Visualizing omics data in the OptFlux workbench

OptFlux is an open-source and extensible workbench for Metabolic Engineering (ME) tasks. Since 2012, OptFlux adoption has been steadily increasing among users, making it one of the reference go-to platforms among the ME community. The workbench supports common ME tasks such as phenotype prediction for both wild-type and mutant genotypes, metabolic control analysis and pathway analysis as well as strain optimization procedures. Moreover, a visualization plug-in is included, allowing the navigation and edition of biochemical network layouts in a multitude of standard formats. This plug-in also allows the overlap of specific phenotypic conditions in the network layouts, providing an intuitive mechanism to explore and understand the associated flux distributions. Navigation between multiple layouts is also included. However, for more specialized applications, such as the inclusion of experimental data, this framework was still lagging behind. In this work, the current visualization platform included in OptFlux is extended to support loading generic experimental data sources (e.g. transcript, protein, metabolite and flux measurements) and mapping it to the model information for posterior overlap with the layouts. The visualization features that will represent this data are also fully customizable. The inclusion of multiple conditions or time-dependent measurements is also supported for metabolite-associated data with intuitive bar-plots being displayed for immediate visual comparison. Finally, compound structural information from KEGG is also automatically downloaded and presented

Metaheuristics for strain optimization using transcriptional information enriched metabolic models

Publicado em "Evolutionary computation, machine learning and data mining in bioinformatics : 8th European Conference, EvoBIO 2010...", ISBN 978-3-642-12210-1The identification of a set of genetic manipulations that result in a microbial strain with improved production capabilities of a metabolite with industrial interest is a big challenge in Metabolic Engineering. Evolutionary Algorithms and Simulated Annealing have been used in this task to identify sets of reaction deletions, towards the maximization of a desired objective function. To simulate the cell phenotype for each mutant strain, the Flux Balance Analysis approach is used, assuming organisms have maximized their growth along evolution. In this work, transcriptional information is added to the models using gene-reaction rules. The aim is to find the (near-)optimal set of gene knockouts necessary to reach a given productivity goal. The results obtained are compared with the ones reached using the deletion of reactions, showing that we obtain solutions with similar quality levels and number of knockouts, but biologically more feasible. Indeed, we show that several of the previous solutions are not viable using the provided rules.This work was partially funded by Portuguese FCT through the AspectGrid project and also through project MIT-PT/BS-BB/0082/2008