On 25 June 2010, during the 8th [BC]2 Computational Biology Conference in Basel, SIB Swiss Institute of Bioinformatics announced the two winners of the SIB Awards 2010.
SIB Young Bioinformatician Award 2010
The winner of the SIB Young Bioinformatician Award is SIB Member Aitana Morton de Lachapelle, 27, PhD student in the Computational Biology Group led by Prof. Sven Bergmann at the Department of Medical Genetics of the University of Lausanne, which she joined after graduating in Physics from the EPFL (Swiss Federal Institute of Technology in Lausanne). During her PhD thesis, she has been investigating how robust pattern formation can be achieved during development.
The Young Bioinformatician Award is given yearly by SIB Swiss Institute of Bioinformatics. It recognises a graduate student or young researcher who has carried out a research project centered on the in silico analysis of biological sequences, structures and processes. The award is given competitively by a jury of experts and is doted with a cash prize of CHF 10'000.
SIB Best Graduate Paper Award 2010
The winner of the 2010 SIB Best Graduate Paper Award is Rajesh Ramaswamy, 27, PhD student in the MOSAIC Group of Prof. Ivo Sbalzarini at ETH Zurich (Swiss Federal Institute of Technology). The title of his award paper is «A new class of highly efficient exact stochastic simulation algorithms for chemical reaction networks».
The Best Graduate Paper Award is given yearly by SIB Swiss Institute of Bioinformatics. It recognises outstanding contributions to the fields of bioinformatics and computational biology made by young researchers who have not yet completed their PhD. The award is given competitively by a jury of experts and is doted with a cash prize of CHF 5'000.
More information about the research work of the winners
Aitana Morton de Lachapelle, SIB Young Bioinformatician Award
Within a developing organism, cells need to know where they are in order to differentiate into the correct cell-type. Pattern formation is the process by which cells acquire positional information and thus determine their fate. This can be achieved by the production and release of a diffusible signaling molecule, called a "morphogen", which forms a concentration gradient: exposure to different morphogen levels then leads to different cell fates. Though morphogens have been known for decades, Mrs. Morton de Lachapelle explains that "it is not yet clear how these gradients form and yield such robust patterns. We have been investigating the properties of Bicoid and Decapentaplegic, two morphogens involved in the patterning of the anterior-posterior axis of Drosophila embryo and wing primordium, respectively". In particular, she is interested in understanding how the pattern proportions are maintained across embryos of different sizes or within a growing tissue, which is essential to yield a correctly proportioned organism or organ. Ultimately, the general understanding of how cells respond to signals and coordinate their actions could bring new insights into some diseases like cancer and, theoretically, provide the ground to make artificial tissues.
In their published work, Mrs. Morton de Lachapelle and Prof. Bergmann investigated two systems properties of Drosophila early embryo development: using staining images for three gap genes and the pair-rule gene Eve, they investigated the precision and scaling of their expression domains. Their results suggest that these properties are, at least in part, already achieved at the level of the Bicoid gradient itself and then passed on to its target genes. Investigating models that can reproduce the position-dependent signatures of precision and scaling, they identified two necessary ingredients: it is essential to include nuclear trapping and an external pr e-steady state morphogen gradient to achieve both maximal precision at mid-embryo and almost perfect scaling away from the source. Current work within the SystemsX.ch WingX collaboration aims at understanding how scaling can be achieved by the Decapentaplegic signaling pathway during wing imaginal disc growth.
Rajesh Ramaswamy, SIB Best Graduate Paper Award
Mr. Ramaswamy found a way of reducing, by orders of magnitude, the computational cost of one of the most important simulation algorithms in bioinformatics and systems biology: the stochastic simulation algorithm (SSA). This algorithm is used to exactly simulate the dynamics of networks of chemical reactions in regimes where differential equation models are invalid (low copy number, burst noise, confinement in small compartments, etc.), as typically found in living cells. Due to the importance of the field, the algorithm is well studied since the 1970's and even small improvements on the order of 10% in computational speed resulted in high-impact publications in the past.
Mr. Ramaswamy's approach to the problem is fundamentally new and innovative. It started with his discovery that there are two distinct classes of chemical reaction networks and that all previously published improvements of the algorithm only work on one of these two classes. Most biological networks, however, fall into the other class. This includes scale-free networks from systems biology, but also the aggregation-and-growth processes governing the organisation of cell membranes or the assembly of virus capsids. Following this discovery, Mr. Ramaswamy then found a novel class of simulation algorithms, whose computational cost is reduced by orders of magnitude also in these cases, while remaining competitive with the best previously known algorithm in the other cases.