Gene Knockout Identification Using an Extension of Bees Hill Flux Balance Analysis

Microbial strain optimisation for the overproduction of a desired phenotype has been a popular topic in recent years. Gene knockout is a genetic engineering technique that can modify the metabolism of microbial cells to obtain desirable phenotypes. Optimisation algorithms have been developed to identify the effects of gene knockout. However, the complexities of metabolic networks have made the process of identifying the effects of genetic modification on desirable phenotypes challenging. Furthermore, a vast number of reactions in cellular metabolism often lead to a combinatorial problem in obtaining optimal gene knockout. The computational time increases exponentially as the size of the problem increases. This work reports an extension of Bees Hill Flux Balance Analysis (BHFBA) to identify optimal gene knockouts to maximise the production yield of desired phenotypes while sustaining the growth rate. This proposed method functions by integrating OptKnock into BHFBA for validating the results automatically. The results show that the extension of BHFBA is suitable, reliable, and applicable in predicting gene knockout. Through several experiments conducted on Escherichia coli, Bacillus subtilis, and Clostridium thermocellum as model organisms, extension of BHFBA has shown better performance in terms of computational time, stability, growth rate, and production yield of desired phenotypes

Identifying gene knockout strategies using a hybrid of bees algorithm and flux balance analysis for in silico optimization of microbial strains

Genome-scale metabolic networks reconstructions from different organisms have become popular in recent years. Genetic engineering is proven to be able to obtain the desirable phenotypes. Optimization algorithms are implemented in previous works to identify the effects of gene knockout on the results. However, the previous works face the problem of falling into local minima. Thus, a hybrid of Bees Algorithm and Flux Balance Analysis (BAFBA) is proposed in this paper to solve the local minima problem and to predict optimal sets of gene deletion for maximizing the growth rate of certain metabolite. This paper involves two case studies that consider the production of succinate and lactate as targets, by using E.coli as model organism. The results from this experiment are the list of knockout genes and the growth rate after the deletion. BAFBA shows better results compared to the other methods. The identified list suggests gene modifications over several pathways and may be useful in solving challenging genetic engineering problems

Improvements on the bees algorithm for continuous optimisation problems

This work focuses on the improvements of the Bees Algorithm in order to enhance the algorithm’s performance especially in terms of convergence rate. For the first enhancement, a pseudo-gradient Bees Algorithm (PG-BA) compares the fitness as well as the position of previous and current bees so that the best bees in each patch are appropriately guided towards a better search direction after each consecutive cycle. This method eliminates the need to differentiate the objective function which is unlike the typical gradient search method. The improved algorithm is subjected to several numerical benchmark test functions as well as the training of neural network. The results from the experiments are then compared to the standard variant of the Bees Algorithm and other swarm intelligence procedures. The data analysis generally confirmed that the PG-BA is effective at speeding up the convergence time to optimum. Next, an approach to avoid the formation of overlapping patches is proposed. The Patch Overlap Avoidance Bees Algorithm (POA-BA) is designed to avoid redundancy in search area especially if the site is deemed unprofitable. This method is quite similar to Tabu Search (TS) with the POA-BA forbids the exact exploitation of previously visited solutions along with their corresponding neighbourhood. Patches are not allowed to intersect not just in the next generation but also in the current cycle. This reduces the number of patches materialise in the same peak (maximisation) or valley (minimisation) which ensures a thorough search of the problem landscape as bees are distributed around the scaled down area. The same benchmark problems as PG-BA were applied against this modified strategy to a reasonable success. Finally, the Bees Algorithm is revised to have the capability of locating all of the global optimum as well as the substantial local peaks in a single run. These multi-solutions of comparable fitness offers some alternatives for the decision makers to choose from. The patches are formed only if the bees are the fittest from different peaks by using a hill-valley mechanism in this so called Extended Bees Algorithm (EBA). This permits the maintenance of diversified solutions throughout the search process in addition to minimising the chances of getting trap. This version is proven beneficial when tested with numerous multimodal optimisation problems

Genome-scale metabolic reconstruction and analysis of the Trypanosoma brucei metabolism from a Systems biology perspective

Les progrès récents dans la modélisation informatique des réseaux biologiques permettent maintenant aux chercheurs d'étudier le métabolisme cellulaire des organismes. Dans ce projet, ces approches ont été utilisées pour analyser le métabolisme de Trypanosoma brucei. Ce parasite protozoaire est responsable de la trypanosomiase africaine, une maladie mortelle chez l'homme et qui entraine des dégâts importants dans les élevages. Ce parasite est principalement retrouvé dans les régions d'Afrique sub-sahariennes. Durant cette thèse, des informations sur le métabolisme de T. brucei ont été recueillies à partir d'études publiées, bases de données et de communication personnelle avec des experts qui étudient les différents aspects du métabolisme des trypanosomatides. Cette information a été mise à disposition de la communauté à travers la base de données TrypanoCyc. La base de données a été publiée en Novembre 2014 et a eu plus de 4200 visiteurs provenant de plus de cent pays depuis Novembre 2015. Un modèle métabolique à l'échelle du génome de T. brucei a également été reconstruit sur la base des informations recueillies. Ce modèle a permis de faciliter l'étude du métabolisme de T. brucei en utilisant une approche de biologie des systèmes. Des algorithmes basés sur l'analyse de balance des flux ont été conçus pour optimiser la visualisation et l'étude des propriétés métaboliques du parasite. En utilisant l'algorithme iMat, des modèles spécifiques de la forme sanguine de T. brucei ont été générés à partir des informations fournies par les études publiées et les annotations présentent dans. Enfin, un algorithme a été conçu pour optimiser encore ces modèles spécifiques afin d'améliorer la cohérence de leurs prédictions avec les résultats publiés. Les modèles ainsi créés, spécifiques à la forme sanguine, ont montré une meilleure puissance prédictive que le modèle initial à l'échelle du génome, en particulier pour prédire le comportement métabolique spécifique de différents mutants de T. brucei. ABSTRACT : Recent advances in computational modelling of biological networks have helped researchers study the cellular metabolism of organisms. In this project, these approaches were used to analyze Trypanosoma brucei metabolism. This protozoan parasite is the causative agent of African trypanosomiasis, a lethal disease which has been responsible for huge loss of lives and livestock in Sub- Saharan Africa since ancient times. Information on T. brucei metabolism was gathered from published studies, databases and from personal communication with experts studying different areas of Trypanosomatid research. This information has been presented to the public through the TrypanoCyc Database, a community annotated T. brucei database. The database was published in November 2014 and has had over 4200 visitors from more than 100 countries as of November 2015. A manually curated genome-scale metabolic model for T. brucei was also built based on the gathered information to facilitate the study of T. brucei metabolism using systems biology approaches. Flux balance analysis based algorithms were designed to optimize visualization and study interesting metabolic properties. Blood-stream form specific metabolic models were generated using information available from published studies and the TrypanoCyc annotations with the help of the iMAT algorithm. Finally, an algorithm was designed to further optimize these stage specific models to improve the consistency of their predictions with results published in previous studies. These stage-specific models were observed to have a clear advantage over the genome-scale model when predicting stage-specific behaviour of T. brucei, particularly when predicting mutant behaviour