Inhalt:

Cell classifiers are synthetic decision-making biological circuits that may be built in the wet lab and delivered to cells. Inside, they classify a cell state as healthy or cancerous and release a drug in the latter case. During the Praktikum we will design a population of cancer cell classifiers that may together distinguish whether a given sample should be diagnosed as positive (cancerous) or negative (healthy). To optimize such classifiers we will apply so-called genetic algorithms. Genetic algorithms (GAs) are population-based metaheuristics inspired by Darwin’s theory of evolution used for solving optimization and search problems. GAs mimic a process occurring in evolution called natural selection. Briefly, the process allows for evolving a population of individuals based on their different survival and reproduction abilities. GAs are applied to solve various problems in different fields of research, e.g., in synthetic biology to design synthetic circuits.

Schedule:

1. Introduction to genetic algorithms and the problem of classifier design
2. Design of the genetic algorithm
3. Implementation
4. Presentation of results