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MIT6_047f08_lec21_note21

MIT6_047f08_lec21_note21 - MIT OpenCourseWare...

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MIT OpenCourseWare http://ocw.mit.edu 6.047 / 6.878 Computational Biology: Genomes, Networks, Evolution Fall 2008 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms .
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Lecture 21: Introduction to Steady State Metabolic Modeling November 18, 2008 1 Introduction Metabolic modeling allows us to use mathematical models to represent complex biological systems. This lecture discusses the role that modeling biological sys- tems at the steady state plays in understanding the metabolic capabilities of interesting organisms and how well steady state models are able to replicate in vitro experiments. 1.1 What is Metabolism? According to Matthews and van Holde, metabolism is “the totality of all chem- ical reactions that occur in living matter”. This includes catabolic reactions, which are reactions that lead the break down of molecules into smaller com- ponents and anabolic reactions, which are responsible for the creation of more complex molecules (e.g. proteins, lipids, carbohydrates, and nucleic acids) from smaller components. These reactions are responsible for the release of energy from chemical bonds and the storage of energy respectively. Metabolic reactions are also responsible for the transduction and transmission of information (for example, via the generation of cGMP as a secondary messenger or mRNA as a substrate for protein translation). 1.2 Why Model Metabolism? An important application of metabolic modeling is in the prediction of drug effects. An important subject of modeling is the organism Mycobacterium tu- berculosis [1]. The disruption of the mycolic acid synthesis pathways of this organism would help control TB infection. Computational modeling gives us a platform for identifying the best drug targets in this system. Gene knock- out studies in Escherichia coli have allowed scientists to determine which genes and gene combinations affect the growth of that important model organism [2]. Both agreements and disagreements between models and experimental data can help us assess our knowledge of biological systems and help us improve our 1
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predictions about metabolic capabilities. In the next lecture, we will learn the importance of incorporating expression data into metabolic models. 2 Model building 2.1 Chemical reactions In metabolic models, we are concerned with modeling chemical reactions that are catalyzed by enzymes. Enzymes work by facilitating a transition state of the enzyme-substrate complex that lowers the activation energy of a chemical reaction. The diagram on slide 5 of page 1 of the lecture slides demonstrates this phenomenon. A typical rate equation (which describes the conversion of the substrates of the enzyme reaction into its products S=P) can be described by a Michaelis-Menten rate law: V = [ S ] , where V is the rate of the V max K m +[ S ] equation as a function of substrate concentration. K m and V max are the two parameters necessary to characterize the equation.
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