On the integration of process engineering with metabolomics for the production of muconic acid: the case for Saccharomyces cerevisiae

Ξενιός, Στέφανος; Xenios, Stefanos

dc.contributor.author	Ξενιός, Στέφανος	el
dc.contributor.author	Xenios, Stefanos	en
dc.date.accessioned	2022-10-03T09:02:37Z
dc.date.available	2022-10-03T09:02:37Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/55826
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.23524
dc.rights	Default License
dc.subject	Μυκονικό οξύ	el
dc.subject	Μεταβολικο μοντέλο Γονιδιακής Κλίμακας	el
dc.subject	Μεταβολική Μηχανική	el
dc.subject	Μηχανική μάθηση	el
dc.subject	Κινητικά μοντέλα	el
dc.subject	ORACLE	en
dc.subject	Metabolic Control Analysis	en
dc.subject	Metabolic engineering	en
dc.subject	large-scale kinetics	en
dc.subject	Saccharomyces cerevisiae	en
dc.title	On the integration of process engineering with metabolomics for the production of muconic acid: the case for Saccharomyces cerevisiae	en
heal.type	bachelorThesis
heal.classification	Μεταβολική μηχανική	el
heal.classification	Μηχανική διεργασιών	el
heal.language	el
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2022-02-02
heal.abstract	Muconic acid is a high value product which has gathered interest in applications in the manufacture of new resins, bio plastics, food additives, agrochemicals and pharmaceuticals. Lots of efforts have been made for an economically viable biotechnological strategy for muconic acid production but as of yet have been fruitless. Directed evolution and DBTL cycles hold important promises for the development of future catalysts with high efficiency and productivity. However, process engineering is typically disjointed from these cycles and more often than not the mismatch of kinetics presents a major challenge and a bottleneck in the scaling up of novel bioprocesses. The dissertation addresses the integration of metabolomics and experimental data using the optimization and risk analysis of complex living entities (ORACLE) platform combined with clustering and advanced analytics. The methodology consists of six steps. In the first step, the stoichiometry of the system is defined through biochemical data and experimental data are integrated into the model to further constrain it. In the second step, steady state fluxes and metabolite concentrations are calculated based on metabolomics analysis. In the third step, through stoichiometric analysis conserved moieties are identified and the dependent metabolites are separated from the independent. In the fourth step, kinetic parameters for every reaction are sampled to fit in with the steady state fluxes based on mechanistic kinetics expressions. In the fifth step, consistency checks and pruning consider the stability of the system and the consistency with experimental data. In the fifth step, the flux control coefficients for the desired metabolite flux are calculated based on the well-established metabolic control analysis (MCA) framework. In the sixth step, clustering and advanced statistical analysis on the control coefficient population is performed to determine the impact of key enzymes on the desired flux. In this project, large-scale mechanistic kinetic models for a muconic acid producing S.cerevisiae strain were developed using the aforementioned ORACLE platform. The yeast8 genome scale model [1] was used and experimental data from this paper [2] were integrated into the model. Three heterologous reactions (PaAroZ, KpAroY, CaCatA) were added to the GEM for the muconic acid production pathway via shikimate pathway branching. Τhe reduced genome scale model for S.cerevisiae used in this project consisted of 306 reactions and 300 metabolites. A total of 23500 of potential kinetic models were generated out of which 372(1.58%) agreed to the experimental data thus passing the pruning step. Lastly 29(0.12%) models out of the 372 passed the consistency check and showed stability in random perturbations performed on them. Those 29 models were used to indicate key enzymes that affect muconic acid flux and possible bottlenecks. Enzyme perturbations were performed to further quantify the influence of various enzymes on muconic flux. A big number of enzymes seem to have a significant impact on muconic acid production, excluding those that express the heterologous reactions of the muconic acid pathway, such as glucose-6-phosphate isomerase (PGI), transketolase (TKT2) and enolase (ENO). This study aims to offer metabolic engineering strategies for a muconic acid production yeast strain while taking into consideration stoichiometry, thermodynamics and kinetics.	en
heal.advisorName	Κοκόσης, Αντώνης	el
heal.advisorName	Τόπακας	el
heal.committeeMemberName	Δέτση, Αναστασία	el
heal.committeeMemberName	Τόπακας, Ευάγγελος	el
heal.committeeMemberName	Κοκόσης, Αντώνης	el
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Χημικών Μηχανικών. Τομέας Ανάλυσης, Σχεδιασμού και Ανάπτυξης Διεργασιών και Συστημάτων (ΙΙ)	el
heal.academicPublisherID	ntua
heal.numberOfPages	64 σ.	el
heal.fullTextAvailability	false