In the present article, we introduce a new method for identifying a set of optimal gene regulatory pathways by using structural equations as a tool for modeling gene regulatory networks. The method, first of all, generates data on reaction flows in a pathway. A set of constraints is formulated incorporating weighting coefficients. Finally the gene regulatory pathways are obtained through optimization of an objective function with respect to these weighting coefficients. The effectiveness of the present method is successfully tested on ten gene regulatory networks existing in the literature. A comparative study with the existing extreme pathway analysis also forms a part of this investigation. The results compare favorably with earlier experimental results. The validated pathways point to a combination of previously documented and novel findings.

We show that our method can correctly identify the causal genes and effectively output experimentally verified pathways. The present method has been successful in deriving the optimal regulatory pathways for all the regulatory networks considered. The biological significance and applicability of the optimal pathways has also been discussed. Finally the usefulness of the present method on genetic engineering is depicted with an example.

Here, we use a number of case studies for describing the results of biological experiments. We investigate the ways in which these could be represented using both realist and non-realist approaches; we consider the limitations and advantages of each of these models.

From our analysis, we conclude that while realist principles may enable straight-forward modelling for some topics, there are crucial aspects of science and the phenomena it studies that do not fit into this approach; realism appears to be over-simplistic which, perversely, results in overly complex ontological models. We suggest that it is impossible to avoid compromise in modelling ontology; a clearer understanding of these compromises will better enable appropriate modelling, fulfilling the many needs for discrete mathematical models within computational biology.

Here we introduce the novel concept of ModuLand, an integrative method family determining overlapping network modules as hills of an influence function-based, centrality-type community landscape, and including several widely used modularization methods as special cases. As various adaptations of the method family, we developed several algorithms, which provide an efficient analysis of weighted and directed networks, and (1) determine pervasively overlapping modules with high resolution; (2) uncover a detailed hierarchical network structure allowing an efficient, zoom-in analysis of large networks; (3) allow the determination of key network nodes and (4) help to predict network dynamics.

The concept opens a wide range of possibilities to develop new approaches and applications including network routing, classification, comparison and prediction.

All analysed plasmids share a common replicon-type related to theta-replicating plasmid pAMbeta1. Nonetheless plasmids could be broadly divided into two distinct groups based on replicon diversity and the genetic content of the respective plasmid groups. Listeria plasmids are characterized by the presence of a large number of diverse mobile genetic elements and a commonly occurring translesion DNA polymerase both of which have probably contributed to the evolution of these plasmids. We detected small non-coding RNAs on some plasmids that were homologous to those present on the chromosome of L. monocytogenes EGD-e. Multiple genes involved in heavy metal resistance (cadmium, copper, arsenite) as well as multidrug efflux (MDR, SMR, MATE) were detected on all listerial plasmids. These factors promote bacterial growth and survival in the environment and may have been acquired as a result of selective pressure due to the use of disinfectants in food processing environments. MDR efflux pumps have also recently been shown to promote transport of cyclic diadenosine monophosphate (c-di-AMP) as a secreted molecule able to trigger a cytosolic host immune response following infection.

The comparative analysis of 14 plasmids of genus Listeria implied the existence of a common ancestor. Ubiquitously-occurring MDR genes on plasmids and their role in listerial infection now deserve further attention.

Here, the minimum redundancy maximum relevance (mRMR) feature selection algorithm, the nearest neighbor algorithm (NNA), and the incremental feature selection (IFS) method were used to identify the most important TFs that either favor or inhibit nucleosome positioning by analyzing the numbers of transcription factor binding sites (TFBSs) in 53,021 nucleosomal DNA sequences and 50,299 linker DNA sequences. A total of nine important families of TFs were extracted from 35 families, and the overall prediction accuracy was 87.4% as evaluated by the jackknife cross-validation test.

Our results are consistent with the notion that TFs are more likely to bind linker DNA sequences than the sequences in the nucleosomes. In addition, our results imply that there may be some TFs that are important for nucleosome positioning but that play an insignificant role in discriminating nucleosome-forming DNA sequences from nucleosome-inhibiting DNA sequences. The hypothesis that TFs play a role in nucleosome positioning is, thus, confirmed by the results of this study.

Sequence-based mutual information analysis revealed five characteristic (completely conserved and unique), putative functional residues of the Hpx domain of MMP-13 (these residues hereafter are referred to as HCR-13_pf). Binding of a ligand to as many of the HCR-13_pf is postulated to result in an increased selective inhibition of the Hpx domain of MMP-13. Through the in silico structure-based high-throughput virtual screening (HTVS) method of Glide, against a large public library of 16908 molecules from Maybridge, PubChem and Binding, we identified 25 ligands that interact with at least one of the HCR-13_pf. Assessment of cross-reactivity of the 25 ligands with MMP-1 and MMP-8, members of the collagenase family as MMP-13, returned seven lead molecules that did not bind to any one of the putative functional residues of Hpx domain of MMP-1 and any of the catalytic active site residues of MMP-1 and -8, suggesting that the ligands are not likely to interact with the functional or catalytic residues of other MMPs. Further, in silico analysis of physicochemical and pharmacokinetic parameters based on Lipinski's rule of five and ADMET (absorption, distribution, metabolism, excretion and toxicity) respectively, suggested potential utility of the compounds as drug leads.

We have identified seven distinct drug-like molecules binding to the HCR-13_pf of MMP-13 with no observable cross-reactivity to MMP-1 and MMP-8. These molecules are potential selective inhibitors of MMP-13 that can be experimentally validated and their backbone structural scaffold could serve as building blocks in designing drug-like molecules for OA, RA and other inflammatory disorders. The systematic cheminformatics-based drug design approach applied herein can be used for rational search of other public/commercial combinatorial libraries for more potent molecules, capable of selectively inhibiting the collagenolytic activity of MMP-13.

Hilbert-Huang Transformation is a non-stationary transformation used in signal processing. We provide a novel algorithm for preprocessing that can deal with MALDI and SELDI spectra. We use the Hilbert-Huang Transformation to decompose the spectrum and filter-out the very high frequencies and very low frequencies signal. We think the noise in mass spectrometry comes from many sources and some of the noises can be removed by analysis of signal frequence domain. Since the protein in the spectrum is expected to be a unique peak, its frequence domain should be in the middle part of frequence domain and will not be removed. The results show that HHT, when used for preprocessing, is generally better than other preprocessing methods. The approach not only is able to detect peaks successfully, but HHT has the advantage of denoising spectra efficiently, especially when the data is complex. The drawback of HHT is that this approach takes much longer for the processing than the wavlet and traditional methods. However, the processing time is still manageable and is worth the wait to obtain high quality data.

We introduce the cREMaG (cis-Regulatory Elements in the Mammalian Genome) system designed for in silico studies of the promoter properties of co-regulated mammalian genes. The cREMaG system offers an analysis of data obtained from human, mouse, rat, bovine and canine gene expression-profiling studies. More than eight analysis parameters can be utilized in user-defined combinations. The selection of alternative transcription start sites and information about CpG islands are also available.

Using the cREMaG system, we successfully identified TFs mediating transcriptional responses in reference gene sets. The cREMaG system facilitates in silico studies of mammalian transcriptional gene regulation. The resource is freely available at http://www.cremag.org.