Lecture

Thermodynamics in Metabolic Pathways

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In course

ChE-411: Principles and applications of systems biology

The course introduces and develops the key concepts from systems biology and systems engineering in the context of complex biological networks. The lectures elaborate on techniques and methods to model and analyze complex biological problems. ...

Description

This lecture explores the application of thermodynamics in metabolic pathways, focusing on the use of Flux Balance Analysis and the formulation of the problem as a Mixed Integer Linear Programming. It discusses the constraints imposed by thermodynamics, the role of bi-directional reactions, and the group contribution method for estimating Gibbs free energy. The uncertainty in linear regression models and the impact of experimental errors are also covered. Additionally, it delves into the influence of factors like pH and ionic strength on free energy calculations, as well as the effect of crowding conditions on metabolic pathway feasibility.

Instructor

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).

Official source

Related concepts (32)

Laws of thermodynamics

The laws of thermodynamics are a set of scientific laws which define a group of physical quantities, such as temperature, energy, and entropy, that characterize thermodynamic systems in thermodynamic equilibrium. The laws also use various parameters for thermodynamic processes, such as thermodynamic work and heat, and establish relationships between them. They state empirical facts that form a basis of precluding the possibility of certain phenomena, such as perpetual motion.

Chemical thermodynamics

Chemical thermodynamics is the study of the interrelation of heat and work with chemical reactions or with physical changes of state within the confines of the laws of thermodynamics. Chemical thermodynamics involves not only laboratory measurements of various thermodynamic properties, but also the application of mathematical methods to the study of chemical questions and the spontaneity of processes. The structure of chemical thermodynamics is based on the first two laws of thermodynamics.

Measurement uncertainty

In metrology, measurement uncertainty is the expression of the statistical dispersion of the values attributed to a measured quantity. All measurements are subject to uncertainty and a measurement result is complete only when it is accompanied by a statement of the associated uncertainty, such as the standard deviation. By international agreement, this uncertainty has a probabilistic basis and reflects incomplete knowledge of the quantity value. It is a non-negative parameter.

Experimental uncertainty analysis

Experimental uncertainty analysis is a technique that analyses a derived quantity, based on the uncertainties in the experimentally measured quantities that are used in some form of mathematical relationship ("model") to calculate that derived quantity. The model used to convert the measurements into the derived quantity is usually based on fundamental principles of a science or engineering discipline. The uncertainty has two components, namely, bias (related to accuracy) and the unavoidable random variation that occurs when making repeated measurements (related to precision).

Uncertainty quantification

Uncertainty quantification (UQ) is the science of quantitative characterization and estimation of uncertainties in both computational and real world applications. It tries to determine how likely certain outcomes are if some aspects of the system are not exactly known. An example would be to predict the acceleration of a human body in a head-on crash with another car: even if the speed was exactly known, small differences in the manufacturing of individual cars, how tightly every bolt has been tightened, etc.

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