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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Predicting development has been a major challenge in genetics, medicine and evolutionary biology. More than half a century ago, Conrad Hal Waddington famously raised several issues in understanding ‘cybernetics’ of development in his book ‘The Strategy of the Genes’, such as impact of environment, maternal effect and developmental buffering. My previous study showed that buffering level is maternally inherited, and identified key maternal mRNAs that predicts level of developmental buffering. Since maternal mRNAs are a major cause of embryonic defects, delivering necessary maternal mRNAs can safeguard eggs from environmental impacts. I will introduce my ongoing work on predicting impact of thermal stress on development.

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Gene editing is revolutionizing bioscience and therapy. Our lab has developed a gene writing tool combining the precision of CRISPR and gene transfer capacity of a transposase (Pallarès-Masmitjà et al, Nat Com 2021). We are planning to develop improved gene writers for safer and more efficient therapies with aid of the B-bio (new retrovirus-based directed evolution platform developed by our lab (Ivancic et al, in preparation). A key part for the success of this massively genotype testing platform is library design and analysis using AI. Large language models are being very powerful to represent protein language and will be used to fuel genotypes and accelerate evolution. Coupling this biological hardware with AI is generating a fascinating synergy for enhancing the evolutionary rate of new CRISPR based editors and writers.

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In brains, symmetry, asymmetry, and complexity are brought together; and affect both structure and function. Symmetric structures, thanks to their redundancy, might aid in computing with faulty parts and under noisy conditions. Mirror symmetric counterparts in the brain might also act as backups when a circuit fails. This redundancy, however, might be costly in metabolic terms. It might also require the coordination of parallel, independent computations, which can take a toll as well. In this talk we discuss complexity, symmetry, and symmetry breaking in the brain, and their implications in evolutionary dynamics and for certain pathologies.

Paper (Accepted at Phys. Rev. X): https://journals.aps.org/prx/accepted/b007aKadA7a14c0935de57b3dd09f47e3c5296c0a

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Big Data analytical techniques and AI have the potential to transform drug discovery, as they are reshaping other areas of science and technology, but we need to blend biology and chemistry in a format that is amenable for modern machine learning. In this talk, I will present the Chemical Checker (CC), a resource that provides processed, harmonized and integrated bioactivity data on small molecules. The CC divides data into five levels of increasing complexity, ranging from the chemical properties of compounds to their clinical outcomes. In between, it considers targets, off-targets, perturbed biological networks and several cell-based assays such as gene expression, growth inhibition and morphological profiles. I will also present the Bioteque, a resource of unprecedented size and scope that contains pre-calculated biomedical embeddings around 11 biological entities (e.g. genes, cells, tissues, disease, etc), derived from a gigantic knowledge graph, so that each entity can be described considering different contexts (e.g. interactions, expression, etc). With small molecule and biological bioactivity descriptors in hand, we now face a new scenario for chemical and biological entities where they both are translated into a common numerical format. In this computational framework, complex connections between entities can be unveiled by means of simple arithmetic operations. Indeed, we demonstrate and experimentally validate that these descriptors can be used to reverse and mimic biological signatures of disease models and genetic perturbations in vitro and in vivo, options that are otherwise impossible using chemical information alone.

References
Duran-Frigola et al. Extending the small molecule similarity principle to all levels of biology with the Chemical Checker. 2020. Nat Biotechnol. 38: 1087-1096.

Bertoni et al. Bioactivity descriptors for uncharacterized chemical compounds. 2021. Nat Commun. 12: 3932.

Pauls et al. Identification and drug-induced reversion of molecular signatures of Alzheimer's disease onset and progression in AppNL-G-F, AppNL-F, and 3xTg-AD mouse models. 2021. Genome Med. 13:168.

Fernández-Torras et al. Connecting chemistry and biology through molecular descriptors. 2022. Curr Opin Chem Biol, 66: 102090.

Fernández-Torras et al. Integrating and formatting biomedical data in the Bioteque, a comprehensive repository of pre-calculated knowledge graph embeddings. 2022. Nat Commun. 13: 5304.

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Variation, i.e. the molecular and phenotypic changes caused by random mutations, is a crucial component of evolutionary processes. One biologically relevant example for which variation can be modelled computationally is the RNA genotype-phenotype (GP) map. This GP map links RNA sequences to their folded secondary structures and thus allows us to identify structural changes after sequence mutations. In this talk, I will describe some recent progress on the biophysics of this GP map, show what the properties of this GP map mean for evolutionary processes, and discuss what we can learn from this GP map for evolutionary processes beyond RNA