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Metabolic flux modeling of Gluconobacter oxydans enables improved production of bioleaching organic acids. Process Biochem 2022. [DOI: 10.1016/j.procbio.2022.10.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Environment Constrains Fitness Advantages of Division of Labor in Microbial Consortia Engineered for Metabolite Push or Pull Interactions. mSystems 2022; 7:e0005122. [PMID: 35762764 PMCID: PMC9426560 DOI: 10.1128/msystems.00051-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Fitness benefits from division of labor are well documented in microbial consortia, but the dependency of the benefits on environmental context is poorly understood. Two synthetic Escherichia coli consortia were built to test the relationships between exchanged organic acid, local environment, and opportunity costs of different metabolic strategies. Opportunity costs quantify benefits not realized due to selecting one phenotype over another. The consortia catabolized glucose and exchanged either acetic or lactic acid to create producer-consumer food webs. The organic acids had different inhibitory properties and different opportunity costs associated with their positions in central metabolism. The exchanged metabolites modulated different consortial dynamics. The acetic acid-exchanging (AAE) consortium had a “push” interaction motif where acetic acid was secreted faster by the producer than the consumer imported it, while the lactic acid-exchanging (LAE) consortium had a “pull” interaction motif where the consumer imported lactic acid at a comparable rate to its production. The LAE consortium outperformed wild-type (WT) batch cultures under the environmental context of weakly buffered conditions, achieving a 55% increase in biomass titer, a 51% increase in biomass per proton yield, an 86% increase in substrate conversion, and the complete elimination of by-product accumulation all relative to the WT. However, the LAE consortium had the trade-off of a 42% lower specific growth rate. The AAE consortium did not outperform the WT in any considered performance metric. Performance advantages of the LAE consortium were sensitive to environment; increasing the medium buffering capacity negated the performance advantages compared to WT. IMPORTANCE Most naturally occurring microorganisms persist in consortia where metabolic interactions are common and often essential to ecosystem function. This study uses synthetic ecology to test how different cellular interaction motifs influence performance properties of consortia. Environmental context ultimately controlled the division of labor performance as shifts from weakly buffered to highly buffered conditions negated the benefits of the strategy. Understanding the limits of division of labor advances our understanding of natural community functioning, which is central to nutrient cycling and provides design rules for assembling consortia used in applied bioprocessing.
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Singh A, Satapathy SC, Roy A, Gutub A. AI-Based Mobile Edge Computing for IoT: Applications, Challenges, and Future Scope. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022. [DOI: 10.1007/s13369-021-06348-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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In Silico Analysis of Functionalized Hydrocarbon Production Using Ehrlich Pathway and Fatty Acid Derivatives in an Endophytic Fungus. J Fungi (Basel) 2021; 7:jof7060435. [PMID: 34072611 PMCID: PMC8228540 DOI: 10.3390/jof7060435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 05/25/2021] [Accepted: 05/26/2021] [Indexed: 11/17/2022] Open
Abstract
Functionalized hydrocarbons have various ecological and industrial uses, from signaling molecules and antifungal/antibacterial agents to fuels and specialty chemicals. The potential to produce functionalized hydrocarbons using the cellulolytic, endophytic fungus, Ascocoryne sarcoides, was quantified using genome-enabled, stoichiometric modeling. In silico analysis identified available routes to produce these hydrocarbons, including both anabolic- and catabolic-associated strategies, and determined correlations between the type and size of the hydrocarbons and culturing conditions. The analysis quantified the limits of the wild-type metabolic network to produce functionalized hydrocarbons from cellulose-based substrates and identified metabolic engineering targets, including cellobiose phosphorylase (CP) and cytosolic pyruvate dehydrogenase complex (PDHcyt). CP and PDHcyt activity increased the theoretical production limits under anoxic conditions where less energy was extracted from the substrate. The incorporation of both engineering targets resulted in near-complete conservation of substrate electrons in functionalized hydrocarbons. The in silico framework was integrated with in vitro fungal batch growth experiments to support O2 limitation and functionalized hydrocarbon production predictions. The metabolic reconstruction of this endophytic filamentous fungus describes pathways for both specific and general production strategies of 161 functionalized hydrocarbons applicable to many eukaryotic hosts.
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Bao T, Hou W, Wu X, Lu L, Zhang X, Yang ST. Engineering Clostridium cellulovorans for highly selective n-butanol production from cellulose in consolidated bioprocessing. Biotechnol Bioeng 2021; 118:2703-2718. [PMID: 33844271 DOI: 10.1002/bit.27789] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 03/06/2021] [Accepted: 04/09/2021] [Indexed: 01/05/2023]
Abstract
Cellulosic n-butanol from renewable lignocellulosic biomass has gained increased interest. Previously, we have engineered Clostridium cellulovorans, a cellulolytic acidogen, to overexpress the bifunctional butyraldehyde/butanol dehydrogenase gene adhE2 from C. acetobutylicum for n-butanol production from crystalline cellulose. However, butanol production by this engineered strain had a relatively low yield of approximately 0.22 g/g cellulose due to the coproduction of ethanol and acids. We hypothesized that strengthening the carbon flux through the central butyryl-CoA biosynthesis pathway and increasing intracellular NADH availability in C. cellulovorans adhE2 would enhance n-butanol production. In this study, thiolase (thlACA ) from C. acetobutylicum and 3-hydroxybutyryl-CoA dehydrogenase (hbdCT ) from C. tyrobutyricum were overexpressed in C. cellulovorans adhE2 to increase the flux from acetyl-CoA to butyryl-CoA. In addition, ferredoxin-NAD(P)+ oxidoreductase (fnr), which can regenerate the intracellular NAD(P)H and thus increase butanol biosynthesis, was also overexpressed. Metabolic flux analyses showed that mutants overexpressing these genes had a significantly increased carbon flux toward butyryl-CoA, which resulted in increased production of butyrate and butanol. The addition of methyl viologen as an electron carrier in batch fermentation further directed more carbon flux towards n-butanol biosynthesis due to increased reducing equivalent or NADH. The engineered strain C. cellulovorans adhE2-fnrCA -thlACA -hbdCT produced n-butanol from cellulose at a 50% higher yield (0.34 g/g), the highest ever obtained in batch fermentation by any known bacterial strain. The engineered C. cellulovorans is thus a promising host for n-butanol production from cellulosic biomass in consolidated bioprocessing.
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Affiliation(s)
- Teng Bao
- William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio, USA
| | - Wenjie Hou
- William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio, USA.,College of Life Sciences, Northwest A&F University, Yangling, Shanxi, China
| | - Xuefeng Wu
- William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio, USA.,School of Food and Biological Engineering, Hefei University of Technology, Hefei, China
| | - Li Lu
- William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio, USA
| | - Xian Zhang
- William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio, USA.,School of Biotechnology, Jiangnan University, Wuxi, China
| | - Shang-Tian Yang
- William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio, USA
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6
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Bao T, Zhao J, Li J, Liu X, Yang ST. n-Butanol and ethanol production from cellulose by Clostridium cellulovorans overexpressing heterologous aldehyde/alcohol dehydrogenases. BIORESOURCE TECHNOLOGY 2019; 285:121316. [PMID: 30959389 DOI: 10.1016/j.biortech.2019.121316] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 04/01/2019] [Accepted: 04/02/2019] [Indexed: 05/22/2023]
Abstract
With high cellulolytic and acetic/butyric acids production abilities, Clostridium cellulovorans is promising for use to produce cellulosic n-butanol. Here, we introduced three different aldehyde/alcohol dehydrogenases encoded by bdhB, adhE1, and adhE2 from Clostridium acetobutylicum into C. cellulovorans and studied their effects on ethanol and n-butanol production. Compared to AdhE2, AdhE1 was more specific for n-butanol biosynthesis over ethanol. Co-expressing adhE1 with bdhB produced a comparable amount of butanol but significantly less ethanol, leading to a high butanol/ethanol ratio of 7.0 and 5.6 (g/g) in glucose and cellulose fermentation, respectively. Co-expressing adhE1 or adhE2 with bdhB did not increase butanol production because the activity of BdhB was limited by the NADPH availability in C. cellulovorans. Overall, the strain overexpressing adhE2 alone produced the most n-butanol (4.0 g/L, yield: 0.22 ± 0.01 g/g). Based on the insights from this study, further metabolic engineering of C. cellulovorans for cellulosic n-butanol production is suggested.
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Affiliation(s)
- Teng Bao
- William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, 151 W. Woodruff Avenue, Columbus, OH 43210, USA
| | - Jingbo Zhao
- William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, 151 W. Woodruff Avenue, Columbus, OH 43210, USA
| | - Jing Li
- William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, 151 W. Woodruff Avenue, Columbus, OH 43210, USA; College of Biology & Engineering, Hebei University of Economics & Business, Shijiazhuang 050061, PR China
| | - Xin Liu
- William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, 151 W. Woodruff Avenue, Columbus, OH 43210, USA; School of Chemical Engineering, Changchun University of Technology, Changchun 130012, PR China
| | - Shang-Tian Yang
- William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, 151 W. Woodruff Avenue, Columbus, OH 43210, USA.
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Heirendt L, Arreckx S, Pfau T, Mendoza SN, Richelle A, Heinken A, Haraldsdóttir HS, Wachowiak J, Keating SM, Vlasov V, Magnusdóttir S, Ng CY, Preciat G, Žagare A, Chan SHJ, Aurich MK, Clancy CM, Modamio J, Sauls JT, Noronha A, Bordbar A, Cousins B, El Assal DC, Valcarcel LV, Apaolaza I, Ghaderi S, Ahookhosh M, Ben Guebila M, Kostromins A, Sompairac N, Le HM, Ma D, Sun Y, Wang L, Yurkovich JT, Oliveira MAP, Vuong PT, El Assal LP, Kuperstein I, Zinovyev A, Hinton HS, Bryant WA, Aragón Artacho FJ, Planes FJ, Stalidzans E, Maass A, Vempala S, Hucka M, Saunders MA, Maranas CD, Lewis NE, Sauter T, Palsson BØ, Thiele I, Fleming RMT. Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0. Nat Protoc 2019; 14:639-702. [PMID: 30787451 PMCID: PMC6635304 DOI: 10.1038/s41596-018-0098-2] [Citation(s) in RCA: 622] [Impact Index Per Article: 124.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods.
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Affiliation(s)
- Laurent Heirendt
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Sylvain Arreckx
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Thomas Pfau
- Life Sciences Research Unit, University of Luxembourg, Belvaux, Luxembourg
| | - Sebastián N Mendoza
- Center for Genome Regulation (Fondap 15090007), University of Chile, Santiago, Chile
- Mathomics, Center for Mathematical Modeling, University of Chile, Santiago, Chile
| | - Anne Richelle
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA, USA
| | - Almut Heinken
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Hulda S Haraldsdóttir
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Jacek Wachowiak
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Sarah M Keating
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, UK
| | - Vanja Vlasov
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Stefania Magnusdóttir
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Chiam Yu Ng
- Department of Chemical Engineering, The Pennsylvania State University, State College, PA, USA
| | - German Preciat
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Alise Žagare
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Siu H J Chan
- Department of Chemical Engineering, The Pennsylvania State University, State College, PA, USA
| | - Maike K Aurich
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Catherine M Clancy
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Jennifer Modamio
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - John T Sauls
- Department of Physics, and Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA
| | - Alberto Noronha
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | | | - Benjamin Cousins
- Algorithms and Randomness Center, School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA
| | - Diana C El Assal
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Luis V Valcarcel
- Biomedical Engineering and Sciences Department, TECNUN, University of Navarra, San Sebastián, Spain
| | - Iñigo Apaolaza
- Biomedical Engineering and Sciences Department, TECNUN, University of Navarra, San Sebastián, Spain
| | - Susan Ghaderi
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Masoud Ahookhosh
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Marouen Ben Guebila
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Andrejs Kostromins
- Institute of Microbiology and Biotechnology, University of Latvia, Riga, Latvia
| | - Nicolas Sompairac
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France
| | - Hoai M Le
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Ding Ma
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
| | - Yuekai Sun
- Department of Statistics, University of Michigan, Ann Arbor, MI, USA
| | - Lin Wang
- Department of Chemical Engineering, The Pennsylvania State University, State College, PA, USA
| | - James T Yurkovich
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Miguel A P Oliveira
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Phan T Vuong
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Lemmer P El Assal
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Inna Kuperstein
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France
| | - Andrei Zinovyev
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France
| | - H Scott Hinton
- Utah State University Research Foundation, North Logan, UT, USA
| | - William A Bryant
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, UK
| | | | - Francisco J Planes
- Biomedical Engineering and Sciences Department, TECNUN, University of Navarra, San Sebastián, Spain
| | - Egils Stalidzans
- Institute of Microbiology and Biotechnology, University of Latvia, Riga, Latvia
| | - Alejandro Maass
- Center for Genome Regulation (Fondap 15090007), University of Chile, Santiago, Chile
- Mathomics, Center for Mathematical Modeling, University of Chile, Santiago, Chile
| | - Santosh Vempala
- Algorithms and Randomness Center, School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA
| | - Michael Hucka
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
| | - Michael A Saunders
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, State College, PA, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA, USA
- Novo Nordisk Foundation Center for Biosustainability, University of California, San Diego, La Jolla, CA, USA
| | - Thomas Sauter
- Life Sciences Research Unit, University of Luxembourg, Belvaux, Luxembourg
| | - Bernhard Ø Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Lyngby, Denmark
| | - Ines Thiele
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Ronan M T Fleming
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg.
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Faculty of Science, Leiden University, Leiden, The Netherlands.
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Castillo S, Patil KR, Jouhten P. Yeast Genome-Scale Metabolic Models for Simulating Genotype-Phenotype Relations. PROGRESS IN MOLECULAR AND SUBCELLULAR BIOLOGY 2019; 58:111-133. [PMID: 30911891 DOI: 10.1007/978-3-030-13035-0_5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Understanding genotype-phenotype dependency is a universal aim for all life sciences. While the complete genotype-phenotype relations remain challenging to resolve, metabolic phenotypes are moving within the reach through genome-scale metabolic model simulations. Genome-scale metabolic models are available for commonly investigated yeasts, such as model eukaryote and domesticated fermentation species Saccharomyces cerevisiae, and automatic reconstruction methods facilitate obtaining models for any sequenced species. The models allow for investigating genotype-phenotype relations through simulations simultaneously considering the effects of nutrient availability, and redox and energy homeostasis in cells. Genome-scale models also offer frameworks for omics data integration to help to uncover how the translation of genotypes to the apparent phenotypes is regulated at different levels. In this chapter, we provide an overview of the yeast genome-scale metabolic models and the simulation approaches for using these models to interrogate genotype-phenotype relations. We review the methodological approaches according to the underlying biological reasoning in order to inspire formulating novel questions and applications that the genome-scale metabolic models could contribute to. Finally, we discuss current challenges and opportunities in the genome-scale metabolic model simulations.
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Affiliation(s)
- Sandra Castillo
- VTT Technical Research Centre of Finland Ltd., Tietotie 2, 02044, Espoo, Finland
| | - Kiran Raosaheb Patil
- European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117, Heidelberg, Germany
| | - Paula Jouhten
- VTT Technical Research Centre of Finland Ltd., Tietotie 2, 02044, Espoo, Finland.
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Schmitz R, Sabra W, Arbter P, Hong Y, Utesch T, Zeng AP. Improved electrocompetence and metabolic engineering of Clostridium pasteurianum reveals a new regulation pattern of glycerol fermentation. Eng Life Sci 2018; 19:412-422. [PMID: 32625019 DOI: 10.1002/elsc.201800118] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Revised: 10/06/2018] [Accepted: 11/16/2018] [Indexed: 12/11/2022] Open
Abstract
Clostridium pasteurianum produces industrially valuable chemicals such as n-butanol and 1,3-propanediol from fermentations of glycerol and glucose. Metabolic engineering for increased yields of selective compounds is not well established in this microorganism. In order to study carbon fluxes and to selectively increase butanol yields, we integrated the latest advances in genome editing to obtain an electrocompetent Clostridium pasteurianum strain for further engineering. Deletion of the glycerol dehydratase large subunit (dhaB) using an adapted S. pyogenes Type II CRISPR/Cas9 nickase system resulted in a 1,3-propanediol-deficient mutant producing butanol as the main product. Surprisingly, the mutant was able to grow on glycerol as the sole carbon source. In spite of reduced growth, butanol yields were highly increased. Metabolic flux analysis revealed an important role of the newly identified electron bifurcation pathway for crotonyl-CoA to butyryl-CoA conversion in the regulation of redox balance. Compared to the parental strain, the electron bifurcation pathway flux of the dhaB mutant increased from 8 to 46% of the overall flux from crotonyl-CoA to butyryl-CoA and butanol, indicating a new, 1,3-propanediol-independent pattern of glycerol fermentation in Clostridium pasteurianum.
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Affiliation(s)
- Rebekka Schmitz
- Institute of Bioprocess and Biosystems Engineering Hamburg University of Technology Hamburg Germany
| | - Wael Sabra
- Institute of Bioprocess and Biosystems Engineering Hamburg University of Technology Hamburg Germany
| | - Philipp Arbter
- Institute of Bioprocess and Biosystems Engineering Hamburg University of Technology Hamburg Germany
| | - Yaeseong Hong
- Institute of Bioprocess and Biosystems Engineering Hamburg University of Technology Hamburg Germany
| | - Tyll Utesch
- Institute of Bioprocess and Biosystems Engineering Hamburg University of Technology Hamburg Germany
| | - An-Ping Zeng
- Institute of Bioprocess and Biosystems Engineering Hamburg University of Technology Hamburg Germany
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Hunt KA, Jennings RM, Inskeep WP, Carlson RP. Multiscale analysis of autotroph-heterotroph interactions in a high-temperature microbial community. PLoS Comput Biol 2018; 14:e1006431. [PMID: 30260956 PMCID: PMC6177205 DOI: 10.1371/journal.pcbi.1006431] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Revised: 10/09/2018] [Accepted: 08/13/2018] [Indexed: 11/18/2022] Open
Abstract
Interactions among microbial community members can lead to emergent properties, such as enhanced productivity, stability, and robustness. Iron-oxide mats in acidic (pH 2-4), high-temperature (> 65 °C) springs of Yellowstone National Park contain relatively simple microbial communities and are well-characterized geochemically. Consequently, these communities are excellent model systems for studying the metabolic activity of individual populations and key microbial interactions. The primary goals of the current study were to integrate data collected in situ with in silico calculations across process-scales encompassing enzymatic activity, cellular metabolism, community interactions, and ecosystem biogeochemistry, as well as to predict and quantify the functional limits of autotroph-heterotroph interactions. Metagenomic and transcriptomic data were used to reconstruct carbon and energy metabolisms of an important autotroph (Metallosphaera yellowstonensis) and heterotroph (Geoarchaeum sp. OSPB) from the studied Fe(III)-oxide mat communities. Standard and hybrid elementary flux mode and flux balance analyses of metabolic models predicted cellular- and community-level metabolic acclimations to simulated environmental stresses, respectively. In situ geochemical analyses, including oxygen depth-profiles, Fe(III)-oxide deposition rates, stable carbon isotopes and mat biomass concentrations, were combined with cellular models to explore autotroph-heterotroph interactions important to community structure-function. Integration of metabolic modeling with in situ measurements, including the relative population abundance of autotrophs to heterotrophs, demonstrated that Fe(III)-oxide mat communities operate at their maximum total community growth rate (i.e. sum of autotroph and heterotroph growth rates), as opposed to net community growth rate (i.e. total community growth rate subtracting autotroph consumed by heterotroph), as predicted from the maximum power principle. Integration of multiscale data with ecological theory provides a basis for predicting autotroph-heterotroph interactions and community-level cellular organization.
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Affiliation(s)
- Kristopher A. Hunt
- Thermal Biology Institute, Montana State University, Bozeman, Montana, United States of America
- Center for Biofilm Engineering, Montana State University, Bozeman, Montana, United States of America
- Department of Chemical and Biological Engineering, Montana State University, Bozeman, Montana, United States of America
| | - Ryan M. Jennings
- Thermal Biology Institute, Montana State University, Bozeman, Montana, United States of America
- Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, Montana, United States of America
| | - William P. Inskeep
- Thermal Biology Institute, Montana State University, Bozeman, Montana, United States of America
- Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, Montana, United States of America
- * E-mail: (WPI); (RPC)
| | - Ross P. Carlson
- Thermal Biology Institute, Montana State University, Bozeman, Montana, United States of America
- Center for Biofilm Engineering, Montana State University, Bozeman, Montana, United States of America
- Department of Chemical and Biological Engineering, Montana State University, Bozeman, Montana, United States of America
- * E-mail: (WPI); (RPC)
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Hädicke O, von Kamp A, Aydogan T, Klamt S. OptMDFpathway: Identification of metabolic pathways with maximal thermodynamic driving force and its application for analyzing the endogenous CO2 fixation potential of Escherichia coli. PLoS Comput Biol 2018; 14:e1006492. [PMID: 30248096 PMCID: PMC6171959 DOI: 10.1371/journal.pcbi.1006492] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 10/04/2018] [Accepted: 09/07/2018] [Indexed: 12/02/2022] Open
Abstract
Constraint-based modeling techniques have become a standard tool for the in silico analysis of metabolic networks. To further improve their accuracy, recent methodological developments focused on integration of thermodynamic information in metabolic models to assess the feasibility of flux distributions by thermodynamic driving forces. Here we present OptMDFpathway, a method that extends the recently proposed framework of Max-min Driving Force (MDF) for thermodynamic pathway analysis. Given a metabolic network model, OptMDFpathway identifies both the optimal MDF for a desired phenotypic behavior as well as the respective pathway itself that supports the optimal driving force. OptMDFpathway is formulated as a mixed-integer linear program and is applicable to genome-scale metabolic networks. As an important theoretical result, we also show that there exists always at least one elementary mode in the network that reaches the maximal MDF. We employed our new approach to systematically identify all substrate-product combinations in Escherichia coli where product synthesis allows for concomitant net CO2 assimilation via thermodynamically feasible pathways. Although biomass synthesis cannot be coupled to net CO2 fixation in E. coli we found that as many as 145 of the 949 cytosolic carbon metabolites contained in the genome-scale model iJO1366 enable net CO2 incorporation along thermodynamically feasible pathways with glycerol as substrate and 34 with glucose. The most promising products in terms of carbon assimilation yield and thermodynamic driving forces are orotate, aspartate and the C4-metabolites of the tricarboxylic acid cycle. We also identified thermodynamic bottlenecks frequently limiting the maximal driving force of the CO2-fixing pathways. Our results indicate that heterotrophic organisms like E. coli hold a possibly underestimated potential for CO2 assimilation which may complement existing biotechnological approaches for capturing CO2. Furthermore, we envision that the developed OptMDFpathway approach can be used for many other applications within the framework of constrained-based modeling and for rational design of metabolic networks. When analyzing metabolic networks, one often searches for metabolic pathways with certain (desired) properties, for example, conversion routes that maximize the yield of a product from a given substrate. While those problems can be solved with established methods of constraint-based modeling, no algorithm is currently available for genome-scale models to identify the pathway that has the highest possible thermodynamic driving force among all solutions with predefined stoichiometric properties. This gap is closed with our new approach OptMDFpathway which is based on the recently introduced concept of Max-min Driving Force (MDF). OptMDFpathway offers various applications, especially in the context of metabolic design of cell factories. To demonstrate the power and usefulness of OptMDFpathway, we employed it to analyze the endogenous CO2 fixation potential of Escherichia coli. While E. coli cannot assimilate CO2 into biomass, net CO2 fixation can take place along linear pathways from substrate to product and we show that thermodynamically feasible pathways with net CO2 assimilation exist for 145 (34) products when choosing glycerol (glucose) as substrate. Our results indicate that heterotrophic organisms like E. coli hold a possibly underestimated potential for CO2 assimilation which may complement existing biotechnological approaches for capturing CO2.
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Affiliation(s)
- Oliver Hädicke
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
- * E-mail: (OH); (SK)
| | - Axel von Kamp
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Timur Aydogan
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Steffen Klamt
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
- * E-mail: (OH); (SK)
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12
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Measuring Cellular Biomass Composition for Computational Biology Applications. Processes (Basel) 2018. [DOI: 10.3390/pr6050038] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
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13
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Song HS, Goldberg N, Mahajan A, Ramkrishna D. Sequential computation of elementary modes and minimal cut sets in genome-scale metabolic networks using alternate integer linear programming. Bioinformatics 2018; 33:2345-2353. [PMID: 28369193 DOI: 10.1093/bioinformatics/btx171] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2016] [Accepted: 03/23/2017] [Indexed: 01/22/2023] Open
Abstract
Motivation Elementary (flux) modes (EMs) have served as a valuable tool for investigating structural and functional properties of metabolic networks. Identification of the full set of EMs in genome-scale networks remains challenging due to combinatorial explosion of EMs in complex networks. It is often, however, that only a small subset of relevant EMs needs to be known, for which optimization-based sequential computation is a useful alternative. Most of the currently available methods along this line are based on the iterative use of mixed integer linear programming (MILP), the effectiveness of which significantly deteriorates as the number of iterations builds up. To alleviate the computational burden associated with the MILP implementation, we here present a novel optimization algorithm termed alternate integer linear programming (AILP). Results Our algorithm was designed to iteratively solve a pair of integer programming (IP) and linear programming (LP) to compute EMs in a sequential manner. In each step, the IP identifies a minimal subset of reactions, the deletion of which disables all previously identified EMs. Thus, a subsequent LP solution subject to this reaction deletion constraint becomes a distinct EM. In cases where no feasible LP solution is available, IP-derived reaction deletion sets represent minimal cut sets (MCSs). Despite the additional computation of MCSs, AILP achieved significant time reduction in computing EMs by orders of magnitude. The proposed AILP algorithm not only offers a computational advantage in the EM analysis of genome-scale networks, but also improves the understanding of the linkage between EMs and MCSs. Availability and Implementation The software is implemented in Matlab, and is provided as supplementary information . Contact hyunseob.song@pnnl.gov. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Hyun-Seob Song
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Noam Goldberg
- Department of Management, Bar-Ilan University, Ramat Gan 52900, Israel
| | - Ashutosh Mahajan
- Industrial Engineering and Operations Research, IIT Bombay, Powai, Mumbai 400076, India
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14
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Contreras A, Ribbeck M, Gutiérrez GD, Cañon PM, Mendoza SN, Agosin E. Mapping the Physiological Response of Oenococcus oeni to Ethanol Stress Using an Extended Genome-Scale Metabolic Model. Front Microbiol 2018; 9:291. [PMID: 29545779 PMCID: PMC5838312 DOI: 10.3389/fmicb.2018.00291] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2017] [Accepted: 02/07/2018] [Indexed: 11/13/2022] Open
Abstract
The effect of ethanol on the metabolism of Oenococcus oeni, the bacterium responsible for the malolactic fermentation (MLF) of wine, is still scarcely understood. Here, we characterized the global metabolic response in O. oeni PSU-1 to increasing ethanol contents, ranging from 0 to 12% (v/v). We first optimized a wine-like, defined culture medium, MaxOeno, to allow sufficient bacterial growth to be able to quantitate different metabolites in batch cultures of O. oeni. Then, taking advantage of the recently reconstructed genome-scale metabolic model iSM454 for O. oeni PSU-1 and the resulting experimental data, we determined the redistribution of intracellular metabolic fluxes, under the different ethanol conditions. Four growth phases were clearly identified during the batch cultivation of O. oeni PSU-1 strain, according to the temporal consumption of malic and citric acids, sugar and amino acids uptake, and biosynthesis rates of metabolic products - biomass, erythritol, mannitol and acetic acid, among others. We showed that, under increasing ethanol conditions, O. oeni favors anabolic reactions related with cell maintenance, as the requirements of NAD(P)+ and ATP increased with ethanol content. Specifically, cultures containing 9 and 12% ethanol required 10 and 17 times more NGAM (non-growth associated maintenance ATP) during phase I, respectively, than cultures without ethanol. MLF and citric acid consumption are vital at high ethanol concentrations, as they are the main source for proton extrusion, allowing higher ATP production by F0F1-ATPase, the main route of ATP synthesis under these conditions. Mannitol and erythritol synthesis are the main sources of NAD(P)+, countervailing for 51-57% of its usage, as predicted by the model. Finally, cysteine shows the fastest specific consumption rate among the amino acids, confirming its key role for bacterial survival under ethanol stress. As a whole, this study provides a global insight into how ethanol content exerts a differential physiological response in O. oeni PSU-1 strain. It will help to design better strategies of nutrient addition to achieve a successful MLF of wine.
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Affiliation(s)
- Angela Contreras
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Magdalena Ribbeck
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Guillermo D Gutiérrez
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Pablo M Cañon
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Sebastián N Mendoza
- Mathomics, Center for Mathematical Modeling, Universidad de Chile, Santiago, Chile.,Center for Genome Regulation, Universidad de Chile, Santiago, Chile
| | - Eduardo Agosin
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
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15
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Use of CellNetAnalyzer in biotechnology and metabolic engineering. J Biotechnol 2017; 261:221-228. [DOI: 10.1016/j.jbiotec.2017.05.001] [Citation(s) in RCA: 66] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2017] [Revised: 04/28/2017] [Accepted: 05/03/2017] [Indexed: 01/28/2023]
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16
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Brietz A, Schuch KV, Wangorsch G, Lorenz K, Dandekar T. Analyzing ERK 1/2 signalling and targets. MOLECULAR BIOSYSTEMS 2017; 12:2436-46. [PMID: 27301697 DOI: 10.1039/c6mb00255b] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The ERK cascade (e.g. Raf-1) protects the heart from cell death and ischemic injury but can also turn maladaptive. Furthermore, an additional autophosphorylation of ERK2 at Thr188 (Erk1 at Thr208) allows ERK to phosphorylate nuclear targets involved in hypertrophy, stressing this additional phosphorylation as a promising pharmacological target. An in silico model was assembled and setup to reproduce different phosphorylation states of ERK 1/2 and various types of stimuli (hypertrophic versus non-hypertrophic). Synergistic and antagonistic receptor stimuli can be predicted in a semi-quantitative model, simulated time courses were experimentally validated. Furthermore, we detected new targets of ERK 1/2, which possibly contribute to the development of pathological hypertrophy. In addition we modeled further interaction partners involved in the protective and maladaptive cascade. Experimental validation included different gene expression data sets supporting key components and novel interaction partners as well as time courses in chronic heart failure.
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Affiliation(s)
- Alexandra Brietz
- Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany.
| | | | - Gaby Wangorsch
- Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany.
| | - Kristina Lorenz
- Biomedizinsche Forschung, Leibniz Institut für Analytische Wissenschaften - ISAS - e.V, Bunsen-Kirchhoff Straße 11, 44139 Dortmund, Germany and West German Heart and Vascular Center Essen, University Hospital Essen-Duisburg, Duisburg, Germany
| | - Thomas Dandekar
- Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany.
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17
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Stoichiometric Network Analysis of Cyanobacterial Acclimation to Photosynthesis-Associated Stresses Identifies Heterotrophic Niches. Processes (Basel) 2017. [DOI: 10.3390/pr5020032] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
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18
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Hunt KA, Jennings RD, Inskeep WP, Carlson RP. Stoichiometric modelling of assimilatory and dissimilatory biomass utilisation in a microbial community. Environ Microbiol 2016; 18:4946-4960. [PMID: 27387069 PMCID: PMC5629010 DOI: 10.1111/1462-2920.13444] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2016] [Accepted: 06/30/2016] [Indexed: 11/26/2022]
Abstract
Assimilatory and dissimilatory utilisation of autotroph biomass by heterotrophs is a fundamental mechanism for the transfer of nutrients and energy across trophic levels. Metagenome data from a tractable, thermoacidophilic microbial community in Yellowstone National Park was used to build an in silico model to study heterotrophic utilisation of autotroph biomass using elementary flux mode analysis and flux balance analysis. Assimilatory and dissimilatory biomass utilisation was investigated using 29 forms of biomass-derived dissolved organic carbon (DOC) including individual monomer pools, individual macromolecular pools and aggregate biomass. The simulations identified ecologically competitive strategies for utilizing DOC under conditions of varying electron donor, electron acceptor or enzyme limitation. The simulated growth environment affected which form of DOC was the most competitive use of nutrients; for instance, oxygen limitation favoured utilisation of less reduced and fermentable DOC while carbon-limited environments favoured more reduced DOC. Additionally, metabolism was studied considering two encompassing metabolic strategies: simultaneous versus sequential use of DOC. Results of this study bound the transfer of nutrients and energy through microbial food webs, providing a quantitative foundation relevant to most microbial ecosystems.
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Affiliation(s)
- Kristopher A. Hunt
- Center for Biofilm Engineering, Montana State University, Bozeman, MT, USA
- Department of Chemical and Biological Engineering, Montana State University, Bozeman, MT, USA
- Thermal Biology Institute, Montana State University, Bozeman, MT, USA
| | - Ryan deM. Jennings
- Thermal Biology Institute, Montana State University, Bozeman, MT, USA
- Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT, USA
| | - William P. Inskeep
- Thermal Biology Institute, Montana State University, Bozeman, MT, USA
- Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT, USA
| | - Ross P. Carlson
- Center for Biofilm Engineering, Montana State University, Bozeman, MT, USA
- Department of Chemical and Biological Engineering, Montana State University, Bozeman, MT, USA
- Thermal Biology Institute, Montana State University, Bozeman, MT, USA
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Identifying model error in metabolic flux analysis - a generalized least squares approach. BMC SYSTEMS BIOLOGY 2016; 10:91. [PMID: 27619919 PMCID: PMC5020535 DOI: 10.1186/s12918-016-0335-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2016] [Accepted: 08/30/2016] [Indexed: 01/22/2023]
Abstract
BACKGROUND The estimation of intracellular flux through traditional metabolic flux analysis (MFA) using an overdetermined system of equations is a well established practice in metabolic engineering. Despite the continued evolution of the methodology since its introduction, there has been little focus on validation and identification of poor model fit outside of identifying "gross measurement error". The growing complexity of metabolic models, which are increasingly generated from genome-level data, has necessitated robust validation that can directly assess model fit. RESULTS In this work, MFA calculation is framed as a generalized least squares (GLS) problem, highlighting the applicability of the common t-test for model validation. To differentiate between measurement and model error, we simulate ideal flux profiles directly from the model, perturb them with estimated measurement error, and compare their validation to real data. Application of this strategy to an established Chinese Hamster Ovary (CHO) cell model shows how fluxes validated by traditional means may be largely non-significant due to a lack of model fit. With further simulation, we explore how t-test significance relates to calculation error and show that fluxes found to be non-significant have 2-4 fold larger error (if measurement uncertainty is in the 5-10 % range). CONCLUSIONS The proposed validation method goes beyond traditional detection of "gross measurement error" to identify lack of fit between model and data. Although the focus of this work is on t-test validation and traditional MFA, the presented framework is readily applicable to other regression analysis methods and MFA formulations.
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20
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Smita S, Lange F, Wolkenhauer O, Köhling R. Deciphering hallmark processes of aging from interaction networks. Biochim Biophys Acta Gen Subj 2016; 1860:2706-15. [PMID: 27456767 DOI: 10.1016/j.bbagen.2016.07.017] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2016] [Revised: 07/18/2016] [Accepted: 07/20/2016] [Indexed: 12/18/2022]
Abstract
BACKGROUND Aging is broadly considered to be a dynamic process that accumulates unfavourable structural and functional changes in a time dependent fashion, leading to a progressive loss of physiological integrity of an organism, which eventually leads to age-related diseases and finally to death. SCOPE OF REVIEW The majority of aging-related studies are based on reductionist approaches, focusing on single genes/proteins or on individual pathways without considering possible interactions between them. Over the last few decades, several such genes/proteins were independently analysed and linked to a role that is affecting the longevity of an organism. However, an isolated analysis on genes and proteins largely fails to explain the mechanistic insight of a complex phenotype due to the involvement and integration of multiple factors. MAJOR CONCLUSIONS Technological advance makes it possible to generate high-throughput temporal and spatial data that provide an opportunity to use computer-based methods. These techniques allow us to go beyond reductionist approaches to analyse large-scale networks that provide deeper understanding of the processes that drive aging. GENERAL SIGNIFICANCE In this review, we focus on systems biology approaches, based on network inference methods to understand the dynamics of hallmark processes leading to aging phenotypes. We also describe computational methods for the interpretation and identification of important molecular hubs involved in the mechanistic linkage between aging related processes. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.
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Affiliation(s)
- Suchi Smita
- Department of Systems Biology & Bioinformatics, University of Rostock, Rostock, Germany; Oscar-Langendorff-Institute of Physiology, Rostock University Medical Center, Rostock, Germany.
| | - Falko Lange
- Oscar-Langendorff-Institute of Physiology, Rostock University Medical Center, Rostock, Germany.
| | - Olaf Wolkenhauer
- Department of Systems Biology & Bioinformatics, University of Rostock, Rostock, Germany; Stellenbosch Institute for Advanced Study (STIAS), Wallenberg Research Centre at Stellenbosch University, Stellenbosch, South Africa.
| | - Rüdiger Köhling
- Oscar-Langendorff-Institute of Physiology, Rostock University Medical Center, Rostock, Germany.
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21
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Cuevas DA, Edirisinghe J, Henry CS, Overbeek R, O’Connell TG, Edwards RA. From DNA to FBA: How to Build Your Own Genome-Scale Metabolic Model. Front Microbiol 2016; 7:907. [PMID: 27379044 PMCID: PMC4911401 DOI: 10.3389/fmicb.2016.00907] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2016] [Accepted: 05/27/2016] [Indexed: 11/19/2022] Open
Abstract
Microbiological studies are increasingly relying on in silico methods to perform exploration and rapid analysis of genomic data, and functional genomics studies are supplemented by the new perspectives that genome-scale metabolic models offer. A mathematical model consisting of a microbe's entire metabolic map can be rapidly determined from whole-genome sequencing and annotating the genomic material encoded in its DNA. Flux-balance analysis (FBA), a linear programming technique that uses metabolic models to predict the phenotypic responses imposed by environmental elements and factors, is the leading method to simulate and manipulate cellular growth in silico. However, the process of creating an accurate model to use in FBA consists of a series of steps involving a multitude of connections between bioinformatics databases, enzyme resources, and metabolic pathways. We present the methodology and procedure to obtain a metabolic model using PyFBA, an extensible Python-based open-source software package aimed to provide a platform where functional annotations are used to build metabolic models (http://linsalrob.github.io/PyFBA). Backed by the Model SEED biochemistry database, PyFBA contains methods to reconstruct a microbe's metabolic map, run FBA upon different media conditions, and gap-fill its metabolism. The extensibility of PyFBA facilitates novel techniques in creating accurate genome-scale metabolic models.
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Affiliation(s)
- Daniel A. Cuevas
- Computational Science Research Center, San Diego State University, San DiegoCA, USA
| | - Janaka Edirisinghe
- Mathematics and Computer Science Division, Argonne National Laboratory, ArgonneIL, USA
| | - Chris S. Henry
- Mathematics and Computer Science Division, Argonne National Laboratory, ArgonneIL, USA
| | - Ross Overbeek
- Fellowship for Interpretation of Genomes, Burr RidgeIL, USA
| | - Taylor G. O’Connell
- Biological and Medical Informatics Research Center, San Diego State University, San DiegoCA, USA
| | - Robert A. Edwards
- Computational Science Research Center, San Diego State University, San DiegoCA, USA
- Biological and Medical Informatics Research Center, San Diego State University, San DiegoCA, USA
- Department of Computer Science, San Diego State University, San DiegoCA, USA
- Department of Biology, San Diego State University, San DiegoCA, USA
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22
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Kracke F, Virdis B, Bernhardt PV, Rabaey K, Krömer JO. Redox dependent metabolic shift in Clostridium autoethanogenum by extracellular electron supply. BIOTECHNOLOGY FOR BIOFUELS 2016; 9:249. [PMID: 27882076 PMCID: PMC5112729 DOI: 10.1186/s13068-016-0663-2] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2016] [Accepted: 11/04/2016] [Indexed: 05/22/2023]
Abstract
BACKGROUND Microbial electrosynthesis is a novel approach that aims at shifting the cellular metabolism towards electron-dense target products by extracellular electron supply. Many organisms including several acetogenic bacteria have been shown to be able to consume electrical current. However, suitable hosts for relevant industrial processes are yet to be discovered, and major knowledge gaps about the underlying fundamental processes still remain. RESULTS In this paper, we present the first report of electron uptake by the Gram-positive, ethanol-producing acetogen, Clostridium autoethanogenum. Under heterotrophic conditions, extracellular electron supply induced a significant metabolic shift away from acetate. In electrically enhanced fermentations on fructose, acetate production was cut by more than half, while production of lactate and 2,3-butanediol increased by 35-fold and threefold, respectively. The use of mediators with different redox potential revealed a direct dependency of the metabolic effect on the redox potential at which electrons are supplied. Only electrons delivered at a redox potential low enough to reduce ferredoxin caused the reported effect. CONCLUSIONS Production in acetogenic organisms is usually challenged by cellular energy limitations if the target product does not lead to a net energy gain as in the case of acetate. The presented results demonstrate a significant shift of carbon fluxes away from acetate towards the products, lactate and 2,3-butanediol, induced by small electricity input (~0.09 mol of electrons per mol of substrate). This presents a simple and attractive method to optimize acetogenic fermentations for production of chemicals and fuels using electrochemical techniques. The relationship between metabolic shift and redox potential of electron feed gives an indication of possible electron-transfer mechanisms and helps to prioritize further research efforts.
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Affiliation(s)
- Frauke Kracke
- Centre for Microbial Electrochemical Systems, The University of Queensland, Brisbane, QLD 4072 Australia
- Advanced Water Management Centre, The University of Queensland, Brisbane, QLD 4072 Australia
| | - Bernardino Virdis
- Centre for Microbial Electrochemical Systems, The University of Queensland, Brisbane, QLD 4072 Australia
- Advanced Water Management Centre, The University of Queensland, Brisbane, QLD 4072 Australia
| | - Paul V. Bernhardt
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, QLD 4072 Australia
| | - Korneel Rabaey
- Centre for Microbial Electrochemical Systems, The University of Queensland, Brisbane, QLD 4072 Australia
- Laboratory of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Universiteit Ghent, Ghent, Belgium
| | - Jens O. Krömer
- Centre for Microbial Electrochemical Systems, The University of Queensland, Brisbane, QLD 4072 Australia
- Advanced Water Management Centre, The University of Queensland, Brisbane, QLD 4072 Australia
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Ben Yahia B, Malphettes L, Heinzle E. Macroscopic modeling of mammalian cell growth and metabolism. Appl Microbiol Biotechnol 2015; 99:7009-24. [PMID: 26198881 PMCID: PMC4536272 DOI: 10.1007/s00253-015-6743-6] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2015] [Revised: 05/28/2015] [Accepted: 05/30/2015] [Indexed: 12/24/2022]
Abstract
We review major modeling strategies and methods to understand and simulate the macroscopic behavior of mammalian cells. These strategies comprise two important steps: the first step is to identify stoichiometric relationships for the cultured cells connecting the extracellular inputs and outputs. In a second step, macroscopic kinetic models are introduced. These relationships together with bioreactor and metabolite balances provide a complete description of a system in the form of a set of differential equations. These can be used for the simulation of cell culture performance and further for optimization of production.
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Affiliation(s)
- Bassem Ben Yahia
- />Biochemical Engineering Institute, Saarland University, Campus A1.5, D-66123 Saarbruecken, Germany
- />Upstream Process Sciences Biotech Sciences, UCB Pharma S.A., Avenue de l’Industrie, B-1420, Braine l’Alleud, Belgium
| | - Laetitia Malphettes
- />Upstream Process Sciences Biotech Sciences, UCB Pharma S.A., Avenue de l’Industrie, B-1420, Braine l’Alleud, Belgium
| | - Elmar Heinzle
- />Biochemical Engineering Institute, Saarland University, Campus A1.5, D-66123 Saarbruecken, Germany
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Signaling network of lipids as a comprehensive scaffold for omics data integration in sputum of COPD patients. Biochim Biophys Acta Mol Cell Biol Lipids 2015. [PMID: 26215076 DOI: 10.1016/j.bbalip.2015.07.005] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Chronic obstructive pulmonary disease (COPD) is a heterogeneous and progressive inflammatory condition that has been linked to the dysregulation of many metabolic pathways including lipid biosynthesis. How lipid metabolism could affect disease progression in smokers with COPD remains unclear. We cross-examined the transcriptomics, proteomics, metabolomics, and phenomics data available on the public domain to elucidate the mechanisms by which lipid metabolism is perturbed in COPD. We reconstructed a sputum lipid COPD (SpLiCO) signaling network utilizing active/inactive, and functional/dysfunctional lipid-mediated signaling pathways to explore how lipid-metabolism could promote COPD pathogenesis in smokers. SpLiCO was further utilized to investigate signal amplifiers, distributers, propagators, feed-forward and/or -back loops that link COPD disease severity and hypoxia to disruption in the metabolism of sphingolipids, fatty acids and energy. Also, hypergraph analysis and calculations for dependency of molecules identified several important nodes in the network with modular regulatory and signal distribution activities. Our systems-based analyses indicate that arachidonic acid is a critical and early signal distributer that is upregulated by the sphingolipid signaling pathway in COPD, while hypoxia plays a critical role in the elevated dependency to glucose as a major energy source. Integration of SpLiCo and clinical data shows a strong association between hypoxia and the upregulation of sphingolipids in smokers with emphysema, vascular disease, hypertension and those with increased risk of lung cancer.
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25
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Reconstruction and analysis of a signal transduction network using HeLa cell protein–protein interaction data. J Taiwan Inst Chem Eng 2014. [DOI: 10.1016/j.jtice.2014.07.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Quek LE, Nielsen LK. A depth-first search algorithm to compute elementary flux modes by linear programming. BMC SYSTEMS BIOLOGY 2014; 8:94. [PMID: 25074068 PMCID: PMC4236763 DOI: 10.1186/s12918-014-0094-2] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2014] [Accepted: 07/24/2014] [Indexed: 11/10/2022]
Abstract
Background The decomposition of complex metabolic networks into elementary flux modes (EFMs) provides a useful framework for exploring reaction interactions systematically. Generating a complete set of EFMs for large-scale models, however, is near impossible. Even for moderately-sized models (<400 reactions), existing approaches based on the Double Description method must iterate through a large number of combinatorial candidates, thus imposing an immense processor and memory demand. Results Based on an alternative elementarity test, we developed a depth-first search algorithm using linear programming (LP) to enumerate EFMs in an exhaustive fashion. Constraints can be introduced to directly generate a subset of EFMs satisfying the set of constraints. The depth-first search algorithm has a constant memory overhead. Using flux constraints, a large LP problem can be massively divided and parallelized into independent sub-jobs for deployment into computing clusters. Since the sub-jobs do not overlap, the approach scales to utilize all available computing nodes with minimal coordination overhead or memory limitations. Conclusions The speed of the algorithm was comparable to efmtool, a mainstream Double Description method, when enumerating all EFMs; the attrition power gained from performing flux feasibility tests offsets the increased computational demand of running an LP solver. Unlike the Double Description method, the algorithm enables accelerated enumeration of all EFMs satisfying a set of constraints.
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Ryll A, Bucher J, Bonin A, Bongard S, Gonçalves E, Saez-Rodriguez J, Niklas J, Klamt S. A model integration approach linking signalling and gene-regulatory logic with kinetic metabolic models. Biosystems 2014; 124:26-38. [PMID: 25063553 DOI: 10.1016/j.biosystems.2014.07.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2014] [Revised: 07/11/2014] [Accepted: 07/18/2014] [Indexed: 12/16/2022]
Abstract
Systems biology has to increasingly cope with large- and multi-scale biological systems. Many successful in silico representations and simulations of various cellular modules proved mathematical modelling to be an important tool in gaining a solid understanding of biological phenomena. However, models spanning different functional layers (e.g. metabolism, signalling and gene regulation) are still scarce. Consequently, model integration methods capable of fusing different types of biological networks and various model formalisms become a key methodology to increase the scope of cellular processes covered by mathematical models. Here we propose a new integration approach to couple logical models of signalling or/and gene-regulatory networks with kinetic models of metabolic processes. The procedure ends up with an integrated dynamic model of both layers relying on differential equations. The feasibility of the approach is shown in an illustrative case study integrating a kinetic model of central metabolic pathways in hepatocytes with a Boolean logical network depicting the hormonally induced signal transduction and gene regulation events involved. In silico simulations demonstrate the integrated model to qualitatively describe the physiological switch-like behaviour of hepatocytes in response to nutritionally regulated changes in extracellular glucagon and insulin levels. A simulated failure mode scenario addressing insulin resistance furthermore illustrates the pharmacological potential of a model covering interactions between signalling, gene regulation and metabolism.
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Affiliation(s)
- A Ryll
- Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstraße 1, D-39106 Magdeburg, Germany.
| | - J Bucher
- Insilico Biotechnology AG, Meitnerstraße 8, D-70563 Stuttgart, Germany
| | - A Bonin
- Insilico Biotechnology AG, Meitnerstraße 8, D-70563 Stuttgart, Germany
| | - S Bongard
- Insilico Biotechnology AG, Meitnerstraße 8, D-70563 Stuttgart, Germany
| | - E Gonçalves
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SD, Cambridge, United Kingdom
| | - J Saez-Rodriguez
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SD, Cambridge, United Kingdom
| | - J Niklas
- Insilico Biotechnology AG, Meitnerstraße 8, D-70563 Stuttgart, Germany
| | - S Klamt
- Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstraße 1, D-39106 Magdeburg, Germany.
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Ahmed Z, Zeeshan S, Dandekar T. Developing sustainable software solutions for bioinformatics by the " Butterfly" paradigm. F1000Res 2014; 3:71. [PMID: 25383181 PMCID: PMC4215756 DOI: 10.12688/f1000research.3681.2] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/01/2014] [Indexed: 11/29/2022] Open
Abstract
Software design and sustainable software engineering are essential for the long-term development of bioinformatics software. Typical challenges in an academic environment are short-term contracts, island solutions, pragmatic approaches and loose documentation. Upcoming new challenges are big data, complex data sets, software compatibility and rapid changes in data representation. Our approach to cope with these challenges consists of iterative intertwined cycles of development (“
Butterfly” paradigm) for key steps in scientific software engineering. User feedback is valued as well as software planning in a sustainable and interoperable way. Tool usage should be easy and intuitive. A middleware supports a user-friendly Graphical User Interface (GUI) as well as a database/tool development independently. We validated the approach of our own software development and compared the different design paradigms in various software solutions.
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Affiliation(s)
- Zeeshan Ahmed
- Department of Neurobiology and Genetics, Biocenter, University of Wuerzburg, Wuerzburg, 97074, Germany ; Department of Bioinformatics, Biocenter, University of Wuerzburg, Wuerzburg, 97074, Germany
| | - Saman Zeeshan
- Department of Bioinformatics, Biocenter, University of Wuerzburg, Wuerzburg, 97074, Germany
| | - Thomas Dandekar
- EMBL, Structural and Computational Biology Unit, Heidelberg, 69117, Germany
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Enumeration of smallest intervention strategies in genome-scale metabolic networks. PLoS Comput Biol 2014; 10:e1003378. [PMID: 24391481 PMCID: PMC3879096 DOI: 10.1371/journal.pcbi.1003378] [Citation(s) in RCA: 76] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2013] [Accepted: 10/18/2013] [Indexed: 11/24/2022] Open
Abstract
One ultimate goal of metabolic network modeling is the rational redesign of biochemical networks to optimize the production of certain compounds by cellular systems. Although several constraint-based optimization techniques have been developed for this purpose, methods for systematic enumeration of intervention strategies in genome-scale metabolic networks are still lacking. In principle, Minimal Cut Sets (MCSs; inclusion-minimal combinations of reaction or gene deletions that lead to the fulfilment of a given intervention goal) provide an exhaustive enumeration approach. However, their disadvantage is the combinatorial explosion in larger networks and the requirement to compute first the elementary modes (EMs) which itself is impractical in genome-scale networks. We present MCSEnumerator, a new method for effective enumeration of the smallest MCSs (with fewest interventions) in genome-scale metabolic network models. For this we combine two approaches, namely (i) the mapping of MCSs to EMs in a dual network, and (ii) a modified algorithm by which shortest EMs can be effectively determined in large networks. In this way, we can identify the smallest MCSs by calculating the shortest EMs in the dual network. Realistic application examples demonstrate that our algorithm is able to list thousands of the most efficient intervention strategies in genome-scale networks for various intervention problems. For instance, for the first time we could enumerate all synthetic lethals in E.coli with combinations of up to 5 reactions. We also applied the new algorithm exemplarily to compute strain designs for growth-coupled synthesis of different products (ethanol, fumarate, serine) by E.coli. We found numerous new engineering strategies partially requiring less knockouts and guaranteeing higher product yields (even without the assumption of optimal growth) than reported previously. The strength of the presented approach is that smallest intervention strategies can be quickly calculated and screened with neither network size nor the number of required interventions posing major challenges. Mathematical modeling has become an essential tool for investigating metabolic networks. One ultimate goal of metabolic network modeling is the rational redesign of biochemical networks to optimize the production of certain compounds by cellular systems. Accordingly, several optimization techniques have been proposed for this purpose. However, for large-scale networks, an effective method for systematic enumeration of the most efficient intervention strategies is still lacking. Herein we present MCSEnumerator, a new mathematical approach by which thousands of the smallest intervention strategies (with fewest targets) can be readily computed in large-scale metabolic models. Our approach is built upon an extended concept of Minimal Cut Sets, the latter being minimal (irreducible) combinations of reaction (or gene) deletions that will lead to the fulfilment of a given intervention goal. The strength of the presented approach is that smallest intervention strategies can be quickly calculated with neither network size nor the number of required interventions posing major challenges. Realistic application examples with E.coli demonstrate that our algorithm is able to list thousands of the most efficient intervention strategies in genome-scale networks for various intervention problems.
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COBRApy: COnstraints-Based Reconstruction and Analysis for Python. BMC SYSTEMS BIOLOGY 2013; 7:74. [PMID: 23927696 PMCID: PMC3751080 DOI: 10.1186/1752-0509-7-74] [Citation(s) in RCA: 687] [Impact Index Per Article: 62.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2012] [Accepted: 08/02/2013] [Indexed: 12/21/2022]
Abstract
Background COnstraint-Based Reconstruction and Analysis (COBRA) methods are widely used for genome-scale modeling of metabolic networks in both prokaryotes and eukaryotes. Due to the successes with metabolism, there is an increasing effort to apply COBRA methods to reconstruct and analyze integrated models of cellular processes. The COBRA Toolbox for MATLAB is a leading software package for genome-scale analysis of metabolism; however, it was not designed to elegantly capture the complexity inherent in integrated biological networks and lacks an integration framework for the multiomics data used in systems biology. The openCOBRA Project is a community effort to promote constraints-based research through the distribution of freely available software. Results Here, we describe COBRA for Python (COBRApy), a Python package that provides support for basic COBRA methods. COBRApy is designed in an object-oriented fashion that facilitates the representation of the complex biological processes of metabolism and gene expression. COBRApy does not require MATLAB to function; however, it includes an interface to the COBRA Toolbox for MATLAB to facilitate use of legacy codes. For improved performance, COBRApy includes parallel processing support for computationally intensive processes. Conclusion COBRApy is an object-oriented framework designed to meet the computational challenges associated with the next generation of stoichiometric constraint-based models and high-density omics data sets. Availability http://opencobra.sourceforge.net/
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Chowdhury S, Pradhan RN, Sarkar RR. Structural and logical analysis of a comprehensive hedgehog signaling pathway to identify alternative drug targets for glioma, colon and pancreatic cancer. PLoS One 2013; 8:e69132. [PMID: 23935937 PMCID: PMC3720582 DOI: 10.1371/journal.pone.0069132] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2013] [Accepted: 06/04/2013] [Indexed: 12/19/2022] Open
Abstract
Hedgehog is an evolutionarily conserved developmental pathway, widely implicated in controlling various cellular responses such as cellular proliferation and stem cell renewal in human and other organisms, through external stimuli. Aberrant activation of this pathway in human adult stem cell line may cause different types of cancers. Hence, targeting this pathway in cancer therapy has become indispensable, but the non availability of detailed molecular interactions, complex regulations by extra- and intra-cellular proteins and cross talks with other pathways pose a serious challenge to get a coherent understanding of this signaling pathway for making therapeutic strategy. This motivated us to perform a computational study of the pathway and to identify probable drug targets. In this work, from available databases and literature, we reconstructed a complete hedgehog pathway which reports the largest number of molecules and interactions to date. Using recently developed computational techniques, we further performed structural and logical analysis of this pathway. In structural analysis, the connectivity and centrality parameters were calculated to identify the important proteins from the network. To capture the regulations of the molecules, we developed a master Boolean model of all the interactions between the proteins and created different cancer scenarios, such as Glioma, Colon and Pancreatic. We performed perturbation analysis on these cancer conditions to identify the important and minimal combinations of proteins that can be used as drug targets. From our study we observed the under expressions of various oncoproteins in Hedgehog pathway while perturbing at a time the combinations of the proteins GLI1, GLI2 and SMO in Glioma; SMO, HFU, ULK3 and RAS in Colon cancer; SMO, HFU, ULK3, RAS and ERK12 in Pancreatic cancer. This reconstructed Hedgehog signaling pathway and the computational analysis for identifying new combinatory drug targets will be useful for future in-vitro and in-vivo analysis to control different cancers.
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Affiliation(s)
- Saikat Chowdhury
- Chemical Engineering and Process Development, CSIR-National Chemical Laboratory, Pune, Maharashtra, India
| | - Rachana N. Pradhan
- Chemical Engineering and Process Development, CSIR-National Chemical Laboratory, Pune, Maharashtra, India
| | - Ram Rup Sarkar
- Chemical Engineering and Process Development, CSIR-National Chemical Laboratory, Pune, Maharashtra, India
- * E-mail:
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Flowers D, Thompson RA, Birdwell D, Wang T, Trinh CT. SMET: Systematic multiple enzyme targeting - a method to rationally design optimal strains for target chemical overproduction. Biotechnol J 2013; 8:605-18. [DOI: 10.1002/biot.201200233] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2012] [Revised: 03/26/2013] [Accepted: 04/03/2013] [Indexed: 01/07/2023]
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Huard J, Mueller S, Gilles ED, Klingmüller U, Klamt S. An integrative model links multiple inputs and signaling pathways to the onset of DNA synthesis in hepatocytes. FEBS J 2012; 279:3290-313. [PMID: 22443451 PMCID: PMC3466406 DOI: 10.1111/j.1742-4658.2012.08572.x] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
During liver regeneration, quiescent hepatocytes re-enter the cell cycle to proliferate and compensate for lost tissue. Multiple signals including hepatocyte growth factor, epidermal growth factor, tumor necrosis factor α, interleukin-6, insulin and transforming growth factor β orchestrate these responses and are integrated during the G1 phase of the cell cycle. To investigate how these inputs influence DNA synthesis as a measure for proliferation, we established a large-scale integrated logical model connecting multiple signaling pathways and the cell cycle. We constructed our model based upon established literature knowledge, and successively improved and validated its structure using hepatocyte-specific literature as well as experimental DNA synthesis data. Model analyses showed that activation of the mitogen-activated protein kinase and phosphatidylinositol 3-kinase pathways was sufficient and necessary for triggering DNA synthesis. In addition, we identified key species in these pathways that mediate DNA replication. Our model predicted oncogenic mutations that were compared with the COSMIC database, and proposed intervention targets to block hepatocyte growth factor-induced DNA synthesis, which we validated experimentally. Our integrative approach demonstrates that, despite the complexity and size of the underlying interlaced network, logical modeling enables an integrative understanding of signaling-controlled proliferation at the cellular level, and thus can provide intervention strategies for distinct perturbation scenarios at various regulatory levels.
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Affiliation(s)
- Jérémy Huard
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
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Ballerstein K, von Kamp A, Klamt S, Haus UU. Minimal cut sets in a metabolic network are elementary modes in a dual network. ACTA ACUST UNITED AC 2011; 28:381-7. [PMID: 22190691 DOI: 10.1093/bioinformatics/btr674] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
MOTIVATION Elementary modes (EMs) and minimal cut sets (MCSs) provide important techniques for metabolic network modeling. Whereas EMs describe minimal subnetworks that can function in steady state, MCSs are sets of reactions whose removal will disable certain network functions. Effective algorithms were developed for EM computation while calculation of MCSs is typically addressed by indirect methods requiring the computation of EMs as initial step. RESULTS In this contribution, we provide a method that determines MCSs directly without calculating the EMs. We introduce a duality framework for metabolic networks where the enumeration of MCSs in the original network is reduced to identifying the EMs in a dual network. As a further extension, we propose a generalization of MCSs in metabolic networks by allowing the combination of inhomogeneous constraints on reaction rates. This framework provides a promising tool to open the concept of EMs and MCSs to a wider class of applications. CONTACT utz-uwe.haus@math.ethz.ch; klamt@mpi-magdeburg.mpg.de SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Kathrin Ballerstein
- Institute for Operations Research, Department of Mathematics, ETH Zürich, Rämistrasse 101, 8092 Zürich, Switzerland
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