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

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

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

Magnitude da interação genótipos X ambientes para o caráter teor de óleo em linhagens de soja.

RESUMO: Com o objetivo de se estimar a interação genótipos x ambientes sobre o teor de óleo, avaliaram-se 28 linhagens de soja em três locais do município de Piracicaba (Anhembi, Areão e ESALQ), SP, com altitude de 540 m, 22 o 45' de latitude sul e 47 o 38' de longitude oeste, nos anos agrícolas de 1996/97, 1997/98, 1998/99 e 1999/00, totalizando 12 ambientes. O delineamento experimental utilizado foi o de blocos completos casualisados com duas repetições, estratificadas em conjuntos experimentais com quatro testemunhas comuns. A parcela experimental correspondeu a quatro fileiras de 5,0 x 0,5 m, avaliando-se os 4 m centrais das duas fileiras intermediárias de cada parcela. Pelos resultados obtidos pode-se evidenciar significância para os efeitos de genótipos, ambientes e interação genótipos x ambientes. A interação locais x anos contribuiu mais que os efeitos isolados de locais e anos para a variação ambiental, enquanto a interação genótipos x anos foi responsável pela maior parte da interação genótipos x ambientes. Destacaram-se as linhagens USPs 93-1188, 93-1024, 93-1042, 93-1043, 93-1044, 93-1012, 94-1195 e 94-1203, com teores de óleo acima de 22%. Na seleção de linhagens para estabilidade e adaptabilidade do teor de óleo no município de Piracicaba, SP, deve-se considerar os testes realizados em mais de um ano, com o propósito de amenizar os efeitos da interação genótipos x ambientes e obter maior garantia na recomendação de cultivares. Termos para indexação: comportamento genotípico, Glycine max, resposta ambiental, variação fenotípica. ABSTRACT: With the objective of estimating the magnitude of the genotype x environment interaction on the oil content, twenty eight soybean lines were evaluated in three location (Anhembi, Areão and Esalq) of Piracicaba, state of São Paulo, Brazil, at 540 m of altitude, 22 o 45' South latitude, and 47 o 38' West longitude, during the agricultural years of 1996/ 97, 1997/98, 1998/99 and 1999/00, totalizing 12 environments. A randomized complete block experiment was designed, with two replications stratified in experimental sets with four common checks. The experimental plot corresponded to four rows 5,0 x 0,5 m, where the four central meters of the two intermediate rows were evaluated. The results evidenced that significative effects were detected for genotypes, environments and genotype x environment interaction. The locations x years interaction contributed more than the effects isolated of locations and years for the environmental variation, while the genotypes x years was responsible for most of the genotype x environment interaction. USP 93-1188, 93-1024, 93-1042, 93-1043, 93-1044, 93-1012, 94-1195 and 94-1203 presented superiority, with oil percentage above 22%. In the selection of lines for stability and adaptability of the oil content in Piracicaba, SP, trials conducted in more than one year should be considered, with the purpose to decrease the effects of the genotype x environment interaction and obtain larger reliability in the recommendation of cultivars. Index terms: genotypic behavior, Glycine max, environmental response, phenotypic variation

Comportamento produtivo de genótipos de soja no Município de Piracicaba, São Paulo.

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