A bioengineering group is bringing the worlds of computational modeling and experimentation closer together by developing a methodology to help analyze the wealth of imaging data provided by advancements in imaging tools and automated microscopes. They show how using approximate Bayesian computation (ABC) can help infer useful quantitative information for experimental design.
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