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Metabolic Network Analysis Module 1 Summary

These are the key points from this module:
Living cells require energy for biosynthesis, transport of nutrients, motility and maintenance. The energy is obtained from the catabolism of carbon compounds, mainly carbohydrates.
Catabolism – Intracellular process of degrading a compound into smaller molecules, which produces energy for the cell.
Anabolism – It is involved in the synthesis of more complex compounds (e.g. glucose to glycogen) and it requires energy.
Bioenergetics highlights the following:
1. Production of biomass, enzymes for biofuel synthesis, plasmids/mRNA synthesis, etc.
2. Large amounts of ATP must be consumed to support cell maintenance processes, macromolecule synthesis, etc.
3. The synthesis of biofuel molecules needs ATP and NAD+(P)H.
The overall reaction of the TCA cycle is as shown below:
The overall reaction of glycolysis is as shown below:
Glucose + 2ADP + 2NAD+ + 2pi –> 2pyruvate + 2ATP + 2H+
The process of forming ATP from the electron transport chain is known as phosphorylation.
The law of mass action states that: The reaction rate is proportional to the probability of collision of the reactants. This probability is in turn proportional to the concentration of reactants to the power of the molecularity, e.g. the number in which they enter the specific reaction. Mathematically it is represented as:

The dynamics of the equation can be described by ordinary differential equations (ODE).
One dimension (1D) annotation of genome sequences
The gene/genome sequence annotated based on the open reading frame (ORF): Where sequence analysis is used to locate the ORF and assign functions to the gene product. This allows the identification of the total genes involved in the metabolism.
Two dimension (1D) annotation of genome sequences
This approach starts with the genome sequence and identifies how many genes are there, and then give functions to the gene. The component interactions can be represented in a matrix form, which shows the metabolites’ protein-protein interactions, and DNA-protein interaction presented in a mathematical form.
The (2D) network construction gives rise to metabolic network (consists of the network of the metabolites and mechanisms of the reaction, the regulatory network, and signaling network).
Measuring the metabolic fluxes (also known as the phenotype of the cell) is a systems biology problem. What is systems biology?
1. It is the study of biological systems by identifying the components of a living system, understanding how those parts fit together, and determining how the parts function as a whole.
2. It seeks to identify how biological functions evolve, and how emergent properties in cells and communities arise from seemingly simple, linear genetic sequences.
Why systems biology?
1. Systems biology is essential because an enormous amount of biological component is available. High-throughput technologies have advanced in recent years. Thus, we can now acquire an understanding of the wiring diagrams of life.
2. Microbial systems are suitable for systems analysis because:
• Microbes are easy to culture
• They can be grown asexually
• Have small genome
• They are dynamic with diverse conditions
• They are compact and multi-scale
• They offer insights into intracellular individual cells and communal cells.
Some key features of biological networks are:
1. They are constrained by basic physicochemical laws
2. Selected by evolution
3. Possess many equivalent states
4. Every microbial cell has a sense of purpose, which is survival by natural selection.
There are two kinds of biological networks:
1. Biochemical reaction network – Is basically the metabolic network or biochemistry of the cell.
2. Statistical inference network – Deals with the integrated network data including Transcriptomics, proteomics and metabolomics, and other concepts.
The three fundamental data types for regulatory networks:
Component data – Data types in this category include those used for the identification of binding sites, riboswitches, etc.
Interaction data – Links are formed by chemical interaction between components, and includes, DNA-protein interaction, metabolite-RNA interactions, etc.
Network state data – It involves assessing a whole network via genome-scale expression data and phenotyping data.
Bottom-up data types
Data derived from classical biochemistry or genetics that are focused on a single or a few variables are often celled bottom-up data.
Top-down data types
High-throughput data types that simultaneously measure many variables or stats are often referred to as top-down data.
Biochemical network reconstruction – The network reconstruction process identifies all the reactions that comprise a network.
The four-level hierarchy of functional metabolism simplifies the conceptualization of network functions.
Level 1 – Deals with cellular inputs and outputs
Level 2 - It is subdivided into two basic sectors, anabolism and catabolism.
Level 3 - It considers the metabolic pathways and the enzymes which catalyze the reactions.
Level 4 - Uses high-throughput data for genome-scale reconstruction of stoichiometric matrix.
Genome-scale metabolic model reconstruction involves three kinds of data, which are the:
1. Genome annotation data
2. Biochemistry data
3. Physiological data
Steps for genome annotation
1. ORF identification – First, step is ORF identification, which considers the stop codons, GIMMERS etc.
2. The Genome annotation – via traditional and annotation and new annotation methods.