Session D27: DBIO Early Career Prize Session

Early Career Award for Biological Physics Research: Shaping living matter through mechanochemical feedback

Dynamics of living matter involve complex interplays between mechanical forces and biochemical signaling. However, the Integration of chemical signaling into physical models of active matter is challenging due to the complexity of the underlying interaction pathways. In this talk I will discuss our recent work on mechanochemical modeling of morphogenetic processes in cells that integrates theory with tractable biophysical measurements. First, I will present our work on mechanochemical patterning in cellular actin cortex, showing that the feedback loops between Rho-GTPase signaling and actomyosin contractility enable adaptive mechanical response and non-equilibrium dynamical behaviors observed in many physiological contexts, such as excitability, waves, and pulsatile contractions. I will then discuss how the feedback between actomyosin contractility and tension at cell-cell junctions promotes robust control of cell shapes, active topological transitions and solid-fluid transitions in tissues. Taken together, these studies provide concrete examples for how active biological systems couple physical forces with biochemical signaling for adaptive mechanical response.   

Elucidating the Role of Filament Turnover in Cortical Flow using Simulations and Representation Learning

Cell polarization relies on long-range cortical flows, which are driven by active stresses and resisted by the cytoskeletal network. While the general mechanisms that contribute to cortical flows are known, a quantitative understanding of the factors that tune flow speeds has remained lacking. Here, we combine physical simulation, representation learning, and theory to elucidate the role of actin turnover in cortical flows. We show how turnover tunes the actin density and filament curvature and use representation learning to demonstrate that these quantities are sufficient to predict cortical flow speeds. We extend a recent theory for contractility to account for filament curvature in addition to the nonuniform distribution of crosslinkers along actin filaments due to turnover. We obtain formulas that can be used to fit data from simulations and microscopy experiments. Our work provides insights into the mechanisms of contractility that contribute to cortical flows and how they can be controlled quantitatively.   

Noisy microbial population growth carries an imprint of initial conditions in its first-passage-time statistics

In exponential population growth, variability in the timing of individual division events and environmental factors (including stochastic inoculation) compound to produce variable growth trajectories. Interactions between noisy population growth and other biological processes (e.g., host immune response) can propagate, contributing to stochasticity in macroscopic biological functions. In several stochastic models of exponential growth we show power-law relationships that relate variability in the time required to reach a threshold population size to growth rate and inoculum size. Population-growth experiments in E. coli and S. aureus with inoculum sizes ranging between 1 and 100 are consistent with these relationships. We quantify how noise accumulates over time, finding that it encodes—and can be used to deduce—information about the early growth rate of a population.   

A bifurcation integrates information from many noisy ion channels and allows for milli-Kelvin thermal sensitivity in the snake pit organ

The thermal imaging organ of pit vipers is a remarkable example for how biological systems integrate information from many noisy sensors into a collective response. Single nerve fibers innervating the organ robustly respond to milli-Kelvin changes in temperature, even though the opening probability of each individual temperature-sensitive ion channel only changes by 0.1%. Here, we propose a mechanism for the integration of this noisy molecular information into an amplified response. Amplification arises due to proximity to a dynamical bifurcation, separating a regime with frequent and regular firing of action potentials (APs), from one with irregular and infrequent firing. Near the transition, AP frequency can have an extremely sharp dependence on temperature, and most of the information in molecular receptors is efficiently transmitted to AP firing even if additional noise corrupts the signal or readout. Our model explains several key features of experimental data. Most significantly, it predicts that the coefficient of variation in the distribution of times between APs decreases for larger AP frequency. It also suggests that the intrinsic channel timescale is slower than the timescale of the cell's voltage dynamics, thereby leading to memory in the state of the channels.   

Award for Outstanding Doctoral Thesis Research in Biological Physics: Fundamental limits to cell replication in extreme heat and cold

An important challenge is explaining how myriad cellular processes together dictate a "pace" at which a cell's life progresses. We intuitively think of life progressing at some pace, but it is unclear how to quantitatively define that pace and mathematically derive it from cellular processes. Several systems-level quantities may represent the pace. Important examples are a cell's doubling time or how rapidly a cell irreversibly loses its viability. Here, we focus on these two quantities in the context of temperature. Temperature is a universal parameter that affects the rates of virtually all cellular processes and thus the pace of a cell's life. Increasing or decreasing the temperature beyond some "optimal" range causes a cell to replicate more slowly. However, it remains unclear whether there is a limit to how slowly the cell can grow and replicate. More generally, it is unclear how temperature quantitatively constrains replication and the viability of a cell. We answer these open questions for the budding yeast, Saccharomyces cerevisiae, through an interplay of mathematical modeling and experiments at single-cell and genome-wide levels. Our findings revise the textbook view – yeast die at extreme temperatures due to protein misfolding or other damages that cells cannot autonomously repair – by revealing that cells help each other survive and replicate. These cooperative behaviors emerge from cells collectively fighting heat- and cold-induced reactive oxygen species. We quantify power-laws and phase diagrams that summarize the population dynamics of yeast in extreme heat and cold, and reveal "speed limits" – a fastest and slowest possible pace at which yeast can complete the cell cycle – for yeast's life at frigid temperatures. Together, our findings uncover quantitative, fundamental principles governing viability and replication of cells at extreme temperatures, and encourage further explorations of how thermal energy drives and constrains life.   

Optimal mechanical interactions direct multicellular network formation on elastic substrates

Cells probe their local environment by exerting mechanical forces on the surrounding medium. During tissue morphogenesis, cells may be driven by matrix-mediated mechanical interactions, which align them into functional, ordered structures. By combining a linear elastic model for substrate-mediated cell-cell mechanical interactions and an agent-based model for cell movement, we show that force dipoles modeling contractile cells on elastic substrates form branched networks that percolate when the interactions are sufficiently strong. We validate model predictions of an intermediate substrate stiffness which optimizes mechanical interactions and network formation by conducting experiments with endothelial cells cultured on hydrogel substrates of varying stiffness. Additionally, we quantitatively analyze experimental images and compare percolation and cell cluster shapes to simulations. Ultimately, we generate a phase diagram of a composite order parameter which captures large scale transport properties and small-scale morphological features which demonstrates strong agreement between simulations and experiments.   

Catch your breath: time-dependent bacterial turbulence in the presence of oxygen diffusion

The swimming of the bacterium Escherichia coli is powered by the local availability of dissolved oxygen in water. Swimming bacteria in a dense suspension drive fluid flows giving rise to jet and vortex-like patterns termed “bacterial turbulence.” Here, we use confocal microscopy to image the bacterial turbulent flow in a cylindrical well where the top surface is in contact with ambient air. We find that the interplay between the molecular diffusion of oxygen, the consumption of oxygen by the swimming bacteria, and the self-generated advective flows results in an unexpected time-dependent dynamics of bacterial turbulence flows. Furthermore, by simultaneous fluorescent imaging of active swimming bacteria alongside passive tracers, we measure the relative contributions of self-swimming and background turbulent flow in the transport of an individual bacterium at different local availabilities of oxygen. We construct a mathematical model incorporating diffusion and consumption of oxygen along with advection due to bacterial turbulence that explains our experimental observations. Our work helps elucidate the role of collective motion and nutrient consumption in the transport of nutrients through a population of bacteria.   

Modelling cell fate transitions with signal-driven attractor network bifurcations

A major problem in development is understanding how signaling pathways mediate cell identity. Current mathematical models fall into one of two categories: geometric landscape models of cell fate bifurcations driven by signals, or gene network models that capture the interactions between a handful of genes. However, no models currently bridge the gap between signal-driven bifurcations in an abstract cell fate landscape and the dynamics of gene expression. To address this, we introduce a new model for cell fate transitions that combines modern Hopfield networks with geometric landscapes and bifurcations. The dynamics occur in gene expression space while minimizing a chosen potential in cell type space, and the potential is controlled by signaling-related parameters. Any geometric landscape may be inserted into the model to generate testable predictions of the corresponding gene expression dynamics. Using single-cell RNA-sequencing data of cell types as our attractor states, we demonstrate our model's ability to predict gene expression changes during differentiation according to a changing cell fate landscape.   

Glioma cells in vitro display liquid crystal characteristics

Glioblastomas (GBM) are the most common adult brain tumors, characterized by rapid invasion into the normal brain and therapeutic resistance. We have previously shown that GBM tumors exhibit self-organized, nematically aligned, multicellular structures, termed "oncostreams," that influence tumor invasion and malignancy. To further understand oncostream dynamics and the biomechanical interactions between glioma cells and the ECM we established a novel in vitro system. Time-lapse imaging revealed the presence of topological defects in the GBM cultures. Two types of topological defects were mainly found in the system: comets (+1/2 charge) and trefoils (-1/2 charge). Our investigation of topological defects aims to reveal their potential functions within brain tumors related to cancer cell invasion, collective migration, and apoptosis. Our results show, on average, high levels of apoptosis within the trefoil defect core and at the head of comet defects. This also impacts local cell density, with about 40% fewer cells at the trefoil core compared to elsewhere in the culture. Future work aims to study this phenomenon in 3D, utilizing a gel platform to allow for the hypothesized migration upwards at defect sites. Our results demonstrate that glioma cells grown in vitro behave as liquid crystals. Our data will define novel physical functional structures in GBMs, leading to the development of therapeutic strategies targeting oncostreams and topological defects.   

Clone size statistics of tumor-inhabiting bacteria

Bacteria inhabit different areas of the human body and perform essential functions. Increasing evidence of bacterial effects on cancer progression has brought interest to tumor-inhabiting bacteria. However, it is not understood how bacteria affect the tumor, nor how the tumor environment affects bacterial dynamics. Recent experiments, done with barcoded bacterial colonies, show that clone sizes of bacteria inhabiting tumors in mice exhibit universal statistical patterns. The patterns are robust across experiments and collection times, and unique to bacteria grown in the tumor environment rather than in liquid culture. We find that the liquid experiments can be explained by a simple birth-death process that cannot capture the observed statistics in the tumor. In this work, we develop a mechanistic understanding of the microecological dynamics of tumor-inhabiting bacteria. We present a physical model that captures the observed statistics with simple assumptions and explains the uniqueness of this observation to the tumor environment.