Session K06: PRX Life Invited Session: Exploring the Frontiers of Physics and Living Systems

Data-Driven Model Construction for Anisotropic Dynamics of Active Matter

The ability of cells to reorganize in response to external stimuli is important in areas ranging from morphogenesis to tissue engineering. Recently, we have discovered that flat substrates with nematic order can induce nematic alignment of dense, spindle-like cells, thereby influencing cell organization on the scale of the entire substrate. Remarkably, single cells are not sensitive to the substrate's anisotropy. Rather, the emergence of global nematic order is a collective phenomenon that requires both steric effects and molecular-scale anisotropy of the substrate. We develop new statistical learning approaches to extend state-of-the-art physics models for quantifying both effects by efficient feature selection that avoids fitting models by all combinations of features. By including these features, such as non-Gaussian, anisotropic fluctuations, and limiting interactions to only neighboring cells with similar velocity directions, this model reproduces the temporal progression of the velocity orientational order and the variability of velocity vectors, whereas models missing any of the features fail to recapitulate these temporally dependent properties. We found that the alignment order is facilitated by enhanced cell division along the substrate's nematic axis, and associated extensile stresses that restructure the cells' actomyosin networks. Our work provides a new understanding of cellular reorganization among weakly interacting cells.   

Precision control by protein counting (not concentration sensing)

Balanced biosynthesis is the hallmark of bacterial cell physiology, where the protein concentrations remain steady during cell elongation. This presents a challenge when attempting to model bacterial cell-cycle and cell-size controls, as the concentration-based models used for eukaryotes do not directly apply. In this seminar, I will delve into the initiator-titration model, a concept introduced three decades ago, which elucidates how bacteria can precisely and robustly control replication initiation by sensing the number of protein copies. At its heart, this model proposes a two-step Poisson process for initiation: first, the titration of initiator proteins by high-affinity chromosome sites, and then their accumulation at the replication origin in a manner akin to a first-passage-time process. This results in profoundly enhanced initiation synchrony, with a CV scaling of approximately 1/N, contrasting with the typical 1/√N scaling in the Poisson process, where N represents the total initiator proteins. The mechanism presented in this work provides a satisfying general solution to how the cell can achieve precision control without sensing protein concentrations, with broad implications from evolution to the design of synthetic cells.   

Synthetic biology of Vector-Borne disease

The recent pandemic has taught us that the modern world is vulnerable to infectious diseases and demonstrated that we need to train ourselves to deal with future pandemics. One way to expand our knowledge of epidemics is to build laboratory systems that mimic aspects of disease spreading under safe conditions. To this end, we have developed a model system using a dimorphic bacteriophage that allowed us to recapitulate the spread of a vector-borne disease. The characteristic that renders a disease vector-borne is a life cycle that requires alternating types of host. Our "malaria in a petri dish" system is based on an engineered, nonhazardous, dimorphic bacteriophage with a life cycle that requires alternation between two host bacteria. We quantified the growth properties of the hosts and virus forms and examined the spread between independent bacterial microcolonies in an immobilized suspension. A mathematical model was able to describe the observed spread of the virus by treating the microcolonies as independent communities. The model correctly predicted both the efficiency of herd immunity and a reduced spread of infection when colonies are larger and more separated.   

Odd elastohydrodynamics: non-reciprocal living material in a viscous fluid

Microswimmers are a central target in understanding the biological system from a physical point of view. Microswimmers consume energy to generate shape change to propel themselves at low Reynolds numbers. In this talk, we introduce the concept of odd elasticity, a relatively new framework for describing the non-equilibrium state of matter and an extension of linear elasticity, to the field of microswimmer elastohydrodynamics. We start by presenting the simplest theoretical models, three-sphere swimmer and Purcell's three-link swimmer, and inflict not only ordinal elasticity but also odd elasticity. We demonstrate that the odd-elastic properties enable the swimmer to exhibit average locomotion without any prescribed actuation and discuss the role of the thermal fluctuations [1,2]. Furthermore, we generalize our concrete odd elastic microswimmer to odd elastohydrodynamics, describing a general swimmer subjected to periodic deformations by extending into a non-linear regime [3]. Through the analysis of Chlamydomonas flagella waveforms and experimental data for human sperm, we demonstrate the wide applicability of a nonlocal and non-reciprocal description of internal interactions within living materials in viscous fluids, offering a unified framework for active and living matter physics.   

Self-organization in multicellular systems: from collective motion to stem cell patterning

Embryonic development is a spectacular display of self-organization of multi-cellular systems, combining transformations of tissue mechanics and patterns of gene expression. These processes are driven by the ability of cells to communicate through mechanical and chemical signaling, allowing coordination of both collective movement and patterning of cellular states. To ensure proper biological function, such patterns must be established reproducibly, by controlling and even harnessing intrinsic and extrinsic fluctuations. While the relevant molecular processes are increasingly well understood, we lack principled frameworks to understand how tissues obtain information to generate reproducible patterns. I will present an information-theoretic framework to mathematically define and interpret the reproducibility and robustness of fate patterns. This framework provides a normative approach for optimization of cell signaling and mechanics, which I showcase using a variety of mechanistic models ranging from reaction-diffusion systems to delta-notch signaling. Furthermore, our approach allows us to estimate the reproducibility of experimental gene expression profiles of stem cell gastruloids, a self-organizing in vitro model of mammalian development. We demonstrate that these estimators assess the total amount of information contained in these expression profiles. This information is, in principle, accessible to the cells by local or non-local readouts, and provides a generalization of the concept of positional information, which is strictly local. Our work opens up an avenue towards unifying the zoo of chemical and mechanical signaling processes encountered across different developmental systems by using a common information-theoretic language.