Bogju Lee

A hybrid approach to modeling metabolic systems using a genetic algorithm and simplex method

One of the main obstacles in applying genetic algorithms (GAs) to complex problems has been the high computational cost due to their slow convergence rate. We encountered such a difficulty in our attempt to use the classical GA for estimating parameters of a metabolic model. To alleviate this difficulty, we developed a hybrid approach that combines a GA with a stochastic variant of the simplex method in function optimization. Our motivation for developing the stochastic simplex method is to introduce a cost-effective exploration component into the conventional simplex method. In an attempt to make effective use of the simplex operation in a hybrid GA framework, we used an elite-based hybrid architecture that applies one simplex step to a top portion of the ranked population. We compared our approach with five alternative optimization techniques including a simplex-GA hybrid independently developed by Renders-Bersini (R-B) and adaptive simulated annealing (ASA). Our empirical evaluations showed that our hybrid approach for the metabolic modeling problem outperformed all other techniques in terms of accuracy and convergence rate. We used two additional function optimization problems to compare our approach with the five alternative methods.

A hybrid approach to modeling metabolic systems using genetic algorithm and simplex method

The genetic algorithm is applied to the parameter estimation problem to optimize a model of the glucose cycle of an E. Coli cell. Since the evaluation of the model is computationally expensive, a hybrid algorithm is proposed which grafts a proposed variant of J.A. Nelder and R. Meads (1965) downhill simplex-called concurrent simplex-with the genetic algorithm by using the simplex as an additional operator. The addition of the operator speeds up the rate of convergence of the genetic algorithm in some cases. The advantages and disadvantages of the simplex hybrid are discussed and the hybrid is tested against several different problem sets to verify its improvement over the generic genetic algorithm.<<ETX>>

Incorporating qualitative knowledge in enzyme kinetic models using fuzzy logic

Modeling of metabolic pathway dynamics requires detailed kinetic equations at the enzyme level. In particular, the kinetic equations must account for metabolite effectors that contribute significantly to the pathway regulation in vivo. Unfortunately, most kinetic rate laws available in the literature do not consider all the effectors simultaneously, and much kinetic information exists in a qualitative or semiquantitative form. In this article, we present a strategy to incorporate such information into the kinetic equation. This strategy uses fuzzy logic-based factors to modify algebraic rate laws that account for partial kinetic characteristics. The parameters introduced by the fuzzy factors are then optimized by use of a hybrid of simplex and genetic algorithms. The resulting model provides a flexible form that can simulate various kinetic behaviors. Such kinetic models are suitable for pathway modeling without complete enzyme mechanisms. Three enzymes in Escherichia coli central metabolism are used as examples: phosphoenolpyruvate carboxylase; phosphoenolpyruvate carboxykinase; and pyruvate kinase I. Results show that, with fuzzy logic-augmented models, the kinetic data can be much better described. In particular, complex behavior, such as allosteric inhibition, can be captured using fuzzy rules. The resulting models, even though they do not provide additional physical meaning in enzyme mechanisms, allow the model to incorporate semiquantitative information in metabolic pathway models.

A simplex genetic algorithm hybrid

One of the main obstacles in applying genetic algorithms (GAs) to complex problems has been the high computational cost due to their slow convergence rate. To alleviate this difficulty, we developed a hybrid approach that combines a GA with a stochastic variant of the simplex method in function optimization. Our motivation for developing the stochastic simplex method is to introduce a cost-effective exploration component into the conventional simplex method. In an attempt to make effective use of the simplex operation in a hybrid GA framework, we used an elite-based hybrid architecture that applies one simplex step to a top portion of the ranked population. We compared our approach with five alternative optimization techniques, including another simplex-GA hybrid, developed independently by Renders and Bersini (1994), and adaptive simulated annealing (ASA). We used two function optimization problems to compare our approach with the five alternative methods. Overall, these tests showed that our hybrid approach is an effective and robust optimization technique. We also tested our hybrid GA on the seven function benchmark problems on real space and showed its results.

Using fuzzy logic and a hybrid genetic algorithm for metabolic modeling

The identification of metabolic systems such as metabolic pathways, enzyme actions, and gene regulations is a complex task due to the complexity of the system and limited knowledge about the model. Mathematical equations and ordinary differential equations have been used to capture the structure of the model, and the conventional optimization techniques have been used to identify the parameters of the model. In general, however, a pure mathematical formulation of the model is difficult due to parametric uncertainty and incomplete knowledge of mechanisms. In this paper, we propose a modeling approach that: (1) uses fuzzy rule-based model to augment algebraic enzyme models that are incomplete; and (2) uses a hybrid genetic algorithm to identify uncertain parameters in the model.

A soft computing approach to the metabolic modeling

The identification of metabolic systems such as metabolic pathways, enzyme actions and gene regulations, is a complex task, due to the complexity of the system and limited knowledge about the model. In the past, mathematical equations and ODEs have been used to capture the structure of the model, and conventional optimization techniques have been used to identify the parameters of the model. In general, however, a pure mathematical formulation of the model is difficult, due to parametric uncertainty and incomplete knowledge of mechanisms. In this paper, we propose a modeling approach that uses (1) a fuzzy rule-based model to augment algebraic enzyme models that are incomplete, and (2) a hybrid genetic algorithm (GA) to identify uncertain parameters in the model. The hybrid GA integrates a GA with the simplex method in functional optimization to improve the GAs convergence rate. We have applied this approach to modeling the rate of enzyme reactions in E. colis central metabolism. The proposed modeling strategy allows (1) easy incorporation of qualitative insights into a pure mathematical model and (2) adaptive identification and optimization of key parameters to fit the system behaviors observed in biochemical experiments.

A hybrid genetic algorithm for the identification of metabolic models

Genetic algorithms (GA) have been demonstrated to be a promising search and optimization technique that is more likely to converge to a global optimum than most alternative techniques. In an attempt to apply GA to estimate parameters of a metabolic model, however, we found that the slow convergence rate of GA becomes a major problem for its applications to model identification of dynamic systems due to the high computational costs associated with the evaluation of models. To alleviate this difficulty, we developed a hybrid approach that combines Nelder and Meads (1965) simplex method with the genetic algorithm. The hybrid approach not only speeds up GAs rate of convergence, but also improves the quality of the solution found by pure GA.