Survey of Engineering Models for Systems Biology

In recent years, the field of systems biology has emerged from a confluence of an increase both in molecular biotechnology and in computing storage and power. As a discipline, systems biology shares many characteristics with engineering. However, before the benefits of engineering-based modeling formalisms and analysis tools can be applied to systems biology, the engineering discipline(s) most related to systems biology must be identified. In this paper, we identify the cell as an embedded computing system and, as such, demonstrate that systems biology shares many aspects in common with computer systems engineering, electrical engineering, and chemical engineering. This realization solidifies the grounds for using modeling formalisms from these engineering subdisciplines to be applied to biological systems. While we document several examples where this is already happening, our goal is that identifying the cell as an embedded computing system would motivate and facilitate further discovery through more widespread use of the modeling formalisms described here.

Structural Differences in KIR3DL1 and LILRB1 Interaction with HLA-B and the Loading Peptide Polymorphisms: In Silico Evidences

KIR3DL1 and LILRB1 interact with HLA class I. Using KIR3DL1/HLA-B interaction to set up the procedure, structural immune-informatics approaches have been performed in LILRB1/HLA-B alleles’ combination also considering the contribution of the HLA bound peptide. All KIR3DL1 alleles interact strongly with HLA-B alleles carrying Bw4 epitope and negative charged amino acid residues in peptide position P8 disrupt KIR3DL1 binding. HLA-B alleles carrying Ile 194 show a higher strength of interaction with LILRB1 in all the analyzed haplotypes. Finally, we hypothesize a contribution of the amino acid at position 1 of the HLA bound peptide in the modulation of HLA-B/LILRB1 interaction.

Asymmetric Behavior of Thymidylate Synthase Dimer Subunits in Denaturating Solvent Observed with Molecular Dynamics

A molecular dynamics simulations of the thymidylate synthase denaturation in chaotrope solvents (urea, guanidinium hydrochloride) were performed on 600 ns timescale. It appeared that this dimeric enzyme undergoes partial unfolding asymmetrically. It was shown also that urea is a better denaturant in the MD condition, as compared to guanidinium chloride. The unfolding occurs first at the external helices (AA 88-118) and follows by the AA 188-200 region. The present results correspond to the suggested in the literature activity of thymidylate synthase through a half-the-site mechanism.

Simplified Algorithms for Determining Cycle Shift between qPCR Fluorescence Curves

The polymerase chain reaction is a central component of current molecular biology. It is a cyclic process, in each early cycle of which the template DNA approximately doubles. An indicator which fluoresces when bound to DNA quantifies the DNA present at the end of each cycle, giving rise to a fluorescence curve which is characteristically sigmoid in shape. The fluorescence curve quantifies the amount of DNA initially present; the more the initial DNA, the earlier the rise in the fluorescence. Accordingly the amount of DNA initially present in two samples can be compared: the sample with the less DNA gives rise to a relatively delayed fluorescence curve and the ratio of the DNAs can be deduced from the separation of the curves. There is, however, a second determinant of this separation, the fold increase in DNA per cycle: ideally a twofold increase but frequently less. Current guidelines recommend that this be determined experimentally by carrying out PCR on a series of dilutions. If the value of the fold increase is known, then the algorithm for determining the separation can be reduced to a relatively simple computation, rather than employing a multidimensional nonlinear optimization such as the Marquardt-Levenberg as currently employed.

Mining Association Rules in Dengue Gene Sequence with Latent Periodicity

The mining of periodic patterns in dengue database is an interesting research problem that can be used for predicting the future evolution of dengue viruses. In this paper, we propose an algorithm called Recurrence Finder (RECFIN) that uses the suffix tree for detecting the periodic patterns of dengue gene sequence. Also, the RECFIN finds the presence of palindrome which indicates the possibilities of formation of proteins. Further, this paper computes the periodicity of nucleic acid and amino acid sequences of any length. The periodicity based association rules are used to diagnose the type of dengue. The time complexity of the proposed algorithm is O(n2). We demonstrate the effectiveness of the proposed approach by comparing the experimental results performed on dengue virus serotypes dataset with NCBI-BLAST algorithm.

Identification of Plant Homologues of Dual Specificity Yak1-Related Kinases

Currently, Dual Specificity YAK1-Related Kinases (MNB/DYRK) were found in slime molds, protista, fungi, and animals, but the existence of plant homologues is still unclear. In the present study, we have identified 14 potential plant homologues with the previously unknown functions, based on the strong sequence similarity. The results of bioinformatics analysis revealed their correspondence to DYRK1A, DYRK1B, DYRK3, and DYRK4. For two plant homologues of animal DYRK1A from Physcomitrella patens and Arabidopsis thaliana spatial structures of catalytic domains were predicted, as well as their complexes with ADP and selective inhibitor d15. Comparative analysis of 3D-structures of the human DYRK1A and plant homologues, their complexes with the specific inhibitors, and results of molecular dynamics confirm their structural and functional similarity with high probability. Preliminary data indicate the presence of potential MNB/DYRK specific phosphorylation sites in such proteins associated with plant cytoskeleton as plant microtubule-associated proteins WVD2 and WDL1, and FH5 and SCAR2 involved in the organization and polarity of the actin cytoskeleton and some kinesin-like microtubule motor proteins.

Identification of Synchronized Role of Transcription Factors, Genes, and Enzymes in Arabidopsis thaliana under Four Abiotic Stress Responsive Pathways

Microarray datasets are widely used resources to predict and characterize functional entities of the whole genomics. The study initiated here aims to identify overexpressed stress responsive genes using microarray datasets applying in silico approaches. The target also extended to build a protein-protein interaction model of regulatory genes with their upstream and downstream connection in Arabidopsis thaliana. Four microarray datasets generated treating abiotic stresses like salinity, cold, drought, and abscisic acid (ABA) were chosen. Retrieved datasets were firstly filtered based on their expression comparing to control. Filtered datasets were then used to create an expression hub. Extensive literature mining helped to identify the regulatory molecules from the expression hub. The study brought out 42 genes/TF/enzymes as the role player during abiotic stress response. Further bioinformatics study and also literature mining revealed that thirty genes from those forty-two were highly correlated in all four datasets and only eight from those thirty genes were determined as highly responsive to the above abiotic stresses. Later their protein-protein interaction (PPI), conserved sequences, protein domains, and GO biasness were studied. Some web based tools and software like String database, Gene Ontology, InterProScan, NCBI BLASTn suite, etc. helped to extend the study arena.

Intelligent CAD System for Automatic Detection of Mitotic Cells from Breast Cancer Histology Slide Images Based on Teaching-Learning-Based Optimization

This paper introduces a computer-assisted diagnosis (CAD) system for automatic mitosis detection from breast cancer histopathology slide images. In this system, a new approach for reducing the number of false positives is proposed based on Teaching-Learning-Based optimization (TLBO). The proposed CAD system is implemented on the histopathology slide images acquired by Aperio XT scanner (scanner A). In TLBO algorithm, the number of false positives (falsely detected nonmitosis candidates as mitosis ones) is defined as a cost function and, by minimizing it, many of nonmitosis candidates will be removed. Then some color and texture (textural) features such as those derived from cooccurrence and run-length matrices are extracted from the remaining candidates and finally mitotic cells are classified using a specific support vector machine (SVM) classifier. The simulation results have proven the claims about the high performance and efficiency of the proposed CAD system.

A Transcriptome Post-Scaffolding Method for Assembling High Quality Contigs

With the rapid development of high throughput sequencing technologies, new transcriptomes can be sequenced for little cost with high coverage. Sequence assembly approaches have been modified to meet the requirements for de novo transcriptomes, which have complications not found in traditional genome assemblies such as variation in coverage for each candidate mRNA and alternative splicing. As a consequence, de novo assembly strategies tend to generate a large number of redundant contigs due to sequence variations, which adversely affects downstream analysis and experiments. In this work we proposed TransPS, a transcriptome post-scaffolding method, to generate high quality, nonredundant de novo transcriptomes. TransPS shows promising results on the test transcriptome datasets, where redundancy is greatly reduced by more than 50% and, at the same time, coverage is improved considerably. The web server and source code are available.

Restricted Boltzmann Machines for Classification of Hepatocellular Carcinoma

Multiple antigen miniarrays can provide accurate tools for cancer detection and diagnosis. These miniarrays can be validated by examining their operating characteristics in classifying individuals as either cancer patients or normal (non-cancer) subjects. We describe the use of restricted Boltzmann machines for this classification problem, relative to diagnosis of hepatocellular carcinoma. In this setting, we find that its operating characteristics are similar to a logistic regression standard and suggest that restricted Boltzmann machines merit further consideration for classification problems.