23.1: Introduction

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Metabolic modeling allows us to use mathematical models to represent complex biological systems. This lecture discusses the role of modeling the steady state of biological systems in understanding the metabolic capabilities of organisms. We also briefly discuss how well steady state models are able to replicate in-vitro experiments.

What is Metabolism?

According to Matthews and van Holde, metabolism is the totality of all chemical reactions that occur in living matter. This includes catabolic reactions, which are reactions that lead to the breakdown of molecules into smaller components, 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 this energy. 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).

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 tuberculosis [15]. The disruption of the mycolic acid synthesis pathways of this organism can help control TB infection. Computational modeling gives us a platform for identifying the best drug targets in this system. Gene knockout studies in Escherichia coli have allowed scientists to determine which genes and gene combinations affect the growth of this important model organism [6]. Both agreements and disagreements between models and experimental data can help us assess our knowledge of biological systems and help us improve our predictions about metabolic capabilities. In the next lecture, we will learn the importance of incorporating expression data into metabolic models. In addition, a variety of infectious disease processes involve metabolic changes at the microbial level.