Chaperone therapy is a new concept of molecular therapeutic approach, first developed for lysosomal diseases, utilizing small molecular competitive inhibitors of lysosomal enzymes. This concept has been gradually targeted to many diseases of other categories, utilizing various compounds not necessarily competitive inhibitors but also non-competitive inhibitors or endogenous protein chaperones (heat-shock proteins).

In this session we will discuss current trends of chaperone therapy targeting various types of neurological and non-neurological diseases caused by misfolded mutant proteins. This molecular approach will open a new therapeutic view for wide variety of diseases, genetic and non-genetic, and neurological and non-neurological, in the near future.

"Quantitative and Systems Pharmacology (QSP) is an emerging discipline focused on identifying and validating drug targets, understanding existing therapeutics and discovering new ones. The goal of QSP is to understand, in a precise, predictive manner, how drugs modulate cellular networks in space and time and how they impact human pathophysiology." (QSP White Paper - October, 2011)

Over the past three decades, the predominant paradigm in drug discovery was designing selective ligands for a specific target to avoid unwanted side effects. However, in the current post-genomic era, the aim is to design drugs that perturb biological networks rather than individual targets. The challenge is to be able to consider the complexity of physiological responses to treatments at very early stages of the drug development. In this way, current effort has been put into combining 0 chemogenomics with network biology to implement new network-pharmacology approaches to drug discovery; i.e. polypharmacology approaches combined with systems biology information, which advance further in both improving efficacy and predicting unwanted off-target effects. Furthermore, the use of network biology to understand drug action outputs treasured information, i.e for pharmaceutical companies, such as alternative therapeutic indications for approved drugs, associations between proteins and drug side effects, drug-drug interactions, or pathways and gene associations which provide leads for new drug targets that may drive drug development.

Following the line of QSP Workshops I and II (2008, 2010), the QSP White Paper (2011), or QSP Pittsburgh Workshop (2013), the goal of this symposium is to bring together interdisciplinary experts to help advance the understanding of how drugs act, with regard to their beneficial and toxic effects, by sharing new integrative, systems-based computational or experimental approaches/tools/ideas which allow to increase the probability that the newly discovered drugs will prove therapeutically beneficial, together with a reduction in the risk of serious adverse events.

Sequence Modeling is one of the most important problems in bioinformatics. In the sequential data modeling, Hidden Markov Models(HMMs) have been widely used to find similarity between sequences. Some of the most important topics in this session are:

• Modeling of biological sequences in bioinformatics;
• The application of Hidden Markov Models(HMM);
• HMM in modeling of sequential data;
• The advantages of HMM in biological sequence modeling in compare of other algorithms;
• The new algorithms of training HMM;
• Gene sequence modeling with HMM;

Biomedicine and, particularly, Bioinformatics are increasingly and rapidly becoming data-based sciences, an evolution driven by technological advances in image and signal non-invasive data acquisition (exemplified by the 2014 Nobel Prize in Chemistry for the development of super-resolved fluorescence microscopy). In the Biomedical field, the large amount of data generated from a wide range of devices and patients is creating challenging scenarios for researchers, related to storing, processing and even just transferring information in its electronic form, all these compounded by privacy and anonymity legal issues. This can equally be extended to Bioinformatics, with the burgeoning of the .omics sciences.

New data requirements require new approaches to data analysis, some of the most interesting ones are currently stemming from the fields of Computational Intelligence (CI) and Machine Learning (ML). This session is particularly interested in the proposal of novel CI and ML approaches to problems in the biomedical and bioinformatics domains.

Topics that are of interest to this session include (but are not necessarily limited to):

• Novel applications of existing CI and ML methods to biomedicine and bioinformatics.
• Novel CI and ML techniques for biomedicine and bioinformatics.
• CI and ML based methods to improve model interpretability in biomedical problems, including data/model visualization techniques.
• Novel CI and ML techniques for dealing with non.structured and heterogeneous data formats.

More information at

http://www.cs.upc.edu/~avellido/research/conferences/IWBBIO15-CI-BioInfMed.html

Next Generation Sequencing (NGS) is the main term used to describe a number of different modern sequencing technologies such as Illumina, Roche 454 Sequencing, Ion torrent, SOLiD sequencing and Pacific Biosciences. These technologies allow us to sequence DNA and RNA more quickly and cheaply than Sanger sequencing and have opened new ways for the study of genomics, transcriptomics and molecular biology, among others.

The continuous improvements on those technologies (longer read length, better read quality, greater throughput, etc) and the broad application of NGS in several research fields, have produced (and still produce) a huge amount of software tools for the analysis of NGS genomic/transcriptomic data.

We invite authors to submit original research, pipelines and review articles on topics related to software tools for NGS data analysis such as (but not limited to):

• Tools for data pre-processing (quality control and filtering).
• Tools for sequence alignment.
• Tools for de novo assembly.
• Tools for the analysis of genomic data: identification and annotation of genomic variants (variant calling, variant annotation).
• Tools for functional annotation to describe domains, orthologues, genomic variants, controlled vocabulary (GO, KEGG, InterPro...).
• Tools for the analysis of transcriptomic data: RNA-Seq data (quantification, normalization, filtering, differential expression) and transcripts and isoforms finding.
• Tools for Chip-Seq data.
• Tools for "big-data" analysis of reads and assembled reads.

Over the past two decades, it is increasingly recognized that a biological function can only rarely be attributed to an individual molecule. Instead, most biological functions arise from signaling and regulatory pathways connecting many constituents, such as proteins, and small molecules, allowing them to adapt to environmental changes. "Following on from this principle, a disease phenotype is rarely a consequence of an abnormality in a single effector gene product, but reflects various processes that interact in a complex network". Offering a unifying language to describe relations within such a complex system has made network science a central component of systems biology and recently system medicine. Despite the knowledge that biological networks can change with time and environment, much of the efforts have taken a static view. Time-varying networks support richer dynamics and may better reflect underlying biological changes in abnormal state versus normal state and this provides a powerful motivation and application domain for computational modelling. We introduce this session on the Dynamics Networks in System Medicine to encourage and support the development of computational methods that elucidate the dynamic network and its application in medicine. We will discuss current trends and potential biological and clinical applications of network-based approaches to human disease. We aim to bring together experts in different fields in order to promote cross fertilization between different communities.

Natural sciences demand measurements of the subject of interest as a necessary part of the experimental process. Thus, for the proper understanding of the obtained datasets, it is the necessity to take into the question all mathematical, biological, chemical or technical conditions affecting the process of the measurement itself. While assumptions and recommendations within the field itself are usually concerned, some issues, especially discretization, quantization, experiment time, self-organization and consequent anomalous statistics might cause puzzling behavior.

In this special section we will describe particular examples across disciplines with joint systems theory based approach, including noise and baseline filtration in mass spectrometry, image processing and analysis, and distributed knowledge database. The aim of this section is to present an general overview of the systemic approach.

Post genomic era has ushered to say 'Ome' for integrated biology disciplines. The complete sequencing of the human genome has shown us a new era of Systems Biology (SB) referred to as omics'. From genomics to proteomics and furthermore, 'Omics'-es existing nowadays integrate many areas of biology. This resulted in an essential ascent from Bioinformatics to Systems Biology leaving room for identifying number of interactions in a cell. Tools have been developed to utilize evolutionary relationships towards understanding uncharacterized proteins while there is a need to generate and understand functional interaction networks. A systematic understanding of genes and proteins in a regulatory network has resulted in bringing the birth of System Biology (SB), there by raising several questions unanswered. Through this conference, we will dig some questions on why and how interactions especially protein-protein interactions (PPI) are useful while discussing methods to remove false positives by validating the data. The conference is aimed at the following two focal themes:

• Bioinformatics and systems biology for deciphering the known unknown regions.
• Systems Biology of regulatory networks and machine learning.

The concept of tissues appeared more than 200 years ago, since textures and attendant differences were described within the whole organism components. Instrumental developments in optics and biochemistry subsequently paved the way to transition from classical to molecular histology in order to decipher the molecular contexts associated with physiological or pathological development or function of a tissue. The aim of this special session is to provide an overview of the most cutting edge updates in tissues engineering technologies. This will cover the most recent developments for tissue proteomics, and the applications of the ultimate molecular histology method in pathology and pharmacology: MALDI Mass Spectrometry Imaging. This session will be of great relevance for people willing to have a relevant summary of possibilities in the field of tissue molecular engineering.

In the last 20 years many resources have been spent in experimentation and marketing of telemedicine systems, but - as pointed by several researchers - no real product has been fully realized - neither in developed or in underdeveloped countries. Many factors could be detected:

• lack of a decision support system in analyzing collected data;
• the difficulty of using the specific monitoring devices;
• the caution of patients and/or doctors towards e-health or telemedicine systems;
• the passive role imposed to the patient by the majority of experimented systems;
• the limits of profit-driven outcome measures;
• a lack of involvement of patients and their families as well as an absence of research on the consequences in the patient's life.

The constant improvement of ICT tools should be taken into account: at-home and mobile monitoring are both possible; virtual visits can be seen as a new way to perform an easier and more accepted style of patient-doctor communication (which is the basis of a new active role of patients in monitoring symptoms and evolution of the disease). The sharing of this new approach could be extended from patients to healthy people, obtaining tools for a real preventive medicine: a large amount of data could be gained, stored and analyzed outside the sanitary structures, contributing to a low-cost approach to health.

The goal of this session is to bring together interdisciplinary experts to develop (discuss about) these topics:

• decision support systems for the analysis of collected data;
• customised monitoring based on the acuteness of the disease;
• integration of collected data with e-Health systems;
• attitudes of doctors and sanitary staff;
• patient-doctor communication;
• involvement of patients and of their relatives and care-givers;
• digital divide as an obstacle / hindrance;
• alternative measurements on the effectiveness of telemedicine (quality of life of patients and care-givers, etc)
• mobile vs home monitoring (sensors, signal transmissions, etc)
• technology simplification (auto-calibrating systems, patient interface, physician interface, bio-feedback for improving learning)

The session will have also the ambition of constituting a team of interdisciplinary research, spread over various countries, as a possible basis for an effective participation to European calls.

The goal of this special session is to explore the use of emerging parallel computing architectures as well as High Performance Computing systems (Supercomputers, Clusters, Grids) for the simulation of relevant biological systems and for applications in Bioinformatics, Computational Biology and Computational Chemistry. We welcome papers, not submitted elsewhere for review, with a focus in topics of interest ranging from but not limited to:

• 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.
• Application of HPC developments in Bioinformatics, Computational Biology and Computational Chemistry.

This Workshop has a focus on interdisciplinary nature and is designed to attract the participation from several groups including Computational Scientists, Bioscientists and the fast growing group of Bioinformatics researchers. It is primarily intended for computational scientists who are interested in Biomedical Research and the impact of high performance computing in the analysis of biomedical data and in advancing Biomedical Informatics. Bio-scientists with some background in computational concepts represent another group of intended participants. The interdisciplinary group of research groups with interests in Biomedical Informatics in general and Bioinformatics in particular will likely to be the group attracted the most to the workshop.

The Workshop topics include (but are not limited to) the following:

• HPC for the Analysis of Biological Data
• Bioinformatics Tools for Health Care
• Parallel Algorithms for Bioinformatics Applications
• Ontologies in biology and medicine
• Integration and analysis of molecular and clinical data
• Parallel bioinformatics algorithms
• Algorithms and Tools for Biomedical Imaging and Medical Signal Processing
• Energy Aware Scheduling Techniques for Large Scale Biomedical Applications
• HPC for analyzing Biological Networks
• Next Generation Sequencing and Advanced Tools for DNA Assembly
• HPC for Gene, Protein/RNA Analysis and Structure Prediction
• ...

For more information, you can see the Call for Paper for this special session.

We welcome papers, not submitted elsewhere for review, with a focus in topics of interest ranging from but not limited to:

• Target identification and validation.
• Chemoinformatics & Computational Chemistry: Methodological basis and applications to drug discovery of: QSAR, Docking, CoMFA-like methods, Quantum Chemistry & Molecular Mechanics (QM/MM), High-performance Computing (HPC), Cloud Computing, Biostatistics, Artificial Intelligence (AI), Machine Learning (ML), and Bio-inspired Algorithms like Artificial Neural Networks (ANN), Genetic Algorithms, or Swarm Intelligence.
• Bioinformatics & Biosystems: Methodological basis and applications to drug design, target or biomarkers discovery of: Alignment tools, Pathway analysis, Complex Networks, Non-linear methods, Microarray analysis, Software and Web servers.
• High Throughput Screening (HTS) of drugs; Fragment Based Drug Discovery; Combinatorial chemistry and synthesis.

Accomplishment of "1000 Genomes Project" revealed immense amount of information about variation, mutation dynamics, and evolution of the human DNA sequences. These genomes have been already used in a number of bioinformatics studies, which added essential information about human populations, allele frequencies, local haplotype structures, distribution of common and rare genetic variants, and determination of human ancestry and familial relationships. Humans have modest intra-species genetic variations among mammals. Even so, the number of genetic variations between two persons from the same ethnic group is in the range of 3.4-5.2 millions. This gigantic pool of nucleotide variations is constantly updating by 40-100 novel mutations arriving in each person. Closely located mutations on the same DNA molecule are linked together forming haplotypes that are inherited as whole units and span over a considerable portion of a gene or several neighboring gene. An intense intermixture of millions of mutations occurs in every individual due to frequent meiotic recombinations during gametogenesis. Scientists and doctors are overwhelmed with this incredible amount of information revealed by new-generation sequencing techniques. Due to this complexity, we encountered significant challenges in deciphering genomic information and interpretation of genome-wide association studies.

The goal of this session is to discuss novel approaches and algorithms for processing of whole-genome SNP datasets in order to understand human health, history, and evolution.

Emotions have a strong influence in our vital signs and in our behavior. Systems that take our emotions and vital signs into account can improve our quality of life. The World Health Organization (WHO) characterizes a healthy life first of all with the prevention of diseases and secondly, in the case of the presence of disease, with the ability to adapt and self manage. Smart measurement of vital signs and of behavior can help to prevent diseases or to detect them before they become persistent. These signs are key to obtain individual data relevant to contribute to this understanding of healthy life.

The objective of this session is to present and discuss smart and unobtrusive methods to measure vital signs and capture emotions of the users and methods to process these data to improve their behavior and health.