Session B02: Biophysics II

Mechanical Inference Reveals the Germband Epithelium as a Non-Conventional Material

Embryo and organ development depend on mechanical forces that shape and regulate cell and tissue structures. Measuring force is thus essential for analyzing the mechanisms that control tissue morphogenesis, and recent non-invasive methods based on cell shape and connectivity offer promising insights. In this study, we apply Bayesian inference to the converging and extending Drosophila germband epithelium, which exhibits planar-polarized myosin II and experiences anisotropic forces. We found that edges oriented along the dorsal-ventral (DV) axis exhibit higher tensions and shorter lengths compared to the longer, lower-tension edges along the anterior-posterior (AP) axis. These tension results are consistent with the observed higher myosin intensities at vertical cell edges compared to horizontal ones and with prior laser ablation measurements. This confirms a clear correlation between junctional myosin II intensity and edge tension. Interestingly, the tissue-level stress-strain curve shows an initial elastic buildup followed by plastic flow or failure, while the stress-strain rate plot displays highly non-Newtonian behavior, characterized by shear stiffening followed by shear thinning.   

Individual actions and collective movement on ant trails

Navigating through crowds is a major challenge for collective movement across various domains, from large animal migrations to intracellular transport systems. Experiments have shown that Argentine ants possess an unusual capability to evade common traffic jamming patterns at high densities, sustaining a stable two-way flow between their nest and a food source (Poissonnier et al., eLife 8:e48945, 2019). These ants do not exhibit large-scale spatiotemporal arrangements such as lane formation or oscillatory flow, suggesting the presence of a different dynamic process allowing them to maneuver efficiently without jamming.

In this work, we combine individual-based analysis of extant experimental data with agent-based modeling to explore individual ant movements and characteristics that contribute to the colony's collective behavior. By assessing spatiotemporal metrics such as density, interaction rate, and velocity, we examine how interaction types and their rates affect the colony's overall traffic dynamics. Our results lead us to suggest feedback mechanisms in which individual ants adjust their actions based on local environmental conditions over time. These mechanisms likely underpin the efficient bidirectional movement observed in crowded ant traffic.   

Immune Memory and Viral Mutation in CRISPR-Based Systems: A Computational Study of Bacterial-Phage Dynamics

Viruses evolve by accumulating mutations that allow them to evade recognition by the host immune system, creating a co-evolutionary dynamic between viral populations and adaptive immune responses. In this study, we present a computational model that simulates the interaction between bacteria and bacteriophages, focusing on the CRISPR-Cas immune system that bacteria use to defend against phage infections. Using a stochastic Gillespie algorithm, our model captures fundamental biological processes such as bacterial growth, phage production, spacer acquisition (immunity), and phage mutation. We model how bacteria gain immunity by incorporating viral DNA fragments (spacers) into their CRISPR arrays after successfully defending against phages. These spacers enable bacteria to recognize and neutralize phages in subsequent infections. The ability of bacteria to acquire new spacers and adapt to evolving phage strains results in dynamic population shifts driven by the ongoing evolutionary arms race between bacterial immunity and phage mutations. Our findings suggest that immune memory mediated by retention of CRISPR spacers plays an important role in maintaining bacterial population stability. However, when phage mutation rates are high, phages can still evade bacterial immunity, leading to a continuous cycle of immune adaptation. By applying both stochastic simulations and mean-field models, we determine the conditions under which bacterial populations can suppress phage infections and maintain stable population levels. This study provides new insights into the co-evolutionary dynamics between bacteria and phages, highlighting the importance of immune recognition and memory retention for bacterial survival. Our model provides a theoretical framework for understanding how CRISPR-based immunity impacts the long-term stability of microbial ecosystems.   

Exploring confinement effects on lipid diffusion across length scales with a scalable platform

Confinement effects are one of the main drivers of the heterogeneity observed in membrane dynamics, yet many open questions remain regarding their physical origins and manifestations across various experimental techniques. We have developed a simple platform that enables us to systematically probe the effect of confinement size and geometry over many different length scales in supported lipid bilayer systems. Here, we form fixed obstacles on a SiO₂ chip and then investigate how the two-dimensional diffusion of DLPC lipids is affected by the presence of these structures. Using optical microscopy techniques-- namely Fluorescence Correlation Spectroscopy (FCS) and Fluorescence Recovery After Photobleaching (FRAP)-- we observe that FCS and FRAP reveal different trends in diffusion as a function of confinement size; these variations ultimately originate from the different length scales probed by each technique. Our platform enables us to generate arbitrary patterns, which we use to experimentally test several previously published models, finding substantial discrepancies between simple analytical descriptions of confined diffusion and our experimental data.

A key advantage of our platform is its scalability and adaptability; we are not restricted to forming patterns on SiO₂, and the size of these patterns can be adjusted. This flexibility will allow for further exploration of confinement effects on nanoscale dynamics using new techniques, such as color centers in diamonds. By investigating how confinement influences diffusion at the nanoscale, we aim to construct a comprehensive understanding of how local behaviors contribute to the global dynamics observed by FCS and FRAP.   

Can Biophysics Predict Fitness of SARS-CoV-2 Variants?

SARS-CoV-2 employs its spike protein's receptor binding domain (RBD) to enter host cells. The RBD is constantly subjected to immune responses, while requiring efficient binding to host cell receptors for successful infection. However, our understanding of how RBD's biophysical properties contribute to SARS-CoV-2's epidemiological fitness remains largely incomplete. Through a comprehensive approach, comprising large-scale sequence analysis of SARS-CoV-2 variants and the identification of a fitness function based on binding thermodynamics, we unravel the relationship between the biophysical properties of RBD variants and their contribution to viral fitness. We developed a biophysical model that uses statistical mechanics to map the molecular phenotype space, characterized by dissociation constants of RBD to ACE2, LY-CoV016, LY-CoV555, REGN10987, and S309, onto an epistatic fitness landscape. We validate our findings through experimentally measured and machine learning (ML) estimated binding affinities, coupled with infectivity data derived from population-level sequencing. Our analysis reveals that this model effectively predicts the fitness of novel RBD variants and can account for the epistatic interactions among mutations, including explaining the later reversal of Q493R. Our study sheds light on the impact of specific mutations on viral fitness and delivers a tool for predicting the future epidemiological trajectory of previously unseen or emerging low-frequency variants. These insights offer not only greater understanding of viral evolution but also potentially aid in guiding public health decisions in the battle against COVID-19 and future pandemics.   

Origin of yield stress and mechanical plasticity in biological tissues

During development and under normal physiological conditions, biological tissues are constantly exposed to significant mechanical stresses. In response to large deformations, cells within a tissue must undergo collective rearrangements to maintain integrity and resilience. However, the temporal and spatial connections between these events remain unclear. In this study, through computational and theoretical modeling, we explored the mechanical plasticity of epithelial monolayers under substantial deformation. Our findings reveal that the jamming-unjamming (solid-fluid) transition in tissues varies markedly with the level of deformation, highlighting that tissues behave as highly unconventional materials. Through analytical modeling, we uncovered the underlying mechanisms driving this behavior. Additionally, we demonstrate that tissues accommodate large deformations through a series of collective rearrangements, resembling avalanches seen in non-living materials. These "tissue avalanches" are regulated by stress redistribution and the spatial arrangement of vulnerable regions. Finally, we present a straightforward and experimentally accessible framework to predict these avalanches and estimate mechanical stress in tissues using static images.   

Role of RyR cooperativity in Ca2+-wave-mediated triggered activity in cardiomyocytes

Ca²⁺ waves are known to trigger delayed afterdepolarizations (DADs) that can cause malignant cardiac arrhythmias. However, modeling Ca²⁺ waves using physiologically realistic models has remained a major challenge. Existing models with low Ca²⁺ sensitivity of RyRs necessitate large release currents, leading to an unrealistically large Ca²⁺ transient (CaT) amplitude incompatible with the experimental observations. Consequently, current physiologically detailed models of DADs resort to unrealistic cell architectures to produce Ca²⁺ waves with a normal CaT amplitude. Here, we address these challenges by incorporating RyR cooperativity into a physiologically detailed model with a realistic cell architecture. We represent RyR cooperativity phenomenologically through a Hill coefficient within the sigmoid function of RyR open probability. Simulations in permeabilized myocytes with high Ca²⁺ sensitivity reveal that a sufficiently large Hill coefficient is required for Ca²⁺ wave propagation via the fire-diffuse-fire mechanism. In intact myocytes, propagating Ca²⁺ waves can only occur within an intermediate Hill coefficient range. Within this range, the spark rate is neither too low, enabling Ca²⁺ wave propagation, nor too high, allowing for the maintenance of a high SR load during diastole of the action potential. Moreover, this model successfully replicates other experimentally observed manifestations of Ca²⁺-wave-mediated triggered activity, including phase 2 and phase 3 early afterdepolarizations, and high-frequency voltage-Ca²⁺ oscillations. These oscillations feature an elevated take-off potential with depolarization mediated by the L-type Ca²⁺ current. The model also sheds light on the roles of luminal gating of RyRs and the mobile buffer ATP in the genesis of these arrhythmogenic phenomena.