Call for Abstract
10th International Conference on Proteomics and Bioinformatics, will be organized around the theme “Recent innovations and solutions adopted in the fields of Proteomics”
Proteomics 2018 is comprised of keynote and speakers sessions on latest cutting edge research designed to offer comprehensive global discussions that address current issues in Proteomics 2018
Submit your abstract to any of the mentioned tracks.
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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 1-1Evolutionary bioinformatics
- Track 1-2Structural bioinformatics
- Track 1-3Next generation sequencing
- Track 1-4Web services in bioinformatics
- Track 1-5Programming languages in bioinformatics
- Track 1-6High performance computing in bioinformatics
- Track 1-7Pattern recognition, clustering & classification
- Track 1-8Bayesian inference for gene expression & proteomics
Protein structure is the three-dimensional arrangement of atoms in a protein molecule. Proteins are polymers specifically polypeptides formed from sequences of amino acids, the monomers of the polymer. A single amino acid monomer may also be called a residue indicating a repeating unit of a polymer. Proteins form by amino acids undergoing condensation reactions, in which the amino acids lose one water molecule per reaction in order to attach to one another with a peptide bond. By convention, a chain under 30 amino acids is often identified as a peptide, rather than a protein. To be able to perform their biological function, proteins fold into one or more specific spatial conformations driven by a number of non-covalent interactions such as hydrogen bonding, ionic interactions, Van der Waals forces, and hydrophobic packing. To understand the functions of proteins at a molecular level, it is often necessary to determine their three-dimensional structure. This is the topic of the scientific field of structural biology, which employs techniques such as X-ray crystallography, NMR spectroscopy, and dual polarisation interferometry to determine the structure of proteins.
- Track 2-1Protein expression
- Track 2-2Protein analysis
- Track 2-3 Protein characterization
- Track 2-4 Protein profiling
- Track 2-5 Protein identification
- Track 2-6 Protein interaction
- Track 2-7 Protein biochemistry
- Track 2-8 Gel-free & based proteomics techniques
- Track 2-9 Functional proteomics
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 3-2 Over expression and purification of the proteins
- Track 3-3Protein identification and validation
- Track 3-4 Multidimensional protein identification technology – MudPIT
- Track 3-5 Liquid chromatography mass spectrometry (LC-MS)
- Track 3-6 Electrospray ionization mass spectrometry (ESI-MS)
- Track 3-7 Matrix-assisted laser desorption (MALDI-TOF-MS)
- Track 3-8 Computational methods of mass spectrometry in proteomics
- Track 3-9 Analysis of protein and proteome by mass spectrometry
- Track 3-10Mass spectrometry based quantitative proteomics
- Track 3-11Mass spectrometry data analysis in proteomics
- Track 3-12 Molecular imaging by mass spectrometry
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 4-1 Cardiovascular proteome biology
- Track 4-2 Cardiovascular prevention
- Track 4-3Cardiovascular diseases
- Track 4-4 Cardiac gene expression
- Track 4-5 Haematological proteomics
- Track 4-6 Applications of cardiovascular proteomics
- Track 4-7 Cardiovascular medicine
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 5-1Proteomics animal science
- Track 5-2 Plant genomics & proteomics
- Track 5-3 Plant growth & development
- Track 5-4plant proteomics
- Track 5-5 Animal proteomics
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 6-1 Molecular biochemistry
- Track 6-2 Analytical biochemistry
- Track 6-3 Clinical biochemistry
- Track 6-4 Medical biochemistry
- Track 6-5Structural biochemistry
- Track 6-6Nano biochemistry
- Track 6-7 Nutritional biochemistry
- Track 6-8Agricultural biochemistry
- Track 6-9 Lipids biochemistry
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 7-1Genome analysis
- Track 7-2Metagenomics
- Track 7-3Genomic medicine
- Track 7-4Human genomics
- Track 7-5Structural & Functional genomics
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 8-1Proteomics in clinical neurosciences
- Track 8-2 Proteomics studies in nerological diseases
- Track 8-3 Proteomic analysis of neural epigenetic mechanisms
- Track 8-4 Proteomics in nephrology
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 interdisciplinary 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. 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. One of the outreaching aims of systems biology is to model and discover emergent properties, properties of cells, tissues and organisms functioning as a system whose theoretical description is only possible using techniques which fall under the remit of systems biology. These typically involve metabolic networks or cell signaling networks.
Computational biology involves the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, behavioral, and social systems. The field is broadly defined and includes foundations in computer science, applied mathematics, animation, statistics, biochemistry, chemistry, biophysics, molecular biology, genetics, genomics, ecology, evolution, anatomy, neuroscience, and visualization.
- Track 9-1Cancer systems biology
- Track 9-2Systems theory for complex dynamical systems
- Track 9-3Transcriptomics
- Track 9-4Neural Networks
- Track 9-5Visual Analytics
Epigenetics are stable heritable traits (or "phenotypes") that cannot be explained by changes in DNA sequence. Epigenetics often refers to changes in a chromosome that affect gene activity and expression, but can also be used to describe any heritable phenotypic change that doesn't derive from a modification of the genome, such as prions. Such effects on cellular and physiological phenotypic traits may result from external or environmental factors, or be part of normal developmental program. The standard definition of epigenetic requires these alterations to be heritable, either in the progeny of cells or of organisms.
- Track 10-1Methylation
- Track 10-2Epigenetics in bacteria
- Track 10-3Prions
- Track 10-4Transgenerational
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 11-1Systems biomedicine
- Track 11-2Bioimaging & bioengineering
- Track 11-3 Molecular and cellular principles
- Track 11-4 Biochemical reactions and enzyme kinetics
- Track 11-5 Functional disulfide bonds in health and disease
Protein sequencing is the practical process of determining the amino acid sequence of all or part of a protein or peptide. This may serve to identify the protein or characterize its post-translational modifications. Typically, partial sequencing of a protein provides sufficient information (one or more sequence tags) to identify it with reference to databases of protein sequences derived from the conceptual translation of genes.
Molecular Interactions are attractive or repulsive forces between molecules and between non-bonded atoms. Molecular interactions are important in diverse fields of protein folding, drug design, material science, sensors, nanotechnology, separations, and origin of life. Molecular interactions are also known as noncovalent interactions or intermolecular interactions. Molecular interactions are not bonds.
- Track 12-1Amino acid sequences
- Track 12-2Protein–ligand interaction
- Track 12-3Protein–polynucleotide interaction
- Track 12-4Protein–solvent interaction
- Track 12-5Protein–protein interaction
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 13-1 Bioinformatics Tools & Software
- Track 13-2 Pattern recognition and machine learning
- Track 13-3Practical machine learning tools and techniques
- Track 13-4 Support vector machines
- Track 13-5 Machine learning in prediction of protein secondary structure
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 14-1 Transcriptomics & proteomics in microorganisms
- Track 14-2 Epigenetics
- Track 14-3 Single cell transcriptomics
- Track 14-4 Transcriptome analysis & gene expression
- Track 14-5 Transcriptomics & proteomics in microorganisms