Current Bioinformatics - Volume 6, Issue 1, 2011

In the present days, there has been an explosion on the use of Topological Indices (TIs) described from graph theory to study Complex Networks on a broad spectrum of topics related to Bioinformatics and Proteomics. These topics cover many biomedical fields from Virology, Parasitology, and Microbiology in general to Toxicology, and Cancer research to cite only some of the more investigated. The main reason for this success of TIs, is the high flexibility of this theory to solve in a fast but rigorous way many apparently unrelated problems in all these disciplines. This determined the recent development of several interesting software and theoretical methods to handle with structure-function information and data mining in this field. In fact, recently, there has been an explosion on the use of TIs and Complex Networks on a broad spectrum of topics related to Bioinformatics but clearly differentiated from it. One important reason is that using TIs as inputs we can find Quantitative Structure-Property Relationships (QSPR) models for any kind of bio-systems; at least in principle. We see QSPR model as a function that predicts the properties of different systems using parameters that numerically describe the structure of the system (like TIs). We refer to almost any class of systems ranging from molecules to social networks such as: Drugs, Proteins and Proteomes, RNA, Diseasomes, Brain cortex Interactome, Disease spreading networks or Internet. There are many QSPR-like terms that fit to these and more specific situations, for instance Quantitative Structure-Activity Relationships (QSAR), Quantitative Structure-Toxicity Relationships (QSPR), Quantitative Proteome-Property Relationships (QPPR), Quantitative Sequence- Action Model (QSAM), or Quantitative Structure-Reactivity Relationships (QSRR), to cite a few examples. In all these cases we can find models that use the TIs of the system as input to predict the properties of this system (output). In any case, we miss a collection of manuscripts or issues focused on QSAR-like models with TIs and networks more focused on Bioinformatics. That is why; we have extended the discussion to a collection of authors guest-editing this special issue. Consequently, it is necessary to have a topic issue because many of the previous ones limit to a narrow field of application and ignore the several applications at different bioinformatics levels. On the other hand, many researchers, which move by the frontiers of these fields, miss the journal issues reviewing the actual applications and future perspectives of these software and methods and the possible relationships of data flow between them in a common theoretic framework. Such a collection of papers could be of major interest for many specialists on Bioinformatics and may increase the interchange between these specialists of different but related fields with a common root: proteomics and graph theory. In addition, it could be the seed for further improvement of software performance and compatibility. Taking into consideration all these aspects, this journal (Current Bioinformatics) presents this special issue composed of a collection of papers devoted to review the common theoretic basis, applications, and inter-connections between methods based on TIs and networks and their applications to Bioinformatics-related areas. We hope that the present issue may serve as a bridge between theoretical scientists in graph theory and experimentalists in order to suggest new areas of mutual interchange and collaboration. Based on all these reasons we edited the present issue including QSPR/QSAR studies as well as other types of studies using TIs and complex networks from the point of view of Bioinformatics but still including applications to related areas like Microbiology, Parasitology, Pharmacology, Chemoterapy, Epidemiology, Toxicology, and others. In this sense, the present issue provides state of the art reviews of some of these new computational approaches in this rapidly expanding area of Bioinformatics. Taking these aspects into consideration, in the first work of this issue Speck-Planche and Cordeiro focused on the role of Bioinformatics toward the design of compounds with anti-herpetic activity. In the second paper Chis et al. reviewed the state-of-art in yeast gene interaction networks studies and their own results. The authors of the third paper have presented here a work that reviews some bioinformatics concepts and previous studies related to Complex networks, GOs, and related methods that can be used for the study of CNP regions. In the next work of this issue, García et al. revised QSAR and other bioinformatics studies of GSK-3 inhibitory activity. On the other hand, Wan, Cai, Chou et al. reviewed previous models and introduced a new predictor called Sort-PLoc to tackle the difficult and challenging problem of protein subcellular localization prediction....

Alzheimer disease (ADa) is the most common form of senile dementia, and it is characterized pathologically by decreased brain mass. An important problem to inhibiting β-secretase, is to cross the blood-brain barrier (BBB) using drugs not derived from proteins and thus more efficient to design drugs to treat Alzheimer's disease. In this sense, quantitative structure-activity relationships (QSAR) could play an important role in studying these β-secretase inhibitors. QSAR models are necessary in order to guide the β-secretase synthesis. In the present work, we firstly revised two servers like ChEMBL or PDB to obtain databases of β-secretase inhibitors. Next, we review previous works based on 2D-QSAR, 3DQSAR, CoMFA, CoMSIA and Docking techniques, which studied different compounds to find out the structural requirements. Last, we carried out new QSAR studies using Artificial Neural Network (ANN) method and the software Modes- Lab in order to understand the essential structural requirement for binding with receptor for β-secretase inhibitors.

1R,25-Dihydroxyvitamin D3, the hormonally active form of vitamin D3, besides regulating the homeostasis of calcium and classical bone mineralization, also promotes cellular differentiation and induces some biological functions related to the immunological system. Extensive structure-function studies have shown that it is possible to modify the calcitriol structure to obtain vitamin D3 analogs that are capable of inducing, in a selective manner, the biological functions related to the same hormone. In this article, we revised QSAR studies with conceptual parameters such as flexibility of rotation, probability of availability, etc. we then used the method of regression analysis and QSAR studies in order to understand the essential structural requirement for binding with receptor. Next, we reviewed Radial Distribution Function, 4DQSAR, CoMFA and Docking with different compounds to find out the structural requirements for GSK-3 inhibitory activity.

The complex network induced by a sequence of substitution reactions on a chemical structure generates a partially ordered set (or poset) oriented graph. Such a poset can be used to develop network-QSAR models to predict various molecular properties with quantitative superstructure-activity relationships (QSSARs). These novel network- QSAR models look beyond simple molecular structure and chemical descriptors, and predict molecular properties from the topology of a poset network and from the embedding of a chemical compound into a reaction network. We demonstrate this novel quantitative structure-activity relationship (QSAR) approach for the prediction of chromatographic retention properties of polychlorinated biphenyls (PCBs). PCBs have become worldwide pollutants due to their presence in the environment. Exposure to PCBs can permanently damage the nervous, reproductive, and immune systems. PCBs are known carcinogens and have been linked with the development of various forms of cancer including skin and liver. To predict the chromatographic properties for PCBs we generate the substitution reaction poset, which is a formal chlorosubstitution network which progresses from biphenyl to decachlorobiphenyl. Three network-QSAR models are compared, namely poset-average, splinoid poset, and cluster expansion QSSAR models, to estimate the chromatographic properties in different conditions (of column, temperature, or detector) for all 209 PCB congeners. Excellent results are obtained for all QSSAR chromatographic models. Based on the poset reaction diagram, all these three QSSAR models reflect in distinct ways the topology of the network describing the interconversion of chemical species. QSSAR equations based on poset reaction networks add a supramolecular dimension to QSAR models.

A cell division cycle is a set of events that take place in a cell, characterized by a period of cell growing and cell duplication, these processes taking place in five phases. Many genes and proteins regulate cell division. There are presented mechanisms that stop and restart cell divisions for budding yeast, by considering stochastic noise (extrinsic noise). Fuzzy phenomenon was introduced by Li and Liu and it is based on credibility measure. A Wiener-Liu process (hybrid process) is a mixture of randomness and fuzziness. We consider the study of a generic cell cycle model for budding yeast, representing a complex network of interactions between genes and proteins. In this sense, the present work has two parts: first we give a short review the state-of-art in yeast gene interaction networks studies, and next we report our own results. We study this model by considering stochastic approach (by writing stochastic system of differential equations), fuzzy approach (the fuzzy system of differential equations associated to the deterministic system) and hybrid system of differential equations, as a combination of randomness and fuzziness.

The specificity of drug targets is a great challenge in the pharma-proteomics field of cancer biology. In particular, bioinformatics methods based on complex networks and Gene Ontologies (GOs) are very useful in this area. This work reviews some bioinformatics concepts and previous studies related to Colorectal Cancer (CRC) research using GOs, complex networks, and related methods. Also, we report new results mapping natural compounds on potential drug target CRC network using GOs. The literature mining along with OMIM database gives the details of diseased genes which are further subjected to design a well connected gene regulatory network for cancer. The resultant network is then extrapolated to proteomics level to sort out the genes only expressed in the specific cancer types. The network is statistically analyzed and represented by the graphical interpretation to encounter the hub nodes and their locally parsed neighbors, ligands multi-receptor docking, and the propensity of drug targets in hub nodes and related sub-networks.

Bionformatics models describe that structure-property relationships of systems may play an important role to reduce costs in terms of time, human resources, material resources, as well as allow certain laboratory animals replacement in biomedical sciences. Many of these models are in essence Quantitative Structure-Activity or Property Relationships (QSARs/QSPRs). In other words, QSPRs are models that connect the structure of a system with external properties of these systems that are not self-evident after direct inspection of the structure. In particular, QSARs are models that connect the chemical structure of drugs, target (protein, gen, RNA, microorganism, tissue, disease…), or both (drug and target at the same time) with drug biological activity over this target. Many of these QSAR techniques are based on the use of structural parameters, which are numerical series that codify useful structural information and enable correlations between strcture and biological properties. In parallel, graph theory and Complex Network analysis tools are expanding to new potential fields of application of Information Sciences at different levels from molecules to populations, social or technological networks such as genome, protein-protein networks, sexual disease transmission networks, power electric power network or internet. In all these cases, we can calculate parameters called Topological Indices (TIs) that numerically described the connectedness patterns (structure) between the nodes or actors in a network. Consequently, TIs are very useful as inputs for QSPR models at all structural levels. In fact, even legal systems may be approached using computing and information techniques like networks. So we can construct a complex network of legal systems connecting laws (nodes) that regulate common biological topics for instance. On the other hand, a systematic judicial framework is needed to provide appropriate and relevant guidance for addressing various computing techniques as applied to scientific research. Bioinformatics and computational biology are two areas within the field of biosciences that require more attention from the legal operators. Taking all the previous aspects into consideration, this article reviews both: the use of legal sciences to regulate and protect QSAR models of molecular sytems and the application of QSPR-like models to study legal systems per se. Consequently, as stated in a title, in this review we are going to travel from Cheminformatics, Bioinformatics, and Networks to Legal Sciences and later go back. In the first direction, we review the various legal procedures that are available to protect QSAR software, the acceptance and legal treatment of scientific results and techniques derived from such software, as well as some of the specific tax issues from the computer programs field. In the second direction, we review the representation of legal systems using complex networks, the description of legal social networks with TIs, the development of QSPR models to predict legal and social phenomena. In this sense, the issues reviewed here are: 1- Networks, Topological Indices and QSPR models: Theoretical Background. 2- Notes on reviews of legal issues related to QSAR models. 3- Complex Network Representations in Legal Sciences. 4- Topological Indices of Legal networks. 5. QSPR models to predict causality in crime law networks.

Knowing the subcellular locations of a protein helps to explore its functions in vivo since a protein can only play its roles properly if and only if it is located at certain subcellular compartments. Since it is both time-consuming and costly to determine protein subcellular localization purely by means of the conventional biotechnology experiments, computational methods play an important complementary role in this regard. Although a number of computational methods have been developed for predicting protein subcellular localization, it remains a challenge to deal with the multiplex proteins that may simultaneously exist at, or move between, two or more different locations. Here, a new predictor called Sort-PLoc was developed to tackle such a difficult and challenging problem. The key step was to select protein domains to code the protein samples by Incremental Feature Selection method. In each prediction, a series of subcellular locations were sorted descendingly according to their likelihood to be the site where the query protein may reside. Based on the selected domain set, the importance of Gene Ontology (GO) terms and domains in the contribution to the prediction was analyzed that may provide useful insights to the relevant areas. For the convenience of the broad experimental scientists, a user-friendly web-server for Sort-PLoc was established that is freely accessible to the public at http://yscl.biosino.org/.

Human herpes viruses (HHV) are leading cause of human viral diseases, second after influenza and cold viruses. They cause overt disease or can remain silent for many years waiting for reactivation. HHV are associated to several side-effects or co-conditions like Alzheimer's disease, cholangiocarcinoma and pancreatic cancer, and with the time, they have become resistant to the available commercial drugs like acyclovir, whose final target is the DNApolymerase. For this reason there is an increasing interest for the search of novel compounds against herpes viruses. In this sense, Bioinformatics is determinant for a better understanding of the mechanisms of resistance, and also for the search of new biomolecular targets for the design of more potent and versatile anti-herpes agents. This review is focused on the role of Bioinformatics toward the design of compounds with anti-herpetic activity and also we propose a model based fragment descriptors for the design, search and prediction of anti-herpes compounds through the inhibition of 5 targets belonging to different herpes viruses.

Graph and Complex Network theories are applied to different levels of matter organization such as genome networks, protein-protein networks, sexual disease transmission networks, linguistic networks, law and social networks, power electric networks or Internet. A very important fact is that we can use the numeric parameters called Topological Indices (TIs) to describe the connectivity patterns in all these kinds of Complex Networks despite the nature of the object they represent. The main reason for this success of TIs is the high flexibility of this theory to solve in a fast but rigorous way many apparently unrelated problems in all these disciplines. Another important reason for the success of TIs is that using these parameters as inputs we can find Quantitative Structure-Property Relationships (QSPR) models for any kind of bio-systems, at least in principle. In any case, there is a lack of manuscripts or issues focused on QSAR-like models with TIs and of networks more focused on Bioinformatics. In this sense, the present issue provides state-of-the-art reviews of some of these new computational approaches in this rapidly expanding area of Bioinformatics. Taking into account all the above-mentioned aspects, the present work intends to offer a common background to all the manuscripts presented in this special issue. In so doing, we make a review of classic TIs and Databases of Biology, Parasitology, Technology, and Social-Legal Networks. After that, we report a definition of a new class of TIs, coined here as Markov-Harary invariants. We also present the calculation of this class of TIs for different classes of networks. Next, we carry out a comparative study of these networks using the values of the new Markov-Harary TIs. Finally, we compare these new indices with another new class of TIs called Markov entropy values, which has been previously developed.

Genes showing copy number variation have been the subject of much study in recent years. Copy number variation has been found to be surprisingly common among humans. As much as 12% of the human genome is polymorphic in copy number and this can disrupt genes and alter gene dosage to influence gene expression, phenotypic variation and adaptation, but little is known about the significance of these copy number polymorphic genes. On-line databases, Complex Networks software, Gene Ontologies (GOs) analysis web servers, and other Bioinformatics tools may be very useful in this branch. In this sense, the present work reviews some bioinformatics concepts and previous studies related to Complex networks, GOs, and related methods that can be used for the study of Copy Number Polymorphic regions. The second objective of the envisaged work was to trace the divergence of human genomes in copy number variant genes and ascertain three conditions disease pathways and Functional Categories with Whole Genome TilePath (WGTP) array and 500K early access (500KEA) array data. These tests were performed to see significant departures from the divergence/ polymorphism ratio. The analysis of genes from WGTP and 500KEA gave the evidence for reciprocal disease and their pathways.

MicroRNAs (miRNAs) are a recently identified class of cellular non-coding RNAs that regulates protein expression and growth of a biological system during different stages of life. The active, mature miRNAs are 17-24 bases long, single-stranded RNA molecules expressed in both eukaryotic and prokaryotic cells and are known to affect the translation or stability of target messenger RNAs (mRNAs). Each miRNA is believed to regulate multiple genes and it is believed that greater than one third of all human genes may be regulated by miRNA molecules. Here in this review we try to focus on the role of these tiny molecules at different aspects of bioprocesses, prediction of miRNAs and their targets from a bioinformatics point of view.