Spontaneously flowing active nematic films serve as an ideal model for more complex biologically relevant out-of-equilibrium biofluids, such as dense ensembles of bacteria and the subcellular cytoplasmic fluids. These biofluids are composed of anisotropic constituents that are intrinsically out-of-equilibrium due to their ability to do microscopic work on their surroundings, which results in collective motion and disorderly flows, called active turbulence. However, biofluids are typically more complex mixtures than model active nematics and often include a diverse assortment of inclusions. Such inclusions might include particulate matter, macromolecules, fixed obstacles or any number of solutes. In this seminar, I will introduce a numerical approach that is amenable to simulating mobile inclusions embedded in biofluids. I will present results on biofluids flowing through a porous medium of obstacles, discuss the dispersion of passive polymers suspended in flowing biofluids and unpick the interactions between colloidal discs and the topological defects that accompany active turbulence. These results demonstrate how active flows represent a pathway by which biological systems can control and impact the steady-state properties of passive inclusions.

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Ternary lipid membranes—comprising a high-melting species, a low-melting species, and cholesterol—have long served as minimal model systems for studying lipid organization. Despite their ostensible simplicity, they reproduce a surprising range of the complex mixing behavior observed in biological membranes, including fluid-fluid phase coexistence and its associated critical point. A longstanding motivation behind these studies has been the hope that ternary mixtures might help unravel the enduring mystery of lipid rafts. Extensive research on well-controlled model systems has indeed revealed many of the physical principles that govern ternary lipid phase behavior, while complementary discoveries in living cells have added both support and intrigue. Yet the physiological reality remains perplexing.

Recent findings from multiple groups suggest that further progress will likely require addressing a second fundamental feature of biomembranes: their pronounced asymmetry across the two leaflets. This asymmetry is not limited to composition (i.e., the presence of distinct ternary mixtures in each leaflet) but likely extends to mechanical properties as well. There is growing evidence that the two leaflets may experience very different lateral tensions, resulting in a differential stress that strongly influences cholesterol partitioning—arguably one of the central players in membrane organization.

In this talk, I will examine these two intertwined aspects of biomembrane complexity—compositional and mechanical asymmetry—and propose a generic (though not yet very predictive) thermodynamic framework for describing their interplay. I will also present initial coarse-grained simulation results that begin to elucidate the cross-talk between asymmetry and lipid mixing thermodynamics.

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The Arrhenius law explains how a single enzymatic step speeds up with temperature, yet many multi-step complex biological processes still look roughly Arrhenius, until they don’t. Here we will address this question. 

We start with the biochemical cell-cycle oscillator driving early embryonic cleavages. Using new measurements and modeling across ectotherms, we find that cycle periods share similar apparent activation energies and are approximately Arrhenius over a broad range, but break down at cold and hot ends. These deviations are traced to concrete circuit features: biphasic temperature responses in key regulators and imbalances in activation energies across partially rate-determining steps, supported by Xenopus extract and in-vitro assays (Rombouts et al., Nature Comm. 2025).

We then generalize beyond oscillations: viewing biological durations as mean-first-passage times through reversible multi-step networks yields a robust curved middle regime (quadratic-exponential) produced by averaging across many steps, flanked by Arrhenius-like extremes where a few steps dominate. Simple network motifs also account for warm-edge slowdowns and apparent negative activation energies without invoking denaturation. This framework matches more than 100 datasets across species and developmental processes (Jacobs et al., BioRxiv 2025). We then test these rules in new systems and study how they help predict when biological timing will stay coordinated, drift, or fail as environments warm.

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Life systems are complex and hierarchical, with diverse components at different scales, yet they sustain themselves, grow and evolve over time. Here we note that for a hierarchical biological system to be robust, it must achieve consistency between micro-scale (e.g. molecular) and macro-scale (e.g. cellular) phenomena, which allows for a universal theory of adaptive changes in biological systems. The talk will present a demonstration of how adaptive changes in high-dimensional phenotypes (biological states) are constrained to low-dimensional manifold, leading to a macroscopic law for cellular states, as confirmed by adaptation experiments of bacteria. The theory is then extended to evolution, leading to the proportionality between phenotypic variations due to environmental adaptation and genetic changes. This finding allows the prediction of evolution, as demonstrated experimentally. Finally, we extend this theory to the development of multicellular organisms, and discuss how irreversible cell differentiation and the robustness of developmental pathways (homeorhesis) are acquired. Overall, this talk highlights the potential for physics to the study of biology through a universal perspective and the development of macroscopic theories for living systems.

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How morphogen gradients are formed has been under debate since the term was first coined by Alan Turing. Can diffusion alone lead to the robust formation of morphogen gradients? Or are cell processes like transcytosis important to move molecules across tissues? How does the complex geometry of the extracellular space determine the diffusive and transport properties of morphogen molecules? In the first part of my talk, I will present a theoretical framework that addresses these questions, and I will discuss the design principles for morphogen gradient formation and the ways these can lead to robustness to perturbations such as tissue size and the molecular numbers in the system. I will then present a combination of experimental and theoretical work where we show that transitions in tissue-scale physical properties are coupled to morphogen signalling and transport during early zebrafish development. Our findings show that morphogen transport are actively regulated by cell and tissue architecture in vivo. We propose that feedback loops between morphogen signalling and tissue organization lock patterning and morphogenesis in a closed feedback loop that ensures that their dynamics are kept in sync. 

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Microbial communities provide countless ecological services essential for sustaining life on Earth, and they perform a wide array of functions in biotechnology—from food production to biofuel synthesis. The quantitative functions delivered by microbial communities depend on their composition, i.e. the specific genotypes present and their relative abundances. To engineer microbial consortia that optimize these functions, we must establish a predictive, quantitative link between community composition and function. Yet, developing mechanistic mathematical models to achieve this is exceptionally challenging due to the complex network of interactions involved. In this talk, I will explore how concepts from fitness landscape theory in genetics can help overcome these challenges and lead to the creation of predictive, quantitative models of community function that can guide the optimization of  synthetic microbial consortia.

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Collective cell motion is ubiquitous in biology, occurring in normal development, wound healing and disease (cancer). Over the past decade I have been collaborating with the lab of Paul Kulesa in Kansas on a study of cranial neural crest cell migration in the chick. In this talk, I will review our work and illustrate how a basic hybrid agent-based mathematical model, combined with experimental studies, has led to new insights into this phenomenon. These include understanding the role of (i) environmentally-induced phenotypic switching, (ii) extracellular matrix degrading factors, (iii) DAN-induced cell velocity control, and (iv) Colec12 and Trial as factors confining cells to move along a corridor.

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The availability of time- and disease-dependent single-cell gene expression data has opened new opportunities for integrating these datasets into mathematical models representing the evolution and switching between cellular states. In this talk, I will focus on Hopfield recurrent networks, a mathematical framework from statistical physics that captures the multi-stable dynamics inherent in complex cell signaling networks, interpreting gene expression patterns as associative memories. Hopfield recurrent networks can mathematically implement Waddington’s interpretation of normal and abnormal cell phenotypes as dynamical attractors within epigenetic landscapes. I will discuss applications of this framework in modeling the onset of angiogenesis, the dynamics of disease progression in Multiple Myeloma (MM), and the cell cycle. In our angiogenesis model, we use data to visualize the cellular transition from stalk-like to tip-like endothelial cells, corresponding to the formation of capillary sprouts in blood vessels. The MM model employs scRNA-seq data from bone marrow aspirates of MM patients and those diagnosed with two medical conditions that often progress to full MM.

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Perceptual decisions rely on accumulating sensory evidence. This computation has been studied using phenomenological models, e.g. the drift diffusion model, or neurobiological network models exhibiting attractor dynamics. It remains unclear whether the dynamics of both models are qualitatively equivalent and whether attractor models can integrate evidence optimally. Here, I will present distinctive features of attractor models that allow them to perform flexible temporal weighting of stimulus evidence. In the discrete attractor model, this is due to transitions between decision states that can reverse initially-incorrect categorizations. Moving from categorical choices to continuous perceptual judgments, I will show that a continuous bump attractor network can integrate a circular feature, such as stimulus direction, nearly optimally. As required by optimal integration, the population activity of the network unfolds on a two-dimensional manifold, in which the position of the network’s activity bump tracks the stimulus average, and, simultaneously, the bump amplitude tracks stimulus uncertainty. Moreover, the model can flexibly switch between different temporal weighting profiles by changing a single control parameter, the global excitatory drive. Predictions of the models are validated with psychophysical data. Finally, I will outline how these attractor models can provide a comprehensive and experimentally testable computational framework to study the neural mechanisms underlying stimulus integration and bias effects in combined discrimination-estimation tasks.

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One of the most remarkable examples of self-organized structure formation is the development of a complex organism from a single fertilized egg. With the identification of molecules that participate in this process of morphogenesis, attention has now turned to capturing the physical principles that govern the emergence of biological form. What are the physical laws that govern the dynamics and the formation of structure in living matter? Much of the force generation that drives morphogenesis stems from the actomyosin cortical layer of cells just underneath the cell surface, which endows the surface with the ability to generate active stresses and active torques that can drive reshaping. We combine theory and experiment and investigate how the actomyosin cell surface deforms and how it supports chiral rotations, and how these events together participate in chiral morphogenesis and the establishment of a left-right principal body axis in both the nematode worm and the Japanese quail.

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