GECCO 2017 Workshop “ Evolutionary Computation in Computational Biology”

http://eccsb2017.irlab.org/

http://gecco-2017.sigevo.org/index.html/HomePage

In the last two decades, many computer scientists in Artificial Intelligence have made significant contributions to modeling biological systems as a means of understanding the molecular basis of mechanisms in the healthy and diseased cell. The field of computational biology includes the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, behavioral, and social systems . The focus of this workshop is the use of nature-inspired approaches to central problems in computational biology, including optimization methods under the umbrella of evolutionary computation.

Areas of interest include (but are not restricted to):

* Genome and sequence analysis with nature-inspired approaches. * Computational systems biology. * Biological network modeling and analysis. * Use of artificial life models like cellular automata or Lindenmayer systems in the modeling of biological problems. * Study and analysis of properties of biological systems like self-organization, self-assembled systems, emergent behavior or morphogenesis. * Hybrid approaches and memetic algorithms in the modeling of computational biology problems. * Multi-objective approaches in the modeling of computational biology problems. * Use of natural and evolutionary computation algorithms in protein structure classification and prediction (secondary and tertiary). * Mapping of protein and peptide energy landscapes. * Modeling of temporal folding of proteins. * Protein design. * Protein-ligand and protein-protein docking. * Stability and dynamics of biomolecular systems. * Applications in atomic clusters: Water clusters, Leonard Jones clusters, metal clusters, etc. * Applications in cellular systems: micelle, single organisms, bacterial cells, etc. * Applications in stem cell differentiation and development, lineage programming and cell fates. * Evolutionary search strategies to assist cryo-electron microscopy and other experimental techniques in model building. * Surrogate models and stochastic approximations of computationally expensive fitness functions of biomolecular systems.