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Encyclopedia of Complexity and Systems Science pp 5535–5552Cite as

Metabolic Systems Biology

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Definition of the Subject

Systems biology has various definitions. Common features among accepted definitionsgenerally involve the description and analysis of interacting biomolecular components. Systems analysis of biological network is quickly demonstrating itsutility as it helps to characterize biomolecular behavior that could not otherwise be produced by the individual components alone [46]. Three areas in which systems analysis has been implemented in biology include: (1) the generation andstatistical analysis of high-throughput data in an effort to catalog and characterize cellular components; (2) the construction and analysis ofcomputational models for various biological systems (e.?g., metabolism, signaling, and transcriptional regulation); and (3) the integration ofthe knowledge of parts and computational models to predict and engineer biological systems (synthetic biology ) [18,45,46]. Metabolism , as a system, has played an important role inthe...

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Abbreviations

Bibliome:

The collection of primary literature, review literature and textbooks on a particular topic.

Biochemically, genetically and genomically (BiGG) structured reconstruction:

A structured genome-scale metabolic network reconstruction which incorporates knowledge about the genomic, proteomic, and biochemical components, including relationships between each component in a particular organism or cell (See Sect. “Reconstructions, Knowledge Bases, and Models”).

Biomass function:

A pseudo-reaction representing the stoichiometric consumption of metabolites necessary for cellular growth (i.?e., to produce biomass). When this pseudo-reaction is placed in a model, a flux through it represents the in silico growth rate of the organism or population (See Sect. “Constraint-Based Methods of Analysis”).

Constraint-based reconstruction and analysis (COBRA):

A set of approaches for constructing manually curated, stoichiometric network reconstructions and analyzing the resulting models by applying equality and inequality constraints and computing functional states. In general, mass conservation and thermodynamics (for directionality) are the fundamental constraints. Additional constraints reflecting experimental conditions and other biological constraints (such as regulatory states) can be applied. The analysis approaches generally fall into two classes: biased and unbiased methods. Biased methods involve the application of various optimization approaches which require the definition of an objective function . Unbiased methods do not require an objective function (See Sect. “Constraint-Based Modeling”).

Convex space :

A multi-dimensional space in which a straight line can be drawn from any two points, without leaving the space (see Sect. “Constraint-Based Methods of Analysis”).

Extreme pathways (ExPa) analysis:

An approach for calculating a unique, linearly independent, but biochemically feasible reaction basis that can describe all possible steady state flux combinations in a biochemical network. ExPas are closely related to Elementary Modes (See Sect. “Constraint-Based Methods of Analysis”).

Flux-balance analysis (FBA):

The formalism in which a metabolic network is framed as a linear programming optimization problem. The principal constraints in FBA are those imposed by steady state mass conservation of metabolites in the system (See Sect. “Constraint-Based Methods of Analysis”).

Gene-protein-reaction association (GPR):

A mathematical representation of the relationships between gene loci, gene transcripts, protein sub-units, enzymes, and reactions using logical relationships (and/or) (See Sect. “Reconstructions, Knowledge Bases, and Models”).

Genome-scale:

The characterization of a cellular function/system on its genome scale, i.?e., incorporation/consideration of all known associated components encoded in the organism’s genome .

Isocline:

A line in a phenotypic phase plane diagram, along which the ratio between the shadow prices for two metabolites is fixed (See Sect. “Constraint-Based Methods of Analysis”).

Knowledge base:

A specific type of reconstruction which also accounts for the following information: molecular formulae, subsystem assignments, GPRs, references to primary and review literature, and additional pertinent notes (See Sect. “Reconstructions, Knowledge Bases, and Models”).

Line of optimality:

The isocline in a phenotypic phase plane diagram that achieves the highest value of the objective in the phase plane (See Sect. “Constraint-Based Methods of Analysis”).

Linear programming problem:

A class of optimization problems in which a linear objective function is maximized or minimized subject to linear equality and inequality constraints (See Sect. “Constraint-Based Methods of Analysis”).

Metabolic network null space :

The set of independent vectors that satisfy the equations: \( { \mathbf{S} \bullet \boldsymbol{v} = 0 } \); i.?e., a flux basis satisfying the steady state conditions, also referred to as the steady state flux solution space (See Sect. “Constraint-Based Methods of Analysis”).

Network reconstruction :

An assembly of the components and their interconversions for an organism, based on the genome annotation and the bibliome (See Sect. “Reconstructions, Knowledge Bases, and Models”).

Objective function :

A function which is maximized or minimized in optimization problems. In FBA, the objective function is a linear combination of fluxes. For prokaryotes and simple eukaryotes grown in the laboratory under controlled conditions, the biomass function is often used as the objective function (See Sect. “Constraint-Based Methods of Analysis”).

Open reading frame (ORF):

A DNA segment that has a start and stop site for translation and can encode for a protein product (see Sect. “The Human Metabolic Network Reconstruction: Characterizing the Knowledge Landscape and a Framework for Drug Target Discovery”).

Phenotypic phase plan (PhPP) analysis:

A constraint-based method of analysis which uses FBA simulations to perform a sensitivity analysis by optimizing the objective function as two uptake fluxes are varied simultaneously. The results of generally displayed graphically. Isoclines and the line of optimality can be used to characterize different functional states in the phase plane (See Sect. “Constraint-Based Methods of Analysis”).

Sensitivity analysis :

The analysis of how the output of a model changes as input parameters are varied (See Sect. “Constraint-Based Methods of Analysis”).

Shadow price :

For FBA optimization problems, the (negative) change in the objective function divided by the change in the availability of a particular metabolite (i.?e., the negative sensitivity of the objective function with respect to a particular metabolite) (See Sect. “Constraint-Based Methods of Analysis”).

Single nucleotide polymorphism (SNP) :

A genetic sequence variation that involves a change or variation of a single base (See Sect. “Causal SNP Classification Using Co-sets”).

Solution space:

The set of feasible solutions for a system under a defined set of constraints (See Sect. “Constraint-Based Methods of Analysis”).

Uniform random sampling (Monte Carlo sampling):

A constraint-based method of analysis that uses Monte Carlo sampling methods to obtain a uniform distribution of random samples from the allowable flux space in order to find the range and probability distributions for reaction fluxes (See Sect. “Constraint-Based Methods of Analysis”).

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Acknowledgments

This work was supported in part by NSF IGERT training grant DGE0504645.

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Authors and Affiliations

  1. Department of Bioengineering, University of California, San Diego, La Jolla, USA

    Nathan E. Lewis, Neema Jamshidi & Bernhard Ø. Palsson

  2. Bioinformatics program, University of California, San Diego, USA

    Nathan E. Lewis & Ines Thiele

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  1. Nathan E. Lewis
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    Robert A. Meyers Ph. D. (Editor-in-Chief) (Editor-in-Chief)

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Lewis, N.E., Jamshidi, N., Thiele, I., Palsson, B.Ø. (2009). Metabolic Systems Biology. In: Meyers, R. (eds) Encyclopedia of Complexity and Systems Science. Springer, New York, NY. https://doi.org/10.1007/978-0-387-30440-3_329

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