X-meeting

Michal Linial - University of Jerusalem

Michal Linial (Opening Conference)
Michal Linial

    Michal Linial is a Professor of Biochemistry, Molecular Biology and Bioinformatics at the Hebrew University of Jerusalem, Israel, where she heads the Israel Institute for Advanced Studies.

     She received her PhD from the Hebrew University's Medical School (1986) in Biochemistry and Molecular Biology. During her post-doctoral training in Stanford University, she engaged in molecular neuroscience with the goal of deciphering the molecular makeup of the synapse. She joined the Hebrew University (1989) and was a driving force in merging computational and analytical tools with classical wet biology. She is a founder (1999) and the chair of the undergraduate and graduate joint program in Computer sciences and Life Sciences at the Hebrew University. She heads The Sudarsky Center for Computational Biology at the Hebrew University.

     Her laboratory is active in the two arenas - She leads a wet lab as well as a computational group. Her research interests span a broad range of topics such as stem cells, neuronal differentiation, synapse regulation, cell biology of secretory systems and the molecular mechanisms that underlie behavior and metabolic diseases. With the maturation of large-scale technologies, she has become involved in developing methods for target selection in Structural Genomics, protein family classification and the development of methodologies for the analysis of large-scale biological data sets. She is particularly interested in introducing powerful computational tools to meet the needs of the biological and bio-medical research communities. Among the web tools developed by her research group are PANDORA, ProtoNet, and EVEREST. One of her main recent areas of activity is proteomics where she combines experimental, technological and computational work.

    She is an ISCB’s vice-president, a member of its Board of Directors, and is active in the Conference and Education committees and the ISCB Students’ council. She served as the Chair of the European Conference in Computational Biology, and a member of the steering committees of RECOMB and ECCB.

Ruth Nussinov - Center for Cancer Research

Ruth Nussinov

Ruth Nussinov

Ruth Nussinov’s algorithm for the prediction of RNA secondary structure is still the leading method. She proposed ‘Conformational Selection and Population Shift’ as an alternative to the textbook ‘Induced-Fit’ model in molecular recognition. Her recent studies unveiled the key role of allostery under normal conditions and in disease and the principles of allosteric drug discovery. She uncovered the structural basis for cancer signaling, and its mechanistic principles; predicted GTP-dependent K-Ras dimer structures and suggested that K-Ras4B dimerizes via two distinct interfaces and explained the consequences for Raf’s activation and MAPK signaling; elucidated calmodulin’s role in KRAS-driven adenocarcinomas; the critical role of oncogenic KRAS in the initiation of cancer through deregulation of the G1 cell cycle, and proposed a new view of Ras isoforms. This new view argues for multiple signaling states of palmitoylated Ras isoforms, such as K-Ras4A. This view questions the completeness and accuracy of small GTPase Ras isoform statistics in different cancer types and calls for reevaluation of concepts and protocols. Importantly, the multiple signaling states also call for reconsideration of oncogenic Ras therapeutics.


Abstract:

Ras signaling: a challenge to the biological sciences

Ras proteins are classical members of small GTPases that function as molecular switches by alternating between inactive GDP-bound and active GTP-bound states. Ras activation is regulated by guanine nucleotide exchange factors that catalyze the exchange of GDP by GTP, and inactivation is terminated by GTPase-activating proteins that accelerate the intrinsic GTP hydrolysis rate by orders of magnitude. Ras has multiple partners, signals through several key pathways and fulfills critical functions in the cell life. Mutations in Ras are common in a variety of cancers; yet it is still undruggable. Consequently, it is at the center of an NCI initiative. In my talk, I will provide the background on Ras and an overview of our recent work, highlighting how it may help in elucidating vital questions in Ras biology and hopefully contribute to therapeutic strategies.

Winston Hide - University of Sheffield

Winston Hide

Professor Hide graduated in Zoology with an Upper Second Honours from the University of Wales (Cardiff) in 1981. He attended graduate school at the Temple University, Philadelphia and graduated with a PhD in molecular genetics in 1991.

In 1992 he performed post doctoral training with Wen Hsuing-Li at the University of Texas, Houston, where he published his first Nature paper and in 1993 went on to train with David Pawson at the Smithsonian Institution National Natural History Museum, in 1994 with Richard Gibbs at the Baylor Human Genome Centre, Houston, and with Dan Davison at the University of Houston. In 1995 he gained industrial experience in Silicon Valley at MasPar Computer corporation as Director of Genomics.

In 1996 Professor Hide founded the South African National Bioinformatics Institute at the University of Western Cape, South Africa and was appointed Professor in 1999. 
In 2007, he became visiting Professor of Bioinformatics at Harvard School of Public Health, and won the Oppenheimer Foundation Distinguished Sabbatical Research Fellowship. 
In 2008, Hide was the subject of a directed search and became an associate professor at the Department of Biostatistics at Harvard School of Public Health. Also, in 2008 Hide founded the Harvard School of Public Health bioinformatics Core and became Director of the Harvard Stem Cell Institute Center for Stem Cell Bioinformatics. 
In 2014, Hide accepted a Chair, and became Professor of Computational Biology, at the Sheffield Institute for Translational Neurosciences within the Department of Neuroscience at the University of Sheffield.

Hide has been awarded the National President’s Award for research in 1998, was elected to membership of the Academy of Science of South Africa in 2007 and also in 2007, won the Oppenheimer Foundation Distinguished Sabbatical Research Fellowship . In 2011, he was the first recipient of the International Society for Computational Biology award for Outstanding Achievement - in recognition of his work for the development of computational biology and bioinformatics in Africa.


Abstract:

Correlative approaches to interpretation of genetic contributions to disease

There is a need to move beyond the identification of individual genes associated with a disease. With the goal of using integrative genetic and functional models as a powerful approach to defining therapeutic targets for interventions; we have developed a series of functional maps for relationships between pathways. Current approaches for understanding directed interaction between pathways rely on shared genes, combining information from databases and interaction networks, or using direct physical interaction between genes and gene products to determine likely interaction.
We have extended this concept to define the relationship between pathways by their co-activity. Systematic quantification of the relationship between pathways provides a high-level map of related cellular functions to reveal the relationship between biological functions by their interactions. We interrogate disease variants in combination with disease gene expression signatures to reveal key interacting pathways enriched with disease variants. We extend genome variant associations in specific pathways enabling analysis of influence of previously unknown pathway relationships. A pathway correlation network (PCxN) reveals co-activity between gene sets for integrative genetic and functional models for experiment or genome association study.
As part of the Cure Alzheimer's Genome Project, we have discovered genomic loci that show both familial association with AD and genomic loci associated with disease resistance. These loci are in turn enriched with pathways and genes that undergo rewiring in direct association with plaque formation.
By applying this genetic data to a global functional interaction maps (Hide, Winston (2015): PCxN the Pathway co-activity Map https://dx.doi.org/10.6084/m9.figshare.00 AS subject, as1589792.v4) that reflect changes in the way cellular processes interact; we expose key functional dynamics of disease progression in close correlation with plaque density, neurofibrillary tangles in post-mortem brains, and degradation in cognitive function. Integrating these into comprehensive drug to target perturbation networks we predict those drugs that appear to most specifically interact to correct perturbations that result in disease.

William Stafford Noble - University of Washington


William Stafford Noble

William Stafford Noble (formerly William Noble Grundy) received the Ph.D. in computer science and cognitive science from UC San Diego in 1998. After a one-year postdoc with David Haussler at UC Santa Cruz, he became an Assistant Professor in the Department of Computer Science at Columbia University. In 2002, he joined the faculty of the Department of Genome Sciences at the University of Washington. His research group develops and applies statistical and machine learning techniques for modeling and understanding biological processes at the molecular level. Noble is the recipient of an NSF CAREER award and is a Sloan Research Fellow and a Fellow of the International Society for Computational Biology.


Abstract:

Machine learning and statistical challenges in protein mass spectrometry

Bioinformatics is driven in large part by technological innovation, such as the advances in DNA sequencing for genomics. The field of proteomics is undergoing a similar, technology-driven transformation, as the field begins to switch from data-dependent acquisition to data-independent acquisition. I will explain this transformation and its implications for our understanding of the dynamic proteome, giving examples of new machine learning and statistical challenges that arise from the resulting big data sets.