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Genetic algorithms

Standard theory of Genetic Algorithms (GA) was first introduced by Holland in 1975. A genetic algorithm is a technique used in computing to find exact or approximate solutions to optimization and search problems. They belong to the class of global search heuristics that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover (also called recombination). I am interested in applying GA both to standard optimization problems and in computational biology problems, such as vaccine optimization and targeted therapy design.

Immune system modeling

The human immune system is a complex and adaptive learning system which operates at multiple levels from molecule to cell, organ and organism. Immune cells and molecules exist in many forms which differ in their specificities. Research in immunology leading to vaccine development and drug discovery often involves simulation of immune system responses. Simulating the immune system is and will be a fascinating challenge that involves many researchers coming from various scientific fields. Simulators can be used to help understand the general behavior of immune system related phenomena in different situations.

In particular they can be useful to study the role of single constituents and relations, to plan new experiments, to test theoretical assumptions and to suggest theory modifications.