News: Research

Stephanie Palmer

10/01/2024

Congratulations Stephanie Palmer!

Please join us in congratulating Stephanie Palmer as one of the new Schmidt Science Polymaths! This program offers support to researchers looking to explore different focuses with their work. Stephanie has several projects she is interested in pursuing including, examine the evolution of neural computation in color vision for butterflies and the circadian rhythms of bacteria, specifically how their environment affects computation and migration. Read the full article here.

An experimental image of a moth fly walking across sand paper

Natural environments are rarely flat. Walking animals must navigate substrates that are rough and variable across multiple scales. We do not understand the principles of adaptability in biomechanics that can allow an organism to deal with such variability on a range of length scales. We showed how moth flies (Clogmia albipunctata) adjust stepping kinematics and coordination patterns to deal with substrates of varying levels of roughness.  Above is an experimental image of a moth fly walking across sand paper of a certain grit. The gait changes as grit is modified.  Colored dots are features used to track gait.  This work is published by Brandt et. al., Annals of the New York Academy of Sciences 2024 from Nirody’s lab at the Physics Frontier Center for Living Systems.

Figure depicting chiral active fluids

Pattern formation entails a process of wavelength selection, which can usually be traced to the linear instability of a homogeneous state. By contrast, here we propose a fully nonlinear mechanism triggered by the non-dissipative arrest of turbulent cascades. We show that the tunable wavelength of these cascade-induced patterns can be set by a non-dissipative transport coefficient called odd viscosity. Such cascade-induced patterns naturally arise across scales, from bio-active chiral fluids to stellar plasma or in the coagulation of droplets in which mass rather than energy cascades. This work was published by de Wit et. al. Nature 2024 in the Vitelli group at the Center for Living Systems.

Image of cytoskeletal proteins

Cellular form and function emerge from complex mechanochemical systems within the cells. Currently, no systematic strategy exists to infer large-scale physical properties of a cell from its molecular components. This is an obstacle to understanding processes such as cell adhesion and migration. Here, we develop a data-driven modeling pipeline to learn the mechanical behavior of adherent cells.  We developed a data-driven modeling pipeline to learn the mechanical behavior of adherent cells. We first train neural networks to predict cellular forces from images of cytoskeletal proteins. We then develop two approaches—one constrained by physics and the other agnostic—to construct data-driven continuum models of cellular forces. Both reveal how cellular forces are encoded by two distinct length scales. Beyond adherent cell mechanics, our work serves as a case study for integrating neural networks into predictive models for cell biology. This work, published in Schmitt et al., Cell 2024 is a collaboration between Vitelli and Gardel.

Potassium ion channels acting on a tissue interface, vertical cross-section view

Spiking is a general phenomenon that is crucial in the firing of neurons, beating of hearts, and spread of diseases. In homogeneous media, spiking arises from a local competition between amplifying and suppressing forces. But most real-world systems are far from homogeneous. We demonstrated that inhomogeneities such as interfaces and boundaries (that spatially segregate these two forces) can promote spiking, even if the system does not spike when these forces are evenly mixed. Our findings apply to chemical reactions, predator–prey dynamics, and recent electrophysiology experiments, in which localized action potentials were observed at the interface of distinct, nonspiking bioelectric tissues. This work was published by Scheiber et. al. PNAS 2024.

Plot showing show distributions of the total energy extracted by ecosystems as a function of the number of species

Life on earth relies on sunlight for energy, but this energy can only be exploited through the collective recycling of matter by communities of microbes, plants, and animals. Yet we lack a framework for understanding how ecosystems can organize themselves to collectively capture the sun’s energy by running cycles of matter subject to thermodynamic constraints. We advance a conceptual model to study the collective properties of nutrient-cycling ecosystems. Surprisingly, even though species “greedily” extract energy from the environment, sufficiently diverse communities of species almost always manage to sustain themselves by extracting enough energy.  The plots above show distributions of the total energy extracted by ecosystems as a function of the number of species. As ecosystems become more species-rich, ecosystems extract greater average energy with greater convergence. This result from the Murugan lab at the CLS is described in Goyal et al, PNAS (2023).

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