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Introduction to Metabolic Pathways

Metabolism is the set of life-sustaining chemical transformations within the cells of living organisms. These biochemical reactions are organized into metabolic pathways in which one chemical is transformed through a series of steps into another chemical by a sequence of enzymes. Biochemical reactions and enzymes convert raw materials into molecules necessary for the cell’s survival. Enzymes act as catalysts that allow the reactions to proceed more rapidly, or to regulate the pathways’ response to changes in the cell’s environment or to signals from other cells. The reactants, products, and intermediates of an enzymatic reaction are known as metabolites, which leave behind unique chemical fingerprints that can be studied and profiled.

In the biomanufacturing and pharmaceutical industries, billions of dollars’ worth of products are produced in living cell “factories” more commonly referred to as bioreactors. A bioreactor is an approach to producing products that use microbial cells to convert the raw materials (media) into final products. The optimization process largely depends on metabolic engineering and growth conditions (media composition, temperature, cell density, etc.).

Metabolic engineering is the practice of optimizing genetic and regulatory processes within cells to increase the cells’ production of a certain substance. The ultimate goal of metabolic engineering is to be able to use these organisms to produce valuable substances on an industrial scale in a cost-effective manner. Current examples include producing commodity and specialty chemicals, consumer products, pharmaceuticals, and other biotechnology products.

The Metabolic Complexity Nightmare

Metabolic flux analysis specifically seeks to mathematically model metabolic pathways, calculate a yield of useful products, and pinpoint parts of the pathway that constrain the production of products. Genetic engineering techniques can then be used to modify the pathway in order to relieve these constraints. Since cells use these metabolic pathways for their survival, changes can have drastic effects on the cells’ viability. Therefore, trade-offs in metabolic engineering arise between the cells ability to produce the desired substance and its natural survival needs.

Optimizing the production of a cell factory is both problematic and very time-consuming. For example, consider the metabolism of the E. coli bacteria. A simple model of the E. coli central metabolism reactions, a small subsystem in the overall metabolic network, consists of 112 reactions and contains more than two million modes, which increases to more than 26 million modes when the possible substrate alternatives are included.

Existing techniques are limited in capability, are inherently difficult to understand, or are too time-consuming to be used effectively. Researchers often rely on ineffective manual trial-and-error techniques to understand the metabolic processes involved in their cell factory or disease of interest.

How CoreMFA Works

Metalytics’ flagship CoreMFA offering uses a process called metabolic flux analysis (MFA) to create and more easily analyze visual maps of the complex metabolic pathways in cells. By using our platform, companies can drastically reduce analysis complexity and accelerate their understanding of the metabolic processes involved in their cell factories.

Flux, or metabolic flux, is the turnover rate of molecules through a metabolic pathway. Flux is regulated by a complex series of enzymes, chemical reactions, and metabolites involved in a pathway. Within cells, regulation of flux is vital for all metabolic pathways to regulate the cells activity under different conditions. Flux is therefore of great interest in metabolic network modeling, where it is analyzed via metabolic flux analysis.

Metabolic Flux Analysis (MFA) studies are carried out by feeding cells an isotopically labeled substrate (e.g., 13C-labeled glucose and subsequently measuring the patterns of isotope incorporation that emerge in metabolites as the raw material is converted into final product or energy source. A computational model of the intracellular metabolic network is used to determine pathway fluxes by integrating these isotope labeling data with additional measurements of extracellular nutrient uptake and product excretion rates. By systematically accounting for all extracellular carbon inputs and outputs and all major intracellular pathways, MFA can be used to reconstruct comprehensive flux maps depicting this complex cell metabolism.

Comparison of flux maps obtained under varying experimental conditions or in the presence of targeted genetic manipulations provides a functional readout on the global impact perturbations have on cell metabolism. Such information is essential to understanding how metabolic pathways are regulated in working cells. Furthermore, this information enables identification and subsequent elimination of wasteful byproduct pathways or metabolic bottlenecks in host organisms, thus enhancing production rates and yields.

By providing critical information about cellular metabolic rates in living cells, CoreMFA allows investigators to quickly and rationally engineer improved cells and culture conditions/media for biomanufacturing. By highlighting the most promising targets in metabolism, reduced time-to-market, increased productive output and production capacity, lower capital requirements, and reduced raw material costs can be achieved.

Metalytics is seeking collaborative partnerships to develop new MFA-based assays, new cell line products, and application products that enhance existing cell factory production processes.

CoreMFA Benefits

Custom Analysis
The CoreMFA software is customized to each customer’s application and facilitates the visualization and interpretation of results. CoreMFA identifies bottlenecks and other metabolic inefficiencies and provides actionable results to the customer.

Atomic-level Metabolite Measurement
The platform uses a stable isotope to provide atomic-level details that are easier to interpret.

Small Sample Sizes
The process requires only a small number of samples, which reduces the cost of producing samples and data analysis.

Dynamic View of Metabolism
CoreMFA can visualize the rate at which metabolism is proceeding in measured pathways; thus, can control these rates with processes, nutrients and/or genetic engineering to improve quality and quantity of output.

Compare Process Variables
Metabolic maps with an overlay of flux results are easy to interpret flux rates and to compare multiple process variables to improve output.

Scenario Analysis
CoreMFA can evaluate alternative process conditions after the baseline model has been validated, which reduces the time and cost to optimize process conditions for different cell lines and/or products.