Carlos González-Alcón

Metabolism of citric acid production by Aspergillus niger: Model definition, steady-state analysis and constrained optimization of citric acid production rate

In an attempt to provide a rational basis for the optimization of citric acid production by A. niger, we developed a mathematical model of the metabolism of this filamentous fungus when in conditions of citric acid accumulation. The present model is based in a previous one, but extended with the inclusion of new metabolic processes and updated with currently available kinetic data. Among the different alternatives to represent the system behavior we have chosen the S-system representation within power-law formalism. This type of representation allows us to verify not only the ability of the model to exhibit a stable steady state of the integrated system but also the robustness and quality of the representation. The model analysis is shown to be self-consistent, with a stable steady state, and in good agreement with experimental evidence. Moreover, the model representation is sufficiently robust, as indicated by sensitivity and steady-state and dynamic analyses. From the steady-state results we concluded that the range of accuracy of the S-system representation is wide enough to model realistic deviations from the nominal steady state. The dynamic analysis indicated a reasonable response time, which provided further indication that the model is adequate. The extensive assessment of the reliability and quality of the model put us in a position to address questions of optimization of the system with respect to increased citrate production. We carried out the constrained optimization of A. niger metabolism with the goal of predicting an enzyme activity profile yielding the maximum rate of citrate production, while, at the same time, keeping all enzyme activities within predetermined, physiologically acceptable ranges. The optimization is based on a method described and tested elsewhere that utilizes the fact that the S-system representation of a metabolic system becomes linear at steady state, which allows application of linear programming techniques. Our results show that: (i) while the present profile of enzyme activities in A. niger at idiophase steady state yields high rates of citric acid production, it still leaves room for changes and suggests possible optimization of the activity profile to over five times the basal rate synthesis; (ii) when the total enzyme concentration is allowed to double its basal value, the citric acid production rate can be increased by more than 12-fold, and even larger values can be attained if the total enzyme concentration is allowed to increase even more (up to 50-fold when the total enzyme concentration may rise up to 10-fold the basal value); and (iii) the systematic search of the best combination of subsets of enzymes shows that, under all conditions assayed, a minimum of 13 enzymes need be modified if significant increases in citric acid are to be obtained. This implies that improvements by single enzyme modulation are unlikely, which is in agreement with the findings of some investigators in this and other fields.

Optimization of nonlinear biotechnological processes with linear programming: Application to citric acid production by Aspergillus niger

The metabolic pathway and the properties of many of the enzymes involved in the citric acid biosynthesis in the mold Aspergillus niger are well known. This fact, together with the availability of new theoretical frameworks aimed at quantitative analyses of control and dynamics in metabolic systems, has allowed us to construct a mathematical model of the carbohydrate metabolism in Aspergillus niger under conditions of citric acid accumulation. The model makes use of the S‐system representation of biochemical systems, which renders it possible to use linear programming to optimize the process. It was found that maintaining the metabolite pools within narrow physiological limits (20% around the basal steady‐state level) and allowing the enzyme concentrations to vary within a range of 0.1 to 50 times their basal values it is possible to triple the glycolytic flux while maintaining 100% yield of substrate transformation. To achieve these improvements it is necessary to modulate seven or more enzymes simultaneously. Although this seems difficult to implement at present, the results are useful because they indicate what the theoretical limits are and because they suggest several alternative strategies.

Optimization of biotechnological systems through geometric programming

BackgroundIn the past, tasks of model based yield optimization in metabolic engineering were either approached with stoichiometric models or with structured nonlinear models such as S-systems or linear-logarithmic representations. These models stand out among most others, because they allow the optimization task to be converted into a linear program, for which efficient solution methods are widely available. For pathway models not in one of these formats, an Indirect Optimization Method (IOM) was developed where the original model is sequentially represented as an S-system model, optimized in this format with linear programming methods, reinterpreted in the initial model form, and further optimized as necessary.ResultsA new method is proposed for this task. We show here that the model format of a Generalized Mass Action (GMA) system may be optimized very efficiently with techniques of geometric programming. We briefly review the basics of GMA systems and of geometric programming, demonstrate how the latter may be applied to the former, and illustrate the combined method with a didactic problem and two examples based on models of real systems. The first is a relatively small yet representative model of the anaerobic fermentation pathway in S. cerevisiae, while the second describes the dynamics of the tryptophan operon in E. coli. Both models have previously been used for benchmarking purposes, thus facilitating comparisons with the proposed new method. In these comparisons, the geometric programming method was found to be equal or better than the earlier methods in terms of successful identification of optima and efficiency.ConclusionGMA systems are of importance, because they contain stoichiometric, mass action and S-systems as special cases, along with many other models. Furthermore, it was previously shown that algebraic equivalence transformations of variables are sufficient to convert virtually any types of dynamical models into the GMA form. Thus, efficient methods for optimizing GMA systems have multifold appeal.

Optimization of biochemical systems through mathematical programming: Methods and applications

In this work we present a general (mono and multiobjective) optimization framework for the technological improvement of biochemical systems. The starting point of the method is a mathematical model in ordinary differential equations (ODEs) of the investigated system, based on qualitative biological knowledge and quantitative experimental data. In the method we take advantage of the special structural features of a family of ODEs called power-law models to reduce the computational complexity of the optimization program. In this way, the genetic manipulation of a biochemical system to meet a certain biotechnological goal can be expressed as an optimization program with some desired properties such as linearity or convexity. The general method of optimization is presented and discussed in its linear and geometric programming versions. We furthermore illustrate the use of the method by several real case studies. We conclude that the technological improvement of microorganisms can be afforded using the combination of mathematical modelling and optimization. The systematic nature of this approach facilitates the redesign of biochemical systems and makes this a predictive exercise rather than a trial-and-error procedure.

Modeling of leishmaniasis infection dynamics: novel application to the design of effective therapies

BackgroundThe WHO considers leishmaniasis as one of the six most important tropical diseases worldwide. It is caused by parasites of the genus Leishmania that are passed on to humans and animals by the phlebotomine sandfly. Despite all of the research, there is still a lack of understanding on the metabolism of the parasite and the progression of the disease. In this study, a mathematical model of disease progression was developed based on experimental data of clinical symptoms, immunological responses, and parasite load for Leishmania amazonensis in BALB/c mice.ResultsFour biologically significant variables were chosen to develop a differential equation model based on the GMA power-law formalism. Parameters were determined to minimize error in the model dynamics and time series experimental data. Subsequently, the model robustness was tested and the model predictions were verified by comparing them with experimental observations made in different experimental conditions. The model obtained helps to quantify relationships between the selected variables, leads to a better understanding of disease progression, and aids in the identification of crucial points for introducing therapeutic methods.ConclusionsOur model can be used to identify the biological factors that must be changed to minimize parasite load in the host body, and contributes to the design of effective therapies.

A Composite Run-to-the-Bank Rule for Multi-Issue Allocation Situations

In this paper, we propose a new extension of the run-to-the-bank rule for bankruptcy situations to the class of multi-issue allocation situations. We show that this rule always yields a core element and that it satisfies self-duality. We characterise our rule by means of a new consistency property, issue-consistency.