Sector search strategies for odor trail tracking

Ants, mice, and dogs often use surface-bound scent trails to establish navigation routes or to find food and mates, yet their tracking strategies remain poorly understood. Chemotaxis-based strategies cannot explain casting, a characteristic sequence of wide oscillations with increasing amplitude performed upon sustained loss of contact with the trail. We propose that tracking animals have an intrinsic, geometric notion of continuity, allowing them to exploit past contacts with the trail to form an estimate of where it is headed. This estimate and its uncertainty form an angular sector, and the emergent search patterns resemble a “sector search.” Reinforcement learning agents trained to execute a sector search recapitulate the various phases of experimentally observed tracking behavior. We use ideas from polymer physics to formulate a statistical description of trails and show that search geometry imposes basic limits on how quickly animals can track trails. By formulating trail tracking as a Bellman-type sequential optimization problem, we quantify the geometric elements of optimal sector search strategy, effectively explaining why and when casting is necessary. We propose a set of experiments to infer how tracking animals acquire, integrate, and respond to past information on the tracked trail. More generally, we define navigational strategies relevant for animals and biomimetic robots and formulate trail tracking as a behavioral paradigm for learning, memory, and planning.

Correction: Electrochemistry meets polymer physics: polymerized ionic liquids on an electrified electrode

Correction for ‘Electrochemistry meets polymer physics: polymerized ionic liquids on an electrified electrode’ by Yury A. Budkov et al., Phys. Chem. Chem. Phys., 2022, DOI: 10.1039/d1cp04221a.

Marine Biopolymer Dynamics, Gel Formation, and Carbon Cycling in the Ocean

Much like our own body, our planet is a macroscale dynamic system equipped with a complex set of compartmentalized controls that have made life and evolution possible on earth. Many of these global autoregulatory functions take place in the ocean; paramount among those is its role in global carbon cycling. Understanding the dynamics of organic carbon transport in the ocean remains among the most critical, urgent, and least acknowledged challenges to modern society. Dissolved in seawater is one of the earth’s largest reservoirs of reduced organic carbon, reaching ~700 billion tons. It is composed of a polydisperse collection of marine biopolymers (MBP), that remain in reversible assembled↔dissolved equilibrium forming hydrated networks of marine gels (MG). MGs are among the least understood aspects of marine carbon dynamics. Despite the polymer nature of this gigantic pool of material, polymer physics theory has only recently been applied to study MBP dynamics and gel formation in the ocean. There is a great deal of descriptive phenomenology, rich in classifications, and significant correlations. Still missing, however, is the guide of robust physical theory to figure out the fundamental nature of the supramolecular interactions taking place in seawater that turn out to be critical to understanding carbon transport in the ocean.

Polymer models are a versatile tool to study chromatin 3D organization

The development of new experimental technologies is opening the way to a deeper investigation of the three-dimensional organization of chromosomes inside the cell nucleus. Genome architecture is linked to vital functional purposes, yet a full comprehension of the mechanisms behind DNA folding is still far from being accomplished. Theoretical approaches based on polymer physics have been employed to understand the complexity of chromatin architecture data and to unveil the basic mechanisms shaping its structure. Here, we review some recent advances in the field to discuss how Polymer Physics, combined with numerical Molecular Dynamics simulation and Machine Learning based inference, can capture important aspects of genome organization, including the description of tissue-specific structural rearrangements, the detection of novel, regulatory-linked architectural elements and the structural variability of chromatin at the single-cell level.

Scaling Concepts in Serpin Polymer Physics

α1-Antitrypsin is a protease inhibitor belonging to the serpin family. Serpin polymerisation is at the core of a class of genetic conformational diseases called serpinopathies. These polymers are known to be unbranched, flexible, and heterogeneous in size with a beads-on-a-string appearance viewed by negative stain electron microscopy. Here, we use atomic force microscopy and time-lapse dynamic light scattering to measure polymer size and shape for wild-type (M) and Glu342→Lys (Z) α1-antitrypsin, the most common variant that leads to severe pathological deficiency. Our data for small polymers deposited onto mica and in solution reveal a power law relation between the polymer size, namely the end-to-end distance or the hydrodynamic radius, and the polymer mass, proportional to the contour length. We use the scaling concepts of polymer physics to assess that α1-antitrypsin polymers are random linear chains with a low persistence length.