The spontaneous generation of patterns and structures occurs in many living systems and is linked to biological form and function. Such processes often take place on domains which themselves evolve in time, and they can be guided by or coupled to geometrical features. The role of geometry in the self-organisation of functional structures however is not understood. I will present two biophysical examples that illustrate how geometry directs spatial organization at different scales. I will discuss how boundary geometry controls a topological defect transition that guides lumen nucleation in embryonic development [1], and how shape can act as a form of memory in cell-cell signaling [2].

[1] Guruciaga et al. arXiv:2403.08710 (2024)

[2] Dullweber et al. arXiv:2402.08664v2 (2024)

In this tutorial, we will examine the contents of a bacterial cell as well as the timing of the processes of the central dogma through the lens of simple, order magnitude estimates. The goal of these estimates is to develop a quantitative intuition about cells. For example, we might try to gain intuition about why bacterial cells cannot divide faster than every few minutes. Participants should bring pen and paper.
 

Computational methods are an ideal complement for inferring non directly measurable quantities such as forces or growth distribution. I will present a continuum approach where growth tensor is deduced with different objectives: for computing optimal locomotion of soft bodies on non-isotropic substrates, and for inferring the growth distribution that best matches a set of experimental measurements. Importantly, the uncertainty on the boundary conditions is also included in the inverse analysis, jointly with an iterative regularisation process. The methodology is applied  to different worm locomotion strategies, and axolotl limb growth. In each case it is revealed that non-isotropic friction and growth pattern are necessary conditions for reproducing experimental motions and deformations.

Complex systems unfold nonlinear interactions and can suffer regime shifts which are of paramount importance since they can involve the emergence and persistence of a disease or the extinction of a species within an ecosystem. Such regime shifts can be achieved by different mechanisms and their understanding is crucial to potentially anticipate them and have information about the time scales of such shifts. Dynamical systems theory and computational research allows the study of such mechanisms in a general way or for specific complex systems such as ecosystems or viral infections. In this seminar, I will discuss different mechanisms causing regime shifts, paying special attention to critical transitions given by tipping points. I will discuss important dynamical properties arising close to critical transitions such as transients and the so-called warning signals. These signals provide a powerful way to detect that a given system is approaching a critical transition without the necessity to have a detailed knowledge of the underlying system dynamics. To foster discussion and possible new collaborations, I will provide examples of these topics identified in experimental and natural systems.

TBA

Organisms are subject to the laws of physics, so the process of evolution is constrained by these fundamental laws. Classic and recent studies of the biophysical limits facing organisms have shown how fundamental physical constraints can be used to predict broad-scale relationships between body size and organismal biomechanics and physiology. In this talk I will focus on recent work which derives scaling laws for myriad features of single-cell organisms. I will then show how these laws are useful for ecological modeling, detecting life beyond Earth, and building new types of life. 

Correctly sized body parts are crucial for organismal function. For example, small discrepancies in limb length severely obstruct motility, and overgrowth of cardiac muscle is a prevalent cause of heart failure. The growth of different cells and organs must therefore be tightly coordinated to prevent that even small differences in growth rates amplify to large differences in size during development. How growth signals are propagated from cell to cell, and how organs integrate combinatorial signals from different tissues is a fundamental, yet poorly addressed question of high biomedical relevance. We address this question by combining live imaging and genetics using C. elegans with mathematical modelling.

We have developed time-lapse microscopy to precisely monitor the growth of the digestive tract of C. elegans in coordination with total body growth in hundreds of individual animals at high time resolution. Using these tools, we find a remarkable robustness of organ size that persists even under strong tissue-specific perturbation of growth and involves feedback control across multiple tissues. In a genetic screen, we identified a role for mechano-transduction via the transcriptional co-activator YAP-1 and stress signaling via p38 and Jun kinases in this control. We currently investigate how these conserved pathways coordinate growth in space and time during development to robustly yield an appropriate body plan.

Biological neurons grown in the laboratory in the form of neuronal cultures are one of the most compelling examples of a complex system, in which an ensemble of initially disconnected neurons is able to reconnect and form a de novo complex network within days. This network may exhibit different forms of collective activity that depend on the underpinned connectivity between neurons. By using resources from neuroengineering, one can control this connectivity and build in vitro systems that mimic key organizational features of the brain, specifically modularity. Such systems are useful not only to understand the emergence of collective behavior in neuronal assemblies, but also to design brain-on-chip models to explore and treat neurological disorders in a controlled manner. In this talk, I will review the strategies that we have implemented in my laboratory at the University of Barcelona to prepare and analyze neuronal cultures for both the physics and medical communities. I will also introduce recent developments in the context of biological computation on biohybrid devices, where electronic and biological circuits interact with one another.

Research in the field of "stochastic thermodynamics" has uncovered a set of fundamental relationships between energy, information, and temporal irreversibility in nonequilibrium stochastic processes. Recently, these relationships have been used to derive thermodynamic bounds on the performance of molecular machines, including very general tradeoffs such as “thermodynamic uncertainty relations” and “thermodynamic speed limits”. I will review the basics of the field, as well as some promising recent developments and applications to molecular-scale biological systems. I will also discuss the challenges faced when applying these approaches at the cellular scale.

Several molecular markers have been described to predict stem cell potential with great success, e.g., in tissues like the blood, where a clear hierarchy of functional cell types can be identified. In other tissues, neutral competitive dynamics has been reported to remove a large fraction of biochemically identifiable stem cells, thereby creating a severe mismatch between cell-specific biochemical identity and the actual role of them as functional stem cells within the collective. In the case of the intestinal epithelium, this mismatch goes even further: In spite the number of biochemically identifiable (LGR5+) stem cells is the same in the crypts of both large and small intestines, lineage tracing techniques show that the number of them that actually play the role of stem cell displays remarkable differences. In that context, a natural question arises: How to determine the functional number and location of stem cells? To address this problem, I will revise a recent mathematical approach that proposes a new stem cell regulation mechanism, leading the final stem cell population to emerge only from the collective stochastic dynamics of cell movements and geometric considerations. Within this framework, one can accurately predict the robust emergence of a region made of functional stem cells and the actual number of them, as well as the lineage-survival probabilities, among other observables. The presented approach does not neglect the key role of biomarkers: Instead, it points towards the existence of complementary regulatory mechanisms --based on collective phenomena, stochastic dynamics and organ geometry--, playing an active role in determining the emergence and location of different cell functionalities.