Fluxomics

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Fluxomics describes the various approaches that seek to determine the rates of metabolic reactions within a biological entity. [1] While metabolomics can provide instantaneous information on the metabolites in a biological sample, metabolism is a dynamic process. [2] The significance of fluxomics is that metabolic fluxes determine the cellular phenotype. [3] It has the added advantage of being based on the metabolome which has fewer components than the genome or proteome. [4]

Contents

Fluxomics falls within the field of systems biology which developed with the appearance of high throughput technologies. [5] Systems biology recognizes the complexity of biological systems and has the broader goal of explaining and predicting this complex behavior. [2]

Metabolic flux

Metabolic flux refers to the rate of metabolite conversion in a metabolic network. [1] [6] For a reaction this rate is a function of both enzyme abundance and enzyme activity. [1] Enzyme concentration is itself a function of transcriptional and translational regulation in addition to the stability of the protein. [1] Enzyme activity is affected by the kinetic parameters of the enzyme, the substrate concentrations, the product concentrations, and the effector molecules concentration. [1] The genomic and environmental effects on metabolic flux are what determine healthy or diseased phenotype. [6]

Fluxome

Similar to genome, transcriptome, proteome, and metabolome, the fluxome is defined as the complete set of metabolic fluxes in a cell. [5] However, unlike the others the fluxome is a dynamic representation of the phenotype. [5] This is due to the fluxome resulting from the interactions of the metabolome, genome, transcriptome, proteome, post-translational modifications and the environment. [5]

Flux analysis technologies

Two important technologies are flux balance analysis (FBA) and 13C-fluxomics. In FBA metabolic fluxes are estimated by first representing the metabolic reactions of a metabolic network in a numerical matrix containing the stoichiometric coefficients of each reaction. [7] The stoichiometric coefficients constrain the system model and are why FBA is only applicable to steady state conditions. [7] Additional constraints can be imposed. [7] By providing constraints the possible set of solutions to the system are reduced. Following the addition of constraints the system model is optimized. [7] Flux-balance analysis resources include the BIGG database, [8] the COBRA toolbox, [9] and FASIMU. [10]

In 13C-fluxomics, metabolic precursors are enriched with 13C before being introduced to the system. [11] Using an imaging technique such as mass spectrometry or nuclear magnetic resonance spectroscopy the level of incorporation of 13C into metabolites can be measured and with stoichiometry the metabolic fluxes can be estimated. [11]

Stoichiometric and kinetic paradigms

A number of different methods, broadly divided into stoichiometric and kinetic paradigms.

Within the stoichiometric paradigm, a number of relatively simple linear algebra methods use restricted metabolic networks or genome-scale metabolic network models to perform flux balance analysis and the array of techniques derived from it. These linear equations are useful for steady state conditions. Dynamic methods are not yet usable. [12] On the more experimental side, metabolic flux analysis allows the empirical estimation of reaction rates by stable isotope labelling.

Within the kinetic paradigm, kinetic modelling of metabolic networks can be purely theoretical, exploring the potential space of dynamic metabolic fluxes under perturbations away from steady state using formalisms such as biochemical systems theory. Such explorations are most informative when accompanied by empirical measurements of the system under study following actual perturbations, as is the case in metabolic control analysis. [13]

Constraint based reconstruction and analysis

Collected methods in fluxomics have been described as "COBRA" methods, for constraint based reconstruction and analysis. A number of software tools and environments have been created for this purpose. [14] [15] [16] [17] [18] [19] [20]

Although it can only be measured indirectly, metabolic flux is the critical link between genes, proteins and the observable phenotype. This is due to the fluxome integrating mass-energy, information, and signaling networks. [21] Fluxomics has the potential to provide a quantifiable representation of the effect the environment has on the phenotype because the fluxome describes the genome environment interaction. [21] In the fields of metabolic engineering [22] and systems biology, [23] fluxomic methods are considered a key enabling technology due to their unique position in the ontology of biological processes, allowing genome scale stoichiometric models to act as a framework for the integration of diverse biological datasets. [24]

Examples of use in research

One potential application of fluxomic techniques is in drug design. Rama et al. [25] used FBA to study the mycolic acid pathway in Mycobacterium tuberculosis. Mycolic acids are known to be important to M. tuberculosis survival and as such its pathway has been studied extensively. [25] This allowed the construction of a model of the pathway and for FBA to analyze it. The results of this found multiple possible drug targets for future investigation.

FBA was used to analyze the metabolic networks of multidrug-resistant Staphylococcus aureus. [26] By performing in silico single and double gene deletions many enzymes essential to growth were identified.

Related Research Articles

<span class="mw-page-title-main">Systems biology</span> Computational and mathematical modeling of complex biological systems

Systems biology is the computational and mathematical analysis and modeling of complex biological systems. It is a biology-based interdisciplinary field of study that focuses on complex interactions within biological systems, using a holistic approach to biological research.

<span class="mw-page-title-main">Metabolomics</span> Scientific study of chemical processes involving metabolites

Metabolomics is the scientific study of chemical processes involving metabolites, the small molecule substrates, intermediates, and products of cell metabolism. Specifically, metabolomics is the "systematic study of the unique chemical fingerprints that specific cellular processes leave behind", the study of their small-molecule metabolite profiles. The metabolome represents the complete set of metabolites in a biological cell, tissue, organ, or organism, which are the end products of cellular processes. Messenger RNA (mRNA), gene expression data, and proteomic analyses reveal the set of gene products being produced in the cell, data that represents one aspect of cellular function. Conversely, metabolic profiling can give an instantaneous snapshot of the physiology of that cell, and thus, metabolomics provides a direct "functional readout of the physiological state" of an organism. There are indeed quantifiable correlations between the metabolome and the other cellular ensembles, which can be used to predict metabolite abundances in biological samples from, for example mRNA abundances. One of the ultimate challenges of systems biology is to integrate metabolomics with all other -omics information to provide a better understanding of cellular biology.

<span class="mw-page-title-main">Metabolome</span>

The metabolome refers to the complete set of small-molecule chemicals found within a biological sample. The biological sample can be a cell, a cellular organelle, an organ, a tissue, a tissue extract, a biofluid or an entire organism. The small molecule chemicals found in a given metabolome may include both endogenous metabolites that are naturally produced by an organism as well as exogenous chemicals that are not naturally produced by an organism.

Modelling biological systems is a significant task of systems biology and mathematical biology. Computational systems biology aims to develop and use efficient algorithms, data structures, visualization and communication tools with the goal of computer modelling of biological systems. It involves the use of computer simulations of biological systems, including cellular subsystems, to both analyze and visualize the complex connections of these cellular processes.

<span class="mw-page-title-main">Metabolic engineering</span>

Metabolic engineering is the practice of optimizing genetic and regulatory processes within cells to increase the cell's production of a certain substance. These processes are chemical networks that use a series of biochemical reactions and enzymes that allow cells to convert raw materials into molecules necessary for the cell's survival. Metabolic engineering specifically seeks to mathematically model these networks, calculate a yield of useful products, and pin point parts of the network that constrain the production of these products. Genetic engineering techniques can then be used to modify the network in order to relieve these constraints. Once again this modified network can be modeled to calculate the new product yield.

<span class="mw-page-title-main">Metabolic network</span> Set of biological pathways

A metabolic network is the complete set of metabolic and physical processes that determine the physiological and biochemical properties of a cell. As such, these networks comprise the chemical reactions of metabolism, the metabolic pathways, as well as the regulatory interactions that guide these reactions.

<span class="mw-page-title-main">Metabolic network modelling</span> Form of biological modelling

Metabolic network modelling, also known as metabolic network reconstruction or metabolic pathway analysis, allows for an in-depth insight into the molecular mechanisms of a particular organism. In particular, these models correlate the genome with molecular physiology. A reconstruction breaks down metabolic pathways into their respective reactions and enzymes, and analyzes them within the perspective of the entire network. In simplified terms, a reconstruction collects all of the relevant metabolic information of an organism and compiles it in a mathematical model. Validation and analysis of reconstructions can allow identification of key features of metabolism such as growth yield, resource distribution, network robustness, and gene essentiality. This knowledge can then be applied to create novel biotechnology.

<span class="mw-page-title-main">KEGG</span> Collection of bioinformatics databases

KEGG is a collection of databases dealing with genomes, biological pathways, diseases, drugs, and chemical substances. KEGG is utilized for bioinformatics research and education, including data analysis in genomics, metagenomics, metabolomics and other omics studies, modeling and simulation in systems biology, and translational research in drug development.

Phenomics is the systematic study of traits that make up a phenotype. It was coined by UC Berkeley and LBNL scientist Steven A. Garan. As such, it is a transdisciplinary area of research that involves biology, data sciences, engineering and other fields. Phenomics is concerned with the measurement of the phenotype where a phenome is a set of traits that can be produced by a given organism over the course of development and in response to genetic mutation and environmental influences. It is also important to remember that an organisms phenotype changes with time. The relationship between phenotype and genotype enables researchers to understand and study pleiotropy. Phenomics concepts are used in functional genomics, pharmaceutical research, metabolic engineering, agricultural research, and increasingly in phylogenetics.

<span class="mw-page-title-main">Flux balance analysis</span>

Flux balance analysis (FBA) is a mathematical method for simulating metabolism in genome-scale reconstructions of metabolic networks. In comparison to traditional methods of modeling, FBA is less intensive in terms of the input data required for constructing the model. Simulations performed using FBA are computationally inexpensive and can calculate steady-state metabolic fluxes for large models in a few seconds on modern personal computers. The related method of metabolic pathway analysis seeks to find and list all possible pathways between metabolites.

The Systems Biology Markup Language (SBML) is a representation format, based on XML, for communicating and storing computational models of biological processes. It is a free and open standard with widespread software support and a community of users and developers. SBML can represent many different classes of biological phenomena, including metabolic networks, cell signaling pathways, regulatory networks, infectious diseases, and many others. It has been proposed as a standard for representing computational models in systems biology today.

Flux, or metabolic flux is the rate of turnover of molecules through a metabolic pathway. Flux is regulated by the enzymes involved in a pathway. Within cells, regulation of flux is vital for all metabolic pathways to regulate the pathway's activity under different conditions. Flux is therefore of great interest in metabolic network modelling, where it is analysed via flux balance analysis and metabolic control analysis.

<span class="mw-page-title-main">Metabolic flux analysis</span> Experimental fluxomics technique

Metabolic flux analysis (MFA) is an experimental fluxomics technique used to examine production and consumption rates of metabolites in a biological system. At an intracellular level, it allows for the quantification of metabolic fluxes, thereby elucidating the central metabolism of the cell. Various methods of MFA, including isotopically stationary metabolic flux analysis, isotopically non-stationary metabolic flux analysis, and thermodynamics-based metabolic flux analysis, can be coupled with stoichiometric models of metabolism and mass spectrometry methods with isotopic mass resolution to elucidate the transfer of moieties containing isotopic tracers from one metabolite into another and derive information about the metabolic network. Metabolic flux analysis (MFA) has many applications such as determining the limits on the ability of a biological system to produce a biochemical such as ethanol, predicting the response to gene knockout, and guiding the identification of bottleneck enzymes in metabolic networks for metabolic engineering efforts.

Biochemical systems theory is a mathematical modelling framework for biochemical systems, based on ordinary differential equations (ODE), in which biochemical processes are represented using power-law expansions in the variables of the system.

<span class="mw-page-title-main">Reinhart Heinrich</span> German biophysicist

Reinhart Heinrich was a German biophysicist.

Rhodobacter sphaeroides is a kind of purple bacterium; a group of bacteria that can obtain energy through photosynthesis. Its best growth conditions are anaerobic phototrophy and aerobic chemoheterotrophy in the absence of light. R. sphaeroides is also able to fix nitrogen. It is remarkably metabolically diverse, as it is able to grow heterotrophically via fermentation and aerobic and anaerobic respiration. Such a metabolic versatility has motivated the investigation of R. sphaeroides as a microbial cell factory for biotechnological applications.

Bernhard Ørn Palsson is the Galletti Professor of Bioengineering and an adjunct professor of Medicine at the University of California, San Diego.

<span class="mw-page-title-main">Stefan Schuster</span> German biophysicist

Stefan Schuster is a German biophysicist. He is professor for bioinformatics at the University of Jena.

David A. Fell is a British biochemist and professor of systems biology at Oxford Brookes University. He has published over 200 publications, including a textbook on Understanding the control of metabolism in 1996.

<span class="mw-page-title-main">Markus Ralser</span> Italian biologist

Markus Ralser is an Italian biologist. His main research interest is metabolism of microorganisms. He is also known for his work on the origin of metabolism during the origin of life, and proteomics.

References

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