Abstract: Studying DNA (deoxyribonucleic acid) sequence is useful in biological processes and it is applied in the fields such as diagnostic and forensic research. DNA is the hereditary information in human and almost all other organisms. It is passed to their generations. Earlier stage detection of defective DNA sequence may lead to many developments in the field of Bioinformatics. Nowadays various tedious techniques are used to identify defective DNA. The proposed work is to analyze and identify the cancer-causing DNA motif in a given sequence. Initially the human DNA sequence is separated as k-mers using k-mer separation rule. The separated k-mers are clustered using Self Organizing Map (SOM). Using Levenshtein distance measure, cancer associated DNA motif is identified from the k-mer clusters. Experimental results of this work indicate the presence or absence of cancer causing DNA motif. If the cancer associated DNA motif is found in DNA, it is declared as the cancer disease causing DNA sequence. Otherwise the input human DNA is declared as normal sequence. Finally, elapsed time is calculated for finding the presence of cancer causing DNA motif using clustering formation. It is compared with normal process of finding cancer causing DNA motif. Locating cancer associated motif is easier in cluster formation process than the other one. The proposed work will be an initiative aid for finding genetic disease related research.
Abstract: The study of microbial ecology and their function in anaerobic digestion processes are essential to control the biological processes. This is to know the symbiotic relationship between the microorganisms that are involved in the conversion of complex organic matter in the industrial wastewater to simple molecules. In this study, diversity and quantity of bacterial community in the granular sludge taken from the different compartments of a full-scale upflow anaerobic sludge blanket (UASB) reactor treating brewery wastewater was investigated using polymerase chain reaction (PCR) and real-time quantitative PCR (qPCR). The phylogenetic analysis showed three major eubacteria phyla that belong to Proteobacteria, Firmicutes and Chloroflexi in the full-scale UASB reactor, with different groups populating different compartment. The result of qPCR assay showed high amount of eubacteria with increase in concentration along the reactor’s compartment. This study extends our understanding on the diverse, topological distribution and shifts in concentration of microbial communities in the different compartments of a full-scale UASB reactor treating brewery wastewater. The colonization and the trophic interactions among these microbial populations in reducing and transforming complex organic matter within the UASB reactors were established.
Abstract: Biological processes based on oxidation of sulfur
compounds by chemolithotrophic microorganisms are emerging as an
efficient and eco-friendly technique for removal of sulfur from the
coal. In the present article, study was carried out to investigate the
potential of biodesulfurization process in removing the sulfur from
lignite coal sample collected from a Mongolian coal mine. The batch
biodesulfurization experiments were conducted in 2.5 L borosilicate
baffle type reactors at 35 ºC using Acidithiobacillus ferrooxidans.
The effect of pulp density on efficiency of biodesulfurization was
investigated at different solids concentration (1-10%) of coal. The
results of the present study suggested that the rate of desulfurization
was retarded at higher coal pulp density. The optimum pulp density
found 5% at which about 48% of the total sulfur was removed from
the coal.
Abstract: MicroRNAs are small non-coding RNA found in
many different species. They play crucial roles in cancer such as
biological processes of apoptosis and proliferation. The identification
of microRNA-target genes can be an essential first step towards to
reveal the role of microRNA in various cancer types. In this paper,
we predict miRNA-target genes for lung cancer by integrating
prediction scores from miRanda and PITA algorithms used as a
feature vector of miRNA-target interaction. Then, machine-learning
algorithms were implemented for making a final prediction. The
approach developed in this study should be of value for future studies
into understanding the role of miRNAs in molecular mechanisms
enabling lung cancer formation.
Abstract: Nitrification is essential to biological processes
designed to remove ammonia and/or total nitrogen. It removes excess
nitrogenous compound in wastewater which could be very toxic to
the aquatic fauna or cause serious imbalance of such aquatic
ecosystem. Efficient nitrification is linked to an in-depth knowledge
of the structure and dynamics of the nitrifying community structure
within the wastewater treatment systems. In this study, molecular
technique was employed for characterizing the microbial structure of
activated sludge [ammonia oxidizing bacteria (AOB) and nitrite
oxidizing bacteria (NOB)] in a municipal wastewater treatment with
intention of linking it to the plant efficiency. PCR based phylogenetic
analysis was also carried out. The average operating and
environmental parameters as well as specific nitrification rate of plant
was investigated during the study. During the investigation the average temperature was 23±1.5oC.
Other operational parameters such as mixed liquor suspended solids
and chemical oxygen demand inversely correlated with ammonia
removal. The dissolved oxygen level in the plant was constantly
lower than the optimum (between 0.24 and 1.267 mg/l) during this
study. The plant was treating wastewater with influent ammonia
concentration of 31.69 and 24.47 mg/L. The influent flow rates
(ML/Day) was 96.81 during period. The dominant nitrifiers include:
Nitrosomonas spp. Nitrobacter spp. and Nitrospira spp. The AOB
had correlation with nitrification efficiency and temperature. This
study shows that the specific ammonia oxidizing rate and the specific
nitrate formation rates can serve as good indicator of the plant overall
nitrification performance.
Abstract: Chrome tannery wastewater causes serious environmental hazard due to its high pollution potential. As a result, rigorous treatment is necessary for abatement of pollution from this type of wastewater. There are many research studies on chrome tannery wastewater treatment in the field of physical, chemical, and biological methods. In general, biological treatment process is found ineffective for direct application because of adverse effects by toxic chromium, sulphide, chloride etc. However, biological methods were employed mainly for a few sub processes generating significant amount of organic matter and without chromium, chlorides etc. In this context the present paper reviews the characteristics feature and pollution potential of wastewater generated from chrome tannery units and treatment of the same. The different biological processes used earlier and their chronological development for treatment of the chrome tannery wastewater are thoroughly reviewed in this paper. In this regard, the scope of hybrid bioreactor - an advanced technology option has also been explored, as this kind of treatment is well suited for the wastewater having inhibitory substances.
Abstract: In this paper, we employ a directed hypergraph model
to investigate the extent to which environmental variability influences
the set of available biochemical reactions within a living cell.
Such an approach avoids the limitations of the usual complex
network formalism by allowing for the multilateral relationships (i.e.
connections involving more than two nodes) that naturally occur
within many biological processes. More specifically, we extend the
concept of network reciprocity to complex hyper-networks, thus
enabling us to characterise a network in terms of the existence
of mutual hyper-connections, which may be considered a proxy
for metabolic network complexity. To demonstrate these ideas, we
study 115 metabolic hyper-networks of bacteria, each of which
can be classified into one of 6 increasingly varied habitats.
In particular, we found that reciprocity increases significantly
with increased environmental variability, supporting the view that
organism adaptability leads to increased complexities in the resultant
biochemical networks.
Abstract: The rapid expansion of deserts in recent decades as a result of human actions combined with climatic changes has highlighted the necessity to understand biological processes in arid environments. Whereas physical processes and the biology of flora and fauna have been relatively well studied in marginally used arid areas, knowledge of desert soil micro-organisms remains fragmentary. The objective of this study is to conduct a diversity analysis of bacterial communities in unvegetated arid soils. Several biological phenomena in hot deserts related to microbial populations and the potential use of micro-organisms for restoring hot desert environments. Dry land ecosystems have a highly heterogeneous distribution of resources, with greater nutrient concentrations and microbial densities occurring in vegetated than in bare soils. In this work, we found it useful to use techniques of artificial intelligence in their treatment especially artificial neural networks (ANN). The use of the ANN model, demonstrate his capability for addressing the complex problems of uncertainty data.
Abstract: Microarray gene expression data play a vital in biological processes, gene regulation and disease mechanism. Biclustering in gene expression data is a subset of the genes indicating consistent patterns under the subset of the conditions. Finding a biclustering is an optimization problem. In recent years, swarm intelligence techniques are popular due to the fact that many real-world problems are increasingly large, complex and dynamic. By reasons of the size and complexity of the problems, it is necessary to find an optimization technique whose efficiency is measured by finding the near optimal solution within a reasonable amount of time. In this paper, the algorithmic concepts of the Particle Swarm Optimization (PSO), Shuffled Frog Leaping (SFL) and Cuckoo Search (CS) algorithms have been analyzed for the four benchmark gene expression dataset. The experiment results show that CS outperforms PSO and SFL for 3 datasets and SFL give better performance in one dataset. Also this work determines the biological relevance of the biclusters with Gene Ontology in terms of function, process and component.
Abstract: MicroRNAs (miRNAs), a class of approximately 22 nucleotide long non coding RNAs which play critical role in different biological processes. The mature microRNA is usually 19–27 nucleotides long and is derived from a bigger precursor that folds into a flawed stem-loop structure. Mature micro RNAs are involved in many cellular processes that encompass development, proliferation, stress response, apoptosis, and fat metabolism by gene regulation. Resent finding reveals that certain viruses encode their own miRNA that processed by cellular RNAi machinery. In recent research indicate that cellular microRNA can target the genetic material of invading viruses. Cellular microRNA can be used in the virus life cycle; either to up regulate or down regulate viral gene expression Computational tools use in miRNA target prediction has been changing drastically in recent years. Many of the methods have been made available on the web and can be used by experimental researcher and scientist without expert knowledge of bioinformatics. With the development and ease of use of genomic technologies and computational tools in the field of microRNA biology has superior tremendously over the previous decade. This review attempts to give an overview over the genome wide approaches that have allow for the discovery of new miRNAs and development of new miRNA target prediction tools and databases.
Abstract: The DNA microarray technology concurrently monitors the expression levels of thousands of genes during significant biological processes and across the related samples. The better understanding of functional genomics is obtained by extracting the patterns hidden in gene expression data. It is handled by clustering which reveals natural structures and identify interesting patterns in the underlying data. In the proposed work clustering gene expression data is done through an Advanced Nelder Mead (ANM) algorithm. Nelder Mead (NM) method is a method designed for optimization process. In Nelder Mead method, the vertices of a triangle are considered as the solutions. Many operations are performed on this triangle to obtain a better result. In the proposed work, the operations like reflection and expansion is eliminated and a new operation called spread-out is introduced. The spread-out operation will increase the global search area and thus provides a better result on optimization. The spread-out operation will give three points and the best among these three points will be used to replace the worst point. The experiment results are analyzed with optimization benchmark test functions and gene expression benchmark datasets. The results show that ANM outperforms NM in both benchmarks.
Abstract: Protein-protein interactions (PPI) play a crucial role in many biological processes such as cell signalling, transcription, translation, replication, signal transduction, and drug targeting, etc. Structural information about protein-protein interaction is essential for understanding the molecular mechanisms of these processes. Structures of protein-protein complexes are still difficult to obtain by biophysical methods such as NMR and X-ray crystallography, and therefore protein-protein docking computation is considered an important approach for understanding protein-protein interactions. However, reliable prediction of the protein-protein complexes is still under way. In the past decades, several grid-based docking algorithms based on the Katchalski-Katzir scoring scheme were developed, e.g., FTDock, ZDOCK, HADDOCK, RosettaDock, HEX, etc. However, the success rate of protein-protein docking prediction is still far from ideal. In this work, we first propose a more practical measure for evaluating the success of protein-protein docking predictions,the rate of first success (RFS), which is similar to the concept of mean first passage time (MFPT). Accordingly, we have assessed the ZDOCK bound and unbound benchmarks 2.0 and 3.0. We also createda new benchmark set for protein-protein docking predictions, in which the complexes have experimentally determined binding affinity data. We performed free energy calculation based on the solution of non-linear Poisson-Boltzmann equation (nlPBE) to improve the binding mode prediction. We used the well-studied thebarnase-barstarsystem to validate the parameters for free energy calculations. Besides,thenlPBE-based free energy calculations were conducted for the badly predicted cases by ZDOCK and ZRANK. We found that direct molecular mechanics energetics cannot be used to discriminate the native binding pose from the decoys.Our results indicate that nlPBE-based calculations appeared to be one of the promising approaches for improving the success rate of binding pose predictions.
Abstract: The aim of a biological model is to understand the
integrated structure and behavior of complex biological systems as a
function of the underlying molecular networks to achieve simulation
and forecast of their operation. Although several approaches have
been introduced to take into account structural and environment
related features, relatively little attention has been given to represent
the behavior of biological systems. The Abstract Biological Process
(ABP) model illustrated in this paper is an object-oriented model
based on UML (the standard object-oriented language). Its main
objective is to bring into focus the functional aspects of the
biological system under analysis.
Abstract: Poly-β-hydroxybutyrate (PHB) is one of the most
famous biopolymers that has various applications in production of
biodegradable carriers. The most important strategy for enhancing
efficiency in production process and reducing the price of PHB, is the
accurate expression of kinetic model of products formation and
parameters that are effective on it, such as Dry Cell Weight (DCW)
and substrate consumption. Considering the high capabilities of
artificial neural networks in modeling and simulation of non-linear
systems such as biological and chemical industries that mainly are
multivariable systems, kinetic modeling of microbial production of
PHB that is a complex and non-linear biological process, the three
layers perceptron neural network model was used in this study.
Artificial neural network educates itself and finds the hidden laws
behind the data with mapping based on experimental data, of dry cell
weight, substrate concentration as input and PHB concentration as
output. For training the network, a series of experimental data for
PHB production from Hydrogenophaga Pseudoflava by glucose
carbon source was used. After training the network, two other
experimental data sets that have not intervened in the network
education, including dry cell concentration and substrate
concentration were applied as inputs to the network, and PHB
concentration was predicted by the network. Comparison of predicted
data by network and experimental data, indicated a high precision
predicted for both fructose and whey carbon sources. Also in present
study for better understanding of the ability of neural network in
modeling of biological processes, microbial production kinetic of
PHB by Leudeking-Piret experimental equation was modeled. The
Observed result indicated an accurate prediction of PHB
concentration by artificial neural network higher than Leudeking-
Piret model.
Abstract: For many chemical and biological processes, the understanding of the mixing phenomenon and flow behavior in a stirred tank is of major importance. A three-dimensional numerical study was performed using the software Fluent, to study the flow field in a stirred tank with a Rushton turbine. In this work, we first studied the flow generated in the tank with a Rushton turbine. Then, we studied the effect of the variation of turbine’s submergence on the thermodynamic quantities defining the flow field. For that, four submergences were considered, while maintaining the same rotational speed (N =250rpm). This work intends to optimize the aeration performances of a Rushton turbine in a stirred tank.
Abstract: MicroRNAs are an important class of gene expression
regulators that are involved in many biological processes including
embryogenesis. miR-125b is a conserved microRNA that is enriched
in the nervous system. We have previously reported the function of
miR-125b in neuronal differentiation of human cell lines. We also
discovered the function of miR-125b in regulating p53 in human and
zebrafish. Here we further characterize the brain defects in zebrafish
embryos injected with morpholinos against miR-125b. Our data
confirm the essential role of miR-125b in brain morphogenesis
particularly in maintaining the balance between proliferation, cell
death and differentiation. We identified lunatic fringe (lfng) as an
additional target of miR-125b in human and zebrafish and suggest
that lfng may mediate the function of miR-125b in neurogenesis.
Together, this report reveals new insights into the function of miR-
125b during neural development of zebrafish.
Abstract: Water is the main component of biological processes.
Water management is important to obtain higher productivity. In this
study, some of the yield components were investigated together with
different drought levels. Four chickpea genotypes (CDC Frontier,
CDC Luna, Sawyer and Sierra) were grown in pots with 3 different
irrigation levels (a dose of 17.5 ml, 35 ml and 70 ml for each pot per
day) after three weeks from sowing. In the research, flowering, pod
set, pod per plant, fertile pod, double seed/pod, stem diameter, plant
weight, seed per plant, 1000 seed weight, seed diameter, vegetation
length and weekly plant height were measured. Consequently,
significant differences were observed on all the investigated
characteristics owing to genotypes (except double seed/pod and stem
diameter), water levels (except first pod, seed weight and height on
3rd week) and genotype x water level interaction (except first pod,
double seed/pod, seed weight and height).