Lecture

Metabolic Engineering: Systems Biology and Tools

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In course

BIOENG-320: Synthetic biology

This advanced Bachelor/Master level course will cover fundamentals and approaches at the interface of biology, chemistry, engineering and computer science for diverse fields of synthetic biology. This class requires critical and analytical thinking at the ...

Description

This lecture delves into the systems biology of metabolism, exploring genome-scale models, cheminformatic tools, and their applications in metabolic engineering. It covers metabolic networks, GEM reconstruction, enzyme promiscuity, and filling knowledge gaps using bioinformatic approaches.

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Instructors (3)

Vassily Hatzimanikatis

Dr. Vassily Hatzimanikatis is currently Associate Professor of Chemical Engineering and Bioengineering at Ecole Polytechnique Federale de Lausanne (EPFL), in Lausanne, Switzerland. Vassily received a PhD and an MS in Chemical Engineering from the California Institute of Technology, and his Diploma in Chemical Engineering from the University of Patras, in Greece. After the completion of his doctoral studies, he held a research group leader position at the Swiss Federal Institute of Technology in Zurich (ETHZ), Switzerland. Prior to joining EPFL, Vassily worked for three years in DuPont, Cargill, and Cargill Dow, and he has been assistant professor at Northwestern University, at Illinois, USA. Vassilys research interests are in the areas of computational systems biology, biotechnology, and complexity. He is associate editor of the journals Biotechnology & Bioengineering, Metabolic Engineering and Integrative Biology, and he serves on the editorial advisory board of the journals Bioprocess and Biosystems Engineering, Journal of Chemical Technology and Biotechnology, and Industrial Biotechnology. He has written over 70 technical publications and he is co-inventor in three patents and patent applications. Vassily is a fellow of the American Institute for Medical and Biological Engineering (2010), he was a DuPont Young Professor (2001-2004), and he has also received the Jay Bailey Young Investigator Award in Metabolic Engineering (2000), and the ACS Elmar Gaden Award (2011).

Patrick Daniel Barth

Professor Patrick Barth is Associate Professor at EPFL and Adjunct Associate Professor at Baylor College of Medicine, Houston, TX, USA. He received training in Physics, Chemistry and Biology (University of Paris, ENS) in France and performed his PhD at the Commissiariat a l'Energie Atomique in Saclay, France on structure/function studies of membrane proteins (photosystem I) using biochemical and biophysical experimental techniques. He carried out postdoctoral studies at University of California at Berkeley with Tom Alber on computational development for calculating protein electrostatics and designing de novo selective peptide inhibitors of cellular protein interactions. He then went to the University of Washington as a postdoctoral fellow and instructor in David Baker's laboratory to develop computational techniques in the software Rosetta for predicting and designing membrane protein structures. He started his independent career and received tenure at Baylor College of Medicine. He will continue at EPFL to marry computation and experiment for understanding the molecular determinants of signal transduction, as well as modeling and designing membrane proteins with novel functions for various synthetic biology and therapeutic applications.

Official source

Ontological neighbourhood

Biology

Cell biology: Cell metabolism

Related concepts (32)

Metabolic pathway

In biochemistry, a metabolic pathway is a linked series of chemical reactions occurring within a cell. The reactants, products, and intermediates of an enzymatic reaction are known as metabolites, which are modified by a sequence of chemical reactions catalyzed by enzymes. In most cases of a metabolic pathway, the product of one enzyme acts as the substrate for the next. However, side products are considered waste and removed from the cell. These enzymes often require dietary minerals, vitamins, and other cofactors to function.

Metabolic network modelling

Metabolic network modelling, also known as metabolic network reconstruction or metabolic pathway analysis, allows for an in-depth insight into the molecular mechanisms of a particular organism. In particular, these models correlate the genome with molecular physiology. A reconstruction breaks down metabolic pathways (such as glycolysis and the citric acid cycle) into their respective reactions and enzymes, and analyzes them within the perspective of the entire network.

Enzyme catalysis

Enzyme catalysis is the increase in the rate of a process by a biological molecule, an "enzyme". Most enzymes are proteins, and most such processes are chemical reactions. Within the enzyme, generally catalysis occurs at a localized site, called the active site. Most enzymes are made predominantly of proteins, either a single protein chain or many such chains in a multi-subunit complex. Enzymes often also incorporate non-protein components, such as metal ions or specialized organic molecules known as cofactor (e.

Enzyme kinetics

Enzyme kinetics is the study of the rates of enzyme-catalysed chemical reactions. In enzyme kinetics, the reaction rate is measured and the effects of varying the conditions of the reaction are investigated. Studying an enzyme's kinetics in this way can reveal the catalytic mechanism of this enzyme, its role in metabolism, how its activity is controlled, and how a drug or a modifier (inhibitor or activator) might affect the rate. An enzyme (E) is typically a protein molecule that promotes a reaction of another molecule, its substrate (S).

Protein function prediction

Protein function prediction methods are techniques that bioinformatics researchers use to assign biological or biochemical roles to proteins. These proteins are usually ones that are poorly studied or predicted based on genomic sequence data. These predictions are often driven by data-intensive computational procedures. Information may come from nucleic acid sequence homology, gene expression profiles, protein domain structures, text mining of publications, phylogenetic profiles, phenotypic profiles, and protein-protein interaction.

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