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.

The exploration of vast genotype spaces poses a major challenge to evolving populations. As the number of genotype sequences representing viable phenotypes grows exponentially with genome length, understanding how populations navigate and adapt within such spaces becomes paramount. In this contribution, we delve into the dynamics of populations within genotype spaces using data from the environmental adaptation of populations of a small phage infecting E. coli (Qbeta phage) and SARS-CoV-2 genomes. Despite the vastness of the spaces they have in principle access to, even the largest realizable viral populations cover only a tiny fraction of possible sequences, constrained by a local, blind exploration of the nearest attainable. We explore how these populations achieve phenotypic improvements and evolutionary innovations, presenting data-driven insights from extensive datasets. Our analysis reveals crucial features of empirical populations that challenge established theoretical expectations, shedding light on the dynamic interplay between genotype and phenotype in the evolutionary process.

Artificial Intelligence (AI) methods are emerging as powerful tools in fields such as Natural Language Processing (NLP) and Computer Vision (CV), impacting the tools and applications we use in our daily lives. Language models have recently shown incredible performance at understanding and generating human text, producing text often indistinguishable from that written by humans. Inspired by these recent advances, we trained a language model, ProtGTP2, which effectively learned the protein language and generated sequences in unexplored regions of the protein space. A desirable critical feature in protein design is having control over the design process, i.e., designing proteins with specific properties. For this reason, we trained ZymCTRL, a model trained on enzyme sequences and their associated Enzymatic Commission (EC) numbers. ZymCTRL generates enzymes upon user-defined specific catalytic reactions, which show natural-like catalytic activities in wet lab experiments. Lastly, we have trained REXzyme, a translation machine capable of designing enzyme sequences for user-defined chemical reactions.

The collective migration of cohesive groups of cells is a hallmark of the tissue remodeling events that underlie embryonic morphogenesis, wound repair, and cancer invasion. Collective cell migration is characterized by the emergence of supra-cellular properties that control large-scale tissue organization. This suggests that a coarse-grained approach based on a hydrodynamic description of tissues as continuous active materials may shed some light on our understanding of the underlying physics of various tissue processes. Within this spirit, we reflect on the extent to which biological complexity can be encoded in a series of material parameters to build predictive, purely mechanical models of tissues based on very general physical principles. We present an overview of such a hydrodynamic approach to cell tissues as active polar fluids and discuss some examples where relatively simple models have been instrumental in elucidating relevant physical mechanisms behind collective cell behavior in epithelia, including active wetting, collective durotaxis, and spontaneous motility of cell clusters.

Our goal is to better understand why individuals from a population differ from each other in terms of their phenotypes and susceptibility to disease. We focus on genetic effects, considering not only the individual’s own genes but also the genes of their social partners and the genes of their microbiome. I will present the conceptual framework and quantitative genetics models we use to study this extended genotype to phenotype map.

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I will talk about two project involving oriented tissue flows.

1) Most animals display one or more body axes (e.g. head-to-tail, left-right, ventral-dorsal), which usually emerge during early embryogenesis. In our work, we demonstrate that large-scale tissue flows can play an important role for the formation of body axes. To this end, we used aggregates of mouse stem cells, called gastruloids. Gastruloids are initially spherically symmetric, but later form an axis defined by the polarized expression patterns of specific proteins such as T/Brachyury or E-cadherin. While such early embryonic pattern formation is usually studied in the context of genetic and bio-chemical interactions, we show that also advection of cells with tissue flows contribute approximately 1/3 to the overall polarization. We then more closely analyzed the flow field in the gastruloids and found that the dominant component was a recirculating flow. We further showed that this large-scale flow could be understood as a Marangoni flow, i.e. driven by interface and surface tension differences. We further independently confirmed the existence of differential interface and surface tensions though aggregate fusion experiments. Taken together, we found that in polarized gastruloids, differential tensions drive recirculation flows, which in turn further amplify polarization. We expect such a feedback loop to act also in many other systems in vitro and in vivo.

2) Oriented tissue deformation is a fundamental process omnipresent during animal development. However, how exactly cells coordinate to achieve robust oriented deformation on the tissue scale remains elusive. From a physics perspective, deforming tissues can be described as oriented active materials. However, it is known that oriented active materials can inherently exhibit instabilities such as the Simha-Ramaswamy instability. This instability destroys the homogeneously deforming state of active materials. This raises the question of how robust anisoropic tissue deformation can be possible during animal development. In particular, we ask whether the presence of a chemical signalling gradient (e.g. a morphogen gradient) can help stabilize oriented tissue deformation. Using a combination of vertex and hydrodynamic models, we find that stability depends on whether the signalling gradient acts to extend or contract the tissue along the gradient direction. In particular, gradient-extensile coupling can be stable, while gradient-contractile coupling is generally unstable. Intriguingly, developing tissues seem to exclusively use the gradient-extensile and not the unstable gradient-contractile coupling, suggesting that nature might just never have evolved the latter. Our work points to a potential new developmental principle that is directly rooted in active matter physics.

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