake of glucose over lactose in cultures grown on a mixture of carbohydrates, a manifestation of diauxie. The simulated cellular states also show the reprogramming in the content of the proteome in response to fluctuations in the availability of carbon sources, and it captures the associated time lag during the diauxie phenotype. Our models suggest that the diauxic behavior of cells is the result of the evolutionary objective of maximization of the specific growth of the cell. We propose that genetic regulatory networks, such as the operon in , are the biological implementation of a robust control system to ensure optimal growth.
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http://dx.doi.org/10.1073/pnas.2013836118DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7923608PMC
February 2021

PhenoMapping: a protocol to map cellular phenotypes to metabolic bottlenecks, identify conditional essentiality, and curate metabolic models.

STAR Protoc 2021 Mar 22;2(1):100280. Epub 2021 Jan 22.

Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, Lausanne, Switzerland.

Targeted identification of cellular processes responsible for a phenotype is of major importance in guiding efforts in bioengineering and medicine. Genome-scale metabolic models (GEMs) are widely used to integrate various types of omics data and study the cellular physiology under different conditions. Here, we present PhenoMapping, a protocol that uses GEMs, omics, and phenotypic data to map cellular processes and observed phenotypes. PhenoMapping also classifies genes as conditionally and unconditionally essential and guides a comprehensive curation of GEMs. For complete details on the use and execution of this protocol, please refer to Stanway et al. (2019) and Krishnan et al. (2020).
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http://dx.doi.org/10.1016/j.xpro.2020.100280DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7829271PMC
March 2021

Author Correction: Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN.

Nat Commun 2020 07 23;11(1):3757. Epub 2020 Jul 23.

Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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http://dx.doi.org/10.1038/s41467-020-17694-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7378222PMC
July 2020

Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN.

Nat Commun 2020 06 4;11(1):2821. Epub 2020 Jun 4.

Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.

Altered metabolism is associated with many human diseases. Human genome-scale metabolic models (GEMs) were reconstructed within systems biology to study the biochemistry occurring in human cells. However, the complexity of these networks hinders a consistent and concise physiological representation. We present here redHUMAN, a workflow for reconstructing reduced models that focus on parts of the metabolism relevant to a specific physiology using the recently established methods redGEM and lumpGEM. The reductions include the thermodynamic properties of compounds and reactions guaranteeing the consistency of predictions with the bioenergetics of the cell. We introduce a method (redGEMX) to incorporate the pathways used by cells to adapt to the medium. We provide the thermodynamic curation of the human GEMs Recon2 and Recon3D and we apply the redHUMAN workflow to derive leukemia-specific reduced models. The reduced models are powerful platforms for studying metabolic differences between phenotypes, such as diseased and healthy cells.
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http://dx.doi.org/10.1038/s41467-020-16549-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7272419PMC
June 2020

Updated ATLAS of Biochemistry with New Metabolites and Improved Enzyme Prediction Power.

ACS Synth Biol 2020 06 2;9(6):1479-1482. Epub 2020 Jun 2.

Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, CH-1015 Lausanne, Switzerland.

The ATLAS of Biochemistry is a repository of both known and novel predicted biochemical reactions between biological compounds listed in the Kyoto Encyclopedia of Genes and Genomes (KEGG). ATLAS was originally compiled based on KEGG 2015, though the number of KEGG reactions has increased by almost 20 percent since then. Here, we present an updated version of ATLAS created from KEGG 2018 using an increased set of generalized reaction rules. Furthermore, we improved the accuracy of the enzymes that are predicted for catalyzing novel reactions. ATLAS now contains ∼150 000 reactions, out of which 96% are novel. In this report, we present detailed statistics on the updated ATLAS and highlight the improvements with regard to the previous version. Most importantly, 107 reactions predicted in the original ATLAS are now known to KEGG, which validates the predictive power of our approach. The updated ATLAS is available at https://lcsb-databases.epfl.ch/atlas.
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http://dx.doi.org/10.1021/acssynbio.0c00052DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7309321PMC
June 2020

Large-scale kinetic metabolic models of KT2440 for consistent design of metabolic engineering strategies.

Biotechnol Biofuels 2020 28;13:33. Epub 2020 Feb 28.

Laboratory of Computational Systems Biotechnology (LCSB), École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.

Background: is a promising candidate for the industrial production of biofuels and biochemicals because of its high tolerance to toxic compounds and its ability to grow on a wide variety of substrates. Engineering this organism for improved performances and predicting metabolic responses upon genetic perturbations requires reliable descriptions of its metabolism in the form of stoichiometric and kinetic models.

Results: In this work, we developed kinetic models of to predict the metabolic phenotypes and design metabolic engineering interventions for the production of biochemicals. The developed kinetic models contain 775 reactions and 245 metabolites. Furthermore, we introduce here a novel set of constraints within thermodynamics-based flux analysis that allow for considering concentrations of metabolites that exist in several compartments as separate entities. We started by a gap-filling and thermodynamic curation of iJN1411, the genome-scale model of KT2440. We then systematically reduced the curated iJN1411 model, and we created three core stoichiometric models of different complexity that describe the central carbon metabolism of . Using the medium complexity core model as a scaffold, we generated populations of large-scale kinetic models for two studies. In the first study, the developed kinetic models successfully captured the experimentally observed metabolic responses to several single-gene knockouts of a wild-type strain of KT2440 growing on glucose. In the second study, we used the developed models to propose metabolic engineering interventions for improved robustness of this organism to the stress condition of increased ATP demand.

Conclusions: The study demonstrates the potential and predictive capabilities of the kinetic models that allow for rational design and optimization of recombinant strains for improved production of biofuels and biochemicals. The curated genome-scale model of together with the developed large-scale stoichiometric and kinetic models represents a significant resource for researchers in industry and academia.
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http://dx.doi.org/10.1186/s13068-020-1665-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7048048PMC
February 2020

Functional and Computational Genomics Reveal Unprecedented Flexibility in Stage-Specific Toxoplasma Metabolism.

Cell Host Microbe 2020 02 27;27(2):290-306.e11. Epub 2020 Jan 27.

Department of Microbiology & Molecular Medicine, Faculty of Medicine, University of Geneva, Geneva 1211, Switzerland. Electronic address:

To survive and proliferate in diverse host environments with varying nutrient availability, the obligate intracellular parasite Toxoplasma gondii reprograms its metabolism. We have generated and curated a genome-scale metabolic model (iTgo) for the fast-replicating tachyzoite stage, harmonized with experimentally observed phenotypes. To validate the importance of four metabolic pathways predicted by the model, we have performed in-depth in vitro and in vivo phenotyping of mutant parasites including targeted metabolomics and CRISPR-Cas9 fitness screening of all known metabolic genes. This led to unexpected insights into the remarkable flexibility of the parasite, addressing the dependency on biosynthesis or salvage of fatty acids (FAs), purine nucleotides (AMP and GMP), a vitamin (pyridoxal-5P), and a cofactor (heme) in both the acute and latent stages of infection. Taken together, our experimentally validated metabolic network leads to a deeper understanding of the parasite's biology, opening avenues for the development of therapeutic intervention against apicomplexans.
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http://dx.doi.org/10.1016/j.chom.2020.01.002DOI Listing
February 2020

The ETFL formulation allows multi-omics integration in thermodynamics-compliant metabolism and expression models.

Nat Commun 2020 01 13;11(1):30. Epub 2020 Jan 13.

Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.

Systems biology has long been interested in models capturing both metabolism and expression in a cell. We propose here an implementation of the metabolism and expression model formalism (ME-models), which we call ETFL, for Expression and Thermodynamics Flux models. ETFL is a hierarchical model formulation, from metabolism to RNA synthesis, that allows simulating thermodynamics-compliant intracellular fluxes as well as enzyme and mRNA concentration levels. ETFL formulates a mixed-integer linear problem (MILP) that enables both relative and absolute metabolite, protein, and mRNA concentration integration. ETFL is compatible with standard MILP solvers and does not require a non-linear solver, unlike the previous state of the art. It also accounts for growth-dependent parameters, such as relative protein or mRNA content. We present ETFL along with its validation using results obtained from a well-characterized E. coli model. We show that ETFL is able to reproduce proteome-limited growth. We also subject it to several analyses, including the prediction of feasible mRNA and enzyme concentrations and gene essentiality.
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http://dx.doi.org/10.1038/s41467-019-13818-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6959363PMC
January 2020

Statistical inference in ensemble modeling of cellular metabolism.

PLoS Comput Biol 2019 12 9;15(12):e1007536. Epub 2019 Dec 9.

Laboratory of Computational Systems Biotechnology (LCSB), Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland.

Kinetic models of metabolism can be constructed to predict cellular regulation and devise metabolic engineering strategies, and various promising computational workflows have been developed in recent years for this. Due to the uncertainty in the kinetic parameter values required to build kinetic models, these workflows rely on ensemble modeling (EM) principles for sampling and building populations of models describing observed physiologies. Sensitivity coefficients from metabolic control analysis (MCA) of kinetic models can provide important insight about cellular control around a given physiological steady state. However, despite considering populations of kinetic models and their model outputs, current approaches do not provide adequate tools for statistical inference. To derive conclusions from model outputs, such as MCA sensitivity coefficients, it is necessary to rank/compare populations of variables with each other. Currently existing workflows consider confidence intervals (CIs) that are derived independently for each comparable variable. Hence, it is important to derive simultaneous CIs for the variables that we wish to rank/compare. Herein, we used an existing large-scale kinetic model of Escherichia Coli metabolism to present how univariate CIs can lead to incorrect conclusions, and we present a new workflow that applies three different multivariate statistical approaches. We use the Bonferroni and the exact normal methods to build symmetric CIs using the normality assumptions. We then suggest how bootstrapping can compute asymmetric CIs whilst relaxing this normality assumption. We conclude that the Bonferroni and the exact normal methods can provide simple and efficient ways for constructing reliable CIs, with the exact normal method favored over the Bonferroni when the compared variables present dependencies. Bootstrapping, despite its significantly higher computational cost, is recommended when comparing non-normal distributions of variables. Additionally, we show how the Bonferroni method can readily be used to estimate required sample numbers to attain a certain CI size.
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http://dx.doi.org/10.1371/journal.pcbi.1007536DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6922442PMC
December 2019

Genome-Scale Identification of Essential Metabolic Processes for Targeting the Plasmodium Liver Stage.

Cell 2019 11;179(5):1112-1128.e26

Institute of Cell Biology, University of Bern, Bern 3012, Switzerland. Electronic address:

Plasmodium gene functions in mosquito and liver stages remain poorly characterized due to limitations in the throughput of phenotyping at these stages. To fill this gap, we followed more than 1,300 barcoded P. berghei mutants through the life cycle. We discover 461 genes required for efficient parasite transmission to mosquitoes through the liver stage and back into the bloodstream of mice. We analyze the screen in the context of genomic, transcriptomic, and metabolomic data by building a thermodynamic model of P. berghei liver-stage metabolism, which shows a major reprogramming of parasite metabolism to achieve rapid growth in the liver. We identify seven metabolic subsystems that become essential at the liver stages compared with asexual blood stages: type II fatty acid synthesis and elongation (FAE), tricarboxylic acid, amino sugar, heme, lipoate, and shikimate metabolism. Selected predictions from the model are individually validated in single mutants to provide future targets for drug development.
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http://dx.doi.org/10.1016/j.cell.2019.10.030DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6904910PMC
November 2019

Uncertainty reduction in biochemical kinetic models: Enforcing desired model properties.

PLoS Comput Biol 2019 08 20;15(8):e1007242. Epub 2019 Aug 20.

Laboratory of Computational Systems Biology (LCSB), EPFL, CH, Lausanne, Switzerland.

A persistent obstacle for constructing kinetic models of metabolism is uncertainty in the kinetic properties of enzymes. Currently, available methods for building kinetic models can cope indirectly with uncertainties by integrating data from different biological levels and origins into models. In this study, we use the recently proposed computational approach iSCHRUNK (in Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models), which combines Monte Carlo parameter sampling methods and machine learning techniques, in the context of Bayesian inference. Monte Carlo parameter sampling methods allow us to exploit synergies between different data sources and generate a population of kinetic models that are consistent with the available data and physicochemical laws. The machine learning allows us to data-mine the a priori generated kinetic parameters together with the integrated datasets and derive posterior distributions of kinetic parameters consistent with the observed physiology. In this work, we used iSCHRUNK to address a design question: can we identify which are the kinetic parameters and what are their values that give rise to a desired metabolic behavior? Such information is important for a wide variety of studies ranging from biotechnology to medicine. To illustrate the proposed methodology, we performed Metabolic Control Analysis, computed the flux control coefficients of the xylose uptake (XTR), and identified parameters that ensure a rate improvement of XTR in a glucose-xylose co-utilizing S. cerevisiae strain. Our results indicate that only three kinetic parameters need to be accurately characterized to describe the studied physiology, and ultimately to design and control the desired responses of the metabolism. This framework paves the way for a new generation of methods that will systematically integrate the wealth of available omics data and efficiently extract the information necessary for metabolic engineering and synthetic biology decisions.
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http://dx.doi.org/10.1371/journal.pcbi.1007242DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6716680PMC
August 2019

Particle-Based Simulation Reveals Macromolecular Crowding Effects on the Michaelis-Menten Mechanism.

Biophys J 2019 07 25;117(2):355-368. Epub 2019 Jun 25.

Laboratory of Computational Systems Biotechnology, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland. Electronic address:

Many computational models for analyzing and predicting cell physiology rely on in vitro data collected in dilute and controlled buffer solutions. However, this can mislead models because up to 40% of the intracellular volume-depending on the organism, the physiology, and the cellular compartment-is occupied by a dense mixture of proteins, lipids, polysaccharides, RNA, and DNA. These intracellular macromolecules interfere with the interactions of enzymes and their reactants and thus affect the kinetics of biochemical reactions, making in vivo reactions considerably more complex than the in vitro data indicates. In this work, we present a new, to our knowledge, type of kinetics that captures and quantifies the effect of volume exclusion and other spatial phenomena on the kinetics of elementary reactions. We further developed a framework that allows for the efficient parameterization of these kinetics using particle simulations. Our formulation, entitled generalized elementary kinetics, can be used to analyze and predict the effect of intracellular crowding on enzymatic reactions and was herein applied to investigate the influence of crowding on phosphoglycerate mutase in Escherichia coli, which exhibits prototypical reversible Michaelis-Menten kinetics. Current research indicates that many enzymes are reaction limited and not diffusion limited, and our results suggest that the influence of fractal diffusion is minimal for these reaction-limited enzymes. Instead, increased association rates and decreased dissociation rates lead to a strong decrease in the effective maximal velocities V and the effective Michaelis-Menten constants K under physiologically relevant volume occupancies. Finally, the effects of crowding were explored in the context of a linear pathway, with the finding that crowding can have a redistributing effect on the effective flux responses in the case of twofold enzyme overexpression. We suggest that this framework, in combination with detailed kinetics models, will improve our understanding of enzyme reaction networks under nonideal conditions.
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http://dx.doi.org/10.1016/j.bpj.2019.06.017DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6701012PMC
July 2019

Modeling metabolic networks of individual bacterial agents in heterogeneous and dynamic soil habitats (IndiMeSH).

PLoS Comput Biol 2019 06 19;15(6):e1007127. Epub 2019 Jun 19.

Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland.

Natural soil is characterized as a complex habitat with patchy hydrated islands and spatially variable nutrients that is in a constant state of change due to wetting-drying dynamics. Soil microbial activity is often concentrated in sparsely distributed hotspots that contribute disproportionally to macroscopic biogeochemical nutrient cycling and greenhouse gas emissions. The mechanistic representation of such dynamic hotspots requires new modeling approaches capable of representing the interplay between dynamic local conditions and the versatile microbial metabolic adaptations. We have developed IndiMeSH (Individual-based Metabolic network model for Soil Habitats) as a spatially explicit model for the physical and chemical microenvironments of soil, combined with an individual-based representation of bacterial motility and growth using adaptive metabolic networks. The model uses angular pore networks and a physically based description of the aqueous phase as a backbone for nutrient diffusion and bacterial dispersal combined with dynamic flux balance analysis to calculate growth rates depending on local nutrient conditions. To maximize computational efficiency, reduced scale metabolic networks are used for the simulation scenarios and evaluated strategically to the genome scale model. IndiMeSH was compared to a well-established population-based spatiotemporal metabolic network model (COMETS) and to experimental data of bacterial spatial organization in pore networks mimicking soil aggregates. IndiMeSH was then used to strategically study dynamic response of a bacterial community to abrupt environmental perturbations and the influence of habitat geometry and hydration conditions. Results illustrate that IndiMeSH is capable of representing trophic interactions among bacterial species, predicting the spatial organization and segregation of bacterial populations due to oxygen and carbon gradients, and provides insights into dynamic community responses as a consequence of environmental changes. The modular design of IndiMeSH and its implementation are adaptable, allowing it to represent a wide variety of experimental and in silico microbial systems.
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http://dx.doi.org/10.1371/journal.pcbi.1007127DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6583959PMC
June 2019

Dynamic Radiolabeling of S-Palmitoylated Proteins.

Methods Mol Biol 2019 ;2009:111-127

Global Health Institute, School of Life Sciences, EPFL, Lausanne, Switzerland.

Proteins can be radiolabeled either during synthesis, typically using S-cysteine/methionine (S-Cys/Met), or after synthesis, by adding a radiolabeled posttranslational modification. Here we describe how protein S-palmitoylation, and its dynamics, can be monitored by H-palmitate labeling and how the importance of S-palmitoylation in protein biogenesis and turnover can be investigated using S-Cys/Met pulse-chase metabolic labeling. Proteins frequently have multiple palmitoylation sites. The importance thereof on the design and interpretation of metabolic labeling experiments is discussed.
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http://dx.doi.org/10.1007/978-1-4939-9532-5_9DOI Listing
March 2020

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

PLoS Comput Biol 2019 05 13;15(5):e1007036. Epub 2019 May 13.

Laboratory of Computational Systems Biotechnology, EPFL, Lausanne, Switzerland.

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.
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http://dx.doi.org/10.1371/journal.pcbi.1007036DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6532942PMC
May 2019

Impact of iron reduction on the metabolism of Clostridium acetobutylicum.

Environ Microbiol 2019 10 16;21(10):3548-3563. Epub 2019 May 16.

Environmental Microbiology Laboratory, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland.

Iron is essential for most living organisms. In addition, its biogeochemical cycling influences important processes in the geosphere (e.g., the mobilization or immobilization of trace elements and contaminants). The reduction of Fe(III) to Fe(II) can be catalysed microbially, particularly by metal-respiring bacteria utilizing Fe(III) as a terminal electron acceptor. Furthermore, Gram-positive fermentative iron reducers are known to reduce Fe(III) by using it as a sink for excess reducing equivalents, as a form of enhanced fermentation. Here, we use the Gram-positive fermentative bacterium Clostridium acetobutylicum as a model system due to its ability to reduce heavy metals. We investigated the reduction of soluble and solid iron during fermentation. We found that exogenous (resazurin, resorufin, anthraquinone-2,6-disulfonate) as well as endogenous (riboflavin) electron mediators enhance solid iron reduction. In addition, iron reduction buffers the pH, and elicits a shift in the carbon and electron flow to less reduced products relative to fermentation. This study underscores the role fermentative bacteria can play in iron cycling and provides insights into the metabolic profile of coupled fermentation and iron reduction with laboratory experiments and metabolic network modelling.
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http://dx.doi.org/10.1111/1462-2920.14640DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6852232PMC
October 2019

Investigating the deregulation of metabolic tasks via Minimum Network Enrichment Analysis (MiNEA) as applied to nonalcoholic fatty liver disease using mouse and human omics data.

PLoS Comput Biol 2019 04 19;15(4):e1006760. Epub 2019 Apr 19.

Laboratory of Computational Systems Biotechnology, EPFL, Lausanne, Switzerland.

Nonalcoholic fatty liver disease (NAFLD) is associated with metabolic syndromes spanning a wide spectrum of diseases, from simple steatosis to the more complex nonalcoholic steatohepatitis. To identify the deregulation that occurs in metabolic processes at the molecular level that give rise to these various NAFLD phenotypes, algorithms such as pathway enrichment analysis (PEA) can be used. These analyses require the use of predefined pathway maps, which are composed of reactions describing metabolic processes/subsystems. Unfortunately, the annotation of the metabolic subsystems can differ depending on the pathway database used, making these approaches subject to biases associated with different pathway annotations, and these methods cannot capture the balancing of cofactors and byproducts through the complex nature and interactions of genome-scale metabolic networks (GEMs). Here, we introduce a framework entitled Minimum Network Enrichment Analysis (MiNEA) that is applied to GEMs to generate all possible alternative minimal networks (MiNs), which are possible and feasible networks composed of all the reactions pertaining to various metabolic subsystems that can synthesize a target metabolite. We applied MiNEA to investigate deregulated MiNs and to identify key regulators in different NAFLD phenotypes, such as a fatty liver and liver inflammation, in both humans and mice by integrating condition-specific transcriptomics data from liver samples. We identified key deregulations in the synthesis of cholesteryl esters, cholesterol, and hexadecanoate in both humans and mice, and we found that key regulators of the hydrogen peroxide synthesis network were regulated differently in humans and mice. We further identified which MiNs demonstrate the general and specific characteristics of the different NAFLD phenotypes. MiNEA is applicable to any GEM and to any desired target metabolite, making MiNEA flexible enough to study condition-specific metabolism for any given disease or organism.
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http://dx.doi.org/10.1371/journal.pcbi.1006760DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6493771PMC
April 2019

Enzyme annotation for orphan and novel reactions using knowledge of substrate reactive sites.

Proc Natl Acad Sci U S A 2019 04 25;116(15):7298-7307. Epub 2019 Mar 25.

Laboratory of Computational Systems Biology, Institute of Chemistry and Chemical Engineering, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland

Thousands of biochemical reactions with characterized activities are "orphan," meaning they cannot be assigned to a specific enzyme, leaving gaps in metabolic pathways. Novel reactions predicted by pathway-generation tools also lack associated sequences, limiting protein engineering applications. Associating orphan and novel reactions with known biochemistry and suggesting enzymes to catalyze them is a daunting problem. We propose the method BridgIT to identify candidate genes and catalyzing proteins for these reactions. This method introduces information about the enzyme binding pocket into reaction-similarity comparisons. BridgIT assesses the similarity of two reactions, one orphan and one well-characterized nonorphan reaction, using their substrate reactive sites, their surrounding structures, and the structures of the generated products to suggest enzymes that catalyze the most-similar nonorphan reactions as candidates for also catalyzing the orphan ones. We performed two large-scale validation studies to test BridgIT predictions against experimental biochemical evidence. For the 234 orphan reactions from the Kyoto Encyclopedia of Genes and Genomes (KEGG) 2011 (a comprehensive enzymatic-reaction database) that became nonorphan in KEGG 2018, BridgIT predicted the exact or a highly related enzyme for 211 of them. Moreover, for 334 of 379 novel reactions in 2014 that were later cataloged in KEGG 2018, BridgIT predicted the exact or highly similar enzymes. BridgIT requires knowledge about only four connecting bonds around the atoms of the reactive sites to correctly annotate proteins for 93% of analyzed enzymatic reactions. Increasing to seven connecting bonds allowed for the accurate identification of a sequence for nearly all known enzymatic reactions.
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http://dx.doi.org/10.1073/pnas.1818877116DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6462048PMC
April 2019

pyTFA and matTFA: a Python package and a Matlab toolbox for Thermodynamics-based Flux Analysis.

Bioinformatics 2019 01;35(1):167-169

Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.

Summary: pyTFA and matTFA are the first published implementations of the original TFA paper. Specifically, they include explicit formulation of Gibbs energies and metabolite concentrations, which enables straightforward integration of metabolite concentration measurements.

Motivation: High-throughput analytic technologies provide a wealth of omics data that can be used to perform thorough analyses for a multitude of studies in the areas of Systems Biology and Biotechnology. Nevertheless, most studies are still limited to constraint-based Flux Balance Analyses (FBA), neglecting an important physicochemical constraint: thermodynamics. Thermodynamics-based Flux Analysis (TFA) in metabolic models enables the integration of quantitative metabolomics data to study their effects on the net-flux directionality of reactions in the network. In addition, it allows us to estimate how far each reaction operates from thermodynamic equilibrium, which provides critical information for guiding metabolic engineering decisions.

Results: We present a Python package (pyTFA) and a Matlab toolbox (matTFA) that implement TFA. We show an example of application on both a reduced and a genome-scale model of E. coli., and demonstrate TFA and data integration through TFA reduce the feasible flux space with respect to FBA.

Availability And Implementation: Documented implementation of TFA framework both in Python (pyTFA) and Matlab (matTFA) are available on www.github.com/EPFL-LCSB/.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/bty499DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6298055PMC
January 2019

Kinetic models of metabolism that consider alternative steady-state solutions of intracellular fluxes and concentrations.

Metab Eng 2019 03 26;52:29-41. Epub 2018 Oct 26.

Laboratory of Computational Systems Biotechnology (LCSB), Swiss Federal Institute of Technology (EPFL), CH-1015 Lausanne, Switzerland. Electronic address:

Large-scale kinetic models are used for designing, predicting, and understanding the metabolic responses of living cells. Kinetic models are particularly attractive for the biosynthesis of target molecules in cells as they are typically better than other types of models at capturing the complex cellular biochemistry. Using simpler stoichiometric models as scaffolds, kinetic models are built around a steady-state flux profile and a metabolite concentration vector that are typically determined via optimization. However, as the underlying optimization problem is underdetermined, even after incorporating available experimental omics data, one cannot uniquely determine the operational configuration in terms of metabolic fluxes and metabolite concentrations. As a result, some reactions can operate in either the forward or reverse direction while still agreeing with the observed physiology. Here, we analyze how the underlying uncertainty in intracellular fluxes and concentrations affects predictions of constructed kinetic models and their design in metabolic engineering and systems biology studies. To this end, we integrated the omics data of optimally grown Escherichia coli into a stoichiometric model and constructed populations of non-linear large-scale kinetic models of alternative steady-state solutions consistent with the physiology of the E. coli aerobic metabolism. We performed metabolic control analysis (MCA) on these models, highlighting that MCA-based metabolic engineering decisions are strongly affected by the selected steady state and appear to be more sensitive to concentration values rather than flux values. To incorporate this into future studies, we propose a workflow for moving towards more reliable and robust predictions that are consistent with all alternative steady-state solutions. This workflow can be applied to all kinetic models to improve the consistency and accuracy of their predictions. Additionally, we show that, irrespective of the alternative steady-state solution, increased activity of phosphofructokinase and decreased ATP maintenance requirements would improve cellular growth of optimally grown E. coli.
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http://dx.doi.org/10.1016/j.ymben.2018.10.005DOI Listing
March 2019

Efficient cleavage of aryl ether C-O linkages by Rh-Ni and Ru-Ni nanoscale catalysts operating in water.

Chem Sci 2018 Jul 6;9(25):5530-5535. Epub 2018 Jun 6.

Institut des Sciences et Ingénierie Chimiques , Ecole Polytechnique Fédérale de Lausanne (EPFL) , 1015 , Lausanne , Switzerland . Email:

Bimetallic Ru-Ni and Rh-Ni nanocatalysts coated with a phase transfer agent efficiently cleave aryl ether C-O linkages in water in the presence of hydrogen. For dimeric substrates with weaker C-O linkages, α--4 and β--4 bonds, low loadings of the precious metal (Rh or Ru) in the nanocatalysts quantitatively afford monomers, whereas for the stronger 4--5 linkage higher amounts of the precious metal are required to achieve complete conversion. Under the optimized, relatively mild operating conditions, the C-O bonds in a range of substituted ether compounds are efficiently cleaved, and mechanistic insights into the reaction pathways are provided. This work paves the way to sustainable approaches for the hydrogenolysis of C-O bonds.
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http://dx.doi.org/10.1039/c8sc00742jDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6049526PMC
July 2018

Discovery and Evaluation of Biosynthetic Pathways for the Production of Five Methyl Ethyl Ketone Precursors.

ACS Synth Biol 2018 08 7;7(8):1858-1873. Epub 2018 Aug 7.

Laboratory of Computational Systems Biotechnology (LCSB) , Swiss Federal Institute of Technology (EPFL) , CH-1015 Lausanne , Switzerland.

The limited supply of fossil fuels and the establishment of new environmental policies shifted research in industry and academia toward sustainable production of the second generation of biofuels, with methyl ethyl ketone (MEK) being one promising fuel candidate. MEK is a commercially valuable petrochemical with an extensive application as a solvent. However, as of today, a sustainable and economically viable production of MEK has not yet been achieved despite several attempts of introducing biosynthetic pathways in industrial microorganisms. We used BNICE.ch as a retrobiosynthesis tool to discover all novel pathways around MEK. Out of 1325 identified compounds connecting to MEK with one reaction step, we selected 3-oxopentanoate, but-3-en-2-one, but-1-en-2-olate, butylamine, and 2-hydroxy-2-methylbutanenitrile for further study. We reconstructed 3 679 610 novel biosynthetic pathways toward these 5 compounds. We then embedded these pathways into the genome-scale model of E. coli, and a set of 18 622 were found to be the most biologically feasible ones on the basis of thermodynamics and their yields. For each novel reaction in the viable pathways, we proposed the most similar KEGG reactions, with their gene and protein sequences, as candidates for either a direct experimental implementation or as a basis for enzyme engineering. Through pathway similarity analysis we classified the pathways and identified the enzymes and precursors that were indispensable for the production of the target molecules. These retrobiosynthesis studies demonstrate the potential of BNICE.ch for discovery, systematic evaluation, and analysis of novel pathways in synthetic biology and metabolic engineering studies.
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http://dx.doi.org/10.1021/acssynbio.8b00049DOI Listing
August 2018

Single-molecule kinetic analysis of HP1-chromatin binding reveals a dynamic network of histone modification and DNA interactions.

Nucleic Acids Res 2017 Oct;45(18):10504-10517

Laboratory of Biophysical Chemistry of Macromolecules, Institute of Chemical Sciences and Engineering (ISIC), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.

Chromatin recruitment of effector proteins involved in gene regulation depends on multivalent interaction with histone post-translational modifications (PTMs) and structural features of the chromatin fiber. Due to the complex interactions involved, it is currently not understood how effectors dynamically sample the chromatin landscape. Here, we dissect the dynamic chromatin interactions of a family of multivalent effectors, heterochromatin protein 1 (HP1) proteins, using single-molecule fluorescence imaging and computational modeling. We show that the three human HP1 isoforms are recruited and retained on chromatin by a dynamic exchange between histone PTM and DNA bound states. These interactions depend on local chromatin structure, the HP1 isoforms as well as on PTMs on HP1 itself. Of the HP1 isoforms, HP1α exhibits the longest residence times and fastest binding rates due to DNA interactions in addition to PTM binding. HP1α phosphorylation further increases chromatin retention through strengthening of multivalency while reducing DNA binding. As DNA binding in combination with specific PTM recognition is found in many chromatin effectors, we propose a general dynamic capture mechanism for effector recruitment. Multiple weak protein and DNA interactions result in a multivalent interaction network that targets effectors to a specific chromatin modification state, where their activity is required.
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http://dx.doi.org/10.1093/nar/gkx697DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5737501PMC
October 2017

Identification and dynamics of the human ZDHHC16-ZDHHC6 palmitoylation cascade.

Elife 2017 08 15;6. Epub 2017 Aug 15.

Global Health Institute, Faculty of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.

S-Palmitoylation is the only reversible post-translational lipid modification. Knowledge about the DHHC palmitoyltransferase family is still limited. Here we show that human ZDHHC6, which modifies key proteins of the endoplasmic reticulum, is controlled by an upstream palmitoyltransferase, ZDHHC16, revealing the first palmitoylation cascade. The combination of site specific mutagenesis of the three ZDHHC6 palmitoylation sites, experimental determination of kinetic parameters and data-driven mathematical modelling allowed us to obtain detailed information on the eight differentially palmitoylated ZDHHC6 species. We found that species rapidly interconvert through the action of ZDHHC16 and the Acyl Protein Thioesterase APT2, that each species varies in terms of turnover rate and activity, altogether allowing the cell to robustly tune its ZDHHC6 activity.
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http://dx.doi.org/10.7554/eLife.27826DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5582869PMC
August 2017