Abstract: A total of 100 primary caregivers (mothers, fathers, grandparents) with at least one child or grandchild with a diagnosis of congenital bilateral profound deafness were assessed in order to evaluate the functionality of families with a deaf member, who was evaluated by specialists in audiology, molecular biology, genetics and psychology. After confirmation of the clinical diagnosis, DNA from the patients and parents were analyzed in search of the 35delG deletion of the GJB2 gene to determine who possessed the mutation. All primary caregivers were provided psychological support, regardless of whether or not they had the mutation, and prior and subsequent, the family APGAR test was applied. All parents, grandparents were informed of the results of the genetic analysis during the psychological intervention. The family APGAR, after psychological and genetic counseling, showed that 14% perceived their families as functional, 62% moderately functional and 24% dysfunctional. This shows the importance of psychological support in family functionality that has a direct impact on the quality of life of these families.
Abstract: Many inherited diseases and non-hereditary disorders are common in the development of renal cystic diseases. Polycystic kidney disease (PKD) is a disorder developed within the kidneys in which grouping of cysts filled with water like fluid. PKD is responsible for 5-10% of end-stage renal failure treated by dialysis or transplantation. New experimental models, application of molecular biology techniques have provided new insights into the pathogenesis of PKD. Researchers are showing keen interest for developing an automated system by applying computer aided techniques for the diagnosis of diseases. In this paper a multilayered feed forward neural network with one hidden layer is constructed, trained and tested by applying back propagation learning rule for the diagnosis of PKD based on physical symptoms and test results of urinalysis collected from the individual patients. The data collected from 50 patients are used to train and test the network. Among these samples, 75% of the data used for training and remaining 25% of the data are used for testing purpose. Further, this trained network is used to implement for new samples. The output results in normality and abnormality of the patient.
Abstract: Deoxyribonucleic Acid or DNA computing has
emerged as an interdisciplinary field that draws together chemistry,
molecular biology, computer science and mathematics. Thus, in this
paper, the possibility of DNA-based computing to solve an absolute
1-center problem by molecular manipulations is presented. This is
truly the first attempt to solve such a problem by DNA-based
computing approach. Since, part of the procedures involve with
shortest path computation, research works on DNA computing for
shortest path Traveling Salesman Problem, in short, TSP are reviewed.
These approaches are studied and only the appropriate one is adapted
in designing the computation procedures. This DNA-based
computation is designed in such a way that every path is encoded by
oligonucleotides and the path-s length is directly proportional to the
length of oligonucleotides. Using these properties, gel electrophoresis
is performed in order to separate the respective DNA molecules
according to their length. One expectation arise from this paper is that
it is possible to verify the instance absolute 1-center problem using
DNA computing by laboratory experiments.
Abstract: Pattern matching is one of the fundamental applications in molecular biology. Searching DNA related data is a common activity for molecular biologists. In this paper we explore the applicability of a new pattern matching technique called Index based Forward Backward Multiple Pattern Matching algorithm(IFBMPM), for DNA Sequences. Our approach avoids unnecessary comparisons in the DNA Sequence due to this; the number of comparisons of the proposed algorithm is very less compared to other existing popular methods. The number of comparisons rapidly decreases and execution time decreases accordingly and shows better performance.
Abstract: The Far From Most Strings Problem (FFMSP) is to obtain a string which is far from as many as possible of a given set of strings. All the input and the output strings are of the same length, and two strings are said to be far if their hamming distance is greater than or equal to a given positive integer. FFMSP belongs to the class of sequences consensus problems which have applications in molecular biology. The problem is NP-hard; it does not admit a constant-ratio approximation either, unless P = NP. Therefore, in addition to exact and approximate algorithms, (meta)heuristic algorithms have been proposed for the problem in recent years. On the other hand, in the recent years, hybrid algorithms have been proposed and successfully used for many hard problems in a variety of domains. In this paper, a new metaheuristic algorithm, called Constructive Beam and Local Search (CBLS), is investigated for the problem, which is a hybridization of constructive beam search and local search algorithms. More specifically, the proposed algorithm consists of two phases, the first phase is to obtain several candidate solutions via the constructive beam search and the second phase is to apply local search to the candidate solutions obtained by the first phase. The best solution found is returned as the final solution to the problem. The proposed algorithm is also similar to memetic algorithms in the sense that both use local search to further improve individual solutions. The CBLS algorithm is compared with the most recent published algorithm for the problem, GRASP, with significantly positive results; the improvement is by order of magnitudes in most cases.
Abstract: Protein and Esterase electrophoresis were used to
genetically identify two Saudi tick species. Engorged females of the
camel tick Hyalomma dromedarii (Koch) (Acari: Ixodidae) and the
cattle tick Boophilus annulatus (Say) (Acari: Ixodidae) ticks
collected from infested camels and cattle in the animals resting
house at Hail region in KSA were used. The results showed that
there are a variation in both of protein and esterase activity levels and
a high polymorphism within and between the genera and species of
Hyalomma and Boophilus . In conclusion, the protein and esterase
electrophoretic analysis used in the present study could successfully
distinguish among tick species, commonly found in Saudi Arabia.
Abstract: In molecular biology, microarray technology is widely and successfully utilized to efficiently measure gene activity. If working with less studied organisms, methods to design custom-made microarray probes are available. One design criterion is to select probes with minimal melting temperature variances thus ensuring similar hybridization properties. If the microarray application focuses on the investigation of metabolic pathways, it is not necessary to cover the whole genome. It is more efficient to cover each metabolic pathway with a limited number of genes. Firstly, an approach is presented which minimizes the overall melting temperature variance of selected probes for all genes of interest. Secondly, the approach is extended to include the additional constraints of covering all pathways with a limited number of genes while minimizing the overall variance. The new optimization problem is solved by a bottom-up programming approach which reduces the complexity to make it computationally feasible. The new method is exemplary applied for the selection of microarray probes in order to cover all fungal secondary metabolite gene clusters for Aspergillus terreus.
Abstract: Bioinformatics and computational biology involve
the use of techniques including applied mathematics,
informatics, statistics, computer science, artificial intelligence,
chemistry, and biochemistry to solve biological problems
usually on the molecular level. Research in computational
biology often overlaps with systems biology. Major research
efforts in the field include sequence alignment, gene finding,
genome assembly, protein structure alignment, protein structure
prediction, prediction of gene expression and proteinprotein
interactions, and the modeling of evolution. Various
global rearrangements of permutations, such as reversals and
transpositions,have recently become of interest because of their
applications in computational molecular biology. A reversal is
an operation that reverses the order of a substring of a permutation.
A transposition is an operation that swaps two adjacent
substrings of a permutation. The problem of determining the
smallest number of reversals required to transform a given
permutation into the identity permutation is called sorting by
reversals. Similar problems can be defined for transpositions
and other global rearrangements. In this work we perform a
study about some genome rearrangement primitives. We show
how a genome is modelled by a permutation, introduce some
of the existing primitives and the lower and upper bounds
on them. We then provide a comparison of the introduced
primitives.
Abstract: The most common result of analysis of highthroughput
data in molecular biology represents a global list of
genes, ranked accordingly to a certain score. The score can be a
measure of differential expression. Recent work proposed a new
method for selecting a number of genes in a ranked gene list from
microarray gene expression data such that this set forms the
Optimally Functionally Enriched Network (OFTEN), formed by
known physical interactions between genes or their products. Here
we present calculation results of relative connectivity of genes from
META-OFTEN network and tentative biological interpretation of the
most reproducible signal. The relative connectivity and
inbetweenness values of genes from META-OFTEN network were
estimated.