Microbiology for chemical engineers - from macro to micro scale

Recent developments in microbial techniques (such as PCR, GE, FISH) have allowed researchers to detect, identify and quantify microorganisms without the limitation of culture-dependent methods. This has given both engineers and scientists a more fundamental understanding about systems containing microorganisms. These techniques can be used to monitor bacteria in wastewater treatment systems, soil and sea, industrial fermentation, food technology, and improve floccability, etc. However, despite these techniques being readily available and relatively cheap, they are not widely used by engineers. Hence, the aim of this paper is to introduce these techniques, and their applications, to chemical engineers. Two different studies related to industrial wastewater treatment, but applicable to general microorganism systems, will be presented: (1) microbial stability of pure cultures, and (2) bioreactor population shifts during alternating operational conditions. In (1), two bioreactors, inoculated with two different pure cultures, (A) Xanthobacter aut GJ10 and (B) Bulkholderia sp JS150, degrading 1,2-dichloroethane (DCE) and monochlorobenzene (MCB), respectively, were followed over time (Emanuelsson et al ., 2005). Specific and universal 16S rRNA oligonucleotide probes were used to identify the bacteria. It was found that bioreactor (A) remained pure for 290 days, whereas bioreactor (B) became contaminated within one week. The difference in behaviour is attributed to the pathway required to degrade DCE. In (2), the stability of a bacterial strain, which was isolated on the basis of its capability to degrade 2-fluorobenzoate from contaminated soil, in three different, up-flow fixed bed reactors operated under shock loads and starvation periods, was followed by denaturing gradient gel electrophoresis (DGGE) (Emanuelsson et al ., 2006). All bioreactors were rapidly colonised by different bacteria; however, the communities remained fairly stable over time, and shifts in bacterial populations were mainly found during the starvation periods

Enhanced adsorption of cationic and anionic dyes from aqueous solutions by polyacid doped polyaniline

A new high surface area polyaniline (PANI) adsorbent was synthesized by matrix polymerization of aniline in the presence of a polyacid, poly(2-acrylamido-2-methyl-1-propanesulfonic acid) (PAMPSA). Morphological and physicochemical properties of PANI-PAMPSA were characterized by field emission scanning electron microscope (FESEM), Fourier transform infrared spectroscopy (FTIR), X-ray powder diffraction (XRD), nitrogen adsorption/desorption and zeta potential measurement. Adsorption properties were evaluated using methylene blue (MB) and rose bengal (RB) as model dyes.The results showed that PANI-PAMPSA obtained a well-defined porous structure with a specific surface area (126 m2 g−1) over 10 times larger than that of the emeraldine base PANI (PANI-EB) (12 m2 g−1). The maximum adsorption capacities were 466.5 mg g−1 for MB and 440.0 mg g−1 for RB, higher than any other PANI-based materials reported in the literature. The FTIR analysis and zeta potential measurement revealed that the adsorption mechanisms involved π-π interaction and electrostatic interaction. The adsorption kinetics were best described by a pseudo-second-order model, and the adsorption isotherms followed the Langmuir model. The thermodynamic study indicated that the adsorption was a spontaneous endothermic process. Overall, the convenient synthesis and the high adsorption capacity make PANI-PAMPSA a promising adsorbent material for dye removal

Convolutional LSTM Networks for Subcellular Localization of Proteins

Machine learning is widely used to analyze biological sequence data. Non-sequential models such as SVMs or feed-forward neural networks are often used although they have no natural way of handling sequences of varying length. Recurrent neural networks such as the long short term memory (LSTM) model on the other hand are designed to handle sequences. In this study we demonstrate that LSTM networks predict the subcellular location of proteins given only the protein sequence with high accuracy (0.902) outperforming current state of the art algorithms. We further improve the performance by introducing convolutional filters and experiment with an attention mechanism which lets the LSTM focus on specific parts of the protein. Lastly we introduce new visualizations of both the convolutional filters and the attention mechanisms and show how they can be used to extract biological relevant knowledge from the LSTM networks

PROlocalizer: integrated web service for protein subcellular localization prediction

Subcellular localization is an important protein property, which is related to function, interactions and other features. As experimental determination of the localization can be tedious, especially for large numbers of proteins, a number of prediction tools have been developed. We developed the PROlocalizer service that integrates 11 individual methods to predict altogether 12 localizations for animal proteins. The method allows the submission of a number of proteins and mutations and generates a detailed informative document of the prediction and obtained results. PROlocalizer is available at http://bioinf.uta.fi/PROlocalizer/

Prediction of nuclear proteins using SVM and HMM models

<p>Abstract</p> <p>Background</p> <p>The nucleus, a highly organized organelle, plays important role in cellular homeostasis. The nuclear proteins are crucial for chromosomal maintenance/segregation, gene expression, RNA processing/export, and many other processes. Several methods have been developed for predicting the nuclear proteins in the past. The aim of the present study is to develop a new method for predicting nuclear proteins with higher accuracy.</p> <p>Results</p> <p>All modules were trained and tested on a non-redundant dataset and evaluated using five-fold cross-validation technique. Firstly, Support Vector Machines (SVM) based modules have been developed using amino acid and dipeptide compositions and achieved a Mathews correlation coefficient (MCC) of 0.59 and 0.61 respectively. Secondly, we have developed SVM modules using split amino acid compositions (SAAC) and achieved the maximum MCC of 0.66. Thirdly, a hidden Markov model (HMM) based module/profile was developed for searching exclusively nuclear and non-nuclear domains in a protein. Finally, a hybrid module was developed by combining SVM module and HMM profile and achieved a MCC of 0.87 with an accuracy of 94.61%. This method performs better than the existing methods when evaluated on blind/independent datasets. Our method estimated 31.51%, 21.89%, 26.31%, 25.72% and 24.95% of the proteins as nuclear proteins in <it>Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster</it>, mouse and human proteomes respectively. Based on the above modules, we have developed a web server NpPred for predicting nuclear proteins <url>http://www.imtech.res.in/raghava/nppred/</url>.</p> <p>Conclusion</p> <p>This study describes a highly accurate method for predicting nuclear proteins. SVM module has been developed for the first time using SAAC for predicting nuclear proteins, where amino acid composition of N-terminus and the remaining protein were computed separately. In addition, our study is a first documentation where exclusively nuclear and non-nuclear domains have been identified and used for predicting nuclear proteins. The performance of the method improved further by combining both approaches together.</p

Subcellular location prediction of proteins using support vector machines with alignment of block sequences utilizing amino acid composition

Background: Subcellular location prediction of proteins is an important and well-studied problem in bioinformatics. This is a problem of predicting which part in a cell a given protein is transported to, where an amino acid sequence of the protein is given as an input. This problem is becoming more important since information on subcellular location is helpful for annotation of proteins and genes and the number of complete genomes is rapidly increasing. Since existing predictors are based on various heuristics, it is important to develop a simple method with high prediction accuracies. Results: In this paper, we propose a novel and general predicting method by combining techniques for sequence alignment and feature vectors based on amino acid composition. We implemented this method with support vector machines on plant data sets extracted from the TargetP database. Through fivefold cross validation tests, the obtained overall accuracies and average MCC were 0.9096 and 0.8655 respectively. We also applied our method to other datasets including that of WoLF PSORT. Conclusion: Although there is a predictor which uses the information of gene ontology and yields higher accuracy than ours, our accuracies are higher than existing predictors which use only sequence information. Since such information as gene ontology can be obtained only for known proteins, our predictor is considered to be useful for subcellular location prediction of newly-discovered proteins. Furthermore, the idea of combination of alignment and amino acid frequency is novel and general so that it may be applied to other problems in bioinformatics. Our method for plant is also implemented as a web-system and available on http://sunflower.kuicr.kyoto-u.ac.jp/~tamura/slpfa.html webcite