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8th International Conference on Proteomics and Bioinformatics, will be organized around the theme “Proteomics & Bioinformatics: Solving Problems in Health and Life Sciences”

Proteomics 2017 is comprised of 22 tracks and 149 sessions designed to offer comprehensive sessions that address current issues in Proteomics 2017.

Submit your abstract to any of the mentioned tracks. All related abstracts are accepted.

Register now for the conference by choosing an appropriate package suitable to you.

Proteins are polymers of amino acids. Twenty different types of amino acids occur naturally in proteins. Proteins differ from each other in their size, molecular structure and physiochemical properties. These differences allow for protein analysis and characterization by separation and identification. Protein profiling is an emerging independent subspecialty of proteomics that is rapidly expanding and providing unprecedented insight into biological events. A combination of high-resolution two-dimensional (2-D) polyacrylamide gel electrophoresis, highly sensitive biological mass spectrometry, and the rapidly growing protein and DNA databases has paved the way for high-throughput proteomics.  

 

  • Track 1-1Protein expression
  • Track 1-2Protein analysis
  • Track 1-3Protein characterization
  • Track 1-4Protein profiling
  • Track 1-5Protein identification
  • Track 1-6Protein interaction
  • Track 1-7Protein Biochemistry
  • Track 1-8Functional proteomics
  • Track 1-9Gel-free & based proteomics techniques

Plasma Proteome Database (PPD) was developed as a part of Human Proteome Organization's (HUPO) to characterize human plasma proteome. Plasma Proteome Database hosts qualitative and quantitative information on proteins (including those from MRM-based assays) reported in plasma and serum and hence serves as reference platform for biomarker discovery. The Human Protein Reference Database (HPRD) is a protein database accessible through the Internet. 

  • Track 2-1Plasma protein database
  • Track 2-2Human protein database
  • Track 2-3Yeast protein database
  • Track 2-4Plant protein database

Electrospray ionization (ESI) is a technique used in mass spectrometry to produce ions using an electrospray in which a high voltage is applied to a liquid to create an aerosol. Liquid chromatography is an analytical chemistry technique that combines the physical separation capabilities of liquid chromatography (or HPLC) with the mass analysis capabilities of mass spectrometry (MS). Multidimensional Protein Identification Technology (MudPIT) is used to analyze the proteomes of organisms. Protein purification is a series of processes intended to isolate one or a few proteins from a complex mixture, usually cells, tissues or whole organisms. Imaging mass spectrometry (IMS) using matrix-assisted laser desorption ionization (MALDI) is a new and effective tool for molecular studies of complex biological samples such as tissue sections.

 

  • Track 3-1Mass spectrometry based proteomics
  • Track 3-2Over expression and purification of the proteins
  • Track 3-3Protein identification and validation
  • Track 3-4Multidimensional protein identification technology – MudPIT
  • Track 3-5Liquid chromatography mass spectrometry (LC-MS)
  • Track 3-6Electrospray ionization mass spectrometry (ESI-MS)
  • Track 3-7Matrix-assisted laser desorption (MALDI-TOF-MS)
  • Track 3-8Computational methods of mass spectrometry in proteomics
  • Track 3-9Analysis of protein and proteome by mass spectrometry
  • Track 3-10Mass spectrometry based quantitative proteomics
  • Track 3-11Mass spectrometry data analysis in proteomics
  • Track 3-12Molecular imaging by mass spectrometry

The Database for Annotation, Visualization and Integrated Discovery (DAVID) it provides a comprehensive set of functional annotation tools for investigators to understand biological meaning behind large list of genes. Biomarkers (short for biological markers) are biological measures of a biological state. Biomarker discovery is a medical term describing the process by which biomarkers are discovered. Many commonly used blood tests in medicine are biomarkers. A fundamental goal of cell biology is to define the functions of proteins in the context of compartments that organize them in the cellular environment. Actin-binding proteins (also known as ABP) are proteins that bind to actin.

  • Track 4-1Annotation, visualization, integrated discovery
  • Track 4-2Functional organization in yeast proteome
  • Track 4-3Proteomics approaches to identify biomarkers
  • Track 4-4Microarrays approaches in proteomics
  • Track 4-5Molecular and cellular proteomics
  • Track 4-6Biomarkers of bio fluids
  • Track 4-7Global analysis of protein localization
  • Track 4-8Structure and function of actin binding proteins

Genomics refers to the study of the genome in contrast genetics which refers to the study of genes and their roles in inheritance. Genomics can be considered a discipline in genetics. It applies recombinant DNA, DNA sequencing methods, and bioinformatics to sequence, assemble, and analyse the function and structure of genomes (the complete set of DNA within a single cell of an organism). Advances in genomics have triggered a revolution in discovery-based research to understand even the most complex biological systems such as the brain. The field includes efforts to determine the entire of DNA sequence organisms and fine-scale genetic mapping.

  • Track 5-1Protein sequence analysis
  • Track 5-2Analysis of DNA polymorphism data
  • Track 5-3Advanced computational genomic analysis
  • Track 5-4RNA data processing by transcriptomics
  • Track 5-5Structural biology and biophysics
  • Track 5-6Array-based analytical proteomics
  • Track 5-7Glycoproteomics & phosphoproteomics

Proteins are a primary constituent of living things and one of the chief classes of molecules studied in biochemistry. Proteins provide most of the molecular machinery of cells. Many are enzymes or subunits of enzymes. Other proteins play structural or mechanical roles, such as those that form the struts and joints of the cytoskeleton. Each protein is linear polymers built of amino acids. 

 

 

  • Track 6-1Molecular biochemistry
  • Track 6-2Analytical biochemistry
  • Track 6-3Clinical biochemistry
  • Track 6-4Medical biochemistry
  • Track 6-5Structural biochemistry
  • Track 6-6Nano biochemistry
  • Track 6-7Nutritional biochemistry
  • Track 6-8Agricultural biochemistry
  • Track 6-9Lipids biochemistry

Personalized medicine is a medical procedure that separates into different groups with medical decisions, practices, interventions and/or products being tailored to the individuals based on their predicted response or risk of disease. While the tailoring of treatment to patients dates back at least to the time of Hippocrates, the term has risen in usage in recent years given the growth of new diagnostic and informatics approaches that provide understanding of the molecular basis of disease, particularly genomics. This provides a clear evidence base on which to stratify (group) related patients.

 

 

  • Track 7-1Pharmacoproteomics and precision medicine
  • Track 7-2Clinical applications of precision medicine
  • Track 7-3Precision medicine for mental disorders
  • Track 7-4Advanced biomarkers for precision medicine
  • Track 7-5Biomarker analytics for translational research
  • Track 7-6Molecular biological profiling

Cardiovascular disease (CVD) is a class of diseases that involve the heart or blood vessels. Cardiovascular disease includes coronary artery diseases (CAD) such as angina and myocardial infarction (commonly known as a heart attack). Other CVDs are stroke, heart failure, hypertensive heart disease, rheumatic heart disease, cardiomyopathy, heart arrhythmia, congenital heart disease, valvular heart disease, carditis, aortic aneurysms, peripheral artery disease, and venous thrombosis.

 

  • Track 8-1Cardiovascular proteome biology
  • Track 8-2Cardiovascular prevention
  • Track 8-3Cardiovascular diseases
  • Track 8-4Cardiac gene expression
  • Track 8-5Haematological proteomics
  • Track 8-6Applications of cardiovascular proteomics
  • Track 8-7Cardiovascular medicine

Systems biology is the computational and mathematical modeling of complex biological systems. An emerging engineering approach applied to biological scientific research, systems biology is a biology-based inter-disciplinary field of study that focuses on complex interactions within biological systems, using a holistic approach (holism instead of the more traditional reductionism) to biological research. Particularly from year 2000 onwards, the concept has been used widely in the biosciences in a variety of contexts. For example, the Human Genome Project is an example of applied systems thinking in biology which has led to new, collaborative ways of working on problems in the biological field of genetics.

  • Track 9-1Plant systems biology
  • Track 9-2Functional genomics
  • Track 9-3Data integration pathway analysis
  • Track 9-4Data mining and data analysis
  • Track 9-5Medical systems biology
  • Track 9-6Signalling and systems biology
  • Track 9-7Network biology- methods and applications

Bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biological data. As an interdisciplinary field of science, bioinformatics combines computer science, statistics, mathematics, and engineering to analyze and interpret biological data. 

  • Track 10-1Evolutionary bioinformatics
  • Track 10-2Structural bioinformatics
  • Track 10-3Next generation sequencing
  • Track 10-4Web services in bioinformatics
  • Track 10-5Programming languages in bioinformatics
  • Track 10-6High performance computing in bioinformatics
  • Track 10-7Pattern recognition, clustering & classification
  • Track 10-8Bayesian inference for gene expression & proteomics

The proteome of each living cell is dynamic, altering in response to the individual cell's metabolic state and reception of intracellular and extracellular signal molecules, and many of the proteins which are expressed will be post-translationally altered. Thus if the purpose of the proteome analysis is to aid the understanding of protein function and interaction, then it is identification of the proteins in their final state which is required: for this mass spectrometric identification of individual proteins, indicating site and nature of modifications, is essential.

 

  • Track 11-1Gel-free & based proteomics techniques
  • Track 11-2Plant genomics & proteomics
  • Track 11-3Plant growth & development
  • Track 11-4Environmental proteomics
  • Track 11-5Food & plant proteomics
  • Track 11-6Animal proteomics

Nutrigenomics is a branch of nutritional genomics and is the study of the effects of foods and food constituents on gene expression. This means that nutrigenomics is research focusing on identifying and understanding molecular-level interaction between nutrients and other dietary bioactives with the genome. Nutrigenomics has also been described by the influence of genetic variation on nutrition, by correlating gene expression or SNPs with a nutrient's absorption, metabolism, elimination or biological effects. By doing so, nutrigenomics aims to develop rational means to optimise nutrition with respect to the subject's genotype.

  • Track 12-1Proteomics of genetically modified crops
  • Track 12-2Proteomics in nutrition research
  • Track 12-3Dietary metabolites and cellular metabolism
  • Track 12-4Nutrigenomics and plant functional genomics
  • Track 12-5Food safety and contamination assessment using proteomics
  • Track 12-6Applications of proteomics to food processing

Clinical Proteomic Tumor Analysis Consortium (CPTAC), is to link cancer genome to proteome by systematically analyzing the protein content of tumors of which there is comprehensive genomic characterization from initiatives such as The Cancer Genome Atlas. This integrative approach will produce a deeper understanding of cancer biology, with high-quality datasets, reagents, and analytically validated quantitative assays to be made publicly available.

 

  • Track 13-1Cancer biomarker development
  • Track 13-2Clinical applications of proteomics
  • Track 13-3Proteomics in cell biology and disease mechanisms
  • Track 13-4Protein biomarker discovery and delivery
  • Track 13-5Renal and urinary proteomics
  • Track 13-6Proteomics of microbial pathogens
  • Track 13-7Proteomics for mechanistic insight into cancer
  • Track 13-8Proteomics in development of anti-cancer therapeutics

Immunoproteomics is the study of large sets of proteins (proteomics) involved in the immune response like the isolation and mass spectrometric identification of MHC (major histocompatibility complex) binding peptides, purification and identification of protein antigens binding specific antibodies (or other affinity reagents), comparative immunoproteomics to identify proteins and pathways modulated by a specific infectious organism, disease or toxin. The identification of proteins in immunoproteomics is carried out by techniques including gel based, microarray based, and DNA based techniques, with mass spectroscopy typically being the ultimate identification method

  • Track 14-1Computational immunology
  • Track 14-2Systems immunology
  • Track 14-3Epigenetics of trained innate immunity
  • Track 14-4Applied immunology and immunotherapy
  • Track 14-5Application of proteomics in autoimmune diseases

Protein engineering is the process of developing useful or valuable proteins. It is a young discipline, with much research taking place into the understanding of protein folding and recognition for protein design principles. It is also a product and services market, with an estimated value of $168 billion by 2017. There are two general strategies for protein engineering, 'rational' protein design and directed evolution. These techniques are not mutually exclusive; researchers will often apply both. In the future, more detailed knowledge of protein structure and function, as well as advancements in high-throughput technology, may greatly expand the capabilities of protein engineering. Eventually, even unnatural amino acids may be incorporated, thanks to a new method that allows the inclusion of novel amino acids in the genetic code.

  • Track 15-1Genetic, enzyme & antibody engineering
  • Track 15-2Chemistry on drug discovery
  • Track 15-3Molecular docking studies
  • Track 15-4Molecular dynamics & mechanics
  • Track 15-5Genetic function approximation to QSAR
  • Track 15-6Protein phosphatases & folding in the cell
  • Track 15-7Protein-protein interactions
  • Track 15-8Protein arrays, biochips and proteomics
  • Track 15-9Therapeutic protein analysis
  • Track 15-10Targeted drug delivery and gene therapy

Neuroproteomics is a complex field that has a long way to go in terms of profiling the entire neuronal proteome. It is a relatively recent field that has many applications in therapy and science. So far, only small subsets of the neuronal proteome have been mapped, and then only when applied to the proteins involved in the synapse. Neuroproteomics has the difficult task of defining on a molecular level the pathways of consciousness, senses, and self. Neurological disorders are unique in that they do not always exhibit outward symptoms.

 

  • Track 16-1Proteomics in nephrology
  • Track 16-2Neurological disorders
  • Track 16-3Translational neuroimmunology
  • Track 16-4Proteomics in clinical neurosciences
  • Track 16-5Proteomics studies in nerological diseases
  • Track 16-6Proteomic analysis of neural epigenetic mechanisms

Current transcriptomic profiling techniques include DNA microarray, cDNA amplified fragment length polymorphism (cDNA-AFLP), expressed sequence tag (EST) sequencing, serial analysis of gene expression (SAGE), massive parallel signature sequencing (MPSS), RNA-seq etc. The most recent technology for transcriptomic profiling is RNA-Seq which is considered as a revolutionary tool for this purpose. Eukaryotic transcriptomic profiles are primarily analyzed with this technique and it has been already applied for transcriptomic analysis of several organisms including Saccharomyces cerevisiae, Schizosaccharomyces pombe, Arabidopsis thaliana, mouse and human cell.

  • Track 17-1Transcriptomics & proteomics in microorganisms
  • Track 17-2Epigenetics
  • Track 17-3Single cell transcriptomics
  • Track 17-4Transcriptome analysis & gene expression
  • Track 17-5Transcriptomics & proteomics in microorganisms

Proteomics technologies offer considerable opportunities for improved biological understanding and biomarker discovery. The central platform for proteomics is tandem mass spectrometry (MS) but a number of other technologies, resources, and expertise are absolutely required to perform meaningful experiments. These include protein separation science (and protein biochemistry in general), genomics, and bioinformatics. Proteomics produces considerable datasets and resources to facilitate the necessary extended analysis of this data are improving all the time.   

 

  • Track 18-1Biophysics in proteomics research
  • Track 18-2Post translational modifications
  • Track 18-3Protein identification & profiling
  • Track 18-4Targeted proteomics
  • Track 18-5Chemical biology & proteomics
  • Track 18-6Top-down proteomics
  • Track 18-7Tissue proteomics
  • Track 18-8Proteomics for stem cell medicine

Biomedical research is in general simply known as medical research. It is the basic research, applied research, or translational research conducted to aid and supports the development of knowledge in the field of medicine. An important kind of medical research is clinical research, which is distinguished by the involvement of patients. Other kinds of medical research include pre-clinical research, for example on animals, and basic medical research, for example in genetics.

  • Track 19-1Systems biomedicine
  • Track 19-2Bioimaging & bioengineering
  • Track 19-3Molecular and cellular principles
  • Track 19-4Biochemical reactions and enzyme kinetics
  • Track 19-5Functional disulfide bonds in health and disease

Diabetes, often referred to by doctors as diabetes mellitus, describes a group of metabolic diseases in which the person has high blood glucose (blood sugar), either because insulin production is inadequate, or because the body's cells do not respond properly to insulin, or both. Activity-based proteomics, or activity-based protein profiling (ABPP) is a functional proteomic technology that uses chemical probes that react with mechanistically related classes of enzymes. Autoimmune diseases arise from an abnormal immune response of the body against substances and tissues normally present in the body (autoimmunity.) 

  • Track 20-1Diabetes case reports
  • Track 20-2Enzyme based proteomics
  • Track 20-3Auto-immune disorder case report
  • Track 20-4Neurophysiological proteomics case report
  • Track 20-5Cancer case study

Microfluidics-based biochips are soon expected to revolutionize clinical diagnosis, deoxyribonucleic acid (DNA) sequencing, and other laboratory procedures involving molecular biology. Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, and information privacy. Expression proteomics includes the study of the dysregulated proteins as a function of stimulation or patient condition. The patient condition depends on the disease or specific drugs related to any disease.

 

  • Track 21-1Markets trends in biochips and microfluidic chips
  • Track 21-2Big data and corporate evolution
  • Track 21-3Expression proteomics and bioinformatics market

Machine learning is closely related to computational statistics, which also focuses in prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning.

 

  • Track 22-1Bioinformatics Tools & Software
  • Track 22-2Pattern recognition and machine learning
  • Track 22-3Practical machine learning tools and techniques
  • Track 22-4Machine learning in prediction of protein secondary structure
  • Track 22-5Support vector machines