|
Bioinformatics & Computational
Systems Biology 1. Algorithms for Protein
Folding Protein
folding algorithms aim to understand how the amino acid sequence of a protein
determines its unique native
conformation. Experimental methods, such as X-ray crystallography, determine
the native structure of a protein with a great accuracy. However, there is
need for more powerful and “intelligent” optimization algorithms
to simulate the process of folding. This project deals with the development
of parallel optimization algorithms based on traditional and artificial life
paradigms to solve protein-folding problems. These techniques are deployed
onto grid-enabled platforms to facilitate the execution of these compute- and
data-intensive algorithms. 2. Algorithms for Protein Threading To
construct a model of a protein’s tertiary structure one can be use a
known structure (of another protein) and then evaluate its suitability.
Threading can be viewed as “folding” but in reverse. It is also
time-consuming and data-intensive since it uses the same algorithms used in
protein folding. There is also a need for parallel algorithms to speed up the
process of threading and implement the resulting algorithms on Grids
architectures. 3. Parallel Algorithms for Building Comparative Gene Maps Comparative
maps are powerful techniques for the aggregation of genetic information about
related organisms, for inferring phylogenetic relationships, and for
examining hypotheses about the evolution of gene families and the functional
significance of orthologous genes. The construction
of gene maps is a very difficult task and the compilation of such maps across
multiple species is even harder. This work will attempt to automate this
process by using meta-heuristics (e.g. genetic algorithms, Tabu search, swarm algorithms, etc) that are also parallelizable
in nature. This makes such techniques suitable candidates for parallel
implementations and as a result extends their applicability to massive data
sets and a wider range of gene families. 4. Modelling of Complex
Metabolic Pathways for Genomics and Proteomics Complex
multicellular organisms contain large genomes in which each structural gene
is associated with at least one regulatory element and each regulatory
element integrates the activity of at least two other genes. The nature of
such regulation started to be understood from the analysis of small
prokaryotic regulation subsystems and the current picture indicates that the
webs that shape cellular behaviour are very complex. This project deals with
the development of algorithms and tools that can be used to analyse metabolic
networks based on heuristics and meta-heuristics.
|
