Computation and experimentation come closer together

ScienceDaily | 7/9/2019 | Staff
cv2angels (Posted by) Level 3
Their study focuses on embryonic tissue spreading, a process that is critical during wound healing and the progression of many diseases. The article, recently published in PLOS ONE, shows how using approximate Bayesian computation (ABC) -- a statistical inference method -- can help derive useful quantitative information for experimental design.

The work was overseen by Lance Davidson, professor of bioengineering, who runs the MechMorpho Lab in the Swanson School of Engineering. The study was led by Tracy Stepien, a Pitt mathematics graduate alumnus, and Holley Lynch, a former postdoctoral associate in the MechMorpho Lab.

Davidson - Group - Tissue - Xenopus - Embryo

Davidson's group cultured tissue from the Xenopus embryo to uncover the mechanical properties behind embryonic morphogenesis -- the biological process of an organism developing its shape. During the study, they discovered that small explants spread slower than larger ones so they began creating modeling approaches to find out why.

They collected time-lapse image sequences over the course a few weeks, but the challenge when integrating modeling with experiments is determining the best set of parameters.

Models - Systems - Data - Chosen - Parameters

"As models get more complex and the experimental systems produce more data, it is difficult to determine if the chosen parameters are the optimal set," said Stepien, a postdoctoral associate at the University of Arizona. "This is where Bayesian computation is useful -- for each dataset, you can run the model thousands of times to identify sets of parameters that best match the experiment itself."

Once the group applied a Bayesian approach to their model, they found that there was no one unique parameter set. Instead, they identified distributions of 'almost-best' parameters and...
(Excerpt) Read more at: ScienceDaily
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