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MEMO: A Method for Computing Metabolic Modules for Cell-Free Production Systems

ACS SYNTHETIC BIOLOGY 2020年第3期9卷 556-566页

作者： von Kamp, Axel Klamt, Steffen Max Planck Inst Dynam Complex Tech Syst D-39106 Magdeburg Germany

Cell-free bioproduction systems represent a promising alternative to classical microbial fermentation processes to synthesize value-added products from biological feedstocks. An essential step for establishing cell-free production systems is the identification of suitable metabolic modules with defined properties. Here we present MEMO, a novel computational approach to find smallest metabolic modules with specified stoichiometric and thermodynamic constraints supporting the design of cell-free systems in various regards. In particular, one key challenge for a sustained operation of cell-free systems is the regeneration of utilized cofactors (such as ATP and NAD(P)H). Given a production pathway with certain cofactor requirements, MEMO can be used to compute smallest regeneration modules that recover these cofactors with required stoichiometries. MEMO incorporates the stoichiometric and thermodynamic constraints in a single mixed-integer linear program, which can then be solved to find smallest suitable modules from a given reaction database. We illustrate the applicability of MEMO by calculating regeneration modules for the recently published synthetic CETCH cycle for in vitro carbon dioxide fixation. We demonstrate that MEMO is very flexible in taking into account the diverse constraints of the CETCH cycle (e.g., regeneration of 1 ATP, 4 NADPH and of 1 acetyl-group without net production of CO2 and with permitted side production of malate) and is able to determine multiple solutions in reasonable time in two large reaction databases (MetaCyc and BiGG). The most promising regeneration modules found utilize glycerol as substrate and require only 8 enzymatic steps. It is also shown that some of these modules are robust against spontaneous loss of cofactors (e.g., oxidation of NAD(P)H or hydrolysis of ATP). Furthermore, we demonstrate that MEMO can also find cellfree production systems with integrated product synthesis and cofactor regeneration. Overall, MEMO provides a

关键词： synthetic biology design of cell-free systems constraint-based modeling metabolic networks cofactor regeneration mixed-integer linear programming

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Parameterized maximum entropy models predict variability of metabolic scaling across tree communities and populations

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ECOLOGY 2020年第6期101卷 e03011-e03011页

作者： Xu, Meng Pace Univ Dept Math 41 Pk Row New York NY 10038 USA

The maximum entropy theory of ecology (METE) applies the concept of "entropy" from information theory to predict macroecological patterns. The energetic predictions of the METE rely on predetermined metabolic scaling from external theories, and this reliance diminishes the testability of the theory. In this work, I build parameterized METE models by treating the metabolic scaling exponent as a free parameter, and I use the maximum-likelihood method to obtain empirically plausible estimates of the exponent. I test the models using the individual tree data from an oak-dominated deciduous forest in the northeastern United States and from a tropical forest in central Panama. My analysis shows that the metabolic scaling exponents predicted from the parameterized METE models deviate from that of the metabolic theory of ecology and exhibit large variation, at both community and population levels. Assemblage and population abundance may act as ecological constraints that regulate the individual-level metabolic scaling behavior. This study provides a novel example of the use of the parameterized METE models to reveal the biological processes of individual organisms. The implication and possible extensions of the parameterized METE models are discussed.

关键词： constraint-based modeling individual size distribution Kolmogorov-Smirnov test Lagrange multipliers maximum likelihood metabolic energy rate mixed-effects models phenomenological models

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A Systems-based Approach for Cyanide Overproduction byBacillus megateriumfor Gold Bioleaching Enhancement

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FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY 2020年 8卷 528页

作者： Aminian-Dehkordi, Javad Mousavi, Seyyed Mohammad Marashi, Sayed-Amir Jafari, Arezou Mijakovic, Ivan Tarbiat Modares Univ Dept Chem Engn Biotechnol Grp Tehran Iran Univ Tehran Coll Sci Dept Biotechnol Tehran Iran Tarbiat Modares Univ Dept Chem Engn Tehran Iran Chalmers Univ Technol Dept Biol & Biol Engn Gothenburg Sweden Tech Univ Denmark Novo Nordisk Fdn Ctr Biosustainabil Lyngby Denmark

With the constant accumulation of electronic waste, extracting precious metals contained therein is becoming a major challenge for sustainable *** megateriumis currently one of the microbes used for the production of cyanide, which is the main leaching agent for gold recovery. The present study aimed to propose a strategy for metabolic engineering ofB. megateriumto overproduce cyanide, and thus ameliorate the bioleaching process. For this, we employed constraint-based modeling, runningin silicosimulations oniJA1121, the genome-scale metabolic model ofB. megateriumDSM319. Flux balance analysis (FBA) was initially used to identify amino acids to be added to the culture medium. Considering cyanide as the desired product, we used growth-coupled methods, constrained minimal cut sets (cMCSs) and OptKnock to identify gene inactivation targets. To identify gene overexpression targets, flux scanning based on enforced objective flux (FSEOF) was performed. Further analysis was carried out on the identified targets to determine compounds with beneficial regulatory effects. We have proposed a chemical-defined medium for accelerating cyanide production on the basis of microplate assays to evaluate the components with the greatest improving effects. Accordingly, the cultivation ofB. megateriumDSM319 in a chemically-defined medium with 5.56 mM glucose as the carbon source, and supplemented with 413 mu M cysteine, led to the production of considerably increased amounts of cyanide. Bioleaching experiments were successfully performed in this medium to recover gold and copper from telecommunication printed circuit boards. The results of inductively coupled plasma (ICP) analysis confirmed that gold recovery peaked out at around 55% after 4 days, whereas copper recovery continued to increase for several more days, peaking out at around 85%. To further validate the bioleaching results, FESEM, XRD, FTIR, and EDAX mapping analyses were performed. We concluded that the propo

关键词： constraint-based modeling bioleaching cyanide chemically-defined medium genome-scale metabolic model

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Toward modeling metabolic state from single-cell transcriptomics

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MOLECULAR METABOLISM 2022年第0期57卷 101396-101396页

作者： Hrovatin, Karin Fischer, David S. Theis, Fabian J. Helmholtz Ctr Munich Inst Computat Biol Ingolstaedter Landstr 1 D-85764 Neuherberg Germany Tech Univ Munich TUM Sch Life Sci Weihenstephan Alte Akad 8 D-85354 Freising Weihenstephan Germany Tech Univ Munich Dept Math Boltzmannstr 3 D-85748 Garching Germany

Background: Single-cell metabolic studies bring new insights into cellular function, which can often not be captured on other omics layers. Metabolic information has wide applicability, such as for the study of cellular heterogeneity or for the understanding of drug mechanisms and biomarker development. However, metabolic measurements on single-cell level are limited by insufficient scalability and sensitivity, as well as resource intensiveness, and are currently not possible in parallel with measuring transcript state, commonly used to identify cell types. Nevertheless, because omics layers are strongly intertwined, it is possible to make metabolic predictions based on measured data of more easily measurable omics layers together with prior metabolic network *** of Review: We summarize the current state of single-cell metabolic measurement and modeling approaches, motivating the use of computational techniques. We review three main classes of computational methods used for prediction of single-cell metabolism: pathway-level analysis, constraint-based modeling, and kinetic modeling. We describe the unique challenges arising when transitioning from bulk to single-cell modeling. Finally, we propose potential model extensions and computational methods that could be leveraged to achieve these *** Conclusions: Single-cell metabolic modeling is a rising field that provides a new perspective for understanding cellular functions. The presented modeling approaches vary in terms of input requirements and assumptions, scalability, modeled metabolic layers, and newly gained insights. We believe that the use of prior metabolic knowledge will lead to more robust predictions and will pave the way for mechanistic and interpretable machine-learning models. (c) 2021 Published by Elsevier GmbH. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Metabolic modeling Single-cell RNA-seq constraint-based modeling Kinetic modeling Pathway analysis

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Causal inference on microbiome-metabolome relations in observational host-microbiome data via in silico in vivo association pattern analyses

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CELL REPORTS METHODS 2023年第10期3卷 100615页

作者： Hertel, Johannes Heinken, Almut Fassler, Daniel Thiele, Ines Univ Galway Sch Med Galway Ireland Univ Med Greifswald Dept Psychiat & Psychotherapy Greifswald Germany Univ Lorraine Inst Med Res Pole BMS UMR Inserm NGERE Nutrit Genet Environm Risks 1254 Vandoeuvre Les Nancy France Natl Univ Ireland Galway Discipline Bacteriol Galway Ireland Univ Coll Cork APC Microbiome Ireland Cork Ireland Univ Galway Ryan Inst Galway Ireland

Understanding the effects of the microbiome on the host's metabolism is core to enlightening the role of the microbiome in health and disease. Herein, we develop the paradigm of in silico in vivo association pattern analyses, combining microbiome metabolome association studies with in silico constraint-based community modeling. Via theoretical dissection of confounding and causal paths, we show that in silico in vivo association pattern analyses allow for causal inference on microbiome-metabolome relations in observational data. We justify the corresponding theoretical criterion by structural equation modeling of host-microbiome systems, integrating deterministic microbiome community modeling into population statistics approaches. We show the feasibility of our approach on a published multi-omics dataset (n = 347), demonstrating causal microbiome-metabolite relations for 26 out of 54 fecal metabolites. In summary, we generate a promising approach for causal inference in metabolic host-microbiome interactions by integrating hypothesis-free screening association studies with knowledge-based in silico modeling.

关键词： microbiome metabolomics constraint-based modeling causal inference microbiome-host interactions community modeling

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Systematizing the different notions of growth-coupled product synthesis and a single framework for computing corresponding strain designs

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BIOTECHNOLOGY JOURNAL 2021年第12期16卷 e2100236-e2100236页

作者： Schneider, Philipp Mahadevan, Radhakrishnan Klamt, Steffen Max Planck Inst Dynam Complex Tech Syst Sandtorstr 1 D-39106 Magdeburg Germany Univ Toronto Dept Chem Engn & Appl Chem Toronto ON Canada

A widely used design principle for metabolic engineering of microorganisms aims to introduce interventions that enforce growth-coupled product synthesis such that the product of interest becomes a (mandatory) by-product of growth. However, different variants and partially contradicting notions of growth-coupled production (GCP) exist. Herein, we propose an ontology for the different degrees of GCP and clarify their relationships. Ordered by coupling degree, we distinguish four major classes: potentially, weakly, and directionally growth-coupled production (pGCP, wGCP, dGCP) as well as substrate-uptake coupled production (SUCP). We then extend the framework ofMinimal Cut Sets (MCS), previously used to compute dGCP and SUCP strain designs, to allow inclusion of implicit optimality constraints, a feature required to compute pGCP and wGCP designs. This extension closes the gap between MCS-based and bilevelbased strain design approaches and enables computation (and comparison) of designs for all GCP classes within a single framework. By computing GCP strain designs for a range of products, we illustrate the hierarchical relationships between the different coupling degrees. We find that feasibility of coupling is not affected by the chosen GCP degree and that strongest coupling (SUCP) requires often only one or two more interventions than wGCP and dGCP. Finally, we show that the principle of coupling can be generalized to couple product synthesis with other cellular functions than growth, for example, with net ATP formation. This work provides important theoretical results and algorithmic developments and a unified terminology for computational strain design based on GCP.

关键词： computational strain design constraint-based modeling metabolic engineering

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Predicted Metabolic Function of the Gut Microbiota of Drosophila melanogaster

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MSYSTEMS 2021年第3期6卷 e01369-20页

作者： Ankrah, Nana Y. D. Barker, Brandon E. Song, Joan Wu, Cindy McMullen, John G., II Douglas, Angela E. Cornell Univ Dept Entomol Ithaca NY 14853 USA Cornell Univ Ctr Adv Comp Ithaca NY USA Cornell Univ Sch Elect & Comp Engn Ithaca NY USA Cornell Univ Robert Frederick Smith Sch Chem & Biomol Engn Ithaca NY USA Cornell Univ Dept Mol Biol & Genet Ithaca NY USA SUNY Coll Plattsburgh Dept Biol Sci Plattsburgh NY 12901 USA Albert Einstein Coll Med Med Scientist Training Program New York NY 10038 USA

An important goal for many nutrition-based microbiome studies is to identify the metabolic function of microbes in complex microbial communities and their impact on host physiology. This research can be confounded by poorly understood effects of community composition and host diet on the metabolic traits of individual taxa. Here, we investigated these multiway interactions by constructing and analyzing metabolic models comprising every combination of five bacterial members of the Drosophila gut microbiome (from single taxa to the five-member community of Acetobacter and Lactobacillus species) under three nutrient regimes. We show that the metabolic function of Drosophila gut bacteria is dynamic, influenced by community composition, and responsive to dietary modulation. Furthermore, we show that ecological interactions such as competition and mutualism identified from the growth patterns of gut bacteria are underlain by a diversity of metabolic interactions, and show that the bacteria tend to compete for amino acids and B vitamins more frequently than for carbon sources. Our results reveal that, in addition to fermentation products such as acetate, intermediates of the tricarboxylic acid (TCA) cycle, including 2-oxoglutarate and succinate, are produced at high flux and cross-fed between bacterial taxa, suggesting important roles for TCA cycle intermediates in modulating Drosophila gut microbe interactions and the potential to influence host traits. These metabolic models provide specific predictions of the patterns of ecological and metabolic interactions among gut bacteria under different nutrient regimes, with potentially important consequences for overall community metabolic function and nutritional interactions with the host. IMPORTANCE Drosophila is an important model for microbiome research partly because of the low complexity of its mostly culturable gut microbiota. Our current understanding of how Drosophila interacts with its gut microbes and how these inter

关键词： microbiome constraint-based modeling mutualism competition cross-feeding Drosophila

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Proteome Regulation Patterns Determine Escherichia coli Wild-Type and Mutant Phenotypes

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MSYSTEMS 2021年第2期6卷 e00625-20页

作者： Alter, Tobias B. Blank, Lars M. Ebert, Birgitta E. Rhein Westfal TH Aachen Inst Appl Microbiol iAMB Aachen Biol & Biotechnol ABBt Aachen Germany Univ Queensland Australian Inst Bioengn & Nanotechnol AIBN Brisbane Qld Australia Tech Univ Denmark Novo Nordisk Fdn Ctr Biosustainabil Lyngby Denmark

It is generally recognized that proteins constitute the key cellular component in shaping microbial phenotypes. Due to limited cellular resources and space, optimal allocation of proteins is crucial for microbes to facilitate maximum proliferation rates while allowing a flexible response to environmental changes. To account for the growth condition-dependent proteome in the constraint-based metabolic modeling of Escherichia coli, we consolidated a coarse-grained protein allocation approach with the explicit consideration of enzymatic constraints on reaction fluxes. Besides representing physiologically relevant wild-type phenotypes and flux distributions, the resulting protein allocation model (PAM) advances the predictability of the metabolic responses to genetic perturbations. A main driver of mutant phenotypes was ascribed to inherited regulation patterns in protein distribution among metabolic enzymes. Moreover, the PAM correctly reflected metabolic responses to an augmented protein burden imposed by the heterologous expression of green fluorescent protein. In summary, we were able to model the effects of important and frequently applied metabolic engineering approaches on microbial metabolism. Therefore, we want to promote the integration of protein allocation constraints into classical constraint-based models to foster their predictive capabilities and application for strain analysis and engineering purposes. IMPORTANCE Predictive metabolic models are important, e.g., for generating biological knowledge and designing microbes with superior performance for target compound production. Yet today's whole-cell models either show insufficient predictive capabilities or are computationally too expensive to be applied to metabolic engineering purposes. By linking the inherent genotype-phenotype relationship to a complete representation of the proteome, the PAM advances the accuracy of simulated phenotypes and intracellular flux distributions of E. coli. Being equally c

关键词： constraint-based modeling enzyme kinetics metabolic engineering protein allocation transcriptional control Escherichia coli

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Improving the accuracy of flux balance analysis through the implementation of carbon availability constraints for intracellular reactions

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BIOTECHNOLOGY AND BIOENGINEERING 2019年第9期116卷 2339-2352页

作者： Lularevic, Maximilian Racher, Andrew J. Jaques, Colin Kiparissides, Alexandros UCL Dept Biochem Engn London WC1E 6BT England Lonza Biol PLC Res & Technol Slough Berks England

constraint-based modeling methods, such as Flux Balance Analysis (FBA), have been extensively used to decipher complex, information rich -omics datasets to elicit system-wide behavioral patterns of cellular metabolism. FBA has been successfully used to gain insight in a wide range of applications, such as range of substrate utilization, product yields and to design metabolic engineering strategies to improve bioprocess performance. A well-known challenge associated with large genome-scale metabolic networks is that they result in underdetermined problem formulations. Consequently, rather than unique solutions, FBA and related methods examine ranges of reaction flux values that are consistent with the studied physiological conditions. The wider the reported flux ranges, the higher the uncertainty in the determination of basic reaction properties, limiting interpretability of and confidence in the results. Herein, we propose a new, computationally efficient approach that refines flux range predictions by constraining reaction fluxes on the basis of the elemental balance of carbon. We compared carbon constraint FBA (ccFBA) against experimentally-measured intracellular fluxes using the latest CHO GEM (iCHO1766) and were able to substantially improve the accuracy of predicted flux values compared with FBA. ccFBA can be used as a stand-alone method but is also compatible with and complimentary to other constraint-based approaches.

关键词： carbon constraining CHO constraint-based modeling FBA genome-scale metabolic network

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Bioinoculant capability enhancement through metabolomics and systems biology approaches

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BRIEFINGS IN FUNCTIONAL GENOMICS 2019年第3期18卷 159-168页

作者： Chaudhary, Twinkle Shukla, Pratyoosh Maharshi Dayanand Univ Dept Microbiol Rohtak Haryana India

Bioinoculants are eco-friendly microorganisms, and their products are utilized for improving the potential of soil and fulfill the nutrients requirement for the host plant. The agricultural yield has increased due to the use of bioinoculants over chemical-based fertilizers, and thus it generates interest in understanding the innovation process by various methods. By gene-editing tool, the desired gene product can be changed for engineered microbial inoculants. We have also described various modern biotechnological tools like constraint-based modeling, OptKnock, flux balance analysis and modeling of the biological network for enhancing the bioinoculant capability. These fluxes give the fascinating perception of the metabolic network in the absence of comprehensive kinetic information. These tools also help in the stimulation of the metabolic networks by incorporation of enzyme-encoding genes. The present review explains the use of systems biology and gene-editing tools for improving the capability of bioinoculants. Moreover, this review also emphasizes on the challenges and future perspective of systems biology and its multidisciplinary facets.

关键词： bioinoculant systems biology metabolomics gene editing flux balance analysis constraint-based modeling

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