Application Of Genome-scale Metabolic Models Research Articles

Semi-Automatic Detection of Errors in Genome-Scale Metabolic Models.

Genome-Scale Metabolic Models (GSMMs) are used for numerous tasks requiring computational estimates of metabolic fluxes, from predicting novel drug targets to engineering microbes to produce valuable compounds. A key limiting step in most applications of GSMMs is ensuring their representation of the target organism's metabolism is complete and accurate. Identifying and visualizing errors in GSMMs is complicated by the fact that they contain thousands of densely interconnected reactions. Furthermore, many errors in GSMMs only become apparent when considering pathways of connected reactions collectively, as opposed to examining reactions individually. We present Metabolic Accuracy Check and Analysis Workflow (MACAW), a collection of algorithms for detecting errors in GSMMs. The relative frequencies of errors we detect in manually curated GSMMs appear to reflect the different approaches used to curate them. Changing the method used to automatically create a GSMM from a particular organism's genome can have a larger impact on the kinds of errors in the resulting GSMM than using the same method with a different organism's genome. Our algorithms are particularly capable of identifying errors that are only apparent at the pathway level, including loops, and nontrivial cases of dead ends. MACAW is capable of identifying inaccuracies of varying severity in a wide range of GSMMs. Correcting these errors can measurably improve the predictive capacity of a GSMM. The relative prevalence of each type of error we identify in a large collection of GSMMs could help shape future efforts for further automation of error correction and GSMM creation.

GMCSpy: efficient and accurate computation of genetic minimal cut sets in Python.

The identification of minimal genetic interventions that modulate metabolic processes constitutes one of the most relevant applications of genome-scale metabolic models (GEMs). The concept of Minimal Cut Sets (MCSs) and its extension at the gene level, genetic Minimal Cut Sets (gMCSs), have attracted increasing interest in the field of Systems Biology to address this task. Different computational tools have been developed to calculate MCSs and gMCSs using both commercial and open-source software. Here, we present gMCSpy, an efficient Python package to calculate gMCSs in GEMs using both commercial and non-commercial optimization solvers. We show that gMCSpy substantially overperforms our previous computational tool GMCS, which exclusively relied on commercial software. Moreover, we compared gMCSpy with recently published competing algorithms in the literature, finding significant improvements in both accuracy and computation time. All these advances make gMCSpy an attractive tool for researchers in the field of Systems Biology for different applications in health and biotechnology. The Python package gMCSpy and the data underlying this manuscript can be accessed at: https://github.com/PlanesLab/gMCSpy.

Arduino Soft Sensor for Monitoring Schizochytrium sp. Fermentation, a Proof of Concept for the Industrial Application of Genome-Scale Metabolic Models in the Context of Pharma 4.0

Schizochytrium sp. is a microorganism cultured for producing docosahexaenoic acid (DHA). Genome-scale metabolic modeling (GEM) is a promising technique for describing gen-protein-reactions in cells, but with still limited industrial application due to its complexity and high computation requirements. In this work, we simplified GEM results regarding the relationship between the specific oxygen uptake rate (−rO2), the specific growth rate (µ), and the rate of lipid synthesis (rL) using an evolutionary algorithm for developing a model that can be used by a soft sensor for fermentation monitoring. The soft sensor estimated the concentration of active biomass (X), glutamate (N), lipids (L), and DHA in a Schizochytrium sp. fermentation using the dissolved oxygen tension (DO) and the oxygen mass transfer coefficient (kLa) as online input variables. The soft sensor model described the biomass concentration response of four reported experiments characterized by different kLa values. The average range normalized root-mean-square error for X, N, L, and DHA were equal to 1.1, 1.3, 1.1, and 3.2%, respectively, suggesting an acceptable generalization capacity. The feasibility of implementing the soft sensor over a low-cost electronic board was successfully tested using an Arduino UNO, showing a novel path for applying GEM-based soft sensors in the context of Pharma 4.0.

In silico genome-scale metabolic modeling and in vitro static time-kill studies of exogenous metabolites alone and with polymyxin B against Klebsiella pneumoniae.

Multidrug-resistant (MDR) Klebsiella pneumoniae is a top-prioritized Gram-negative pathogen with a high incidence in hospital-acquired infections. Polymyxins have resurged as a last-line therapy to combat Gram-negative “superbugs”, including MDR K. pneumoniae. However, the emergence of polymyxin resistance has increasingly been reported over the past decades when used as monotherapy, and thus combination therapy with non-antibiotics (e.g., metabolites) becomes a promising approach owing to the lower risk of resistance development. Genome-scale metabolic models (GSMMs) were constructed to delineate the altered metabolism of New Delhi metallo-β-lactamase- or extended spectrum β-lactamase-producing K. pneumoniae strains upon addition of exogenous metabolites in media. The metabolites that caused significant metabolic perturbations were then selected to examine their adjuvant effects using in vitro static time–kill studies. Metabolic network simulation shows that feeding of 3-phosphoglycerate and ribose 5-phosphate would lead to enhanced central carbon metabolism, ATP demand, and energy consumption, which is converged with metabolic disruptions by polymyxin treatment. Further static time–kill studies demonstrated enhanced antimicrobial killing of 10 mM 3-phosphoglycerate (1.26 and 1.82 log10 CFU/ml) and 10 mM ribose 5-phosphate (0.53 and 0.91 log10 CFU/ml) combination with 2 mg/L polymyxin B against K. pneumoniae strains. Overall, exogenous metabolite feeding could possibly improve polymyxin B activity via metabolic modulation and hence offers an attractive approach to enhance polymyxin B efficacy. With the application of GSMM in bridging the metabolic analysis and time–kill assay, biological insights into metabolite feeding can be inferred from comparative analyses of both results. Taken together, a systematic framework has been developed to facilitate the clinical translation of antibiotic-resistant infection management.

The Move toward Biopharma 4.0

Genetic Engineering & Biotechnology NewsVol. 39, No. 6 Bioprocessing TutorialThe Move toward Biopharma 4.0Insilico Biotechnology develops “smart” processes that benefit biomanufacturing through Digital TwinsShilpa Nargund, PhD, Kathrin Guenther, PhD, and Klaus MauchShilpa Nargund, PhDShilpa Nargund, PhD, is a Data Scientist & Business Development ManagerSearch for more papers by this author, Kathrin Guenther, PhDKathrin Guenther, PhD, is a Sr. Business Development ManagerSearch for more papers by this author, and Klaus MauchKlaus Mauch E-mail Address: (klaus.mauch@insilico-biotechnology.com) is the CEO & CTO at Insilico Biotechnology. Website: www.insilico-biotechnology.comSearch for more papers by this authorPublished Online:29 May 2019https://doi.org/10.1089/gen.39.06.18AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetailsCited byContinuous integrated manufacturing for biopharmaceuticals: A new paradigm or an empty promise?28 September 2022 | Biotechnology and Bioengineering, Vol. 120, No. 2Exploring the potential of machine learning for more efficient development and production of biopharmaceuticals11 August 2022 | Biotechnology Progress, Vol. 38, No. 6Arduino Soft Sensor for Monitoring Schizochytrium sp. Fermentation, a Proof of Concept for the Industrial Application of Genome-Scale Metabolic Models in the Context of Pharma 4.029 October 2022 | Processes, Vol. 10, No. 11Architectural and Technological Improvements to Integrated Bioprocess Models towards Real-Time Applications9 October 2022 | Bioengineering, Vol. 9, No. 10Artificial intelligence and machine learning applications in biopharmaceutical manufacturingTrends in Biotechnology, Vol. 349AI-ML applications in bioprocessing: ML as an enabler of real time quality prediction in continuous manufacturing of mAbsComputers & Chemical Engineering, Vol. 164Holistic Process Models: A Bayesian Predictive Ensemble Method for Single and Coupled Unit Operation Models29 March 2022 | Processes, Vol. 10, No. 4Modeling and optimization of bioreactor processesReprint of: Basic considerations for a digital twin of biointelligent systems: Applying technical design patterns to biological systemsCIRP Journal of Manufacturing Science and Technology, Vol. 34Computational Efforts for the Development and Scale-up of Antibody-Producing Cell Culture Processes1 January 2022Continuous downstream bioprocessing for intensified manufacture of biopharmaceuticals and antibodiesChemical Engineering ScienceBasic considerations for a digital twin of biointelligent systems: Applying technical design patterns to biological systemsCIRP Journal of Manufacturing Science and Technology, Vol. 31Digitalization in microbiology – Paving the path to sustainable circular bioeconomyNew Biotechnology, Vol. 59ChromaTech: A discontinuous Galerkin spectral element simulator for preparative liquid chromatographyComputers & Chemical Engineering, Vol. 141Digital Twins and Their Role in Model-Assisted Design of Experiments15 August 2020The Kalman Filter for the Supervision of Cultivation Processes11 November 2020History and Evolution of Modeling in Biotechnology: Modeling & Simulation, Application and Hardware PerformanceComputational and Structural Biotechnology Journal, Vol. 18The Rocky Road From Fed-Batch to Continuous Processing With E. coli20 November 2019 | Frontiers in Bioengineering and Biotechnology, Vol. 7 Volume 39Issue 6Jun 2019 InformationCopyright © by GEN PublishingTo cite this article:Shilpa Nargund, PhD, Kathrin Guenther, PhD, and Klaus Mauch.The Move toward Biopharma 4.0.Genetic Engineering & Biotechnology News.Jun 2019.53-55.http://doi.org/10.1089/gen.39.06.18Published in Volume: 39 Issue 6: May 29, 2019PDF download

Identification of Discriminating Metabolic Pathways and Metabolites in Human PBMCs Stimulated by Various Pathogenic Agents.

Immunity and cellular metabolism are tightly interconnected but it is not clear whether different pathogens elicit specific metabolic responses. To address this issue, we studied differential metabolic regulation in peripheral blood mononuclear cells (PBMCs) of healthy volunteers challenged by Candida albicans, Borrelia burgdorferi, lipopolysaccharide, and Mycobacterium tuberculosis in vitro. By integrating gene expression data of stimulated PBMCs of healthy individuals with the KEGG pathways, we identified both common and pathogen-specific regulated pathways depending on the time of incubation. At 4 h of incubation, pathogenic agents inhibited expression of genes involved in both the glycolysis and oxidative phosphorylation pathways. In contrast, at 24 h of incubation, particularly glycolysis was enhanced while genes involved in oxidative phosphorylation remained unaltered in the PBMCs. In general, differential gene expression was less pronounced at 4 h compared to 24 h of incubation. KEGG pathway analysis allowed differentiation between effects induced by Candida and bacterial stimuli. Application of genome-scale metabolic model further generated a Candida-specific set of 103 reporter metabolites (e.g., desmosterol) that might serve as biomarkers discriminating Candida-stimulated PBMCs from bacteria-stimuated PBMCs. Our analysis also identified a set of 49 metabolites that allowed discrimination between the effects of Borrelia burgdorferi, lipopolysaccharide and Mycobacterium tuberculosis. We conclude that analysis of pathogen-induced effects on PBMCs by a combination of KEGG pathways and genome-scale metabolic model provides deep insight in the metabolic changes coupled to host defense.

Model-guided identification of novel gene amplification targets for improving succinate production in Escherichia coli NZN111.

Reconstruction and application of genome-scale metabolic models (GEMs) have facilitated metabolic engineering by providing a platform on which systematic computational analysis of metabolic networks can be performed. In this study, a GEM of Escherichia coli NZN111 was employed by the analysis of production and growth coupling (APGC) algorithm to identify genetic strategies for the overproduction of succinate. Through in silico simulation and reaction expression analysis, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), phosphoglycerate kinase (PGK), triosephosphate isomerase (TPI), and phosphoenolpyruvate carboxylase (PPC), encoded by gapA, pgk, tpiA, and ppc, respectively, were selected for experimental overexpression. The results showed that overexpressing any of these could improve both growth and succinate production. Specifically, overexpression of GAPDH or PGK showed a significant effect with up to 24% increase in succinate production. These results indicate that the APGC algorithm can be effectively used to guide genetic manipulation for strain design by identifying genome-wide gene amplification targets.

Applications of Genome-Scale Metabolic Models in Biotechnology and Systems Medicine.

Genome-scale metabolic models (GEMs) have become a popular tool for systems biology, and they have been used in many fields such as industrial biotechnology and systems medicine. Since more and more studies are being conducted using GEMs, they have recently received considerable attention. In this review, we introduce the basic concept of GEMs and provide an overview of their applications in biotechnology, systems medicine, and some other fields. In addition, we describe the general principle of the applications and analyses built on GEMs. The purpose of this review is to introduce the application of GEMs in biological analysis and to promote its wider use by biologists.

Application of Genome-Scale Metabolic Models in Metabolic Engineering

Abstract Genome-scale metabolic models (GEMs) are growing in accuracy and complexity with the development of advanced computational tools for using these models in simulation and for integrative data analysis. The reconstruction of metabolic networks for various microorganisms is allowing the analysis of phenotypic changes under various environmental and genetic conditions. Thanks to growing datasets on the specific properties of various microorganisms and to the development of computational tools and mathematical modeling, it is becoming possible to evaluate metabolic network modifications in microorganisms in silico, and thereby use GEMs to an increasing extent in metabolic engineering. Here we review the reconstruction of GEMs and their use in developing novel bioprocesses for producing biofuels, biomaterials, food ingredients, and pharmaceuticals.