The emergent dynamics exhibited by collections of living organisms often shows signatures of symmetries that are broken at the single-organism level. At the same time, organism development itself encompasses a well-coordinated sequence of symmetry breaking events that successively transform a single, nearly isotropic cell into an animal with well-defined body axis and various anatomical asymmetries. Combining these key aspects of collective phenomena and embryonic development, we describe here the spontaneous formation of hydrodynamically stabilized active crystals made of hundreds of starfish embryos that gather during early development near fluid surfaces. We describe a minimal hydrodynamic theory that is fully parameterized by experimental measurements of microscopic interactions among embryos. Using this theory, we can quantitatively describe the stability, formation and rotation of crystals and rationalize the emergence of mechanical properties that carry signatures of an odd elastic material. Our work thereby quantitatively connects developmental symmetry breaking events on the single-embryo level with remarkable macroscopic material properties of a novel living chiral crystal system.

Changes in the geometry and topology of self-assembled membranes underlie diverse processes across cellular biology and engineering. Similar to lipid bilayers, monolayer colloidal membranes studied by the Sharma (IISc Bangalore) and Dogic (UCSB) Labs have in-plane fluid-like dynamics and out-of-plane bending elasticity, but their open edges and micron length scale provide a tractable system to study the equilibrium energetics and dynamic pathways of membrane assembly and reconfiguration. First, we discuss how doping colloidal membranes with short miscible rods transforms disk-shaped membranes into saddle-shaped minimal surfaces with complex edge structures. Theoretical modeling demonstrates that their formation is driven by increasing positive Gaussian modulus, which in turn is controlled by the fraction of short rods. Further coalescence of saddle-shaped surfaces leads to exotic topologically distinct structures, including shapes similar to catenoids, tri-noids, four-noids, and higher order structures. We then mathematically explore the mechanics of these catenoid-like structures subject to an external axial force and elucidate their intimate connection to two problems whose solutions date back to Euler: the shape of an area-minimizing soap film and the buckling of a slender rod under compression. A perturbation theory argument directly relates the tensions of membranes to the stability properties of minimal surfaces. We also investigate the effects of including a Gaussian curvature modulus, which, for small enough membranes, causes the axial force to diverge as the ring separation approaches its maximal value.

The fusion of cell aggregates widely exists during biological processes such as development, tissue regeneration, and tumor invasion. Cellular spheroids (spherical cell aggregates) are commonly used to study this phenomenon. In previous studies, with approximated assumptions and measurements, researchers found that the fusion of two spheroids with some cell type is similar to the coalescence of two liquid droplets. However, with more accurate measurements focusing on the overall shape evolution in this process, we find that even in the previously-regarded liquid-like regime, the fusion process of spheroids can be very different from regular liquid coalescence. We conduct numerical simulations using both standard particulate models and vertex models with both Molecular Dynamics and Brownian Dynamics. The simulation results show that the difference between spheroids and regular liquid droplets is caused by the microscopic overdamped dynamics of each cell rather than the topological cell-cell interactions in the vertex model. Our research reveals the necessity of a new continuum theory for “liquid” with microscopically overdamped components, such as cellular and colloidal systems. Detailed analysis of our simulation results of different system sizes provides the basis for developing the new theory.

The question of the origin of homochirality of living matter, or the dominance of one handedness for all molecules of life across the entire biosphere, is a long-standing puzzle in the research on the Origin of Life. In the fifties, Frank proposed a mechanism to explain homochirality based on the properties of a simple autocatalytic network containing only a few chemical species. Following this work, chemists struggled to find experimental realizations of this model, possibly due to a lack of proper methods to identify autocatalysis [1]. In any case, a model based on a few chemical species seems rather limited, because prebiotic earth is likely to have consisted of complex ‘soups’ of chemicals. To include this aspect of the problem, we recently proposed a mechanism based on certain features of large out-of-equilibrium chemical networks [2]. We showed that a phase transition towards an homochiral state is likely to occur as the number of chiral species in the system becomes large or as the amount of free energy injected into the system increases. Through an analysis of large chemical databases, we showed that there is no need for very large molecules for chiral species to dominate over achiral ones; it already happens when molecules contain about 10 heavy atoms. We also analyzed the various conventions used to measure chirality and discussed the relative chiral signs adopted by different groups of molecules [3]. We then proposed a generalization of Frank’s model for large chemical networks, which we characterized using random matrix theory. This analysis includes sparse networks, suggesting that the emergence of homochirality is a robust and generic transition. References: [1] A. Blokhuis, D. Lacoste, and P. Nghe, PNAS (2020), 117, 25230. [2] G. Laurent, D. Lacoste, and P. Gaspard, PNAS (2021) 118 (3) e2012741118. [3] G. Laurent, D. Lacoste, and P. Gaspard, Proc. R. Soc. A 478:20210590 (2022).

Networks of stiff biopolymers are an omnipresent structural motif in cells and tissues. A prominent modeling framework for describing biopolymer network mechanics is rigidity percolation theory. This theory describes model networks as nodes joined by randomly placed, springlike bonds. Increasing the amount of bonds in a network results in an abrupt, dramatic increase in elastic moduli above a certain threshold – an example of a mechanical phase transition. While homogeneous networks are well studied, many tissues are made of disparate components and exhibit spatial fluctuations in the concentrations of their constituents. In this talk, I will first discuss recent work in which we explained the structural basis of the shear mechanics of healthy and chemically degraded cartilage by coupling a rigidity percolation framework with a background gel. Our model takes into account collagen concentration, as well as the concentration of peptidoglycans in the surrounding polyelectrolyte gel, to produce a structureproperty relationship that describes the shear mechanics of both sound and diseased cartilage. I will next discuss the introduction of structural correlation in constructing networks, such that sparse and dense patches emerge. I find moderate correlation allows a network to become rigid with fewer bonds, while this benefit is partly erased by excessive correlation. We explain this phenomenon through analysis of the spatial fluctuations in strained networks’ displacement fields. Finally, I will address our work’s implications for non-invasive diagnosis of pathology, as well as rational design of prostheses and novel soft materials.

The vertebrate retina is an important outpost of the central nervous system, responsible for the perception and transmission of visual information. It consists of five different types of neurons that reproducibly laminate into three layers, a process of crucial importance for the organ’s function. Unsurprisingly, impaired fate decisions as well as impaired neuronal migrations and lamination lead to impaired retinal function. However, how processes are coordinated at the cellular and tissue level and how variable or robust retinal formation is, is currently still underexplored. In my lab, we aim to shed light on these questions from different angles, studying on the one hand differentiation phenomena and their variability and on the other hand the downstream migration and lamination phenomena. We use zebrafish as our main model system due to its excellent possibilities for live imaging and quantitative developmental biology. More recently we also started to use human retinal organoids as a comparative system. We further employ cross disciplinary approaches to address these issues combining work of cell and developmental biology, biomechanics, theory and computer science. Together, this allows us to integrate cell with tissue-wide phenomena and generate an appreciation of the reproducibility and variability of events.

The richness of active matter's spatiotemporal patterns continues to capture our imagination. Shaping these emergent dynamics into pre-determined forms of our choosing is a grand challenge in the field. To complicate matters, multiple dynamical attractors can coexist in such systems, leading to initial condition-dependent dynamics. Consequently, non-trivial spatiotemporal inputs are generally needed to access these states. Optimal control theory provides a general framework for identifying such inputs and represents a promising computational tool for guiding experiments and interacting with various systems in soft active matter and biology. As an exemplar, I first consider an extensile active nematic fluid confined to a disk. In the absence of control, the system produces two topological defects that perpetually circulate. Optimal control identifies a time-varying active stress field that restructures the director field, flipping the system to its other attractor that rotates in the opposite direction. As a second, analogous case, I examine a small network of coupled Belousov-Zhabotinsky chemical oscillators that possesses two dominant attractors, two wave states of opposing chirality. Optimal control similarly achieves the task of attractor switching. I conclude with a few forward-looking remarks on how the same model-based control approach might come to bear on problems in biology.

A major unresolved question in microbiome research is whether the complex ecological patterns observed in surveys of natural communities can be explained and predicted by fundamental, quantitative principles. Bridging theory and experiment is hampered by the multiplicity of ecological processes that simultaneously affect community assembly and a lack of theoretical tools for modeling diverse ecosystems. Here, I will present a simple ecological model of microbial communities that reproduces large-scale ecological patterns observed across multiple natural and experimental settings including compositional gradients, clustering by environment, diversity/harshness correlations, and nestedness. Surprisingly, our model works despite having a “random metabolisms” and “random consumer preferences”. This raises the natural of question of why random ecosystems can describe real-world experimental data. In the second, more theoretical part of the talk, I will answer this question by showing that when a community becomes diverse enough, it will always self-organize into a stable state whose properties are well captured by a “typical random ecosystems”.

Chromosomes are extremely long, active polymers that are spatially organized across multiple scales to promote cellular functions, such as gene transcription and genetic inheritance. During each cell cycle, chromosomes are dramatically compacted as cells divide and dynamically reorganized into less compact, spatiotemporally patterned structures after cell division. These activities are facilitated by DNA/chromatin-binding protein motors called SMC complexes. Each of these motors can perform a unique activity known as “loop extrusion,” in which the motor binds the DNA/chromatin polymer, reels in the polymer fiber, and extrudes it as a loop. Using simulations and theory, I show how loop-extruding motors can collectively compact and spatially organize chromosomes in different scenarios. First, I show that loop-extruding complexes can generate sufficient compaction for cell division, provided that loop-extrusion satisfies stringent physical requirements. Second, while loop-extrusion alone does not uniquely spatially pattern the genome, interactions between SMC complexes and protein “boundary elements” can generate patterns that emerge in the genome after cell division. Intriguingly, these “boundary elements” are not necessarily stationary, which can generate a variety of patterns in the neighborhood of transcriptionally active genes. These predictions, along with supporting experiments, show how SMC complexes and other molecular machinery, such as RNA polymerase, can spatially organize the genome. More generally, this work demonstrates both the versatility of the loop extrusion mechanism for chromosome functional organization and how seemingly subtle microscopic effects can emerge in the spatiotemporal structure of nonequilibrium polymers.

The spontaneous collective motion of self-propelled agents is ubiquitous in the natural world, and it often occurs in complex environments, be it bacteria and cells migrating through polymeric extracellular matrix or animal herds and human crowds navigating structured terrains. Much is known about flocking dynamics in pristine backgrounds, but how do spatio-temporal heterogeneities in the environment impact such collective self-organization? I will present two model systems, a colloidal active fluid negotiating disordered obstacles and a confined dense bacterial suspension in a viscoelastic medium, as controllable platforms to explore this question and highlight general mechanisms for active self-organization in complex environments. By combining theory and experiment, I will show how flocks on disordered substrates organize into a novel dynamic vortex glass phase, akin to vortex glasses in dirty superconductors, while the presence of viscoelasticity can calm the otherwise turbulent swarming of bacteria, allowing the emergence of a large scale coherent and even oscillatory vortex when confined on the millimetre scale.

Self-organized complex structures in nature, e.g., viral capsids, hierarchical biopolymers, and bacterial flagella, offer efficiency, adaptability, robustness, and multi-functionality. Can we program the self-assembly of three-dimensional (3D) complex structures using simple building blocks, and reach similar or higher level of sophistication in engineered materials? Here we present an analytic theory for the self-assembly of polyhedral nanoparticles (NPs) based on their crystal structures in non-Euclidean space. We show that the unavoidable geometrical frustration of these particle shapes, combined with competing attractive and repulsive interparticle interactions, lead to controllable self-assembly of structures of complex order. Applying this theory to tetrahedral NPs, we find high-yield and enantiopure self-assembly of helicoidal ribbons, exhibiting qualitative agreement with experimental observations. We expect that this theory will offer a general framework for the self-assembly of simple polyhedral building blocks into rich complex morphologies with new material capabilities such as tunable optical activity, essential for multiple emerging technologies.

Embryo morphogenesis is impacted by dynamic changes in tissue material properties, which have been proposed to occur via processes akin phase transitions (PTs). Here, we show that rigidity percolation provides a simple and robust theoretical framework to predict material/structural PTs of embryonic tissues from local cell connectivity. By using percolation theory, combined with directly monitoring dynamic changes in tissue rheology and cell contact mechanics, we demonstrate that the zebrafish blastoderm undergoes a genuine rigidity PT, brought about by a small reduction in adhesion-dependent cell connectivity below a critical value. We quantitatively predict and experimentally verify hallmarks of PTs, including power-law exponents and associated discontinuities of macroscopic observables at criticality. Finally, we show that this uniform PT depends on blastoderm cells undergoing meta-synchronous divisions causing random and, consequently, uniform changes in cell connectivity. Collectively, our theoretical and experimental findings reveal the structural basis of material PTs in an organismal context.

Microorganisms can swim in a variety of environments, interacting with chemicals and other proteins in the fluid. In this talk, we will highlight recent computational methods and results for swimming efficiency and hydrodynamic interactions of swimmers in different fluid environments. Sperm are modeled via a centerline representation where forces are solved for using elastic rod theory. The method of regularized Stokeslets is used to solve the fluid-structure interaction where emergent swimming speeds can be compared to asymptotic analysis. In the case of fluids with extra proteins or cells that may act as friction, swimming speeds may be enhanced, and attraction may not occur. We will also highlight how parameter estimation techniques can be utilized to infer fluid and/or swimmer properties.

Cement is the main binding agent in concrete, literally gluing together rocks and sand into the most-used synthetic material on Earth. However, cement production is responsible for significant amounts of man- made greenhouse gases—in fact if the cement industry were a country, it would be the third largest emitter in the world. Alternatives to the current, environmentally harmful cement production process are not available essentially because the gaps in fundamental understanding hamper the development of smarter and more sustainable solutions. The ultimate challenge is to link the chemical composition of cement grains to the nanoscale physics of the cohesive forces that emerge when mixing cement with water. Cement nanoscale cohesion originates from the electrostatics of ions accumulated in a water-based solution between like-charged surfaces but it is not captured by existing theories because of the nature of the ions involved and the high surface charges. Surprisingly enough, this is also the case for unexplained cohesion in a range of colloidal and biological matter. About one century after the early studies of cement hydration, we have quantitatively solved this notoriously hard problem and discovered how cement cohesion develops during hydration. I will discuss how 3D numerical simulations that feature a simple but molecular description of ions and water, together with an analytical theory that goes beyond the traditional continuum approximations, helped us demonstrate that the optimized interlocking of ion-water structures determine the net cohesive forces and their evolution. These findings open the path to scientifically grounded strategies of material design for cements and have implications for a much wider range of materials and systems where ionic water-based solutions feature both strong Coulombic and confinement effects, ranging from biological membranes to soils. Construction materials are central to our society and to our life as humans on this planet, but usually far removed from fundamental science. We can now start to understand how cement physical-chemistry determines performance, durability and sustainability.

Inspired by ongoing experiments on three dimensional active gels composed of sliding microtubule bundles, we study a few idealized problems in a minimal hydrodynamic model for active liquid crystals. Our aim is to use flow to determine the value of the coefficient of activity in a continuum theory. We consider the case of apolar active particles that form a disordered phase in the absence of flow, and study how activity affects the swimming speed of a prescribed swimmer, as well as the stability of a fluid interface. We also consider flows of active matter in channels or past immersed objects.

Physics offers countless examples for which theoretical predictions are astonishingly powerful. But it’s hard to imagine a similar precision in complex systems where the number and interdependencies between components simply prohibits a first-principles approach, look no further than the challenge of the billions of neurons and trillions of connections within our own brains. In such settings how do we even identify the important theoretical questions? We describe a systems-scale perspective in which we integrate information theory, dynamical systems and statistical physics to extract understanding directly from measurements. We demonstrate our approach with a reconstructed state space of the behavior of the nematode C. elegans, revealing a chaotic attractor with symmetric Lyapunov spectrum and a novel perspective of motor control. We then outline a maximally predictive coarse-graining in which nonlinear dynamics are subsumed into a linear, ensemble evolution to obtain a simple yet accurate model on multiple scales. With this coarse-graining we identify long timescales and collective states in the Langevin dynamics of a double-well potential, the Lorenz system and in worm behavior. We suggest that such an ``inverse’’ approach offers an emergent, quantitative framework in which to seek rather than impose effective organizing principles of complex systems.

Neural networks can learn and recognize subtle correlations in high dimensional inputs. However, neural networks are simply many-body systems with strong non-linearities and disordered interactions. Hence, many-body physical systems with similar interactions should be able to show neural network-like behavior. Here we show neural network-like behavior in the nucleation dynamics of promiscuously interacting molecules with multiple stable crystalline phases. Using a combination of theory and experiments, we show how the physics of the system dictates relationships between the difficulty of the pattern recognition task solved, time taken and accuracy. This work shows that high dimensional pattern recognition and learning are not special to software algorithms but can be achieved by the collective dynamics of sufficiently disordered molecular systems.

It is sometimes said that there are two reasons why physics is so successful as a science. One is that it deals with very simple problems. The other is that it attempts to account only for universal aspects of systems at a desired level of description, with lower level phenomena subsumed into a small number of adjustable parameters. It is a widespread belief that this approach seems unlikely to be useful in biology, which is intimidatingly complex, where “everything has an exception”, and where there are a huge number of undetermined parameters. I will try to argue, nonetheless, that there are important, experimentally-testable aspects of biology that exhibit universality, and should be amenable to being tackled from a physics perspective. My suggestion is that this can lead to useful new insights into the existence and universal characteristics of living systems. I will try to justify this point of view by contrasting the goals and practices of the field of condensed matter physics with materials science, and then by extension, the goals and practices of the newly emerging field of “Physics of Living Systems” with biology. Specific biological examples that I will discuss include the following: Universal patterns of gene expression in cell biology Universal scaling laws in ecosystems, including the species-area law, Kleiber’s law, Paradox of the Plankton Universality of the genetic code Universality of thermodynamic utilization in microbial communities Universal scaling laws in the tree of life The question of what can be learned from studying universal phenomena in biology will also be discussed. Universal phenomena, by their very nature, shed little light on detailed microscopic levels of description. Yet there is no point in seeking idiosyncratic mechanistic explanations for phenomena whose explanation is found in rather general principles, such as the central limit theorem, that every microscopic mechanism is constrained to obey. Thus, physical perspectives may be better suited to answering certain questions such as universality than traditional biological perspectives. Concomitantly, it must be recognized that the identification and understanding of universal phenomena may not be a good answer to questions that have traditionally occupied biological scientists. Lastly, I plan to talk about what is perhaps the central question of universality in biology: why does the phenomenon of life occur at all? Is it an inevitable consequence of the laws of physics or some special geochemical accident? What methodology could even begin to answer this question? I will try to explain why traditional approaches to biology do not aim to answer this question, by comparing with our understanding of superconductivity as a physical phenomenon, and with the theory of universal computation. References Nigel Goldenfeld, Tommaso Biancalani, Farshid Jafarpour. Universal biology and the statistical mechanics of early life. Phil. Trans. R. Soc. A 375, 20160341 (14 pages) (2017). Nigel Goldenfeld and Carl R. Woese. Life is Physics: evolution as a collective phenomenon far from equilibrium. Ann. Rev. Cond. Matt. Phys. 2, 375-399 (2011).

When was the last time you ran into a giant? Chances are never. Almost 100 years ago, JBS Haldane posed an outwardly simple yet complex question – what is the most optimal size (for a biological system)? The living world around us contains a huge diversity of organisms, each with its own characteristic size. Even the size of subcellular organelles is tightly controlled. In absence of physical rulers, how do cells and organisms truly “know” how large is large enough? What are the mechanisms in place to enforce size control? Many of these questions have motivated generations of scientists to look for physical principles underlying size control in biological systems. In the next edition of Emory's Theory and Modeling of Living Systems (TMLS) workshop series, our panel of speakers will take a close look at these questions, across the entire scale - from the molecular, all the way to the ecosystem.

Understanding the transport of microorganisms and self-propelled particles in porous media has important consequences in human health as well as for microbial ecology. In this work, we explore models for the dispersion of active particles in both periodic and random porous media. In a first problem, we analyze the long-time transport properties in a dilute system of active Brownian particles swimming in a periodic lattice in the presence of an external flow. Using generalized Taylor dispersion theory, we calculate the mean transport velocity and dispersion dyadic and explain their dependence on flow strength, swimming activity and geometry. In a second approach, we address the case of run-and-tumble particles swimming through unstructured porous media composed of randomly distributed circular pillars. There, we show that the long-time dispersion is described by a universal hindrance function that depends on the medium porosity and ratio of the swimmer run length to the pillar size. An asymptotic expression for the hindrance function is derived in dilute media, and its extension to semi-dilute and dense media is obtained using stochastic simulations. We conclude by discussing the role of hydrodynamic interactions and swimmer concentration effects.

It is often assumed that biological swimmers conform faithfully to certain stereotypes assigned to them by physicists and mathematicians, when the reality is in fact much more complicated. In this talk we will use a combination of theory, experiments, and robotics, to understand the physical and evolutionary basis of motility control in a number of distinguished organisms. These organisms differ markedly in terms of their size, shape, and arrangement of locomotor appendages, but are united in their use of cilia - the ultimate shape-shifting organelle - to achieve self-propulsion and navigation.

Many of us came to biology from physics. There we have been trained on such classic examples as muon g-2, where experimental data and theoretical predictions agree to many significant digits. Now, working in biology, we routinely hear that it is messy, most details matter, and that the best hope for theory in biology is to be semi-qualitative, predict general trends, and to forgo the hope of ever making quantitative predictions with the precision that we are used to in physics. Colloquially, we should be satisfied even if data and models differ so much that plotting them on the same plot makes little sense. However, some of us won’t be satisfied by this. So can we take theory in biology seriously and predict experimental outcomes within (small) error bars? Certainly, we won’t be able to predict everything, but this is never required, even in traditional physics. But we should be able to choose some features of data that are nontrivial and interesting, and focus on them. We also should be able to find different classes of models --- maybe even null models --- that match biology better, and thus allow for a better agreement. It is even possible that large-dimensional datasets of modern high-throughput experiments, and the ensuing “more is different” statistical physics style models will make quantitative, precise theory easier. To explore the role of quantitative theory in biology, in this workshop, eight speakers will address some of the following general questions based on their specific work in different corners of biology: Which features of biological data are predictable? Which types of models are best suited to making quantitative predictions in different fields? Should theorists interested in quantitative predictions focus on different questions, not typically asked by biologists? Do large, multidimensional datasets make theories (and which theories?) more or less likely to succeed? This will be an unapologetically theoretical physics workshop — we won’t focus on a specific subfield of biology, but will explore these questions across the fields, hoping that the underlying theoretical frameworks will help us find the missing connections.

The Smoluchowski equation has often been used as the starting point of many continuum models of active suspensions. However, its six-dimensional nature depending on time, space and orientation requires a huge computational cost, fundamentally limiting its use for large-scale problems, such as mixing and transport of active fluids in turbulent flows. Despite the singular nature in strain-dominant flows, the generalised Taylor dispersion (GTD) theory (Frankel & Brenner 1991, J. Fluid Mech. 230:147-181) has been understood to be one of the most promising ways to reduce the Smoluchowski equation into an advection-diffusion equation, the mean drift and diffusion tensor of which rely on ‘local’ flow information only. In this talk, we will introduce an exact transformation of the Smoluchowski equation into such an advection-diffusion equation requiring only local flow information. Based on this transformation, a new advection-diffusion equation will subsequently be proposed by taking an asymptotic analysis in the limit of small particle velocity. With several examples, it will be demonstrated that the new advection-diffusion model, non-singular in strain-dominant flows, outperforms the GTD theory.