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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Around four billion years ago, chemical and geological processes on the ancient Earth caused increases in complexity of organic molecules, creating RNA, DNA, protein, polysaccharide, membrane-forming amphipaths, and metabolism. But how?

In our center are learning to understand the origins of life through the geology, physics, chemistry and biochemistry of water. Water serves as a medium, as a chemical hub, and as an energy currency during experimental chemical evolution. We have developed an experimental platform that allows us to perform and evaluate chemical evolution. Based in part on our results, here we describe why (we think) the origins of life:

(i) represents an experimentally addressable and solvable problem that one day will be understood and generally accepted,
(ii) is not technically difficult - it happened on the Hadean Earth, in the absence of post docs, pH meters, rotavaps, HPLCs, NMRs, and mass spectrometers,
(iii) is not a series of idiosyncratic and spectacular one-off events but is reproducible and even mundane,
(iv) is a specific example of a class of processes that we call general chemical evolution, and
(v) can be redirected and exploited for a broad array of chemical applications ranging from materials science, to nanotechnology, to medicine, to astrobiology (i.e., there is money to be made).

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During the differentiation of blood stem cells, gene expression needs to be tightly controlled, but it is unclear how gene regulatory elements encode this specificity. Here, we took a bottom-up approach and attempted to engineer lineage- and stage-specific enhancers from scratch, by systematically embedding transcription factor (TF) binding sites into random DNA. In total, we designed 60,000 candidate enhancers, and measured their ability to drive transcription in seven hematopoietic progenitor states, using a primary cell differentiation model. Our results reveal dose- and context-dependent duality behavior of TFs, encoding of specificity by low-affinity sites, and an unexpected ability of stem cell TFs to turn lineage TFs into repressors. I will also discuss ideas to describe our results with mechanistic models.

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A fundamental question of developmental biology is how pattern formation occur. In this article we restrict ourselves to the pattern formation that occurs without cell movement (i.e. no cell division, cell contraction or any cell behaviors leading to cell movement) but just by signaling and gene networks. Our questions are:

1-Which are the topologies of the gene networks that can lead to pattern transformation?

2-Is there a limited number of classes into which pattern-transformation gene networks can be classified according to their topology, dynamics and pattern transformation capacities?

3-Can we characterize such classes and relate them to experimental gene networks underlying specific pattern transformations in embryos?

By gene network topology we mean which gene products regulate each other and which of these regulations are positive or negative (see FIG). In this article we show that, in spite of the huge size of the space of possible gene networks and the complexity of development, the gene network topologies capable of pattern transformation can be classified into just three topological classes and their combinations. Gene networks within these classes share the same logic on how they lead to pattern transformations and very similar pattern transformations.

In this talk, we will show how ELEM Biotech (http://elem.bio), a spinoff company of the Barcelona Supercomputing Center, is developing Virtual Humans to decisively contribute
to develop new therapeutical strategies, helping to personalize and optimize them. We are highly focused on cardiovascular and respiratory diseases, but in the talk we will show some results on other organs and systems. Virtual Humans are combinations of mathematical models and patient's data, which deployed in the cloud in High Performance instances, can predict the outcome of the therapy. We will show how ELEM technology allows us to create a population of such avatars to perform in-silico clinical trials.

Somatic mutations in human cells have a highly heterogeneous genomic distribution, with increased burden in late-replication time (RT), heterochromatic domains of chromosomes. This regional mutation density (RMD) landscape is known to vary between cancer types, in association with tissue-specific RT or chromatin organization. Here, we hypothesized that regional mutation rates additionally vary between individual tumors in a manner independent of cell type, and that recurrent alterations in DNA replication programs and/or chromatin organization may underlie this. Here, we identified various RMD signatures that describe a global genome-wide mutation redistribution across many megabase-sized domains in >4000 tumors. We identified two novel global RMD signatures of somatic mutation landscapes that were universally observed across various cancer types. First, we identified a mutation rate redistribution preferentially affecting facultative heterochromatin, Polycomb-marked domains, and enriched in subtelomeric regions. This RMD signature strongly reflects regional plasticity in DNA replication time and in heterochromatin domains observed across tumors and cultured cells, which was linked with a stem-like phenotype and a higher expression of cell cycle genes. Consistently, occurrence of this global mutation pattern in cancers is associated with altered cell cycle control via loss of activity of the RB1 tumor suppressor gene. Second, we identified another independant global RMD signature associated with loss-of-function of the TP53 pathway, mainly affecting the redistribution of mutation rates away from late RT regions. The local mutation supply towards 26%-75% cancer driver genes is altered in the tumors affected by the global RMD signatures detected herein, including additionally a known pattern of a general loss of mutation rate heterogeneity due to DNA repair failures that we quantify. Our study highlights that somatic mutation rates at the domain scale are variable across tumors in a manner associated with loss of cell cycle control via RB1 or TP53, which may trigger the local remodeling of chromatin state and the RT program in cancers.

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