Current Bioinformatics - Volume 6, Issue 2, 2011

The complexity of the systems and phenomena in Biosciences leads to the necessity of convergence of many scientific fields such as Bioinformatics, Nanotechnology and Artificial Intelligence. These fields make up the Nano-Bio-Info-Cogno (NBIC) convergent technologies with important applications in Biomedicine such as accurate and early personalized treatments or improvement of the body tissues. This hot topic issue contains the contribution of a couple of members of three international scientific networks such as Ibero American Network of Nano-Bio-Info-Cogno Convergent Technologies funded by CYTED (Ibero-NBIC, http://www.ibero-nbic.udc.es/), ACTION-Grid as the first European initiative on Grid Computing, Biomedical Informatics and Nanoinformatics (http://www.action-grid.eu/) and Cooperative Research Thematic Network on Computational Medicine (COMBIOMED, http://combiomed.isciii.es). In addition, different members participated to the Advisory Board of the ACTION-Grid White Paper on Nanoinformatics, revised and approved by the European Commission. The applications of this convergence are demonstrated in some important review articles contained in this issue. The issue contains ten articles. In the very first article, Lopez-Campos et al. show the applications of the microarrays in colon cancer and the biomedical informatics aspects related to this technology and its applications. In the next article, Dave et al. explain the convergence between Nanotechnology and Bioinformatics into Nanobioinformatics, a research field that encompasses the use of all kinds of biomedical information, from genetic and proteomic data to image data associated with a particular disease condition of a patient. In article 3, Sainz de Murieta et al. describe the biological computing devices implemented by the disciplines of biomolecular computation and synthetic biology. In continuation to this, Brea-Fernandez et al. detail the ability assessment of several in silico bioinformatics tools to accurately predict both pathogenic and neutral missense variants. In the next review, Alvarellos et al. propose the use of MEAs containing nerve cells that shows the importance of fusing bioinformatics, micro/nano-technologies, and AI techniques for the study of these neural complex systems. In article 6, Garcia et al. present an interesting update for the QSAR and docking studies in the case of inhibitors for the glycogen synthase kinase 3 (GSK-3β) as candidates of anti-Alzheimer and anti-parasitic compounds. Further, Novoa et al. explain the extraction of quantitative anatomical information from coronary angiographies over the past thirty years by using the Biomedical Informatics and Artificial Intelligence. In the next article, Gonzalez-Diaz et al. present a state-of-art review about generalized string pseudo-folding lattices in Bioinformatics and a new QSAR model for enzyme sub-classes, and study of ESTs on Trichinella spiralis by using the Complex Network theory. In article 9, Xiao and Chou describe in detail the analysis of proteins sequences by the pseudo amino acid (PseAA) composition or PseAAC formulated via cellular automata and the last article of Ivanciuc is dealing with quantitative structure-activity relationships (QSAR) based on data obtained with the MolNet Molecular Graph Machine for the GSK- 3β inhibition by aloisines. Thus issue could be found very interesting by both theoretical and experimental specialists in the NBIC convergent fields namely Bioinformatics (Bio-Info), Nanotechnology (Nano) and Artificial Intelligence techniques (Cogno).

This review covers recent aspects related with microarray technology and their application in colorectal cancer, one of the most prevalent cancers in the world and being one third of cancer related deaths. Since their origin, microarrays have been extensively used in oncological studies with the aim of unravelling the underlying biology of cancer, enabling translational medicine. Some microarray based applications have already been translated into clinics and approved by the regulatory agencies; these applications have been supported by biomedical informatics. In this work we will present different aspects and views related with microarrays and their applications in colon cancer and the biomedical informatics aspects related with this technology and its applications. The schema followed in this work is based on an introduction to microarray technologies and their applications; translational medicine as a consequence of the different approaches based on microarray technologies; data analysis of microarray data focused on gene expression studies in these applications represents 60% of microarray publications; aspects related with the quality of the experiments and the reproducibility of the analyses and technology; major databases for microarray datasets storage and the standards associated with microarray information; other biomedical informatics aspects different to those strictly associated with microarray data analysis; and finally future trends in microarrays in the scope of the relationship with nanotechnology.

Nanotechnology and Bioinformatics are quickly evolving into a research field that encompasses the use of all kinds of biomedical information, from genetic and proteomic data to image data associated with a particular disease condition of a patient. Nanobioinformatics comprises the fields of nanotechnology (e.g., nanobiological particle, Nanomedicine) and Bioinformatics (e.g., genomics and proteomics) and deals with issues related to the approach of understanding the complex biological system and disease network in medicine, the analysis of high throughput genomics data, Nanobiology and computer added drug design, interoperability and integration of data-intensive biomedical applications. This review depicts some new requirements such as the development of new tools and technologies that are critical for the design, modeling, simulation and visualization of Nanosystems that have arisen during the accelerated evolution of Nanobioinformatics applied to medical science. The knowledge obtained at nanoscale implies the answer of new questions and the development of new concepts in different fields. The implementation of new tools and methods in medical science will be a key element to derive the information needed in order to unleash the promise behind this convergent field. This work will shed light upon some applications of Nanoinformatics in medical science, and will discuss how physical, chemical, and biological properties of nanoparticle are fulfilling the new requirements for different scientific fields such as Bioinformatics and Nanotechnology

Biomolecular computation and synthetic biology are the main disciplines in the design and implementation of biological computing devices. This article examines some of the key works concerning this type of logical devices processing biological information. Their design and construction follow two main approaches. The first approach builds computing devices based on the properties of nucleic acids, whereas the second approach focuses on genetic regulatory networks. Examples of the nucleic acid based approach are DNA self-assembly, DNA automata based on restriction enzymes or deoxyribozymes and logic circuits based on DNA strand displacement. Examples of the use of genetic networks are NOT, AND and OR logic gates, a genetic toggle switch that works like a biological memory unit, and several genetic oscillators that work as biological clocks.

Sensitivity improvement in molecular genetic analysis has led to increased detection of novel sequence variants of unknown clinical significance in disease related genes. These unclassified variants (UVs) can often induce pathogenesis by mutating the protein product of the gene. However, they can also manifest non-pathogenic or neutral effects, coding for amino acid changes which do not significantly affect the protein product. Diagnostic laboratories have great difficulty to identify whether an UV is pathogenic or not. Significant characterization of such variants represents a major challenge for medical management of patients in whom they are identified. Functional assays may help to prove whether an UV cause pathogenicity, but these analyses are tedious and laborious. Conversely, in silico prediction tools are very useful to perform a fast bioinformatics analysis which can predict the pathogenicity of a variant based on the change to an amino acid. Despite the amount of in silico tools, only a small number of these are regularly used by genetic testing laboratories. Practice guidelines at the Clinical Molecular Genetics Society for analysis of UVs (UK CMGS UV guidelines) recommend the use of AGVGD, SIFT and Polyphen, but it is unknown whether these are the most useful methods. The aim of the present study was the ability assessment of several in silico bioinformatics tools to accurately predict both pathogenic and neutral missense variants.

The study of the nervous system of human beings is an arduous task. The reasons are that it is very complex and it is internal to the organism. The nervous system is comprised not only of neuronal networks but also of different types of cells that constitute the glial system. Astrocytes, a type of glial cells, have traditionally been considered as passive, supportive cells. However, through the use of neuroscientific techniques, it has recently been demonstrated that astrocytes are actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. Also in recent studies employing artificial intelligence (AI) techniques, it has been shown that adding artificial astrocytes to Artificial Neural Networks (ANNs), the effectiveness of such networks in classification tasks is markedly improved. At present, the actual impact of astrocytes in neural network function is largely unknown. Therefore, our group is placing increasing emphasis on the study of the influence that astrocytes may have on brain information processing using a rather different perspective based on the use of multielectrode arrays (MEAs). This represents a hybrid approach given that it combines a biological component (cultured cells), hardware technology (MEAs), and AI (computer simulations based on AI techniques to control the system). With this in mind, the objective of this paper is to present a review of the state of the art in the use of MEAs containing nerve cells. This review is intended as a preliminary theoretical analysis on the suitability of these devices to achieve the aforementioned future goal of fusing bioinformatics, micro/nano-technologies, and AI techniques to study these complex systems.

GSK-3 inhibitors are interesting candidates to develop Anti-Alzheimer compounds. GSK-3β are also interesting as Anti-parasitic compounds active against Plasmodium falciparum, Trypanosoma brucei, and Leishmania donovani; the causative agents for Malaria, African Trypanosomiasis and Leishmaniosis. The high number of possible candidates creates the necessity of Quantitative Structure-Activity Relationship models in order to guide the GSK-3 synthesis. Linear Discriminant Analysis was used to fit the classification function and it predicted heterogeneous series of compounds such as paullones, indirubins, meridians, etc. This study thus provided a general evaluation of these types of molecules. Plasmodium falciparum, Leishmania, Trypanosomes, among others, are the causers of diseases such as Malaria, Leishmaniasis and African Trypanosomiasis that are nowadays the most serious parasitic health problems. The great number of deaths and the few drugs against these parasites make it necessary to search for new drugs. Some of these antiparasitic drugs are also GSK-3 inhibitors. GSK-3 inhibitors are serious candidates as drugs for Alzheimer's disease and other degenerative disorders. In this work, we revised different computational studies for a very large and heterogeneous series of GSK-3Is. The methods bioinformatics, molecular docking and quantitative structure-activity relationship (QSAR) were applied to develop new GSK-3Is. First, 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 3DQSAR, CoMFA, CoMSIA and Docking, and analyzed a new and alternative QSAR model with different compounds to find out the structural requirements for GSK-3 inhibitory activity.

Cardiovascular diseases and in particular severe coronary stenosis are the main cause of death in the western hemisphere. The diagnostic method considered as gold-standard in the quantification and location of the coronary lesions is the coronary angiography. This procedure is required in patients with a high probability of coronary heart disease. To treat patients, interventional cardiologists analyse the angiographic images, establish a disease diagnosis and may even provide a prognosis, depending on the location, severity, and extent of the coronary disease. From the late 1970s, a large number of studies have been carried out, aimed at building information systems that assess general practitioners in the diagnostic tasks. These systems are based on the quantification of the anatomical information in an objective way. The relevant information is extracted from the coronary angiographies using automatic or semiautomatic image segmentation techniques. The precision of the segmentation is a key element in the subsequent measurement of the stenosis and flow capacity of the arteries, which also enables the establishment of an index or score related to the prognosis of the disease. Multiscalar methods, matching filters, and morphologic mathematical methods present the best balance between precision and processing speed. The best results are frequently obtained through the common use of multiple techniques. The current paper presents a review of the best techniques used for the extraction of anatomical information from coronary angiographies over the past thirty years.

With the avalanche of protein sequences generated in the post-genomic age, many typical topics in bioinformatics, proteomics and system biology are relevant to identification of various attributes of uncharacterized proteins or need this kind of knowledge. Unfortunately, it is both time-consuming and costly to acquire the desired information by purely conducting biochemical experiments. Therefore, it is highly desirable to develop automated methods for fast and accurately identifying various attributes of proteins based on their sequences information alone. This is the convergence between bioinformatics and artificial intelligence techniques (AI). To establish powerful computational methods in this regard, one of the key procedures is to find an effective mathematical expression for the protein samples that can truly reflect their intrinsic correlation with the target to be predicted. To realize this, the pseudo amino acid (PseAA) composition or PseAAC was proposed. Stimulated by the concept of PseAAC, a series of different modes of PseAAC were developed to deal with proteins or proteins-related systems. The current review is mainly focused on those PseAAC modes that were formulated via cellular automata. By using some optimal space-time evolvement rules of cellular automata, a protein sequence can be represented by a unique image, the so-called cellular automata (CA) image or CAI. Many important features, which are deeply hidden in piles of long and complicated amino acid sequences, can be clearly revealed through their CAIs. It is anticipated that, owing to its impressive power, intuitiveness and relative simplicity, the CAI approach holds a great potential in bioinformatics and other related areas.

Quantitative structure-activity relationships (QSAR) are statistical models that may predict various physicochemical and biological properties of chemical compounds. Typical QSAR models use as input molecular descriptors computed from the chemical structure of the compounds in the dataset. QSAR models are based on algorithms or mathematical functions that correlate these molecular descriptors with the experimental property that is modeled. A different approach is explored in molecular graph machines (MGM) which represent a class of QSAR models that actively consider the molecular topology in the process of generating a structure-property model. After a review of the major MGM models, we present a detailed overview of the artificial neural network MolNet, which is a multilayer perceptron that encodes the molecular topology of each chemical presented to the network during learning or prediction. Each nonhydrogen atom in a molecule has a corresponding neuron in the input and hidden layers, whereas the output layer has only one neuron which provides the computed molecular property. The connections between the input and hidden layers encode the topological distance matrix of a molecule, whereas the connections between the hidden and output layers are classified according to atom types. Connection weights corresponding to the same topological distance or to the same atom type have a constant value for all chemicals in the training set. A MolNet application is presented for the glycogen synthase kinase-3β (GSK-3β) inhibition by aloisines.