Evolutionary Computation in Computational Biology and Bioinformatics
The focus of this workshop is the use of nature-inspired approaches to central problems in computational biology and bioinformatics, 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.
· Biological network modeling and analysis.
· Use of artificial life models such as cellular automata or Lindenmayer systems in the modeling of biological problems.
· Study and analysis of properties of biological systems such as 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).
· Integration of evolutionary computation algorithms with deep-learning architectures.
· Mapping of protein and peptide energy landscapes.
· Modeling of temporal folding of proteins.
· Molecular optimization and design.
· Binding, docking, and complexation.
· Prediction of variant effects on stability, function, and dysfunction.
· 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