Pearson

William
Professor, Biochemistry and Molecular Genetics

Some of my most interesting scientific projects began when I realized that something that most scientists in my field took for granted "didn't make sense".  Often, what didn't make sense seemed sensible on the surface, but below the surface there were contradictions. When my students and I explored those contradictions in more detail, we made discoveries.  Science is often presented as a series of logical steps, one discovery leading to the next.  But the logic of the presentation obscures the scientific intuition that is required to pick the direction for the next hypothesis or experiment.  For me, the engagements offer a chance to explore the "does it make sense" nature of the scientific process.  How can we understand what scientists were thinking when they were wrong, and their  "Aha!" moment when they realized that what seemed to make sense didn't, which prompted them to search for better explanations.  For example, it makes complete sense that heavy objects fall faster than light objects, because, after all, they are heavier.  But the obvious observation that heavier objects weigh more than light objects, and thus should fall faster, had some implications that did not make sense, which lead Galileo and Newton to a simpler, but more powerful, perspective.  My engagements course will explore scientific befores and afters with a focus on why the wrong explanation made sense (or didn't), and why the current explanation prevailed.

I was trained as a "wet-lab" molecular biologist, purifying molecules from tissues to learn why different cells in the body, all with essentially the same set of genes, behave differently.  But at the same time, I was writing computer programs to collect and analyze data.  I continued to write computer programs to analyze DNA and protein sequences, which lead to the development of the FASTP and FASTA programs for rapid sequence similarity searching.  While FASTA is no longer widely used for similarity searches, the FASTA format is ubiquitous in genome biology and bioinformatics. I have taught Computational Biology and Bioinformatics to undergraduates, graduate students, post-doctoral fellows, and professors for more than 30 years.  I continue to explore aspects of protein sequences, and protein evolution, that seem to "not make sense".