@inproceedings{garcia-bernardoEvolvingSmallGRNs2014,
  title = {Evolving Small {{GRNs}} with a Top-down Approach},
  booktitle = {{{GECCO}} 2014 - {{Companion Publication}} of the 2014 {{Genetic}} and {{Evolutionary Computation Conference}}},
  author = {{Garcia-Bernardo}, J. and Eppstein, M.J.},
  year = {2014},
  doi = {10.1145/2598394.2598443},
  abstract = {Designing genetic regulatory networks (GRNs) to achieve a desired cellular function is one of the main goals of synthetic biology. However, determining minimal GRNs that produce desired timeseries behaviors is non-trivial. In this paper, we propose a 'topdown' approach, wherein we start with relatively dense GRNs and then use differential evolution (DE) to evolve interaction coefficients. When the target dynamical behavior is found embedded in a dense GRN, we narrow the focus of the search and begin aggressively pruning out excess interactions at the end of each generation. We first show that the method can quickly rediscover known small GRNs for a toggle switch and an oscillatory circuit. Next we include these GRNs as non-evolvable subnetworks in the subsequent evolution of more complex, modular GRNs. By incorporating aggressive pruning and a penalty term, the DE was able to find minimal or nearly minimal GRNs in all test problems.},
  isbn = {978-1-4503-2881-4},
  keywords = {Differential evolution,Genetic network inference,Genetic regulatory networks,Synthetic biology}
}

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