Approaching mathematical model of the immune network based DNA Strand Displacement system

One biggest obstacle in molecular programming is that there is still no direct method to compile any existed mathematical model into biochemical reaction in order to solve a computational problem. In this paper, the implementation of DNA Strand Displacement system based on nature-inspired computation is observed. By using the Immune Network Theory and Chemical Reaction Network, the compilation of DNA-based operation is defined and the formulation of its mathematical model is derived. Furthermore, the implementation on this system is compared with the conventional implementation by using silicon-based programming. From the obtained results, we can see a positive correlation between both. One possible application from this DNA-based model is for a decision making scheme of intelligent computer or molecular robot.     

Effect of essential oils on postharvest decay and quality factors of tomato in vitro and in vivo conditions

For increasing the shelf life and control of devastating fungal pathogen grey mould (Botrytis cinerea), tomato fruits during storage were applied different concentrations of ammi (Carum copticum) and anise (Pimpinella anisum) essential oils. First, antifungal activities of essential oils were tested on artificial growth media. The growth of grey mould was completely inhibited by ammi and anise essential oils at relatively higher concentrations. In second stage, fruits were infected artificially by grey mould spore and then treated with different concentrations of these essential oils. The results of in vivo conditions showed that ammi and anise essential oils applied at all concentrations were increasing the shelf life and inhibited the grey mould growth on tomato fruits completely in comparison to control. Fruits treated with these essential oils had significantly higher total soluble solids (TSS), ascorbic acid, β-carotene and lycopene content compared to control fruits.     

Biological control of Botrytis cinerea on tomato using naturally occurring fungal antagonists

Abstract

In order to evaluate the potential of naturally occurring filamentous fungi having potential as biocontrol agents effective against grey mould and post-harvest fruit rot caused by Botrytis cinerea on tomato, fungal saprophytes were isolated. They were obtained from leaves, fruits and flowers belonging to different species of cultivated and spontaneous Solanaceous plants collected at the horticultural area of La Plata, Argentina. Of 300 isolates screened for inhibition of B. cinerea using the dual culture technique on agar plate, 12 strains inhibited strongly mycelial growth of the pathogen. Among the antagonists one isolate of Epicoccun nigrum (126), four of Trichoderma harzianum (110, 118, 248 and 252) and four isolates of Fusarium spp. decreased the spore germination of B. cinerea between 30 and 70%. These isolates were probed on tomato fruits to evaluate their biocontrol activity against post-harvest grey mould. In growth chamber tests, E. nigrum (27), F. equiseti (22, 105) and T. harzianum (118, 252) reduced the diameter of fruit lesions by 50 – 90% and were selected for further biocontrol assays of tomato plants in the greenhouse. Although there were not significant differences between the treatments and the control, F. equiseti (105), E. nigrum (27) and T. harzianum (118) reduced by 20, 22 and 22 respectively the disease on whole plants. The targeted application of isolates of E. nigrum, T. harzianum and F. equiseti provides a promising alternative to the use of fungicide spray to control B. cinerea on tomatoes.     

SNV and haplotype analysis reveals new CSRP1 variants associated with growth and carcass traits

The cysteine and glycine-rich protein 1 and 2 genes (CSRP1 and CSRP2) are an effective growth factor in promoting skeletal muscle growth in vitro and vivo. However, in cattle, the information on the CSRP1 and CSRP2 genes is very limited. The aim of this study was to examine the association of the CSRP1 and CSRP2 variants with growth and carcass traits in cattle breeds. Three single nucleotide variants (SNVs) were identified within the bovine CSRP1 gene, whereas CSRP2 gene has not detected any SNVs, using DNA pooled sequencing, PCR-RFLP, and forced PCR-RFLP methods. These SNVs include g. 801T>C (Intron 2), g. 46T>C (Exon 3) and g. 99C>G (Intron 3). Besides, we also investigated haplotype frequencies and linkage disequilibrium (LD) coefficients for three SNVs in all study populations. LD and haplotype structure of CSRP1 were different between breeds. The result of haplotype analysis demonstrated eight haplotype present in QC (Qinchuan) and one haplotype in CH (Chinese Holstein). Only haplotype 1 (TTC), shared by all two populations, comprised 10.74% and 100.00%, of all haplotypes observed in QC and CH, respectively. Haplotype 5 (CTC) had the highest haplotype frequencies in QC (30.98%) and haplotype 1 had the highest haplotype frequencies in CH (100.00%). The statistical analyses indicated that one single SNV and 19 combined haplotypes were significantly or highly significantly associated with growth and carcass traits in the QC cattle population (P < 0.05 or P < 0.01). Quantitative real-time PCR (qRT-PCR) analyses showed that the bovine CSRP1 and CSRP2 genes were widely expressed in many tissues. The results of this study suggest that the CSRP1 gene possibly is a strong candidate gene that affects growth and carcass traits in the Chinese beef cattle breeding.     

Comparing methods for metabolic network analysis and an application to metabolic engineering

Bioinformatics tools have facilitated the reconstruction and analysis of cellular metabolism of various organisms based on information encoded in their genomes. Characterization of cellular metabolism is useful to understand the phenotypic capabilities of these organisms. It has been done quantitatively through the analysis of pathway operations. There are several in silico approaches for analyzing metabolic networks, including structural and stoichiometric analysis, metabolic flux analysis, metabolic control analysis, and several kinetic modeling based analyses. They can serve as a virtual laboratory to give insights into basic principles of cellular functions. This article summarizes the progress and advances in software and algorithm development for metabolic network analysis, along with their applications relevant to cellular physiology, and metabolic engineering with an emphasis on microbial strain optimization. Moreover, it provides a detailed comparative analysis of existing approaches under different categories.     

Defective immune responses in mice lacking LUBAC‐mediated linear ubiquitination in B cells

The linear ubiquitin chain assembly complex (LUBAC) plays a crucial role in activating the canonical NF‐κB pathway, which is important for B‐cell development and function. Here, we describe a mouse model (B‐HOIP^Δlinear) in which the linear polyubiquitination activity of LUBAC is specifically ablated in B cells. Canonical NF‐κB and ERK activation, mediated by the tumour necrosis factor (TNF) receptor superfamily receptors CD40 and TACI, was impaired in B cells from B‐HOIP^Δlinear mice due to defective activation of the IKK complex; however, B‐cell receptor (BCR)‐mediated activation of the NF‐κB and ERK pathways was unaffected. B‐HOIP^Δlinear mice show impaired B1‐cell development and defective antibody responses to thymus‐dependent and thymus‐independent II antigens. Taken together, these data suggest that LUBAC‐mediated linear polyubiquitination is essential for B‐cell development and activation, possibly via canonical NF‐κB and ERK activation induced by the TNF receptor superfamily, but not by the BCR.     

Rule extraction in gene–disease relationship discovery

Background

Biomedical data available to researchers and clinicians have increased dramatically over the past years because of the exponential growth of knowledge in medical biology. It is difficult for curators to go through all of the unstructured documents so as to curate the information to the database. Associating genes with diseases is important because it is a fundamental challenge in human health with applications to understanding disease properties and developing new techniques for prevention, diagnosis and therapy.

Methods

Our study uses the automatic rule-learning approach to gene–disease relationship extraction. We first prepare the experimental corpus from MEDLINE and OMIM. A parser is applied to produce some grammatical information. We then learn all possible rules that discriminate relevant from irrelevant sentences. After that, we compute the scores of the learned rules in order to select rules of interest. As a result, a set of rules is generated.

Results

We produce the learned rules automatically from the 1000 positive and 1000 negative sentences. The test set includes 400 sentences composed of 200 positives and 200 negatives. Precision, recall and F-score served as our evaluation metrics. The results reveal that the maximal precision rate is 77.8% and the maximal recall rate is 63.5%. The maximal F-score is 66.9% where the precision rate is 70.6% and the recall rate is 63.5%.

Conclusions

We employ the rule-learning approach to extract gene–disease relationships. Our main contributions are to build rules automatically and to support a more complete set of rules than a manually generated one. The experiments show exhilarating results and some improving efforts will be made in the future.     

Inverse Problem in Materials Science

Abstract

The importance of the inverse problem in materials science, that is, the determination of unknown parameters in the physico-chemical models from the experimental values is emphasized. The main attention is given to the problem of how to take into account experimental systematic errors during the inverse problem.     

Stabilization of Helical Peptides by Mixed Spaced Salt Bridges

Abstract

Whether or not surface salt bridges have a strong stabilizing effect on the native structure in proteins remains uncertain. Previous studies of model peptides have shown that salt bridges spaced at i,i+4 along the chain are more stabilizing than those spaced at i,i+3, with a preference for the order acid-base rather than base-acid from N to C terminus. An analysis of the effect of spacing the ion pairs in short helical peptides is presented, in which acidic and basic side chains spaced two or three residues apart alternate along the chain. The mixed spacing proves to be stabilizing relative to pure spacings. A control peptide in which salt bridges were spaced uniformly three residues apart proved to form a β-sheet structure rather than a-helix. This is due to formation of a silk-like apolar face consisting of alanine side chains; the mesoscopic structure formed by these sheets can be imaged by scanning microscopy.