The cis-regulatory code that instructs gene regulation during development, also known as the genome’s second code, is a fundamentally unresolved problem. Recent progress has provided proof-of-principle evidence that this complex cis-regulatory code can be learned with neural networks. The new approach is fundamentally different from traditional methods in that the sequence rules are learned inside a black box directly from genomic sequences through their ability to better predict a specific genomics data set. This dramatically improves the predictive performance, but requires rigorous approaches for extracting, understanding and validating the learned sequence rules to make sure that they represent biology. I will describe how we use this approach using mouse or Drosophila development as model systems and uncover sequence rules that we can validate with experiments. The goal is to understand the underlying biophysical processes and constraints and to create a general model of how the cis-regulatory code is read out by transcription factors. We strive to use this knowledge to create more powerful deep learning models that learn cis-regulatory sequence rules more broadly across cell types.

Spontaneous pattern formation provides a physical basis for morphogenesis, as put forth by D’Arcy Thompson and Turing. In this talk, I will discuss ongoing work to understand collective invasion during metastasis using similar principles. We study as a model system patient-derived breast cancer organoids in collagen matrices developed by the Khalil group at Utrecht. We propose a hypothesis for a positive feedback loop driving collective invasion, which involves an interplay between mechanical and chemical activity of cells and the nonlinear mechanics of collagen. We examine the consequences of this hypotheses using mathematical and computational modeling. I will also present preliminary experiments testing the proposed mechanism.

Vascular regression is crucial for limb development and pattern formation. We investigated this
phenomenon using experimental modeling and mathematical approaches. In our in vitro model,
we observed that Sox9 expression affects the behavior of endothelial cells, mimicking in vivo
patterns. To further understand vascular regression, we developed a hybrid mathematical
model that incorporates cell interactions, mechanics, and Sox9 pre-pattern formation. By
combining experimental data and modeling, we gained insights into vasculature network
formation and its role in organogenesis. Our interdisciplinary approach represents a first step in
unraveling the vascular regression and advancing our understanding of limb development.

Over the last fifty years, interdisciplinary approaches in biology have become popular with the emergence of fields such as systems biology and biophysics. Mathematical modelling and the use of quantitative approaches have been successful in shedding light into the emergent behavior of cells and tissues. However, despite great advances in molecular cell biology and genetics, we are still far from building predictive models describing the self-organization of living systems. One of the main drawbacks is the poor connection between microscopic and mesoscopic descriptions, a well-known problem in physics. In this talk, I will propose the use of techniques borrowed from soft matter physics to bridge scales in biological systems, thus unveiling how mesoscopic quantities depend on microscopic quantities such as kinetic parameters. In particular, I will focus on the role of solid-to-fluid transitions at the subcellular and supracellular scales and show how multiscale modelling will be key in the future to build predictive theories in biology.

Tissue structure emerges through the process of self-organization. We aim to build a bottom-up understanding of tissue self-organization by studying the biophysical properties of single cells and their interactions. We seek cross-cutting principles by working in organ systems as diverse as the mammary gland, intestine, pancreas and blood. Our long-term goal is to leverage a detailed understanding of tissue self-organization for applications in regenerative medicine, disease modeling, and therapeutics discovery. Current projects aim to elaborate statistical mechanical models of tissue structure, reveal active mechanical mechanisms of tissue patterning, and develop new tools to systematically measure and engineer the structure of tissues both in vitro and in vivo.

Living things undergo an increase in entropy, which manifests itself in a loss of genetic and epigenetic information. Changes in epigenetic landscapes have been identified in cancer and ageing. In this talk, I will present a pipeline based on chemical reaction network theory and dimension reduction techniques to study how such transitions occur under an accumulation of DNA damage and identify epigenetic drivers that could lead to delay/ hinder them.

Aging involves a set of functional declines that occur at timescales several orders of magnitude slower than most physiologic mechanisms in cell biology and metabolism. At such slow aging timescales, the consequences of molecular events anywhere in an individual have sufficient time to propagate broadly across cells, tissues, and organs to influence potentially any aspect of physiology. New methods are needed to measure, model, and understand the complex causal structure of these long-distance, many-to-many interactions.

In this talk I'll discuss a new approach called "Asynch-Seq" that leverages the natural asynchrony in individual aging rates across a population to map organismal-scale mechanistic interactions. I'll explore what this map tells us about where and how inter-individual variation arises during aging.

Morphogenetic patterns at the level of cells and tissues arise from a tight coupling between signaling, mechanics and dynamical geometry. The actomyosin cytoskeleton is a prime orchestrator of this coupling. We will discuss minimalistic theoretical models for the emergence of mechanochemical patterns in such active materials. We will then show that a simple linear turnover reaction for the active stress regulator (myosin) leads to the emergence of nonlinear traveling waves in these models. Considering such active patterns on curved surfaces, we will demonstrate the emergence of non-trivial curvature induced waves in a polar flock confined to a curved surface. Finally, we will develop a framework to study these nonequilibrium patterns on dynamical geometries wherein the mechanical forces needed for shape deformation are controlled by active mechanochemical stresses. As an example of this geometrodynamics of active materials, we will present preliminary results that lead to spontaneous asymmetries during the ingression of the cytokinetic furrow.

Finding precise signatures of different brain states is a central, unsolved question in neuroscience. The difference in brain state can be described as differences in the detailed causal interactions found in the underlying intrinsic brain dynamics. We use a thermodynamics framework to quantify the breaking of the detailed balance captured by the level of asymmetry in temporal processing, i.e. the arrow of time. We also formulate a novel whole-brain model paradigm allowing us to derive the generative underlying mechanisms for changing the arrow of time between brain regions in different conditions. We found precise, distinguishing signatures in terms of the reversibility and hierarchy of large-scale dynamics in three radically different brain states (cognition, rest, deep sleep and anaesthesia) in fMRI and electrocorticography data from human and non-human primates. Overall, this provides signatures of the breaking of detailed balance in different brain states, reflecting different levels of computation

Seen from an evolutionary perspective, cancer is a complex system subject to high mutation rates and strong selection pressures. Mutations, the substrate of selection, are caused by many different mutational processes. A multitude of such mutational processes, or "signatures", has been identified and associated with biochemical mechanisms of DNA lesions and repair. The mutational spectrum of any given tumor can be decomposed into these signatures in order to classify tumors into subtypes, determine exposure times to certain mutagens or characterize individual mutation origins. However, state-of-the-art methods to quantify the contributions of different mutational processes to a tumor sample fail to detect certain mutational signatures, only work well for a relatively high number of mutations and do not provide error estimates of signature contributions. Here, I will describe SigNet, a novel approach to signature decomposition based on an artificial neural network. By leveraging the correlations between signatures present in real data, this approach outperforms existing methods, particularly for samples with small to intermediate numbers of mutations. We applied SigNet to elucidate the effects of hypoxia on the tumor mutational footprint and discovered both known and novel correlations of mutational signatures with hypoxia, including a strong association of hypoxia with a decrease in the activity of DNA repair processes. These and other results demonstrate the potential of a mutational process decomposition that can be applied to DNA sequencing datasets of limited size.