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Metaheuristics in Synthetic Biology

Untertitel: Genetic Algorithms in Design of Cell Classifiers

DozentIn(en): Melania Nowicka, Heike Siebert

Maximale Teilnehmerzahl: 6


Pre-meeting: between 17.02 and 21.02.2020
Introductory week: 12.03-17.03.2020
Dates for follow-up meetings to be discussed.

Ort: Institut für Mathematik, Arnimallee 6, 14195 Berlin

Kurze inhaltliche Beschreibung:

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.


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

Quantitative Aufteilung: (in %)

Praktische Programmierarbeit: 60 %
Soft Skills: 40 %

Verwendete Programmiersprache(n):

Ideally, the algorithm will be implemented in Python, but other programming languages can be considered.

Schwierigkeitsgrad (Acht Sterne verteilt auf drei Bereiche):

A Programmieren ****
B Biologie/Chemie **
C Projektmanagement **

Erforderliche Vorkenntnisse: Python basics would be helpful.

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