TNA4OptFlux : a software tool for the analysis of strain optimization strategies

BACKGROUND:Rational approaches for Metabolic Engineering (ME) deal with the identification of modifications that improve the microbes' production capabilities of target compounds. One of the major challenges created by strain optimization algorithms used in these ME problems is the interpretation of the changes that lead to a given overproduction. Often, a single gene knockout induces changes in the fluxes of several reactions, as compared with the wild-type, and it is therefore difficult to evaluate the physiological differences of the in silico mutant. This is aggravated by the fact that genome-scale models per se are difficult to visualize, given the high number of reactions and metabolites involved.FINDINGS:We introduce a software tool, the Topological Network Analysis for OptFlux (TNA4OptFlux), a plug-in which adds to the open-source ME platform OptFlux the capability of creating and performing topological analysis over metabolic networks. One of the tool's major advantages is the possibility of using these tools in the analysis and comparison of simulated phenotypes, namely those coming from the results of strain optimization algorithms. We illustrate the capabilities of the tool by using it to aid the interpretation of two E. coli strains designed in OptFlux for the overproduction of succinate and glycine.CONCLUSIONS:Besides adding new functionalities to the OptFlux software tool regarding topological analysis, TNA4OptFlux methods greatly facilitate the interpretation of non-intuitive ME strategies by automating the comparison between perturbed and non-perturbed metabolic networks. The plug-in is available on the web site http://www.optflux.org webcite, together with extensive documentation.This work is funded by ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT (Portuguese Foundation for Science and Technology) within projects ref. COMPETE FCOMP-01-0124-FEDER-015079 and PEst-OE/EEI/UI0752/2011. JPP and RP work is funded by PhD grants from the Portuguese FCT (ref. SFRH/BD/41763/ 2007 and SFRH/BD/51111/2010)

An evaluation of 2D SLAM techniques available in Robot Operating System

n this work, a study of several laser-based 2D Simultaneous Localization and Mapping (SLAM) techniques available in Robot Operating System (ROS) is conducted. All the approaches have been evaluated and compared in 2D simulations and real world experiments. In order to draw conclusions on the performance of the tested techniques, the experimental results were collected under the same conditions and a generalized performance metric based on the k-nearest neighbours concept was applied. Moreover, the CPU load of each technique is examined. This work provides insight on the weaknesses and strengths of each solution. Such analysis is fundamental to decide which solution to adopt according to the properties of the intended final application

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

3D Multi-Robot Exploration with a Two-Level Coordination Strategy and Prioritization

This work presents a 3D multi-robot exploration framework for a team of UGVs moving on uneven terrains. The framework was designed by casting the two-level coordination strategy presented in [1] into the context of multi-robot exploration. The resulting distributed exploration technique minimizes and explicitly manages the occurrence of conflicts and interferences in the robot team. Each robot selects where to scan next by using a receding horizon next-best-view approach [2]. A sampling-based tree is directly expanded on segmented traversable regions of the terrain 3D map to generate the candidate next viewpoints. During the exploration, users can assign locations with higher priorities on-demand to steer the robot exploration toward areas of interest. The proposed framework can be also used to perform coverage tasks in the case a map of the environment is a priori provided as input. An open-source implementation is available online

Discovery and implementation of a novel pathway for n-butanol production via 2-oxoglutarate

Background: One of the European Union directives indicates that 10% of all fuels must be bio-synthesized by 2020. In this regard, biobutanol - natively produced by clostridial strains - poses as a promising alternative biofuel. One possible approach to overcome the difficulties of the industrial exploration of the native producers is the expression of more suitable pathways in robust microorganisms such as Escherichia coli. The enumeration of novel pathways is a powerful tool, allowing to identify non-obvious combinations of enzymes to produce a target compound. Results: This work describes the in silico driven design of E. coli strains able to produce butanol via 2-oxoglutarate by a novel pathway. This butanol pathway was generated by a hypergraph algorithm and selected from an initial set of 105,954 different routes by successively applying different filters, such as stoichiometric feasibility, size and novelty. The implementation of this pathway involved seven catalytic steps and required the insertion of nine heterologous genes from various sources in E. coli distributed in three plasmids. Expressing butanol genes in E. coli K12 and cultivation in High-Density Medium formulation seem to favor butanol accumulation via the 2-oxoglutarate pathway. The maximum butanol titer obtained was 85 \ub1 1 mg L-1 by cultivating the cells in bioreactors. Conclusions: In this work, we were able to successfully translate the computational analysis into in vivo applications, designing novel strains of E. coli able to produce n-butanol via an innovative pathway. Our results demonstrate that enumeration algorithms can broad the spectrum of butanol producing pathways. This validation encourages further research to other target compounds

Fusing sonars and LRF data to perform SLAM in reduced visibility scenarios

Simultaneous Localization and Mapping (SLAM) approaches have evolved considerably in recent years. However, there are many situations which are not easily handled, such as the case of smoky, dusty, or foggy environments where commonly used range sensors for SLAM are highly disturbed by noise induced in the measurement process by particles of smoke, dust or steam. This work presents a sensor fusion method for range sensing in Simultaneous Localization and Mapping (SLAM) under reduced visibility conditions. The proposed method uses the complementary characteristics between a Laser Range Finder (LRF) and an array of sonars in order to ultimately map smoky environments. The method was validated through experiments in a smoky indoor scenario, and results showed that it is able to adequately cope with induced disturbances, thus decreasing the impact of smoke particles in the mapping task

Optimization of peptide nucleic acid fluorescence in situ hybridization (PNA-FISH) method for the detection of bacteria: the effect of pH, dextran sulfate and probe concentration

Fluorescence in situ hybridization (FISH) appeared in the 1980’s and is nowadays widely used in the field of microbiology. FISH is affected by a wide variety of abiotic and biotic variables and their interplay. This is translated into a wide variability of FISH procedures that can be found in the literature. The aim of this work is to study the effects of pH, probe and dextran sulphate concentration in the FISH protocol. For this, response surface methodology (RSM) was used to optimize FISH protocol for gram-negative (E. coli and P. fluorescens) and gram-positive bacteria (L. innocua, S. epidermidis and B. cereus), for these 3 parameters. The obtained results show a clear distinction between the two groups: higher pH (>9) combined with lower dextran sulphate concentration (7% [w/v]), for Gram-positive bacteria. The optimal probe concentration was the same for both groups (300 nM). These results seem to result from an interplay of pH and dextran sulphate ability to influence the probe concentration and migration inside the bacteria

Terras raras nos sedimentos Pliocénicos entre os rios Vouga e Mondego (Portugal) = Rare earth elements in the Pliocene sediments between rivers Vouga and Mondego (Portugal)

A geoquímica de sedimentos argilosos do Pliocénico é usada para determinar as principais fases transportadoras de terras raras. Foram consideradas 3 fácies, associadas a sedimentação em planície de inundação e em pântano-lago, e dois sectores. A concentração de terras raras pesadas é substancialmente maior nos sedimentos de pântano-lago que nos de planície de inundação. Ainda que os minerais de argila integrem parte das terras raras, o xenótimo e a monazite são os principais minerais a transportar aqueles elementos. Outras fases (p. ex. matéria orgânica) em sedimentos de pântano-lago devem reter uma parte significativa das terras raras, em particular das terras raras pesadas. As concentrações de terras raras nos dois sectores não são muito diferentes, sugerindo que a proveniência era similar

A fuzzified systematic adjustment of the robotic Darwinian PSO

The Darwinian Particle Swarm Optimization (DPSO) is an evolutionary algorithm that extends the Particle Swarm Optimization using natural selection to enhance the ability to escape from sub-optimal solutions. An extension of the DPSO to multi-robot applications has been recently proposed and denoted as Robotic Darwinian PSO (RDPSO), benefiting from the dynamical partitioning of the whole population of robots, hence decreasing the amount of required information exchange among robots. This paper further extends the previously proposed algorithm adapting the behavior of robots based on a set of context-based evaluation metrics. Those metrics are then used as inputs of a fuzzy system so as to systematically adjust the RDPSO parameters (i.e., outputs of the fuzzy system), thus improving its convergence rate, susceptibility to obstacles and communication constraints. The adapted RDPSO is evaluated in groups of physical robots, being further explored using larger populations of simulated mobile robots within a larger scenario