daptics Literature

Selected Peer-Reviewed Publications

  1. Caschera F., Karim A. S., Gazzola G., d’Aquino A. E., Packard N. H., and Jewett M. C. (2018), High-throughput optimization cycle of a cell-free ribosome assembly and protein synthesis system. ACS Synthetic Biology Article ASAP. DOI: 10.1021/acssynbio.8b00276

  2. Caschera F., Bedau M. A., Buchanan A., Cawse J., de Lucrezia D., Gazzola G., Hanczyc M. M., and Packard N. H. (2011). Coping with complexity: Machine learning optimization of cell-free protein synthesis. Biotechnology and Bioengineering, 108(9): 2218-28.

  3. Cawse J., Gazzola G., and Packard, N. (2011). Efficient discovery and optimization of complex high-throughput experiments. Catalysis today, 159(1): 55-63.

  4. Caschera F., Gazzola G., Bedau M. A., Moreno C., Buchanan A., Cawse J., Packard N. H., Hanczyc M. M. (2010). Automated discovery of novel drug formulations using predictive iterated high-throughput experimentation. PLoS One, 5(1):e8546.

Other Literature

  1. A white paper describing the fundamentals of daptics' machine-learning methods for experimental discovery and optimization. Figures from this white paper can be found in a separate slide deck.

  2. A white paper describing benchmark studies, including comparisons with genetic algorithms.