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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The repressilator, a genetic circuit of three genes repressing each other to create oscillatory behavior, was one of synthetic biology’s earliest breakthroughs, showcasing the ability to engineer complex gene regulatory networks. Twenty-five years later, synthetic oscillators continue to fascinate.

In this talk, I will first present our work on developing a CRISPR-based repressilator—the "CRISPRlator"—in Escherichia coli and its application in Streptococcus pneumoniae to control capsule production, yielding the "CAPSUlator", as well as its use in a light biosensor. I will then introduce the "Optoscillator," an optogenetic version of the repressilator. Analogous to a clock and wavefront mechanism, in a growing bacterial colony harbouring the repressilator, the periodic oscillations are transformed into spatial ring patterns. We analysed the rings when the cells were subjected to different regimes of light exposure. By combining experiments with mathematical modelling, we demonstrated that this simple circuit can exhibit complex dynamics under external periodic forcing, including synchronization, resonance, period doubling, period-4 behaviour, and even chaos. I will conclude with our ongoing efforts to study coupled synthetic oscillators and expand into multicellular consortia.

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Unraveling the origin of life, and how the road to life was punctuated by transitions toward complexity, from astrochemistry to biomolecules and eventually to living organisms, remains one of the greatest challenges that humanity faces. Since Oparin's groundbreaking article a century ago, various scientific disciplines have approached this problem from isolated perspectives. Despite the remarkable progress made, achieving these ambitious goals still entails significant difficulties, and disruptive and more interdisciplinary, holistic approaches have been advocated recently as the way forward.

In this seminar, I will address the main unresolved difficulties associated with the field of the origin and early evolution of life and explore how complexity science, in harmony with recent advances in data science, can play a pivotal role in tackling them. In addition, I will describe several theoretical, modeling and computational approaches under development in our research group [1-4], outlining the opportunities for complexity theory and network science in different scenarios of increasing complexity related to the origin of life. These scenarios range from interstellar astrochemistry, where the most basic building blocks of life are formed, to the emergence of molecular complexity during prebiotic chemistry, and finally to more complex molecular self-organization levels leading to the first replicative cell.

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[1] C. Alejandre, A. Aguirre-Tamaral, C. Briones and J. Aguirre, Polymerization and replication of primordial RNA induced by clay-water interface dynamics, submitted (2025).

[2] M. Fernández-Ruz, I. Jiménez-Serra and J. Aguirre, A theoretical approach to the complex chemical evolution of phosphorus in the interstellar medium, Astrophysical Journal 956, 47 (2023).

[3] M. García-Sánchez, I. Jiménez-Serra, F. Puente-Sánchez, J. Aguirre, The emergence of interstellar molecular complexity explained by interacting networks, PNAS 119 (30), e2119734119 (2022).

[4] J. Aguirre, Life finds a way, Nature Ecology & Evolution 6 (11), 1599 (2022).

Fluorescence microscopy is a key tool for studying biological systems, yet extracting physical insights from 3D images remains challenging. Meanwhile, tissue models are becoming increasingly sophisticated, but direct integration with imaging data is still limited.

In this talk, I will present our recent efforts to bridge this gap. I will introduce a segmentation and 3D tension inference method that generates detailed mechanical atlases of embryos and tissues from microscopy images. I will then discuss our computational foam-like tissue models, which incorporate viscous dissipation, cell division, and mechanochemical feedback.

Finally, I will showcase a fully differentiable optimization pipeline that links mechanical models to microscopy by generating realistic synthetic images from simulations, paving the way for solving inverse mechanical problems. I will conclude with perspectives on integrating AI with biophysical models to uncover cell behavior in development.

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