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Examining organic acid production potential and growth-coupled strategies in Issatchenkia orientalis using constraint-based modeling

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BIOTECHNOLOGY PROGRESS 2022年第5期38卷 e3276-e3276页

作者： Suthers, Patrick F. Maranas, Costas D. Penn State Univ Dept Chem Engn University Pk PA 16802 USA Penn State Univ Ctr Adv Bioenergy & Bioprod Innovat University Pk PA 16802 USA

Growth-coupling product formation can facilitate strain stability by aligning industrial objectives with biological fitness. Organic acids make up many building block chemicals that can be produced from sugars obtainable from renewable biomass. Issatchenkia orientalis is a yeast strain tolerant to acidic conditions and is thus a promising host for industrial production of organic acids. Here, we use constraint-based methods to assess the potential of computationally designing growth-coupled production strains for I. orientalis that produce 22 different organic acids under aerobic or microaerobic conditions. We explore native and engineered pathways using glucose or xylose as the carbon substrates as proxy constituents of hydrolyzed biomass. We identified growth-coupled production strategies for 37 of the substrate-product pairs, with 15 pairs achieving production for any growth rate. We systematically assess the strain design solutions and categorize the underlying principles involved.

关键词： constraint-based modeling metabolic engineering nonmodel yeast

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Regulatory dynamic enzyme-cost flux balance analysis: A unifying framework for constraint-based modeling

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JOURNAL OF THEORETICAL BIOLOGY 2020年 501卷 110317-110317页

作者： Liu, Lin Bockmayr, Alexander Free Univ Berlin Dept Math & Comp Sci Arnimallee 6 D-14195 Berlin Germany

Integrated modeling of metabolism and gene regulation continues to be a major challenge in computational biology. While there exist approaches like regulatory flux balance analysis (rFBA), dynamic flux balance analysis (dFBA), resource balance analysis (RBA) or dynamic enzyme-cost flux balance analysis (deFBA) extending classical flux balance analysis (FBA) in various directions, there have been no constraint-based methods so far that allow predicting the dynamics of metabolism taking into account both macromolecule production costs and regulatory events. In this paper, we introduce a new constraint-based modeling framework named regulatory dynamic enzyme-cost flux balance analysis (r-deFBA), which unifies dynamic modeling of metabolism, cellular resource allocation and transcriptional regulation in a hybrid discrete-continuous setting. With r-deFBA, we can predict discrete regulatory states together with the continuous dynamics of reaction fluxes, external substrates, enzymes, and regulatory proteins needed to achieve a cellular objective such as maximizing biomass over a time interval. The dynamic optimization problem underlying r-deFBA can be reformulated as a mixed-integer linear optimization problem, for which there exist efficient solvers. (C) 2020 The Author(s). Published by Elsevier Ltd.

关键词： constraint-based modeling Resource allocation Enzyme costs Metabolic networks Gene regulation Hybrid dynamical system

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constraint-based modeling in microbial food biotechnology

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BIOCHEMICAL SOCIETY TRANSACTIONS 2018年第2期46卷 249-260页

作者： Rau, Martin H. Zeidan, Ahmad A. Chr Hansen AS Discovery R&D Microbial Platform DK-2970 Horsholm Denmark

Genome-scale metabolic network reconstruction offers a means to leverage the value of the exponentially growing genomics data and integrate it with other biological knowledge in a structured format. constraint-based modeling (CBM) enables both the qualitative and quantitative analyses of the reconstructed networks. The rapid advancements in these areas can benefit both the industrial production of microbial food cultures and their application in food processing. CBM provides several avenues for improving our mechanistic understanding of physiology and genotype-phenotype relationships. This is essential for the rational improvement of industrial strains, which can further be facilitated through various model-guided strain design approaches. CBM of microbial communities offers a valuable tool for the rational design of defined food cultures, where it can catalyze hypothesis generation and provide unintuitive rationales for the development of enhanced community phenotypes and, consequently, novel or improved food products. In the industrial-scale production of microorganisms for food cultures, CBM may enable a knowledge-driven bioprocess optimization by rationally identifying strategies for growth and stability improvement. Through these applications, we believe that CBM can become a powerful tool for guiding the areas of strain development, culture development and process optimization in the production of food cultures. Nevertheless, in order to make the correct choice of the modeling framework for a particular application and to interpret model predictions in a biologically meaningful manner, one should be aware of the current limitations of CBM.

关键词： constraint-based modeling food fermentation metabolic networks microbial community modeling microbial food cultures strain development

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A mathematical framework for yield (vs. rate) optimization in constraint-based modeling and applications in metabolic engineering

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METABOLIC ENGINEERING 2018年 47卷 153-169页

作者： Klamt, Steffen Mueller, Stefan Regensburger, Georg Zanghellini, Juergen Max Planck Inst Dynam Complex Tech Syst Magdeburg Germany Univ Vienna Fac Math Vienna Austria Johannes Kepler Univ Linz Inst Algebra Linz Austria Univ Nat Resources & Life Sci Dept Biotechnol Vienna Austria Austrian Ctr Ind Biotechnol Vienna Austria

Background: The optimization of metabolic rates (as linear objective functions) represents the methodical core of flux-balance analysis techniques which have become a standard tool for the study of genome-scale metabolic models. Besides (growth and synthesis) rates, metabolic yields are key parameters for the characterization of biochemical transformation processes, especially in the context of biotechnological applications. However, yields are ratios of rates, and hence the optimization of yields (as nonlinear objective functions) under arbitrary linear constraints is not possible with current flux-balance analysis techniques. Despite the fundamental importance of yields in constraint-based modeling, a comprehensive mathematical framework for yield optimization is still missing. Results: We present a mathematical theory that allows one to systematically compute and analyze yield-optimal solutions of metabolic models under arbitrary linear constraints. In particular, we formulate yield optimization as a linear-fractional program. For practical computations, we transform the linear-fractional yield optimization problem to a (higher-dimensional) linear problem. Its solutions determine the solutions of the original problem and can be used to predict yield-optimal flux distributions in genome-scale metabolic models. For the theoretical analysis, we consider the linear-fractional problem directly. Most importantly, we show that the yield-optimal solution set (like the rate-optimal solution set) is determined by (yield-optimal) elementary flux vectors of the underlying metabolic model. However, yield- and rate-optimal solutions may differ from each other, and hence optimal (biomass or product) yields are not necessarily obtained at solutions with optimal (growth or synthesis) rates. Moreover, we discuss phase planes/production envelopes and yield spaces, in particular, we prove that yield spaces are convex and provide algorithms for their computation. We illustrate our fi

关键词： constraint-based modeling Elementary flux mode Elementary flux vector Flux-balance analysis Linear-fractional program Metabolic pathway analysis Production envelope Productivity Strain design Yield space

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constraint-based Student modeling in Probability Story Problems with Scaffolding Techniques

INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING

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INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING 2018年第1期13卷 178-205页

作者： Khodeir, Nabila Wanas, Nayer Elazhary, Hanan Elect Res Inst Dept Informat Cairo Egypt Elect Res Inst Informat Giza Egypt King Abdulaziz Univ Fac Comp & Informat Technol Dept Comp Sci Jeddah Saudi Arabia

constraint-based student modeling (CBM) is an important technique employed in intelligent tutoring systems to model student knowledge to provide relevant assistance. This paper introduces the Math Story Problem Tutor (MAST), a Web-based intelligent tutoring system for probability story problems, which is able to generate problems of different contexts, types and difficulty levels for self-paced learning. constraints in MAST are specified at a low-level of granularity to allow fine-grained diagnosis of the student error. Furthermore, MAST extends CBM to address errors due to misunderstanding of the narrative story. It can locate and highlight keywords that may have been overlooked or misunderstood leading to an error. This is achieved by utilizing the roles and syntaxes of the problem segments and the semantic descriptions of the keywords that are defined through the Natural Language Generation (NLG) methods deployed in the story problem generation. MAST also integrates CBM with scaffolding questions and feedback to provide various forms of help and guidance to the student. This allows the student to discover and correct any errors in his/her solution. The tutoring effect of MAST has been evaluated empirically using different tests (paired samples t-test, normalized knowledge gain, Mann-Whitney U test, and power learning curves). The results demonstrated a positive effect of MAST on improving the exam scores, and the normalized knowledge gain. Additionally, keyword highlighting of errors integrated in MAST has been shown to speed up learning and improve reduction of average percentage of violated constraints along the learning curve. This suggests that using various forms of assistance can help speedup learning, and that well-designed tutoring systems providing relevant help can be superior to traditional approaches.

关键词： constraint-based modeling Intelligent Tutoring Systems Scaffolding Feedback Scaffolding Questions

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constraint-based modeling of Heterologous Pathways: Application and Experimental Demonstration for Overproduction of Fatty Acids in Escherichia coli

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BIOTECHNOLOGY AND BIOENGINEERING 2014年第10期111卷 2056-2066页

作者： Ip, Kuhn Donoghue, Neil Kim, Min Kyung Lun, Desmond S. Univ S Australia Phen & Bioinformat Res Ctr Sch Informat Technol & Math Sci Mawson Lakes SA 5095 Australia Univ S Australia Australian Ctr Plant Funct Genom Mawson Lakes SA 5095 Australia Rutgers State Univ Ctr Computat & Integrat Biol Camden NJ 08102 USA Rutgers State Univ Dept Comp Sci Camden NJ 08102 USA

constraint-based modeling has been shown, in many instances, to be useful for metabolic engineering by allowing the prediction of the metabolic phenotype resulting from genetic manipulations. But the basic premise of constraint-based modeling-that of applying constraints to preclude certain behaviors-only makes sense for certain genetic manipulations (such as knockouts and knockdowns). In particular, when genes (such as those associated with a heterologous pathway) are introduced under artificial control, it is unclear how to predict the correct behavior. In this paper, we introduce a modeling method that we call proportional flux forcing (PFF) to model artificially induced enzymatic genes. The model modifications introduced by PFF can be transformed into a set of simple mass balance constraints, which allows computational methods for strain optimization based on flux balance analysis (FBA) to be utilized. We applied PFF to the metabolic engineering of Escherichia coli (E. coli) for free fatty acid (FFA) production-a metabolic engineering problem that has attracted significant attention because FFAs are a precursor to liquid transportation fuels such as biodiesel and biogasoline. We show that PFF used in conjunction with FBA-based computational strain optimization methods can yield non-obvious genetic manipulation strategies that significantly increase FFA production in E. coli. The two mutant strains constructed and successfully tested in this work had peak fatty acid (FA) yields of 0.050 g FA/g carbon source (17.4% theoretical yield) and 0.035 g FA/g carbon source (12.3% theoretical yield) when they were grown using a mixed carbon source of glucose and casamino acids in a ratio of 2-to-1. These yields represent increases of 5.4- and 3.8-fold, respectively, over the baseline strain. (C) 2014 Wiley Periodicals, Inc.

关键词： biofuel computational strain design constraint-based modeling Escherichia coli fatty acids overproduction

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Flux Variability Analysis: Application to Developing Oilseed Rape Embryos Using Toolboxes for constraint-based modeling

Flux Variability Analysis: Application to Developing Oilseed...

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作者： Jordan O. Hay Jörg Schwender

Flux variability analysis enables comprehensive exploration of alternate optimal routes in a metabolic network. This method is especially useful with models such as bna572 for the developing oilseed rape embryo which is highly compartmentalized. Here, we describe a protocol for carrying out flux variability analysis on reactions and network projections of bna572 using well-established software (CellNetAnalyzer and COBRA) for constraint-based analysis of stoichiometric network reconstructions. less

关键词： MATLAB Metabolic Engineering Flux balance analysis Metabolic network constraint-based modeling Linear programming Stoichiometric modeling Flux variability analysis

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Toward a modeling, optimization, and predictive control framework for fed-batch metabolic cybergenetics

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BIOTECHNOLOGY AND BIOENGINEERING 2024年第1期121卷 366-379页

作者： Espinel-Rios, Sebastian Morabito, Bruno Pohlodek, Johannes Bettenbrock, Katja Klamt, Steffen Findeisen, Rolf Max Planck Inst Dynam Complex Tech Syst Anal & Redesign Biol Networks Magdeburg Germany Yokogawa Insil Biotechnol GmbH Stuttgart Germany Tech Univ Darmstadt Control & Cyber Phys Syst Lab Darmstadt Germany

Biotechnology offers many opportunities for the sustainable manufacturing of valuable products. The toolbox to optimize bioprocesses includes extracellular process elements such as the bioreactor design and mode of operation, medium formulation, culture conditions, feeding rates, and so on. However, these elements are frequently insufficient for achieving optimal process performance or precise product composition. One can use metabolic and genetic engineering methods for optimization at the intracellular level. Nevertheless, those are often of static nature, failing when applied to dynamic processes or if disturbances occur. Furthermore, many bioprocesses are optimized empirically and implemented with little-to-no feedback control to counteract disturbances. The concept of cybergenetics has opened new possibilities to optimize bioprocesses by enabling online modulation of the gene expression of metabolism-relevant proteins via external inputs (e.g., light intensity in optogenetics). Here, we fuse cybergenetics with model-based optimization and predictive control for optimizing dynamic bioprocesses. To do so, we propose to use dynamic constraint-based models that integrate the dynamics of metabolic reactions, resource allocation, and inducible gene expression. We formulate a model-based optimal control problem to find the optimal process inputs. Furthermore, we propose using model predictive control to address uncertainties via online feedback. We focus on fed-batch processes, where the substrate feeding rate is an additional optimization variable. As a simulation example, we show the optogenetic control of the ATPase enzyme complex for dynamic modulation of enforced ATP wasting to adjust product yield and productivity. Metabolic cybergenetic feedback in fed-batch bioreactors exploiting dynamic optimization and model predictive control: Key proteins, like enzyme preg ${p}_{reg}$, adjust using inducible gene systems to achieve various metabolic modes, influenced by fa

关键词： constraint-based modeling dynamic metabolic control metabolic cybergenetics model predictive control optogenetics state estimation

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Adjusting for false discoveries in constraint-based differential metabolic flux analysis

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JOURNAL OF BIOMEDICAL INFORMATICS 2024年 150卷 104597页

作者： Galuzzi, Bruno G. Milazzo, Luca Damiani, Chiara Univ Milano Bicocca Dept Biotechnol & Biosci Piazza Ateneo Nuovo 1 I-20126 Milan Italy Univ Milano Bicocca Dept Informat Syst & Commun Piazza Ateneo Nuovo 1 I-20126 Milan Italy Inst Mol Bioimaging & Physiol IBFM I-20054 Segrate Italy ISBE IT SYSBIO Ctr Syst Biol Milan Italy

One of the critical steps to characterize metabolic alterations in multifactorial diseases, as well as their heterogeneity across different patients, is the identification of reactions that exhibit significantly different usage (or flux) between cohorts. However, since metabolic fluxes cannot be determined directly, researchers typically use constraint -based metabolic network models, customized on post-genomics datasets. The use of random sampling within the feasible region of metabolic networks is becoming more prevalent for comparing these networks. While many algorithms have been proposed and compared for efficiently and uniformly sampling the feasible region of metabolic networks, their impact on the risk of making false discoveries when comparing different samples has not been investigated yet, and no sampling strategy has been so far specifically designed to mitigate the problem. To be able to precisely assess the False Discovery Rate (FDR), in this work we compared different samples obtained from the very same metabolic model. We compared the FDR obtained for different model scales, sample sizes, parameters of the sampling algorithm, and strategies to filter out non -significant variations. To be able to compare the largely used hit-and-run strategy with the much less investigated corner -based strategy, we first assessed the intrinsic capability of current corner -based algorithms and of a newly proposed one to visit all vertices of a constraint -based region. We show that false discoveries can occur at high rates even for large samples of small-scale networks. However, we demonstrate that a statistical test based on the empirical null distribution of Kullback-Leibler divergence can effectively correct for false discoveries. We also show that our proposed corner -based algorithm is more efficient than state-of-the-art alternatives and much less prone to false discoveries than hit-and-run strategies. We report that the differences in the marginal distributio

关键词： Flux sampling constraint-based modeling Corner-based Hit and run False discovery rate

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Prediction and integration of metabolite-protein interactions with genome-scale metabolic models

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METABOLIC ENGINEERING 2024年 82卷 216-224页

作者： Habibpour, Mahdis Razaghi-Moghadam, Zahra Nikoloski, Zoran Max Planck Inst Mol Plant Physiol Syst Biol & Math Modeling Grp D-14476 Potsdam Germany Univ Potsdam Inst Biochem & Biol Bioinformat Dept D-14476 Potsdam Germany

Metabolites, as small molecules, can act not only as substrates to enzymes, but also as effectors of activity of proteins with different functions, thereby affecting various cellular processes. While several experimental techniques have started to catalogue the metabolite-protein interactions (MPIs) present in different cellular contexts, characterizing the functional relevance of MPIs remains a challenging problem. Computational approaches from the constrained-based modeling framework allow for predicting MPIs and integrating their effects in the in silico analysis of metabolic and physiological phenotypes, like cell growth. Here, we provide a classification of all existing constraint-based approaches that predict and integrate MPIs using genome-scale metabolic networks as input. In addition, we benchmark the performance of the approaches to predict MPIs in a comparative study using different features extracted from the model structure and predicted metabolic phenotypes with the state-of-the-art metabolic networks of Escherichia coli and Saccharomyces cerevisiae. Lastly, we provide an outlook for future, feasible directions to expand the consideration of MPIs in constraint-based modeling approaches with wide biotechnological applications.

关键词： Metabolite-protein interactions Metabolic networks constraint-based modeling Machine learning Classification

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