Evaluating evolutionary multiobjective algorithms for the in silico optimization of mutant strains

In Metabolic Engineering, the identification of genetic manipulations that lead to mutant strains able to produce a given compound of interest is a promising, while still complex process. Evolutionary Algorithms (EAs) have been a successful approach for tackling the underlying in silico optimization problems. The most common task is to solve a bi-level optimization problem, where the strain that maximizes the production of some compound is sought, while trying to keep the organism viable (maximizing biomass). In this work, this task is viewed as a multiobjective optimization problem and an approach based on multiobjective EAs is proposed. The algorithms are validated with a real world case study that uses E. coli to produce succinic acid. The results obtained are quite promising when compared to the available single objective algorithms.This work was supported by the Portuguese FCT project POSC/EIA/59899/200

Multi-criterion approaches for the in silico optimization of mutant microorganisms

create mutants for the production of valuable compounds is, in Metabolic Engineering, a promising, while complex task. Approaches to tackle this problem have been proposed and included MILP-based techniques (OptKnock [1]), that lacked the possibility of including non-linear objective functions. More recently, meta-heuristic approaches like Evolutionary Algorithms (EAs) (OptGene [2]) have been put forward. Although these are more flexible and have provided good results in some cases, they relied on objective functions that aggregate several potentially conflicting optimization goals. In this work, the problem is interpreted as a Multi-Objective task and an approach based on Multi-Objective EAs (MOEAs) is proposed. The Strength Pareto Evolutionary Algorithm 2 (SPEA2) and the Non-dominated Sorting Genetic Algorithm II (NSGA-II), two state-of-the-art MOEAs were adapted to conduct this task. The optimization goals are to simultaneously maximize the biomass and also the concentration of a desired compound (bi-objective optimization problems). Next, we took the problem further and added two new objectives: minimizing either the number of knockouts in the solution or the sum of all the fluxes present in the model (tri-objective optimization problems). These algorithms are validated using three real world case studies. The selected organisms are S.cerevisiae for the production of succinate and E.coli for the production of both succinate and lactate. The results are quite promising when compared with the available single-objective approaches

Evolutionary multiobjective algorithms for in silico metabolic engineering

The aims of this Metabolic Engineering conference are to provide a forum for academic and industrial researchers in the field; to bring together the different scientific disciplines that contribute to the design, analysis and optimization of metabolic pathways; and to explore the role of Metabolic Engineering in the areas of health and sustainability. Presentations, both written and oral, panel discussions, and workshops will focus on both applications and techniques used for pathway engineering. Various applications including bioenergy, industrial chemicals and materials, drug targets, health, agriculture, and nutrition will be discussed. Workshops focused on technology development for mathematical and experimental techniques important for metabolic engineering applications will be held for more in depth discussion. This 2008 meeting will celebrate our conference tradition of high quality and relevance to both industrial and academic participants, with topics ranging from the frontiers of fundamental science to the practical aspects of metabolic engineering

A study on the effects of using gene-reaction rules on in silico strain optimization

To identify a set of genetic manipulations that will result in a microbial strain with improved production capabilities of a metabolite / product of industrial interest, is one of the greatest challenges in Metabolic Engineering. This problem represents a complex combination between the development of accurate metabolic and regulatory models / networks, plus the need for appropriate simulation and optimization tools. To achieve this end, Evolutionary Algorithms (EAs) and Simulation Annealing (SA) have been previously proposed as tools to perform in silico Metabolic Engineering [1]. These methods are used to identify sets of reaction deletions, towards the maximization of a desired physiological objective function. In order to simulate the cell phenotype for each mutant strain, including its growth and the by-products secretion, the Flux-Balance Analysis approach is used, assuming that microorganisms have maximized their growth along evolution. Currently, the available optimization algorithms work only with reaction deletions, i.e. their result is a set of reactions that have to be removed from the metabolic model. Biologically, it is possible to knockout genes, not reactions. In this work, the transcriptional information is added to the underlying models using gene-reaction rules based on a boolean logic representation. So, for each reaction we have a Boolean expression, where the variables are the encoding genes and including the logical AND and OR operators. The aim is to find the optimal / near-optimal set of gene knockouts necessary to reach a given productivity goal. The results obtained are compared with the ones using the deletion of reactions. A set of computational experiments were performed, using four case studies and the production of succinate and lactic acid as the metabolite to maximize and E. coli as the selected organism. Genome-scale models including both reactions and gene-reaction rules [2] are used to conduct the necessary FBA simulations. The results show that several of the results from reaction deletion optimizations are not feasible using the provided gene-reaction rules, i.e. the genes that would need to be removed in order to delete the reaction also lead to the removal of other reactions causing side effects that make the strain unviable. Nevertheless, basing the optimization algorithms on gene knockouts, we were able to reach solutions where the production of the desired compounds is similar to the ones using reaction deletions.MIT-P

Evaluating simulated annealing algorithms in the optimization of bacterial strains

In this work, a Simulated Annealing (SA) algorithm is proposed for a Metabolic Engineering task: the optimization of the set of gene deletions to apply to a microbial strain to achieve a desired production goal. Each mutant strain is evaluated by simulating its phenotype using the Flux-Balance Analysis approach, under the premise that microorganisms have maximized their growth along natural evolution. A set based representation is used in the SA to encode variable sized solutions, enabling the automatic discovery of the ideal number of gene deletions. The approach was compared to the use of Evolutionary Algorithms (EAs) to solve the same task. Two case studies are presented considering the production of succinic and lactic acid as the target, with the bacterium E. coli. The variable sized SA seems to be the best alternative, outperforming the EAs, showing a fast convergence and low variability among the several runs and also enabing the automatic discovery of the ideal number of knockouts.FEDER.Portuguese Foundation for Science and Technology (FCT) - POSC/EIA/59899/2004

How do Developers Improve Code Readability? An Empirical Study of Pull Requests

Readability models and tools have been proposed to measure the effort to read code. However, these models are not completely able to capture the quality improvements in code as perceived by developers. To investigate possible features for new readability models and production-ready tools, we aim to better understand the types of readability improvements performed by developers when actually improving code readability, and identify discrepancies between suggestions of automatic static tools and the actual improvements performed by developers. We collected 370 code readability improvements from 284 Merged Pull Requests (PRs) under 109 GitHub repositories and produce a catalog with 26 different types of code readability improvements, where in most of the scenarios, the developers improved the code readability to be more intuitive, modular, and less verbose. Surprisingly, SonarQube only detected 26 out of the 370 code readability improvements. This suggests that some of the catalog produced has not yet been addressed by SonarQube rules, highlighting the potential for improvement in Automatic static analysis tools (ASAT) code readability rules as they are perceived by developers

Yield stability of soybean lines using addtive main effects and multiplicative interaction analysis - AMMI.

The grain yield of 27 soybean lines was evaluated at three locations (Anhembi, Areão and Esalq) in Piracicaba, State of São Paulo, Brazil, during four crop years to study the effect of environment (E) on the adaptability and stability of the lines (G) using additive main effects and multiplicative interaction analysis (AMMI). Effects of the G, E, and GE interaction were found to be significant and accounted for 51, 12, and 36% of the variation, respectively. The first and only significant interaction principal component axis (IPCA1) accounted for 26% of the sum of squares due to original GE interaction. This concentrated the largest proportion of the pattern of GE interaction. Environments associated with Anhembi and Esalq proved more favorable, while Areão contributed negatively to the grain yield. However, Anhembi and Areão were more predictable for the crop years. USP 93-5082 and USP 93-5243 lines combined high adaptability and stability