Metabolic engineering is the directed improvement of product formation or cellular properties through the modification of specific biochemical reaction(s) or the introduction of new one(s) with the use of recombinant DNA technology. The field emerged during the past decade, and powered by techniques from applied molecular biology and reaction engineering, it is becoming a focal point of research activity in biological and biochemical engineering, cell physiology, and applied microbiology. Metabolic engineering was firstly suggested by Jay Bailey in 1991.

It was embraced then by both engineers and life scientists who saw in it the opportunity to capture the potential of sequence and other information generated from genomics research. AIMS OF METABOLIC ENGINEERING The aims of metabolic engineering can be summarized as to improve the yield and productivity of native products, to extend the range of substrates, to produce the products that are new to the host cell, or entirely novel, to improve general cellular properties ,to improve the resistance against pests and diseases in plants or human, and to lower the level of undesired compounds. CHARACTERISTICS OF METABOLIC ENGINEERING

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As with all traditional fields of engineering, metabolic engineering too encompasses the two defining steps of analysis and synthesis. Since metabolic engineering emerged with DNA recombination as the enabling technology, attention initially was focused on the synthetic side of this field: expression of new genes in various host cells, amplification of endogenous enzymes, deletion of genes or modulation of enzymatic activity, transcriptional or enzymatic deregulation, etc. As such, metabolic engineering was the tachnological manifestation of applied molecular biology with very little engineering content..

A more important engineering component can be found in the analytical side of this field. Some concerns that are elucidated by analytical side of metabolic engineering are the identification of the important parameters that define the physiological state, the utilization of this information to elucidate the control structure of a metabolic network and then proposal of rational targets for modification to achieve a certain purpose, the assessment of the true biochemical impact of such genetic and enzymatic modifications to design the next step of pathway modifications.

On the synthetic side, a novel aspect of metabolic engineering is the focus on integrated metabolic pathways instead of individual reactions. As such, it examines complete biochemical reaction networks. Observations about the behavior of the overall system are the best guide for further rational decomposition and analysis. IMPORTANCE OF METABOLIC ENGINEERING It is considered that the most important contribution of metabolic engineering is the emphasis on metabolic fluxes and their control under in vivo conditions.

An important objective of metabolic engineering is to understand the function of metabolic pathways in their entirety, preferably through the integration of the constituent biochemical reactions. Another important content of this field is the elucidation of key metabolic branch points, as a result the control of metabolic pathway can be applied effectively. Furthermore, metabolic fluxes provide a generic basis of comparison of strain variants. Besides metabolic engineering opens a field to upgrade the quality of biological information and synthesizing it for the aim of developing useful products.

Finally, metabolic engineering plays a strategic role in defining measurements of critical importance for dissolving reaction networks. THE METHODS Many biological and chemical techniques can be used to apply metabolic engineering. Some of them are amplification, inhibition, deletion and/or overexpression of genes, transfer or deregulation of genes/enzymes, random mutagenesis, gene duplication, gene fusion, and changing the reaction conditions (temperature, pH, dissolved oxygen,etc. ) DNA recombination in a broader sense is routinely employed at various steps of metabolic engineering.

REQUIREMENTS FOR METABOLIC ENGINEERING Two kinds of background information is required to improve product formation: growth conditions of microorganisms and metbolic pathway of the reactions. Since metabolic engineering emphasizes metabolic pathway integration and relies on metabolic fluxes as determinants of cell physiology and measures of metabolic control, cellular metabolism must be known. Specifically transport mechanisms, fueling reactions, biosynthetic reactions, polimerization and growth energetics of the organism of interest must be recognized thoroughly.

As detailed, species may be transported across the plasma membrane by three different mechanisms: (i) free diffusion, (ii) facilitated diffusion, and (iii) active transport. The fueling reactions that must be well-understood are glycolysis, fermentative pahways, TCA cycle and oxidative phosphorylation, anaplerotic pathways, catabolism of fats, organic acids and amino acids. In addition to cellular metabolism, stoichiometry of cellular reactions, material balances, reaction rates and data consistency must be provided.

There are two approaches in assessing the consistency of experimental data. The first one is based on a very simple metabolic model where all cellular reactions are lumped into a single on efor the overall cell biomass growth, and the method is called black box model. In the second approach recognizes more biochemical detail in the overall conversion of substrates into biomass and metabolic products. The second model is mathematically more involved, but provides a more realistic outcome than the black box model. RE-CONSTRUCTING THE MODEL

In metabolic engineering, you need a model of either a particular pathway or all the network of the reactions. If you are dealing with a specific product, it might be enough to construct a limited network to work with. This is easier. But, if you need a detailed information about the microorganism metabolism, a “genome scale model” is required. Therefore, the genome scale model can be defined as a deeper insight into analyzing the metabolism of an organism related with its genome. It’s a reconstruction of the pathways with respect to their reactions and enzymes. TERMINOLOGY METABOLIC FLUX ANALYSIS (MFA)

Metabolic fluxes constitute a fundamental determinant of cell physiology, primarily because they provide a measure of the degree of engagement of various pathways in overall cellular functions and metabolic processes. At that point, MFA is an analysis whereby intracellular fluxes are calculated by using a stoichiometric model for the major intracellular reactions and applying mass balances around intracellular metabolites. The concept of MFA is useful for studying interactions between different pathways and quantifying flux distribution around metabolic branch points. By MFA; Identification of branch point control (nodal rigidity) in cellular pathways, • Identification of alternative pathways • Calculation of non-measured extracellular fluxes • Calculation of maximum theoretical yields …are possible. Steps of MFA are: • Determine the reactions. [pic] • Write the material balances for each metabolite. • Build the stochiometric matrix: The stochiometric matrix S (T) provides the linear relationship of the model between the flux rates of the (enzymatic) reactions and the derivatives of the reactant (enzyme) concentrations. [pic] • Determine the flux vector. • Set the equation.

But the net formation rates of intracellular metabolites are impossible to calculate. So, to solve that equation an assumption is considered: pseudo steady-state assumption. That tells us, the change of formation rates of intracellular metabolites is zero. [pic] [pic] [pic] • Determine an objective function such as maximizing biomass flux or maximizing the flux of a specific product (glutamic acid, ethanol, hydrogen…) • Perform the matrix using a computer program such as MatLab, Gams, etc. METABOLIC CONTROL ANALYSES MCA is a linear perturbation theory of the inherently nonlinear problem of enzymatic kinetics of metabolic networks.

MCA is important for keeping the rates of synthesis and conversion of metabolites closely balanced over a very wide range of external conditions without the catastrophic rise or fall of intracellular metabolite concentrations. MCA quantifies how variables, such as fluxes and species concentrations, depend on network parameters. In particular it is able to describe how network dependent properties, called control coefficients, depend on local properties called elasticities. Control Coefficients The sensitivity of the metabolic pathways can be evaluated by control coefficients. Flux Control Coefficient (FCC): The relative change in the steady-state flux resulting from an infinitesimal change in the activity of an enzyme of the pathway by the relative change in the activity. (J:flux of the reaction, vi: activity of the enzyme) [pic] • Elasticity Coefficient (EC): The elasticity coefficient measures the local response of an enzyme or other chemical reaction to changes in its environment. The most common elasticity coefficients are the sensitivity (elasticities) of the reaction rates with respect to metabolite concentration. S:concentration of the metabolite, v: reaction rate) [pic] • Concentration Control Coefficient (CCC): The change in concentration of a metabolite affected by an enzyme acitivity is simply concentration control coefficient. (S:concentration of the metabolite, vi: activity of the enzyme) [pic] DATABASES While re-constructing the model some databases can be used. In those databases, you may find the reactions in which a specific metabolite is involved, enzymes that are related with a particular pathway or the genes that encode the protein or the enzyme you are interested in.

This kind of databases are also very informative when you design an in silico experiment. Some examples to those databases are NCBI, ERGO, BRENDA, KEGG, ExPASy, METACyC, PubMed. IN SILICO EXPERIMENTS Reconstructing of a model includes both in silico experiments and experimental datas. After constructing the model, you should check the validity of the model and see if the model is consistent with the experimental datas. For that purpose, you run your model with the help of some special computer programs, get some results and then compare those results with the experimental datas obtained from laboratory.

Generally over 80% consistency is required. APPLICATION AREAS OF METABOLIC ENGINEERING • Chemical industry • Food industry • Modern biological applications • Environment • Medical field and some others are the application areas of this field. Some specific examples of the applications are the biosynthesis of petroleum-derived thermoplastics (poly[hydoxyalkanoates]) by fermentation, production of dyes in E. coli, improved production of terpenoid indole alkaloids in Catharanthus roseus cells, production of cortisol from glucose, using genetically engineered S.

Cerevisiae cells production of new drugs/drug-like molecules for the treatment of celiac disease. REFERENCES • G. Stephanopoulos, A. A. Aristodou, J. Nielsen, Metabolic Engineering,Academic Press,1998. • C. Ratledge and B. Kristiansen-Basic Biotechnology-Cambridge University Press • I. Martinez, G. N. Bennett, K. Y. San, Metabolic impact of the level of aeration during cell growth on anaerobic succinate production by an engineered Escherichia coli, Metabolic Engineering 2010 Nov;12(6):499-509. Maria Gonzalez-Pajueloa, Isabelle Meynial-Salles, Filipa Mendesa, Jose Carlos Andradea, Isabel Vasconcelosa, Philippe Soucaillec, Metabolic engineering of Clostridium acetobutylicum for the industrial production of 1,3-propanediol from glycerol, Metabolic Engineering 7(2005)329–336. • R. E. Cripps, K. Eley, D. J. Leak, B. Rudd, M. Taylor, M. Todd, S. Boakes, S. Martin, T. Atkinson Metabolic engineering of Geobacillus thermoglucosidasius for high yield ethanol production, Metabolic Engineering 11(2009)398–408 ———————– 8


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