activity
20172026
most citedE. coli chemotaxis is information-limited

114 citations · 183 across the 6 of their papers we have counts for

collaborators

6 papers

physics.bio-ph2026

Measurement-limited learning of conformational heterogeneity in cryo-electron microscopy

Henry H. Mattingly, Luke Evans, Pilar Cossio

Cryogenic electron microscopy images sample individual biomolecules from their conformational landscapes, offering a route to infer the distributions underlying molecular mechanism…

physics.bio-ph2025★ 1 cited

Ultrasensitivity without conformational spread: A mechanical origin for non-equilibrium cooperativity in the bacterial flagellar motor

Henry H. Mattingly, Yuhai Tu

Flagellar motors enable bacteria to navigate their environments by switching rotation direction in response to external cues with high sensitivity. Previous work suggested that ult…

physics.bio-ph2024★ 1 cited

Chemotaxing E. coli do not count single molecules

Henry H. Mattingly, Keita Kamino, Jude Ong +3

Organisms use specialized sensors to measure their environments, but the fundamental principles that determine their accuracy remain largely unknown. In Escherichia coli chemotaxis…

cond-mat.soft2023★ 7 cited

Bacterial diffusion in disordered media, by forgetting the media

Henry H. Mattingly

We study bacterial diffusion in disordered porous media. Interactions with obstacles, at unknown locations, make this problem challenging. We approach it by abstracting the environ…

physics.bio-ph2021★ 114 cited

E. coli chemotaxis is information-limited

Henry H. Mattingly, Keita Kamino, Benjamin B. Machta +1

Organisms must acquire and use environmental information to guide their behaviors. However, it is unclear whether and how information quantitatively limits behavioral performance.…

physics.data-an2017★ 60 cited

Maximizing the information learned from finite data selects a simple model

Henry H. Mattingly, Mark K. Transtrum, Michael C. Abbott +1

We use the language of uninformative Bayesian prior choice to study the selection of appropriately simple effective models. We advocate for the prior which maximizes the mutual inf…