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Computational design approaches and tools for synthetic biology., , , , and . Integrative biology : quantitative biosciences from nano to macro, 3 (2): 97--108 (Feb 8, 2011)Deep Learning Inference Service at Microsoft., , , , , , , , , and 2 other author(s). OpML, page 15-17. USENIX Association, (2019)Comparison of Athletic Performances across Disciplines.. AusDM, volume 158 of CRPIT, page 131-139. Australian Computer Society, (2014)ABC-SysBio - approximate Bayesian computation in Python with GPU support., , , , , , and . Bioinform., 26 (14): 1797-1799 (2010)A computational method for the investigation of multistable systems and its application to genetic switches., , , and . BMC Syst. Biol., (2016)Bayesian design strategies for synthetic biology., , and . Interface focus, 1 (6): 895--908 (Dec 6, 2011)A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation, , , , , and . Nat. Protocols, 9 (2): 439--456 (Feb 23, 2014)Model Selection in Systems Biology Depends on Experimental Design., , , , and . PLoS Comput. Biol., (2014)FlopR: An Open Source Software Package for Calibration and Normalization of Plate Reader and Flow Cytometry Data, , , and . ACS Synthetic Biology, 9 (9): 2258--2266 (August 2020)Deep reinforcement learning for optimal experimental design in biology., , , and . PLoS Comput. Biol., 18 (11): 1010695 (November 2022)