Category: journal article commentary

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Enhance Your Productivity by Ignoring Biophysics

Usually when I write about biophysics, it’s with the uplifting message that understanding physics helps us make sense of biology, bringing varied phenomena together under umbrellas of general principles. This is true, and there are countless examples. Brownian motion explains the meandering of neurotransmitters and the patterning of embryonic body segments. Electrical interactions influence the … Continue reading Enhance Your Productivity by Ignoring Biophysics

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Things Fall Apart; The Bacterial Cluster Cannot Hold

About a recent paper from my lab: Deepika Sundarraman, T. Jarrod Smith, Jade V. Z. Kast, Karen Guillemin, and Raghuveer Parthasarathy, “Disaggregation as an interaction mechanism among intestinal bacteria,” Biophysical Journal (2022). Some bacteria stick together; others do not. We have seen these variations among bacteria inside the zebrafish gut, and it’s probably the case … Continue reading Things Fall Apart; The Bacterial Cluster Cannot Hold

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Living Gels

About a recent paper from my lab: B. H. Schlomann and R. Parthasarathy, “Gut bacterial aggregates as living gels,” eLife, 10: e71105 (2021). DOI: 10.7554/eLife.71105. What do gut bacterial colonies look like? My group has been exploring this question for years, and ever since our first forays peering inside larval zebrafish it has been evident … Continue reading Living Gels

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Tossing Starfish from the Tidepools — Gut Microbiome Edition

About a paper from my lab [1] on competition and cooperation among gut microbes. Is the whole more than the sum of its parts? This question arises throughout the sciences, as one wonders whether understanding the constituents of some system suffices to understand the system as a whole. Sometimes the answer is “yes.” Electromagnetic fields, … Continue reading Tossing Starfish from the Tidepools — Gut Microbiome Edition

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Comments on over-interpreting results, correlation and causation, and concluding remarks — “Ten common statistical mistakes…” #9-10

This week: the last of my commentaries on Makin and Orban de Xivry’s Common Statistical Mistakes! (Previous posts: #1-2, #3 , #4, #5, #6, #7, #8.) I’m lumping together comments on “Mistake #9” (Over-interpreting non-significant results) and “Mistake #10” (Correlation and causation), as well as concluding remarks, writing one long post instead of two or … Continue reading Comments on over-interpreting results, correlation and causation, and concluding remarks — “Ten common statistical mistakes…” #9-10

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Comments on “Failure to Correct for Multiple Comparisons” — “Ten common statistical mistakes…” #8

This week’s installment of comments on Makin and Orban de Xivry’s Common Statistical Mistakes deals with #8: Failure to Correct for Multiple Comparisons. (Previous posts: #1-2, #3 , #4, #5, #6, #7.) Makin and Orban de Xivry’s description is rather complex, but the error is a simple one. To illustrate: suppose we have a control … Continue reading Comments on “Failure to Correct for Multiple Comparisons” — “Ten common statistical mistakes…” #8

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Comments on “p-Hacking (Flexibility of Analysis)” — “Ten common statistical mistakes…” #7

This week’s commentary on Makin and Orban de Xivry’s Common Statistical Mistakes covers #7: Flexibility of Analysis: p-Hacking. (Previous posts: #1-2, #3 , #4, #5, #6.) I feel like this has been discussed ad nauseum,* yet the problem still exists. The issue is that flexibility in how one analyzes data, even seemingly innocuous flexibility, can … Continue reading Comments on “p-Hacking (Flexibility of Analysis)” — “Ten common statistical mistakes…” #7