Invited Speakers

The following speakers will present a keynote lecture at this year BBC.

  • Jim Haseloff, University of Cambridge

    Jim Haseloff is a plant biologist working at the Department of Plant Sciences, University of Cambridge. His scientific interests are focused on the engineering of plant morphogenesis, using microscopy, molecular genetic, computational and synthetic biology techniques. Prior to joining the Department of Plant Sciences, Jim served as group leader at MRC Laboratory of Molecular Biology in Cambridge and his group developed advanced imaging techniques and modified fluorescent proteins for efficient use in plants. Before this, Jim was a research fellow at Harvard Medical School, working on trans-splicing ribozymes. He has also worked at the CSIRO Division of Plant Industry, Canberra, and developed methods for the design of the first synthetic RNA enzymes with novel substrate specificities. Jim is deeply involved with teaching Synthetic Biology at the University of Cambridge, and is very interested in its wider potential as a tool for engineering biological systems and underpinning sustainable technologies.

    Engineering simple plant systems

    Synthetic Biology has great potential as a tool for the engineering of multicellular organisms. (1) The greatest diversity of cell types and biochemical specialisation is found in multicellular systems, (2) the molecular basis of cell fate determination is increasingly well understood, and (3) it is feasible to consider creating new tissues or organs with specialized biosynthetic or storage functions by remodelling the distribution of existing cell types. Of all multicellular systems, plants are the obvious first target for this type of approach. Plants possess indeterminate and modular body plans, have a wide spectrum of biosynthetic activities, can be genetically manipulated, and are widely used in crop systems for production of biomass, food, polymers, drugs and fuels.
    Current GM crops generally possess new traits conferred by single genes, and expression results in the production of a new metabolic or regulatory activity within the context of normal development. However, cultivated plant varieties often have enlarged flowers, fruit organs or seed, and are morphologically very different from their wild-type ancestors. The next generation of transgenic crops will contain small gene networks that confer self-organizing properties, with the ability to reshape patterns of plant metabolism and growth, and the prospect of producing neomorphic structures suited to bio production. We have developed a battery of computational, imaging and genetic tools to allow clear visualisation of individual cells inside living plant tissues and have the means to reprogram them. These techniques are well suited to study of simple experimental systems such as the lower plant Marchantia polymorpha and surrogate microbial populations. These types of simple systems are becoming increasingly important to explore the next generation of genetic circuits with self-organising properties.

  • Anna Tramontano, Sapienza University of Rome

    Anna Tramontano was trained as a physicist but she soon became fascinated by the complexity of biology and by the promises of computational biology. After a post-doctoral period at UCSF, she joined the Biocomputing Programme of the EMBL in Heidelberg. In 1990 she moved back to Italy to work in the Merck Research Laboratories near Rome. In 2001, she returned to the academic world as a Chair Professor of Biochemistry in "La Sapienza" University in Rome where she continues to pursue her scientific interests on protein structure prediction and analysis and on genomic and post-genomic data interpretation in the Department of Physics. She is a member of the Scientific Council of the ERC, of the European Molecular Biology Organization, the Scientific Council of Institute Pasteur - Fondazione Cenci Bolognetti, and the organizing Committee of the Critical Assessment of Techniques for Protein Structure Prediction (CASP) initiative.She is a member of the Advisory Board of the SIB in Basel, of the CRG in Barcelona, of the CNB in Madrid, of the MPI for Molecular Genetics in Berlin, of the IIMCB in Warsaw and has been a member of the EMBL Scientific Adivsory Committee and of the EBI Advisory Committee. She is Associate Editor of Bioinformatics, Proteins, PLoS One and Current Opinion in Structural Biology.

    She was awarded the prize for Natural Sciences of the Italian Government, the “Marotta Prize” of the Italian National Academy of Science and the Minerva Prize for Scientific Research, the KAUST Investigator Award and has published four books (Bioinformatica - Zanichelli; The ten most wanted solutions in Protein Bioinformatics - CRC Press; Protein Structure Prediction - Wiley; Introduction to Bioinformatics - CRC Press).

    The computational analysis of biomolecular interactions

    The combination of experimental and computational approaches can provide invaluable information on the function of biological systems. The computational approach is usually based on empirical methods for predicting the three-dimensional structure and the function of a protein, as well as its interactions with both macromolecules and smaller compounds. These methods, even if still approximate, can be instrumental for the development of effective rational strategies for experiments such as studies of disease related mutations, site directed mutagenesis, or structure based drug design.
    I will describe some of the methods that we developed to this end, including a method to dissect and predict antibody-antigen interactions. I will also show some examples of their effectiveness in providing relevant information about systems of biomedical interest.

  • Alfonso Valencia, Spanish National Cancer Research Center

    Alfonso Valencia is a biologist with formal training in population genetics and biophysics which he received from the Universidad Complutense de Madrid. He was awarded his PhD in 1988 at the Universidad Autónoma de Madrid. He was a Visiting Scientist at the American Red Cross Laboratory in 1987 and from 1989-1994 was a Postdoctoral Fellow at the laboratory of C. Sander at the European Molecular Biology Laboratory (EMBL), Heidelberg, Germany. In 1994 Alfonso Valencia set up the Protein Design Group at the Centro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas (CSIC) in Madrid where he was appointed as Research Professor in 2005. He is a Member of the European Molecular Biology Organisation (EMBO), Founder and former Vice President of the International Society for Computational Biology where he has been Chair of the Systems Biology and/or Text Mining Tracks of the main Computational Biology Annual Conference (ISMB) since 2003. He was honoured as ISCB-Fellow in 2010. Alfonso Valencia serves on the Scientific Advisory Board of the European Molecular Biology Laboratory; the Swiss Institute for Bioinformatics, Biozentrum, Basel; the INTERPRO database; the Spanish Grant Evaluation Agency (ANEP); as well as the Steering Committee of the European Science Foundation Programme on Functional Genomics (2006-2011). Alfonso Valencia is Co-Executive Editor of Bionformatics, serves on the Editorial Board of EMBO Journal and EMBO Reports, among others. He is the Director of the Spanish National Bioinformatics Institute (INB).

    Co-Evolution Based Methods in the Prediction and Analysis of Protein Interaction Networks

    Co-evolution based methods are potentially able to increase the coverage and accuracy of the information provided by high-throughput protein-protein interaction methods (1). Given the importance of protein interaction networks to contextualize functional information in genome analysis pipelines, it is relevant to explore the possibilities that this methodology open. Recent publications in the area of protein folding have revitalized the interest in the use of co-evolution based methods for the prediction of protein interactions. In this presentation, I will first review the general panorama of co-evolution based methods with particular emphasis in those applied to the prediction of protein interactions (2). In the second part of the talk, I will present a new co-evolution based approach to the prediction of protein interaction networks in a large set of bacterial species (3). The radical innovation of this new approach is that it permits for the first time the prediction of specie specific interactions instead of the general type of predictions for protein families provided by other approaches.

    MICROME. EU Framework Programme 7 Collaborative Project. Grant Agreement Number 222886-2
    Spanish Government grant BIO2007-66855

    (1) de Juan D, Pazos F, Valencia A. (2013) Emerging methods in protein co-evolution. Nat Rev Genet. 14:249-261.
    (2) Mosca E, Pons T, Ceol A, Valencia A, Aloy P. Towards a detailed atlas of protein-protein interactions. Curr Opin Struct Biol 2013 (PMID 23896349).
    (3) de Juan D, de la Torre V, Valencia A (2013) in preparation.