Optimization Of Metabolic Pathways Research Articles

Optimization of metabolic pathways for bioconversion of lignocellulose to ethanol through genetic engineering

The optimization of metabolic pathways is of fundamental importance for strategies aimed at improving the economics and yield of the lignocellulose-to-ethanol processes. Although Escherichia coli is capable of metabolizing a wide variety of substrates including hexoses and pentoses, its hexose metabolism is inferior to that of Zymomonas mobilis, an obligate, ethanologenic bacterium. We therefore inserted and expressed Z. mobilis genes encoding essential enzymes involved in the fermentation pathway, alcohol dehydrogenase II ( adh II) and pyruvate decarboxylase ( pdc), into E. coli, resulting in increased cell growth and ethanol production. Ethanol concentrations of > 30 g/L were obtained on 10% glucose. Additionally, since pyruvate is mainly assimilated through pyruvate formate lyase ( pfl) and forms formic acid and acetyl coenzyme A, metabolic redirection was attempted through gene knockout by Red-mediated recombination to decrease the byproducts of pyruvate metabolism. Under microaerobic conditions, pflA- and pflB-mutants produced more ethanol (163% and 207%, respectively) relative to the parent strain, using glucose as a carbon source.

Steady state optimization with guaranteed stability of a tryptophan biosynthesis model

In this article we address the steady state design of a nonlinear tryptophan biosynthesis model with guaranteed stability of the optimal solution in the presence of parametric uncertainty. The optimization based approach – denoted constructive nonlinear dynamics (CNLD) – is based on constraints that enforce a minimal distance in the space of the uncertain parameters between a stability boundary and the optimal solution. The results obtained with CNLD are compared to results recently presented by Chang and Sahinidis [Chang, Y. J., & Sahinidis, N. V. (2005). Optimization of metabolic pathways under stability considerations. Computers and Chemical Engineering, 29 (3), 467–479], where the optimization of the tryptophan biosynthesis model was addressed by a global optimization algorithm. CNLD takes the nonlinearities of the process exactly into account and satisfies the specified robustness criteria without being overly conservative.

Reduced physical activity and risk of chronic disease: the biology behind the consequences

This review focuses on three preserved, ancient, biological mechanisms (physical activity, insulin sensitivity, and fat storage). Genes in humans and rodents were selected in an environment of high physical activity that favored an optimization of aerobic metabolic pathways to conserve energy for a potential, future food deficiency. Today machines and other technologies have replaced much of the physical activity that selected optimal gene expression for energy metabolism. Distressingly, the negative by-product of a lack of ancient physical activity levels in our modern civilization is an increased risk of chronic disease. We have been employing a rodent wheel-lock model to approximate the reduction in physical activity in humans from the level under which genes were selected to a lower level observed in modern daily functioning. Thus far, two major changes have been identified when rats undertaking daily, natural voluntary running on wheels experience an abrupt cessation of the running (wheel lock model). First, insulin sensitivity in the epitrochlearis muscle of rats falls to sedentary values after 2 days of the cessation of running, confirming the decline to sedentary values in whole-body insulin sensitivity when physically active humans stop high levels of daily exercise. Second, visceral fat increases within 1 week after rats cease daily running, confirming the plasticity of human visceral fat. This review focuses on the supporting data for the aforementioned two outcomes. Our primary goal is to better understand how a physically inactive lifestyle initiates maladaptations that cause chronic disease.

On the local optimal solutions of metabolic regulatory networks using information guided genetic algorithm approach and clustering analysis

Biological information generated by high-throughput technology has made systems approach feasible for many biological problems. By this approach, optimization of metabolic pathway has been successfully applied in the amino acid production. However, in this technique, gene modifications of metabolic control architecture as well as enzyme expression levels are coupled and result in a mixed integer nonlinear programming problem. Furthermore, the stoichiometric complexity of metabolic pathway, along with strong nonlinear behaviour of the regulatory kinetic models, directs a highly rugged contour in the whole optimization problem. There may exist local optimal solutions wherein the same level of production through different flux distributions compared with global optimum. The purpose of this work is to develop a novel stochastic optimization approach-information guided genetic algorithm (IGA) to discover the local optima with different levels of modification of the regulatory loop and production rates. The novelties of this work include the information theory, local search, and clustering analysis to discover the local optima which have physical meaning among the qualified solutions.

Optimization of metabolic pathways under stability considerations

Metabolic networks are often approached through steady-state optimization formulations that are solved to interpret and predict the behavior of the network subject to changes in external fluxes or internal enzyme activity. The major question addressed in this paper is how to ensure that solutions to these steady-state optimization models for metabolic networks are implementable from a stability point of view. The stability of a dynamic system is closely related to matrix stability. Hence, it can be determined through the computation of the largest eigenvalue of a coefficient matrix. While it is straightforward to analyze the stability of a given system, the challenge is to redesign a metabolic network in a way that guarantees that the system will be stable around the new steady-state. For this purpose, we propose to model metabolic networks through classical optimization formulations, such as the classical S -system representation, with an additional constraint to enforce stability within a prespecified neighborhood of the solution point. The proposed formulation is a bilevel optimization problem that is very difficult to solve. We develop a suitable global optimization algorithm to solve this problem after transforming it to a semi-infinite optimization problem. Computational results are presented, including application to tryptophan biosynthesis in bacteria and anaerobic fermentation in Saccharomyces cerevisiae.

Evolutionary Optimization of Metabolic Pathways. Theoretical Reconstruction of the Stoichiometry of ATP and NADH Producing Systems

The structural design of ATP and NADH producing systems, such as glycolysis and the citric acid cycle (TCA), is analysed using optimization principles. It is assumed that these pathways combined with oxidative phosphorylation have reached, during their evolution, a high efficiency with respect to ATP production rates. On the basis of kinetic and thermodynamic principles, conclusions are derived concerning the optimal stoichiometry of such pathways. Extending previous investigations, both the concentrations of adenine nucleotides as well as nicotinamide adenine dinucleotides are considered variable quantities. This implies the consideration of the interaction of an ATP and NADH producing system, an ATP consuming system, a system coupling NADH consumption with ATP production and a system consuming NADH decoupled from ATP production. It is examined in what respect real metabolic pathways can be considered optimal by studying a large number of alternative pathways. The kinetics of the individual reactions are described by linear or bilinear functions of reactant concentrations. In this manner, the steady-state ATP production rate can be calculated for any possible ATP and NADH producing pathway. It is shown that most of the possible pathways result in a very low ATP production rate and that the very efficient pathways share common structural properties. Optimization with respect to the ATP production rate is performed by an evolutionary algorithm. The following results of our analysis are in close correspondence to the real design of glycolysis and the TCA cycle. (1) In all efficient pathways the ATP consuming reactions are located near the beginning. (2) In all efficient pathways NADH producing reactions as well as ATP producing reactions are located near the end. (3) The number of NADH molecules produced by the consumption of one energy-rich molecule (glucose) amounts to four in all efficient pathways. A distance measure and a measure for the internal ordering of reactions are introduced to study differences and similarities in the stoichiometries of metabolic pathways.

New insights into metabolic pathway optimization by analogy with industrial manufacturing processes

New insights into metabolic pathway optimization by analogy with industrial manufacturing processes

Quantitative assessment of uncertainty in the optimization of metabolic pathways

The S-System formalism provides a popular, versatile and mathematically tractable representation of metabolic pathways. At steady-state, after a logarithmic transformation, the S-System representation reduces into a system of linear equations. Thus, the maximization of a particular metabolite concentration or a flux subject to physiological constraints can be expressed as a linear programming (LP) problem which can be solved explicitly and exactly for the optimum enzyme activities. So far, the quantitative effect of parametric/experimental uncertainty on the S-model predictions has been largely ignored. In this work, for the first time, the systematic quantitative description of modeling/experimental uncertainty is attempted by utilizing probability density distributions to model the uncertainty in assigning a unique value to system parameters. This probabilistic description of uncertainty renders both objective and physiological constraints stochastic, demanding a probabilistic description for the optimization of metabolic pathways. Based on notions from chance-constrained programming and statistics, a novel approach is introduced for transforming the original stochastic formulation into a deterministic one which can be solved with existing optimization algorithms. The proposed framework is applied to two metabolic pathways characterized with experimental and modeling uncertainty in the kinetic orders. The computational results indicate the tractability of the method and the significant role that modeling and experimental uncertainty may play in the optimization of networks of metabolic reactions. While optimization results ignoring uncertainty sometimes violate physiological constraints and may fail to correctly assess objective targets, the proposed framework provides quantitative answers to questions regarding how likely it is to achieve a particular metabolic objective without exceeding a prespecified probability of violating the physiological constraints. Trade-off curves between metabolic objectives, probabilities of meeting these objectives, and chances of satisfying the physiological constraints, provide a concise and systematic way to guide enzyme activity alterations to meet an objective in the face of modeling and experimental uncertainty. (c) 1997 John Wiley & Sons, Inc. Biotechnol Bioeng 56: 145-161, 1997.

Simulation and optimization of metabolic pathways

A program to simulate metabolic pathways on the basis of either kinetic data or data on the steady-state levels and fluxes using Biochemical Systems Theory is presented. Having established such a model it is possible to determine the rate-controlling enzymes and metabolites for the system. While this in itself yields extremely valuable information pinpointing the limiting fluxes through the system, a more usual objective is to maximize a production rate or flux or to minimize byproducts or toxic intermediates. For this reason the problem is posed as a constrained optimization problem. While we recognize that the optimum thus found may be unachievable in practical terms due to limitations in the extent to which cells can be manipulated, it is important in that it: (a) gives an upper or lower bound to the production rate or flux; and (b) indicates the ideal combination of variables which should be aimed at. The model system used to test the software is a pathway for the degradation of toluene encoded by the TOL plasmid from Pseudomonas putida transformed into Escherichia coli. Kinetic data has been obtained, partly from the literature and partly by approximation, and realistic metabolite and enzyme concentrations are chosen.

Simulation and optimization of metabolic pathways

A program to simulate metabolic pathways on the basis of either kinetic data or data on the steady state levels and fluxes using Biochemical Systems Theory is presented. Having established such a model it is possible to determine the rate-controlling enzymes and metabolites for the system. While this in itself yields extremely valuable information pinpointing the limiting fluxes through the system, a more usual objective is to maximise a production rate or flux or to minimise byproducts or toxic intermediates. For this reason the problem is posed as a constrained optimization. While we recognise that the optimum thus found may be unachievable in practical terms due to limitations in experimental techniques, it is important in that it (a) gives an upper or lower bound to the production rate or flux, and (b) indicates the ideal combination of variables which should be aimed at. The model system used to test the software is a pathway for the degradation of toluene encoded by the TOL plasmid from Pseudomonas putida transformed into Escherichia coli. Kinetic data has been obtained, partly from the literature and partly by approximation, and realistic metabolite and enzyme concentrations are chosen.