Scientific Program

Conference Series Ltd invites all the participants across the globe to attend 3rd International Conference on Proteomics & Bioinformatics Courtyard by Marriott Philadelphia Downtown, USA.

Day 3 :

  • Track 8: Bioinformatics in Biopharmaceuticals and Therapeutics
    Track 9: Biomedical Informatics
    Track 10: Computational biology
    Track 11: Application of Bioinformatics
Location: Salon I & II (Floor I)

Session Introduction

Agnieszka A. Kaczor

University of Eastern Finland, Finland

Title: Molecular modeling of GPCR dimers

Time : 10:00 - 10:20

Speaker
Biography:

Agnieszka Kaczor obtained her MSc in chemistry and PhD in pharmacy, both in Lublin, Poland. She made her postdoctoral training in GRIB/PRBB, Barcelona, funded by Foundation for Polish Science, and at University of Regensburg, funded by DAAD. At present she is Marie Curie fellow at University of Eastern Finland. Dr. Kaczor is author of 24 scientific articles and book chapters, 2 patents and 80 conference presentations. She has obtained several research and teaching awards, including those by the Polish Minister of Health and multiple travel grants, including Young Investigator Award by IUPAC (twice) and European Science Foundation (twice).

Abstract:

Dimerization or oligomerization of GPCRs is a well-established phenomenon, which has been confirmed by many experimental and molecular modeling data. Molecular modeling approaches can be classified into sequence-based and structure-based methods. The first group is based on the GPCR sequence analysis performed in order to detect evolutionary changes of the GPCR interfaces. Structure-based approaches involve protein-protein docking and molecular dynamics techniques as well as electrostatics analysis with Adaptive Poisson-Boltzmann Solver (APBS) and Normal Mode Analysis. We first demonstrated that protein-protein docking approaches are unsuitable for the prediction of GPCR-dimer structures when applied to transmembrane proteins. Instead of that we elaborated a sophisticated protocol with subsequent docking, scoring, energy minimization and clustering procedures. The following parameters are used for consensus scoring: Rosetta score, dimer interface area, polar contribution to interface area, interface surface roughness, evolutionary conservation score, hydrogen bond interactions, shape complementarity and electrostatic complementarity. Alternatively, we also performed free energy scans on dimers with different interfaces to select the most stable interface. Furthermore, we employed APBS in order to study electrostatic interactions in GPCRs oligomerization interfaces in detail. These methods were successfully tested on dimers with available X-ray structures (opsin, kappa opioid receptor and CXCR4 dimers) and then applied to CB1R and D2R homodimers as well as to mGluR5-D2R heterodimers in different conformational states. The availability of dimer models made it possible to propose the modes of interaction of bivalent ligands with these dimers.

Kou Okuro

The University of Tokyo, Japan

Title: Molecular glues: Adhesion-mediated control of biomolecular functions

Time : 10:20 - 1:40

Speaker
Biography:

Kou Okuro is an assistant professor of Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Japan. He completed his PhD for development of “molecular glue” designed for non-covalent chemical modification of biomacromolecules under the direction of Prof. Takuzo Aida at the University of Tokyo (2011). His research interests include biomimetic chemistry and chemical biology for medicinal and clinical application.

Abstract:

Glue" is an indispensable tool to fix objects in contact. We envisioned if such glue is realized in molecular scale, and can be utilized for biomacromolecules such as proteins, nucleic acids, and so forth. Toward this goal, we designed dendritic macromolecules having multiple guanidinium ion (Gu+) pendants at their periphery. Thanks to multivalent salt-bridge formation with oxyanions such as carboxylate and phosphate, which exist ubiquitously in biomacromolecules, the dendritic molecules can tightly adhere to the surface of biomacromolecules, where the association constant reaches at nearly 108 M–1 even under physiological conditions. We also found that the dendritic "molecular glues" can freeze dynamic nature of protein assemblies by gluing their components together, and control their functions. For example, microtubule, which is a homotropic assembly of tubulin proteins, is stabilized against depolymerisation when treated with molecular glues. Besides, molecular glues can also stabilize a heterotropic protein conjugate of actin and myosin (actomyosin). In the presence of ATP, actomyosin shows sliding motion, which gives rise to muscle contraction. However, once molecular glues attach to the assembly and stabilize it, the ATP-driven sliding motion is completely arrested. In addition to the stabilization capability for protein assemblies, one of the molecular glues shows a high performance in migrating into living cells. Taking this property into account, we successfully delivered proteins or nucleic acids inside the cells by utilizing molecular glue as a carrier. This delivery system can be applied to serum existing conditions, suggesting its potential for in vivo practical use.

Break: Coffee Break: 10:40 - 10:55 @ Salon Ballroom Foyer
Speaker
Biography:

Sinem Nalbantoglu has completed her Ph.D at the age of 27 years from Ege University and postdoctoral studies from Ege University School of Medicine. She is now working as assistant professor in Nisantasi University. She has been publishing papers in reputed medical journals and serving as referee in many peer-reviewed journals.

Abstract:

Hereditary autoinflammatory diseases are heterogenous group of disorders characterized by seemingly unprovoked fever and localized recurrent episodes of excess systemic inflammation without any pathogens stimuli. Unlike autoimmune disorders, autoinflammatory disorders lack the production of high-titer autoantibodies or antigen-specific T cells. The most important determinants of autoinflammatory diseases which are named also as ‘hereditary periodic fevers’ are abdominal pain and fever characterized with acute and usually short attacks of serosal, synovial and cutaneous inflammation. These disorders are caused by primary dysfunction of the innate immune system, without evidence of adaptive immune dysregulation. The protein products of the genes which are associated with these disorders are known to control innate immunity and apoptosis. Role of pyrin (PyD) and/or caspase recruitment domain (CARD) containing regulator and adaptor proteins in inflammation, apoptosis, and innate immunity has been previously outlined. The inflammasome is composed of intracellular receptor NALP and ASC adapter which stimulates caspase-1 activation. Mutations in the NALP3 inflammasome and NLR cause autoinflammatory syndromes by increased inflammasome activity responsible for uncontrolled IL-1β production. In this communication, we have documented molecular analysis data of the studied patient populations with autoinflammatory disorders, and our research activities conducted on autoinflammatory disease genes. Moreover, current work also underlined the critical significance of molecular diagnosis which refers to detailed mutation screening of autoinflammatory disease genes in particular for mutation negative or asymptomatic individuals among at-risk populations. Since there is still way in understanding and exploring the undefined periodic syndromes, further studies are required to discover novel inflammatory pathways.

Cathy H. Wu

University of Delaware, USA

Title: Integrative bioinformatics for knowledge discovery of PTM networks

Time : 11:15 - 11:35

Speaker
Biography:

Cathy H. Wu is the Edward G. Jefferson Chair and Director of the Center for Bioinformatics & Computational Biology, Professor of Departments of Computer & Information Sciences and of Biological Sciences, and Director of Bioinformatics Graduate Programs at the University of Delaware. She has conducted bioinformatics research for over 20 years and led the Protein Information Resource (PIR) since 1999. She is the PI/Co-PI on several large consortium projects, has served on many scientific advisory boards, including the HUPO council, published about 180 peer-reviewed papers and eight books and conference proceedings, and given about 140 invited talks.

Abstract:

Facilitated by proteomic and other high-throughput studies, the number of protein phosphorylation related resources has been growing along with pertinent literature. However, our understanding of phosphorylation events in signaling networks is still fragmented. The iPTMnet is a new bioinformatics resource being developed for integrative understanding of protein post-translational modifications (PTMs) in systems biology context, with the initial focus on phosphorylation. The iPTMnet bioinformatics framework consists of: (i) the PIR iProClass database for molecular and omics data integration, including many phosphorylation, pathway, and interaction databases, (ii) the RLIMS-P/eFIP text mining system for knowledge extraction from scientific literature, (iii) the Protein Ontology (PRO) for knowledge representation of specific protein PTM forms, and (iv) a web portal linking data and analysis tools with Cytoscape network visualization for scientific queries and exploration. The text mining system allows researchers to provide a list of PubMed IDs or proteins of interest as input, and returns a ranked list of abstracts with evidence tagging for phosphsorylation information (kinase, substrate, phosphorylation site) and its functional impact, particularly interaction partners of phosphorylated proteins. The user interface further supports community annotation to validate text mining results and capture knowledge about PTM forms in PRO. A PTM database is under development to combine text mining results and data extracted from related databases to capture relevant kinase-substrate information and their functional impact and biological context. Scientific use cases have been developed to demonstrate the integrative bioinformatics approach for exploring and discovering PTM networks.

Speaker
Biography:

Richard J. Edwards received his PhD in genetics from the University of Nottingham before working as a postdoc in The Royal College of Surgeons in Ireland and University College Dublin on protein bioinformatics. He has been leading his own group at the University of Southampton for over five years, where he is now a Lecturer in molecular evolution and bioinformatics. In addition to more than 25 papers, Rich Edwards has written a number of open source bioinformatics tools available as part of the SLiMSuite and SeqSuite packages, including several webservers available at bioware.ucd.ie.

Abstract:

Short Linear Motifs (SLiMs) are short functional protein sequences that act as ligands to mediate transient protein-protein interactions (PPI) in critical biological pathways and signaling networks. SLiMs are short (3-15aa), generally tolerate considerable sequence variation and typically have fewer than five residues critical for function. These features result in a degree of evolutionary plasticity not seen in domains and SLiMs often add new functions to proteins by convergent evolution. They also present a challenge for computational identification, making it difficult to differentiate biological signal from stochastic patterns. Despite this, discovering new SLiMs is of great interest due to their potential as therapeutic targets. In recent years, we have made great progress in SLiM discovery, particularly through development of the SLiMSuite package of bioinformatics tools. SLiMs generally occur in structurally disordered regions of proteins and exhibit evolutionary conservation relative to other disordered residues. SLiMFinder uses this knowledge and exploits patterns of convergent evolution to predict novel, overrepresented motifs within a statistical framework with high specificity. Applying this approach to a comprehensive set of human PPI data has highlighted interactome complexity and quality as the next challenges for SLiM prediction. Our latest development, QSLiMFinder (“Query” SLiMFinder) tackles some of these issues by incorporating specific interaction data to restrict the motif search space, which improves both the sensitivity and biological relevance of predictions. We are now using QSLiMFinder to combine structurally defined domain-motif interactions with large-scale PPI data to perform large-scale de novo SLiM prediction.

Speaker
Biography:

Hesham H. Ali is a Professor of Computer Science and the Lee and Wilma Seaman Distinguished Dean of the College of Information Science and Technology (IS&T), at the University of Nebraska at Omaha (UNO). He is also the director of UNO Bioinformatics Core Facility that supports a large number of biomedical research projects in Nebraska. He has published numerous articles in various IT areas including scheduling, distributed systems, wireless networks, and Bioinformatics. He has also published two books in scheduling and graph algorithms, and several book chapters in Bioinformatics. He is currently serving as the PI or Co-PI of several projects funded by NSF, NIH and Nebraska Research Initiative (NRI) in the areas of wireless networks and Bioinformatics. He has been leading a Bioinformatics Research Group at UNO that focuses on developing innovative computational approaches to identify and classify biological organisms. The research group is currently developing new graph theoretic models for assembling short reads obtained from high throughput instruments, as well as employing a novel correlation networks approach for integrating and analyzing large heterogeneous biological data associated with various biomedical research areas. He has also been leading two funded projects for developing secure wireless infrastructure and using wireless technologies to study mobility profiling for aging research.

Abstract:

With the development of advanced high throughput instruments in recent years, there has been a tremendous focus on data generation in biomedical research. However, not all generated data has been properly analyzed or utilized to obtain relevant biological knowledge. Hence, there is a urgent need for innovative tools that focus on the analysis of large biological data. Additionally, biological databases continue to be handled in isolated fashion with little focus on connecting the various types of data associated with a particular domain. This in turns limits the impact of such data. Hence, the desire for the data analysis tools to have the ability to integrate heterogeneous data with a focus on applying systems biology approaches. We propose new and innovative network analysis tools that uses networks/graphs to model, integrate, and analysis large and heterogeneous data. While biological data tend to be very large, the potential amount of relationships among such data items is much larger. Using advanced network analysis and systems biology approaches is computationally intensive and hence the need for High Performance Computing (HPC), and potentially cloud computing. The balance between using HPC to integrate/analysis large biological data and taking into consideration security (particularly cloud security) and energy-awareness issues will be critical in advancing this new direction of developing Bioinformatics tools. We use practical applications and specific case studies in aging research, cancer informatics and Parkinson's research to illustrate and validate the proposed concepts and the developed tools.

Yanbin Yin

Northern Illinois University, USA

Title: Bioinformatics data mining of metagenomes for bioenergy-related enzymes

Time : 13:20 - 13:40

Speaker
Biography:

Yin received his Ph.D. in Biology with a specialization in Bioinformatics from Peking University in Beijing, China in 2005. He then did two postdocs both in Bioinformatics first in the State University of New York at Buffalo and then in the University of Georgia at Athens. In 2012, Dr. Yin joined the Department of Biological Sciences of Northern Illinois University as an Assistant Professor. His lab focuses on applying bioinformatics approaches to the bioenergy research. Dr. Yin has published ~40 research papers and book chapters. He is also on the editorial board of three international journals.

Abstract:

The lignocellulosic biofuels have drawn a lot of attentions in the past few years mainly because the fossil-based oil price continues going up and starch-based biofuels compete with human food consumption. However the lignocellulosic biofuels are currently too expensive because plant biomass is recalcitrant to microbial/enzymatic deconstruction. Two parallel approaches are being undertaken to reduce the cost: 1) genetically modify plants to make their biomass (plant cell walls) easier to be degraded and 2) develop more robust microbial systems to get higher biofuel yield with lower cost. We are employing bioinformatics data mining techniques to mine the public microbial metagenome data from various environments, e.g. animal guts and decomposed biomass, for novel enzymes involved in biomass degradation. We are also building a web-based database to host and annotate all these bioenergy-related enzymes so that researchers all around the world can freely access these data.

Speaker
Biography:

Anne Rosenwald completed a Ph.D. in Biochemistry from the Johns Hopkins University School of Public Health in 1989. Following post-doctoral fellowships at the Carnegie Institution of Washington and the National Cancer Institute at NIH, she joined the chemistry faculty at Dickenson College. Subsequently, she joined the biology faculty at Georgetown University in 1997. She is a 2011 winner of the Dean’s Teaching Award at Georgetown and is a 2012 Bioscience Education Network Scholar. Funding for both her research on membrane traffic in yeast and the Genome Solver Project comes from the National Science Foundation.

Abstract:

The Human Microbiome Project is revolutionizing our understanding of the microorganisms that coexist in and on the human body, and the relationship between the microbiome and human health. The sequence information from thousands of bacteria and bacteriophages is available in public repositories. This vast data set represents an opportunity for undergraduates to engage in authentic bioinformatics research. We have developed the Genome Solver online community for faculty and students to share curriculum and research. As an illustration of the work that can be done, we show one project in which students found evidence for gene transfer between Chlamydia (Chlamydophila) pneumoniae isolates and Chlamydia phages. We found that two phage genes are found in a C. pneumoniae isolate which infects koalas, but only one of these, encoding a putative replication initiation protein (PRIP), is found in the isolates that infect humans. We further show by phylogenetic analyses that the PRIP proteins from the phages cluster together while the PRIP proteins from bacteria cluster together. These results are consistent with the hypothesis that phage genes were transferred into a C. pneumoniae ancestor that gave rise to the koala-infecting strain as well as the human-infecting strains, while the immediate ancestor of the human strains lost the second phage gene and retains only the PRIP gene. These observations suggest that the bacterial PRIP gene is retained because it serves an important, though unknown function. We are extending these results to examine transfer of PRIP genes between other phage and their bacterial hosts.

Malini Laloraya

Rajiv Gandhi Centre for Biotechnology, India

Title: Intersection of proteomics and bioinformatics in deciphering novel functions of proteins

Time : 14:00 - 14:20

Speaker
Biography:

Abstract:

  • Track 10: Computational biology

Session Introduction

Cathy H. Wu

University of Delaware, USA

Title: Integrative bioinformatics for knowledge discovery of PTM networks
Speaker
Biography:

Cathy H. Wu is the Edward G. Jefferson Chair and Director of the Center for Bioinformatics & Computational Biology, Professor of Departments of Computer & Information Sciences and of Biological Sciences, and Director of Bioinformatics Graduate Programs at the University of Delaware. She has conducted bioinformatics research for over 20 years and led the Protein Information Resource (PIR) since 1999. She is the PI/Co-PI on several large consortium projects, has served on many scientific advisory boards, including the HUPO council, published about 180 peer-reviewed papers and eight books and conference proceedings, and given about 140 invited talks.

Abstract:

Speaker
Biography:

Victor P Andreev, Ph.D. is an Associate Professor at the Department of Psychiatry & Behavioral Sciences and Department of Biochemistry & Molecular Biology, University of Miami School of Medicine. Dr. Andreev is a bioinformatician and computational scientist with a strong background in mathematics, physics, and analytical chemistry. Prior to University of Miami, he worked for the Northeastern University, Boston, and prior to this for the Institute for Analytical Instrumentation, Russian Academy of Sciences, St. Petersburg, Russia. Dr. Andreev is a member of several editorial boards, including the Journal of Pharmacogenomics and Pharmacoproteomics.

Abstract:

Pathway analysis is an important approach to reveal the biological meaning of the multidimensional transcriptomics and proteomics data sets. Here, we will present the comparison of the capabilities and will demonstrate the advantage of combining several pathway analysis tools, including MetaCore (GeneGO, Thomson Reuters), Pathway Studio (Ariadne Genomics, Elsevier), GeneXplain platform (GeneXplain, GmbH) and PathOlogist (Greenblum et al) based on three proteomics and two transcriptomics case studies. The discussed proteomics case studies are: (i) label-free quantitative LC-MS proteomics study of Alzheimer’s disease and normally aged human brains, (ii) the iTRAQ-labeled LC-MS/MS study of the dynamics of human plasma proteome during leptin replacement therapy in genetically based leptin deficiency, (iii) the spectral count proteomics of the adipose tissue dynamics in leptin replacement. The discussed transcriptomics studies are: (i) the peripheral blood gene expression analysis in intestinal transplantation in adult human patients, (ii) the peripheral blood gene expression analysis in intestinal transplantation in model animals (syngeneic and allogeneic rats) without immunosuppressant treatment.

Speaker
Biography:

Richard J. Edwards received his Ph.D. in genetics from the University of Nottingham before working as a postdoc in The Royal College of Surgeons in Ireland and University College Dublin on protein bioinformatics. He has been leading his own group at the University of Southampton for over five years, where he is now a Lecturer in molecular evolution and bioinformatics. In addition to more than 25 papers, Rich Edwards has written a number of open source bioinformatics tools available as part of the SLiMSuite and SeqSuite packages, including several webservers available at bioware.ucd.ie.

Abstract:

Short Linear Motifs (SLiMs) are short functional protein sequences that act as ligands to mediate transient protein-protein interactions (PPI) in critical biological pathways and signaling networks. SLiMs are short (3-15aa), generally tolerate considerable sequence variation and typically have fewer than five residues critical for function. These features result in a degree of evolutionary plasticity not seen in domains and SLiMs often add new functions to proteins by convergent evolution. They also present a challenge for computational identification, making it difficult to differentiate biological signal from stochastic patterns. Despite this, discovering new SLiMs is of great interest due to their potential as therapeutic targets. In recent years, we have made great progress in SLiM discovery, particularly through development of the SLiMSuite package of bioinformatics tools. SLiMs generally occur in structurally disordered regions of proteins and exhibit evolutionary conservation relative to other disordered residues. SLiMFinder uses this knowledge and exploits patterns of convergent evolution to predict novel, overrepresented motifs within a statistical framework with high specificity. Applying this approach to a comprehensive set of human PPI data has highlighted interactome complexity and quality as the next challenges for SLiM prediction. Our latest development, QSLiMFinder (“Query” SLiMFinder) tackles some of these issues by incorporating specific interaction data to restrict the motif search space, which improves both the sensitivity and biological relevance of predictions. We are now using QSLiMFinder to combine structurally defined domain-motif interactions with large-scale PPI data to perform large-scale de novo SLiM prediction.

Speaker
Biography:

Hesham H. Ali is a Professor of Computer Science and the Lee and Wilma Seaman Distinguished Dean of the College of Information Science and Technology (IS&T), at the University of Nebraska at Omaha (UNO). He is also the director of UNO Bioinformatics Core Facility that supports a large number of biomedical research projects in Nebraska. He has published numerous articles in various IT areas including scheduling, distributed systems, wireless networks, and Bioinformatics. He has also published two books in scheduling and graph algorithms, and several book chapters in Bioinformatics. He is currently serving as the PI or Co-PI of several projects funded by NSF, NIH and Nebraska Research Initiative (NRI) in the areas of wireless networks and Bioinformatics. He has been leading a Bioinformatics Research Group at UNO that focuses on developing innovative computational approaches to identify and classify biological organisms. The research group is currently developing new graph theoretic models for assembling short reads obtained from high throughput instruments, as well as employing a novel correlation networks approach for integrating and analyzing large heterogeneous biological data associated with various biomedical research areas. He has also been leading two funded projects for developing secure wireless infrastructure and using wireless technologies to study mobility profiling for aging research.

Abstract:

With the development of advanced high throughput instruments in recent years, there has been a tremendous focus on data generation in biomedical research. However, not all generated data has been properly analyzed or utilized to obtain relevant biological knowledge. Hence, there is a urgent need for innovative tools that focus on the analysis of large biological data. Additionally, biological databases continue to be handled in isolated fashion with little focus on connecting the various types of data associated with a particular domain. This in turns limits the impact of such data. Hence, the desire for the data analysis tools to have the ability to integrate heterogeneous data with a focus on applying systems biology approaches. We propose new and innovative network analysis tools that uses networks/graphs to model, integrate, and analysis large and heterogeneous data. While biological data tend to be very large, the potential amount of relationships among such data items is much larger. Using advanced network analysis and systems biology approaches is computationally intensive and hence the need for High Performance Computing (HPC), and potentially cloud computing. The balance between using HPC to integrate/analysis large biological data and taking into consideration security (particularly cloud security) and energy-awareness issues will be critical in advancing this new direction of developing Bioinformatics tools. We use practical applications and specific case studies in aging research, cancer informatics and Parkinson's research to illustrate and validate the proposed concepts and the developed tools.

Jason Lu Long

Cincinnati Children's Hospital Medical Center, USA

Title: Network analysis and applications in studying human diseases
Speaker
Biography:

Dr. Lu works in bioinformatics and systems biology. He focuses on using quantitative approaches from disciplines such as computer science and applied mathematics to analyze biomedical big data with the goal of addressing fundamental questions in biology and improving human health. In particular, he is interested in deciphering the human genetic blueprint, modeling complex biological systems (such as biomolecular networks and pathways), analyzing biomedical images and data mining on medical records. He developed a network-based approach that combines proteomics experiments and computational predictions to discover the subspecies in high-density lipoprotein (HDL) cholesterol and correlate them with cardiovascular protection function. Dr. Lu has also analyzed microbial genomes to facilitate drug discovery and development. Most recently, Dr. Lu’s lab has made progress in developing computational algorithms for analyzing MRI brain images.

Abstract:

Speaker
Biography:

Pedro R. Cutillas completed his Ph.D. at University College London and his postdoctoral work at the Ludwig Institute for Cancer Research (London Branch). In 2007 he obtained a lectureship in Barts Cancer Institute, where he set up a research group focusing on the analytical aspects of cancer cell signaling. He has recently moved to the MRC Clinical Sciences Centre at Imperial College London to head the Biological MS and Proteomics Laboratory. In the last 5 years Dr Cutillas has published 19 papers and filed 5 patent applications.

Abstract:

All cancers deregulate the activity of kinase-driven signaling pathways. These enzymes are therefore drug targets for the treatment of different malignancies. However, not all cancer patients respond to these drugs to the same extent, meaning that there are different signaling routes by which cancer cells can sustain the malignant phenotype. In order to shed light into this complexity and to identify biomarkers of response for patient stratification, we developed methods to quantify signaling as comprehensively as possible. These techniques are based on quantification of protein phosphorylation by mass spectrometry. The presentation will summarize our efforts to develop robust workflows for label-free quantitative phosphoproteomics, which involved the optimization of extraction procedures, normalization of MS data and the design of a computer program that allows efficient implementation of the workflow. We also developed a computational approach to obtain readouts of pathway activation from phosphoproteomics data. These techniques were used to quantify kinase activities and thus profile signaling pathways in leukemia cell lines and primary tissues. Fitting quantitative values of kinase activities into mathematical models allowed us to identify the activities that predicted responses to several kinase inhibitors. We observed that pathways parallel to those being targeted predicted sensitivity to inhibitors that target PI3K signaling nodes. The most frequently deregulated pathways in primary leukemia cells (20 cases analyzed) were driven by the activity of kinase such as PI3K, cyclin dependent kinases, casein kinases and PAK. In conclusion, in-depth quantification of phosphorylation by MS represents a general tool to profile signaling and to identify biomarkers of response to inhibitors that target the signaling network.

Nikhil Sharma

Himachal Pradesh University, India

Title: Motif’s of aromatic and aliphatic nitrilases
Speaker
Biography:

A doctorate from Punjab University (Chandigarh, India) and a post doc from Japan Energy Corporation (Japan), Prof. Tek Chand Bhalla is presently the Chairman of the Department of Biotechnology, Himachal Pradesh University. His areas of research are microbial enzymes, traditional food fermentation and bioinformatics. He has published more than ninety research articles in various national/international journals and participated in several international and national conferences. Prof. Bhalla has guided twenty three Ph.D. and thirty two M. Phil students.

Abstract:

The amino acid sequences of some aromatic and aliphatic nitrilases were analyzed for physiochemical properties and specificity towards aromatic or aliphatic nitriles. The multiple sequence alignment studies of these sequences have clearly exhibited differences between aromatic and aliphatic nitrilases in terms of position specific conserved amino acids. Statistical analysis of most of the physiochemical parameters did not show much difference between the two groups of nitrilases. In aromatic group of nitrilases, the conserved amino acid residues besides active site domain triad (Glu, Lys, Cys) were His-129, Asn-168 and Arg- 174 and these were replaced by Arg-129, His -168 and Lys-174 in aliphatic group of nitrilases. There were some difference in the physiochemical properties of these two groups of nitrilases e.g. as compared to aliphatic nitrilases, aromatic nitrilases have lower molecular mass, higher pI values, lesser number of amino acid residues and higher content of Ala and Cys residues.

  • Track 11: Application of Bioinformatics

Session Introduction

Yanbin Yin

Northern Illinois University, USA

Title: Bioinformatics data mining of metagenomes for bioenergy-related enzymes
Speaker
Biography:

Yin received his Ph.D. in Biology with a specialization in Bioinformatics from Peking University in Beijing, China in 2005. He then did two postdocs both in Bioinformatics first in the State University of New York at Buffalo and then in the University of Georgia at Athens. In 2012, Dr. Yin joined the Department of Biological Sciences of Northern Illinois University as an Assistant Professor. His lab focuses on applying bioinformatics approaches to the bioenergy research. Dr. Yin has published ~40 research papers and book chapters. He is also on the editorial board of three international journals.

Abstract:

The lignocellulosic biofuels have drawn a lot of attentions in the past few years mainly because the fossil-based oil price continues going up and starch-based biofuels compete with human food consumption. However the lignocellulosic biofuels are currently too expensive because plant biomass is recalcitrant to microbial/enzymatic deconstruction. Two parallel approaches are being undertaken to reduce the cost: 1) genetically modify plants to make their biomass (plant cell walls) easier to be degraded and 2) develop more robust microbial systems to get higher biofuel yield with lower cost. We are employing bioinformatics data mining techniques to mine the public microbial metagenome data from various environments, e.g. animal guts and decomposed biomass, for novel enzymes involved in biomass degradation. We are also building a web-based database to host and annotate all these bioenergy-related enzymes so that researchers all around the world can freely access these data.

Speaker
Biography:

Anne Rosenwald completed a Ph.D. in Biochemistry from the Johns Hopkins University School of Public Health in 1989. Following post-doctoral fellowships at the Carnegie Institution of Washington and the National Cancer Institute at NIH, she joined the chemistry faculty at Dickenson College. Subsequently, she joined the biology faculty at Georgetown University in 1997. She is a 2011 winner of the Dean’s Teaching Award at Georgetown and is a 2012 Bioscience Education Network Scholar. Funding for both her research on membrane traffic in yeast and the Genome Solver Project comes from the National Science Foundation.

Abstract:

The Human Microbiome Project is revolutionizing our understanding of the microorganisms that coexist in and on the human body, and the relationship between the microbiome and human health. The sequence information from thousands of bacteria and bacteriophages is available in public repositories. This vast data set represents an opportunity for undergraduates to engage in authentic bioinformatics research. We have developed the Genome Solver online community for faculty and students to share curriculum and research. As an illustration of the work that can be done, we show one project in which students found evidence for gene transfer between Chlamydia (Chlamydophila) pneumoniae isolates and Chlamydia phages. We found that two phage genes are found in a C. pneumoniae isolate which infects koalas, but only one of these, encoding a putative replication initiation protein (PRIP), is found in the isolates that infect humans. We further show by phylogenetic analyses that the PRIP proteins from the phages cluster together while the PRIP proteins from bacteria cluster together. These results are consistent with the hypothesis that phage genes were transferred into a C. pneumoniae ancestor that gave rise to the koala-infecting strain as well as the human-infecting strains, while the immediate ancestor of the human strains lost the second phage gene and retains only the PRIP gene. These observations suggest that the bacterial PRIP gene is retained because it serves an important, though unknown function. We are extending these results to examine transfer of PRIP genes between other phage and their bacterial hosts.

Speaker
Biography:

Hoan Nguyen (Ph.D.) is a Researcher at the Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Illkrich, France where he works on projects related to automated variant annotation and prediction, database design and management and large-scale computation. His is responsible for SM2PH-Central framework.

Abstract:

Predictive medicine relies on efficient genome annotation, ‘omic’ data integration and system level analyses to develop new approaches for personalized health care in which patients are treated based on their individual characteristics. We present the SM2PH-Central (from Structural Mutation to Pathology Phenotypes in Human) knowledge base, http://decrypthon.igbmc.fr/ sm2ph/cgi-bin/home, which is part of an overall strategy aimed at the development of a transversal system to better understand and describe the networks of causality linking a particular phenotype, and one or various genes or networks. It incorporates tools and data related to all human genes, including their evolution, tissue expressions, genomic features and associated phenotypes, in a single infrastructure. It also provides access to systematic annotation tools, including sequence database searches, multiple alignment and 3D model exploitation, physico-chemical, functional, structural and evolutionary characterizations of variants. All information is accessible via standardized reports (gene/proteins profiles, etc.), as well as automated services for specific applications, such as gene prioritization or variant prediction.