1	Laboratory for Computational Life Sciences
2	Problems in Computational Biology and Bioinformatics Protein folding: predict 3-D structure of a protein Flexible protein docking: predict affinity of a medicine to an enzyme which has a lot of flexibility Protein interaction network: predict which proteins interact from available data Morphogenesis: simulate cell interaction with genetics to control development of animal embryos
3	Research Projects
4	(1) How to create high-performance software that is easy to use? Goals: Encapsulate optimizations like parallelism and cluster/grid computing so that these can be used easily. MATLAB and Mathematica are examples of easy to use scientific software Allow easy prototyping of algorithms, extensions of the software by computational scientists (not expert computer scientists) Our current solutions use: Generic and object-oriented programming Design patterns XML-based domain specific languages Related publications: Matthey et al. (2004) ACM Trans. Math. Software, 20(3) Cickovski et al. (2004) IEEE/ACM Trans. Comput. Biol. and Bioinformatics Cickovski and Izaguirre (2004) ACM Trans. Prog. Lang. and Systems, in preparation
5	(2) How to make it easy for the user to select software, algorithms, and parameters to solve their problems
6	(3) How to create hierarchical, multiscale, multilevel algorithms? Examples: Algorithms for N-body problem (linear complexity, multiple grids) e.g., Matthey and Izaguirre (2004) J. Par. Dist. Comp. Multiscale integration (15 order of magnitude gap on timescales) e.g. Ma and Izaguirre (2003), Multisc. Model. Simul. Coarse approximations (use averaging or stochastic or ensemble) solutions, e.g. Izaguirre and Hampton (2004), J. Comp. Phys.
7	(4) How to predict protein interaction networks? Goal: Predict proteins in a genome that are likely to interact, thus giving clue as to their function. Our current solution starts from experimental interaction data and uses clustering and a set cover approach to predict novel interactions. This is documented in Huang et al. (2004), IEEE/ACM TCBB, submitted
8	(5) How to combine experiments and simulation to model 3-d Growth and Patterning? Publications include Izaguirre et al. (2004) Bioinformatics and Cickovski et al. (2004) IEEE/ACM TCBB
9	(6) How to create reliable databases of simulations in the grid? Collaboration with Aaron Striegel, Doug Thain (CSE), Peng, Baker (chem.)
10	Acknowledgements JI acknowledges support from: National Science Foundation Biocomplexity grant IBN-0083653 NSF CAREER Award ACI-ACI-0135195 Department of Computer Science and Engineering, Univ. of Notre Dame and our collaborators, particularly Stuart Newman (New York Medical College), James Glazier (IU), Mark Alber (math), H.G.E. Hentschell (Emory) in morphogenesis, and Danny Chen (CSE) and Stefan Wuchty (Physics) in protein interaction network