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Meetings International PTE LTD.28 Maxwell Road, #03-05 Red Dot TrafficSingapore-069120Contact: +1-302-231-6756 | +65 3108 0483Toll Free No. 800-101-2526Email: [email protected]
October 25-26, 2017Dubai, UAE
GlobalProteomics Conference
Biochemistry & Molecular Biology JournalOctober 2017 | Volume 3 | Issue 3 | ISSN: 2471-8084
Proceedings of
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October 25-26, 2017 Dubai, UAE
Global Proteomics Conference
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Proteomics Meeting 2017
Day 1Keynote Forum
Volume 3, Issue 3 (Suppl)Biochem Mol biol J
ISSN: 2471-8084
October 25-26, 2017 Dubai, UAEGlobal Proteomics Conference
Proteomics Meeting 2017October 25-26, 2017
Page 18
Precision Metabolomics™ for precision medicine: From population health to scientific wellness
We have heard much about genomic medicine but
genotype does not equate to phenotype. A potential future of effective precision medicine requires not only estimates of lifetime risk of disease but also accurate measurement of phenotype: molecular phenotype. This will enable assessment of disease risk in the near term, disease progress, effect of intervention on disease progression and so on. Precision Metabolomics™ enables measurement of molecular phenotype in the form of the assessment of an individual’s overall metabolism and changes to metabolism, not just specific, discrete metabolite measurements, in samples such as plasma, urine and tissue. When utilized in a ‘systems medicine’ approach metabolomics becomes a powerful indicator of health and disease. A pragmatic systems medicine approach would be to merge genomics data with large scale phenotypic measurements such as metabolomics, proteomics and imaging. The global metabolomics platform developed over the last15 years by metabolon and its use in recent years in population health studies and applying knowledge of metabolism to precision medicine research will be discussed. These areas include diagnosis of inherited metabolic disease, undiagnosed illness, assessment of penetrance, drug toxicity and ‘scientific wellness’.
References1. Long T, et al. (2017) Whole genome sequencing identifies common to rare variants
associated with human blood metabolites. Nat Genet.; 49: 568-578.2. Guo L, et al. (2015) Plasma metabolomics profiles enhance precision medicine for
volunteers of normal health Proc. Natl. Acad. Sci. USA; 112: E4901-E4910.
BiographyMartin P Hornshaw is the Director of Scientific Marketing at Metabolon Inc., USA. His goal is to educate the public in general as well as scientists specifically as to the power of mass spectrometry based metabolomics. He has many years of experience as a Scientist and of managing scientists working with the ‘mass spec OMICS’ of metabolomics, lipidomics and proteomics as well as clinical and diagnostic applications of mass spectrometry.
Martin P HornshawMetabolon Inc., USA
Martin P Hornshaw, Biochem Mol biol J 2017, 3:3DOI: 10.21767/2471-8084-C1-004
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Volume 3, Issue 3 (Suppl)Biochem Mol biol J
ISSN: 2471-8084
October 25-26, 2017 Dubai, UAEGlobal Proteomics Conference
Proteomics Meeting 2017October 25-26, 2017
Page 19
Protein sequence detection: Recent developments and a new algorithm
There is a flurry of algorithms for sequence detection and
analysis aimed particularly at proteomics. These algorithms almost invariably try to measure the similarity between sequences of symbols drawn from finite sets of such symbols but with different cardinalities. They are in two broad classes: deterministic and stochastic. Deterministic algorithms are often hard to implement and are slow in practice. Stochastic algorithms, on the other hand, are relatively easier to implement and are efficient. However, they do not guarantee to find the correct solution even when it exists. This greatly reduces their applicability particularly where it is essential to know the exact solution as in medical situations. The inherent uncertainty in the outcomes of non-deterministic approaches is exacerbated by the need to arbitrarily set a number of parameters on which they depend. Default values of these parameters are often inappropriate outside the context in which they were estimated. There is, therefore, room for deterministic algorithms particularly when the time constraint is soft. In my talk, I will highlight the limitations of stochastics algorithms and illustrate them on probably the most prominent genomics search tool, namely the Basic Local Search Tool or BLAST. We then present our algorithm which is deterministic and has a strong mathematical basis. Furthermore, we will show that it is easy to understand and implement. We illustrate it on shotgun proteomics data and compare it with a number of other well-known sequence comparison algorithms such as the Needleman-Wunch and Smith-Waterman algorithms.
Recent Publications1. Kheniche A, Brahimi N and Salhi A (2015) A Deterministic Algorithm for Alpha-
Numeric Sequence Comparison with Application to Protein Sequence Detection. Journal of Algorithms and Computational Technology; 9(3): 323-338.
2. Mashwani W K and Salhi A (2014) Multi-Objective Memetic Algorithm Based on Decomposition. Applied Soft Computing; 21: 221-243.
BiographyAbdellah Salhi is a Professor of Operational Research in the Department of Mathematical Science of Essex University, UK. He has obtained his PhD on Interior-Point Methods from the University of Aston in Birmingham, UK. His research interests are in the design, analysis, implementation and application of OR algorithms. He has led a number of research projects and contributes to the ESRC funded Business and Local Government Data Research Centre. He has published over 80 peer-reviewed papers. He has worked as the Head of the Department of Mathematical Sciences from 2010 t0 2016.
Abdellah SalhiUniversity of Essex, UK
Abdellah Salhi, Biochem Mol biol J 2017, 3:3DOI: 10.21767/2471-8084-C1-004
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Figure-1: Shotgun proteomics data generation
Volume 3, Issue 3 (Suppl)Biochem Mol biol J
ISSN: 2471-8084
October 25-26, 2017 Dubai, UAEGlobal Proteomics Conference
Proteomics Meeting 2017October 25-26, 2017
Page 20
Development and validation of targeted metabolomics for clinical applications
Metabolome, representing a vast array of analytes, is the
ultimate functional equivalent of the genome and can be studied through small molecules (<1500 Da) identification and quantification. The global metabolome influences the individual phenotype through environment and clinical interventions. Metabolomics has been shown to identify relevant biomarkers responsible for/or associated with complex phenotypes in diverse biological systems. Combining metabolomics with genomics, transcriptomics and proteomics studies provides a higher level of understanding of the mechanism and the pathophysiology of many diseases and related clinical interventions. Metabolomics plays a major role in clinical practice as it represents >95% of the clinical laboratories routine work load. However, many of these metabolites require different analytical platforms and many clinically relevant metabolites are still not amenable to detection using routinely available assays. Mass spectrometry (MS) coupled with liquid chromatography (LC) is a robust and important analytical tool, where two almost universal techniques merge to accommodate the chemical diversity of the metabolome. Herein, we introduce the establishment of a comprehensive targeted metabolomics method for a panel of 225 metabolites using liquid chromatography tandem mass spectrometry. The sensitivity, reproducibility and molecular stability of each targeted metabolite were assessed under experimental conditions. Quantification of metabolites by peak area was linear, with minimal deviation (R2=0.98). Inter and intraday precision had an average coefficient of variation <20%. The method reported here is robust for the extraction of the maximum number of metabolites from different types of tissues and bio-fluids.
References1. Nawal Alshehri, Shimaa Eissa, Laila Balobaid, Majed Dasouki, Anas Abdel Rahman,
Mohammed Zourob (2017) Electrochemical Immunosensors for the Rapid Screening of Cystic Fibrosis and Duchenne Muscular Dystrophy. Electroanalysis; 29: 1-8.
2. Hani Alhadrami, Raja Chinnappan, Shimaa Eissa, Anas Abdel Rahman, Mohammed Zourob (2017) High affinity truncated DNA aptamers for the development of fluorescence-based progesterone biosensors. Anal Biochem; 525: 78-84.
BiographyAnas M Abdel Rahman has completed his PhD in Bioanalytical Chemistry in Proteomics at Memorial University of Newfoundland, Canada. In 2014, he was appointed as an Associate Scientist at the Research Center of King Faisal Specialist Hospital and as an Assistant Professor at School of Medicine at Al Faisal University. His current scientific interests are related to signaling pathways controlling the metabolic reprogramming in cancer. He has several publications in the field of proteomics and metabolomics.
Anas M Abdel RahmanKing Faisal Specialist Hospital and Research Center (KFSHRC), KSA
Anas M Abdel Rahman, Biochem Mol biol J 2017, 3:3DOI: 10.21767/2471-8084-C1-004
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Figure-1: Workflow for targeted LC-MS/MS analysis of metabolites extracted from Dried Blood Spot (DBS), plasma, whole blood and tissues. Tissue samples were frozen on dry ice, stored at -80 oC and crushed in a crucible on liquid nitrogen just prior to extraction.
