• Parallel stochastic simulation.
• Biological and Numerical parallel computing.
• Parallel and distributed architectures.
• Emerging processing architectures (e.g. GPUs, Intel Xeon Phi, FPGAs, mixed CPU-GPU or CPU-FPGA, etc).
• Parallel Model checking techniques.
• Parallel algorithms for biological analysis.
• Cluster and Grid Deployment for system biology.
• Biologically inspired algorithms.

• Theoretical and practical aspects of protein folding.
• New trends and discoveries in self-avoiding walks.
• Biophysics of protein folding.
• New artificial intelligence trends for PSP software.
• Biological aspects of protein folding.
• Mathematical study of folding dynamics.
• Complexity of the PSP problem.

With the rise of biotechnology and bioinformatics, a number of problems regarding storing, searching and using biological data occur. The data are scattered over a large number of repositories (public or private) and stored in a large number of different formats. Furthermore, some formats are not adequate for automatic computer processing (such as text documents) and require some kind of preprocessing before they can be input into computer algorithms. This situation makes searching and analyzing the data very difficult, leaving facts and knowledge on observed biological phenomena hidden in databases and digital collections.

• database integration techniques,
• data acquisition from heterogeneous sources,
• genotype-phenotype associations researches,
• integrative modeling and analysis of processes and systems in biosciences,
• tool integration and workflow systems,
• computational infrastructure for biological researches, including laboratory management systems,
• biological ontologies and metadata,
• integrative data and text mining approaches.

• Computational methods for assessment and prediction of physical (including mechanical, optical,...) properties of biomaterials;
• Application of biomaterials in Biomedicine;
• Modelling systems of biomaterials;
• Software tools and simulation packages for the evaluation of suitability of biomaterials;

• Artificial Neural Networks Algorithms on Biomedical Signals.
• Fuzzy Systems and Fuzzy Clustering Algorithms on Biomedical Signals.
• Probabilistic Model Algorithms on Biomedical Signals.
• Metaheuristic Algorithms on Biomedical Signals.
• Hybrid Algorithms on Biomedical Signals.

Chaperone therapy is a new concept of molecular therapeutic approach, first developed for lysosomal diseases, based on a paradoxical molecular phenomenon involving lysosomal enzyme proteins and their competitive inhibitors as intracellular enhancers (chaperones). The misfolded mutant enzyme protein is stabilized as a molecular complex with its substrate analogue, chaperone, and transported safely to the lysosome. The complex is automatically dissociated in the lysosome under the acidic condition, the free mutant protein remains stable, and the enzyme activity is expressed. The small chaperone molecule has been confirmed to be delivered to the brain tissue through the blood-brain barrier.

This new trial was targeted initially at a few lysosomal diseases, and then has been expanded to many other diseases with pathological protein dysfunction due to structural misfolding. The advantages of this molecular approach over other currently available therapies for genetic and protein misfolding diseases are summarized twofold; first, oral administration to individuals with intractable diseases; and second, delivery to the central nervous system for therapy of brain diseases currently not available with other experimental or clinical trials. In this session we discuss the present status of chaperone therapy research, focusing on chemical compounds, enzyme-chaperone interactions, and animal and human experiments for a few genetic diseases. This therapeutic approach will become a novel and revolutionary scientific and medical strategy in the near future.

Motivations and objectives: Next generation sequencing (NGS) is an important technology for today's biomedical research. The growing importance of NGS for the clear understanding of various biological systems has induced a competition among several companies, each trying to come up with a sequencing platform which can produce high quality longer read sequences with greater throughput and reduced cost. However, whole genome sequencing is accompanied by higher error rates than capillary sequencing.

There is considerable amount of evidence about context dependency and other systematic errors across different NGS platforms and different releases of same technologies now. The sources of systematic errors are extremely diverse. The error sources range from library preparations, hardware and software artefacts, up to DNA/RNA error-prone patterns, secondary structure and genome function.

In order to make Bioinformatic and Biomedical community aware of well known and newly developed systematic errors in NGS (which could negatively affect their research and clinics) we announce this Special Issue on 'Error models, sources and statistical biases in Next Generation Sequencing'. We invite authors to submit original research, software application and review articles on above topic, including the following:

• Typical systematic biases across different NGS platforms, including methods to correct for them.
• Different NGS experimental techniques: DNA_seq, RNA_seq, CHiP_seq etc, and error sources within them.
• NGS data analysis tools: aligners, assemblers, base callers, SNP-callers. Their impact into statistical error biases.
• Particular cases of NGS applications to Biology, Genetics and Medicine, which are known (or may be) to be affected by systematic sequencing errors, such as GWAS etc.
• Hybrid Algorithms on Biomedical Signals.

• Promoter inference and classification from transcript mapping data.
• Functional classification of regulatory elements based on chromatin features.
• Methods for identifying differentially modified chromatin regions.
• Prediction of nucleosome positioning from DNA sequence and TF binding events
• Discovery of genetic variants associated with changes in chromatin state.
• Usage of epigenetic profiling data for medical diagnosis and treatment decision

This special session will describe and illustrate the development/measurement properties, current use, and future electronic uses of patient-friendly symptom checklists for adults (TRSC) and children (TRSC-C) oncology patients. These checklists can be completed in less than 5 minutes by adults and children/parents, and involve no radical alterations in or increased costs of clinical practice. The adult checklist was developed 1984-1995 and the child checklist 2004-2010. Both checklists now have versions in English, Spanish, Thai, Pilipino, Chinese, and Bahasa Indonesia, and have been used in clinic settings in the USA and other countries.

The checklists were originally developed to reduce observed under-documentation of treatment symptoms of concern to patients in medical records. The hope was that use of these checklists (25 symptoms for adults and 30 for children) would lead to better documentation through integration of the checklists into electronic medical records (EMR), enhance communications among patients and clinicians, and improve health outcomes. Completed research has found high levels of patient/clinician satisfaction with use of the TRSC, no increase in clinic costs, and strong correlations of TRSC/TRSC-C scores with the number of patient symptoms documented and managed, patient functional status, and patient quality of life, A recently published sequential cohort trial with adult outpatients at a Mayo Clinic Health System community cancer center reported that use of the TRSC produced a 7.2% higher covariate adjusted patient quality of life, 116% more symptoms documented and managed, and higher functional status. These results were statistically and substantively significant.

This special session will illustrate transitions among design, measurement, application, and informatics required for good patient care. Audience participation will be solicited, and establishment of relationships among the audience for work along the lines presented and discussed in this special session will be encouraged

According to the "structure-function-paradigm" a stable, folded structure is a pre-requisite of protein function. However, since the turn of the century it became evident via an increasing number of known examples that many proteins are able to serve crucial functions in vivo without adopting a well-defined 3D structure. In accordance with the typical functions these Intrinsically Unstructured/Disordered Proteins (IUPs/IDPs) serve in signalling, regulation and transcription, they were found to be at the heart of many diseases such as cancer, neurodegenerative diseases and diabetes, among others. This sparked interest in the focused research of IUPs not only at a basic science level but also in biomedicine/pharmacology.

However, due to biological and technical reasons, experimental study of these proteins remains difficult and expensive and therefore the exact biological function, mode of action and biophysical/thermodynamical description of the majority of IUPs remain elusive. Because of these difficulties, bioinformatics tools that target protein disorder play an important role in the identification and characterization of IDPs. This is exemplified by the fact that the majority of our systems-, network- and evolutionary level knowledge of IUPs are based in various bioinformatics prediction methods and analyses.

In this session we present the concept of IUPs and focus on the current, state-of-the art bioinformatics approaches, tools and results of analyses concerning protein disorder.

• Computational flow for the functional annotation of uncharacterized proteins using motif discovery, sequence and structural similarity, structural homology, hypothome (interactome of hypothetical proteins) or combination of data from highly similar non-interacting proteins (Similactors).
• Predictive approaches based on Gene Ontology (GO).
• Feature selection and machine learning methods.
• Diseasome approaches.

Recently there has been a significant interest in the stochastic approach for modelling biological systems, mainly because experimental data are providing evidences that random events play significant roles in determining the complex behaviours observed in living organisms. The growing amount of evidences arising from experimental observations at the single cell level are showing that fundamental biological processes such as, e.g., fate decision making, gene expression regulation, phenotypic variability are deeply conditioned by random fluctuations at the molecular level.

Approaches grounding on computational stochastic modelling and simulations are proving to be useful tools for gaining insights about the role played by random events in determining the global dynamics of biological networks. In these cases deterministic descriptions fail both in predicting the observed random fluctuations and in capturing stochastic-driven phenomena such as stochastic focusing, stochastic switching and multiplicative noise effects. The most popular strategy for building stochastic models of biological phenomena consist in describing the temporal evolution of the considered system as a discrete-state, continuous time Markov process. Recently, an increasing number of works proposing non-Markovian methods is emerging, with the aim of providing more accurate representations of the random events observed experimentally. The need for non-Markovian modeling is also related to the finding of long-range memory and non-ergodicity.

In this special session we aim at collecting original research works reporting on topics related to the stochastic modelling of biological systems at different levels, ranging from the inter-molecular scale to the inter-cellular scale, including neural networks. Particular attention will be devoted to those proposals highlighting how the proposed modeling approach allows to gain insights on the investigated biological phenomenon and on the emerging properties of biological networks.

• Identification of genes, transcription factors, biomarkers, SNPs, SSRs, etc.
• Bioinformatics tools in genome and transcriptome data-mining.
• RNA-seq data analysis for the gene and protein discovery.
• Modeling of cellular processes in livings.