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.

In this talk, we explore the emergence of collaboration in multiagent reinforcement learning (MARL) through various examples, highlighting the role of human biases such as loss aversion and their impact on collaborative dynamics. We then delve into a paper co-authored by Ricard Sole and Clement Moulin-Frier, which focuses on modeling adaptive dynamics in complex ecosystems. Building on the Forest-Fire model, the study uses fire as an external, fluctuating force in a simulated environment. Agents must balance tree harvesting and fire avoidance, resulting in the evolution of an ecological engineering strategy that optimally maintains biomass while suppressing large fires. We will discuss the implications of these findings for AI management of complex ecosystems, emphasizing the potential benefits of incorporating MARL and collaboration strategies into environmental management and conservation efforts.

It is often useful to think about Cells and Tissues as Distributed Computing Systems, especially in the context of the processing of noisy molecular information. I will illustrate this in two parts. In the first, I will talk about cellular compartmentalisation and receptor promiscuity as a strategy for accurate inference of position during Morphogenesis. In the second, I will discuss the synthesis of a complex Glycan code in the Golgi cisternae, and how cisternal number and enzyme promiscuity achieves the target distribution with high fidelity.

The surfaces of eukaryotic cells are decorated with glycans: branched sugar polymers that encode cell identity and play key roles in cell-cell interactions. Eukaryotic glycans are manufactured in the Golgi apparatus, so changes in Golgi organization are expected to impact the glycans made by a cell. We study the Golgi as an information channel whose input is some measure of Golgi perturbation and whose output is the multi-dimensional distribution of cell-surface glycan levels. Specifically, we used the drug Brefeldin A (BFA) to disrupt Golgi organisation, then measured the resulting levels of specific cell-surface glycans in single cells using fluorescently-tagged lectins and flow cytometry. We constructed an optimal Bayesian decoder to infer the input BFA level from single-cell glycan measurements. The decoder has two qualitatively distinct regimes: an all-or-nothing regime that decodes to the minimum and maximum BFA concentrations, and a continuous regime that decodes to intermediate BFA concentrations. We estimate a lower bound of approximately 1 bit for the Shannon information capacity of the Golgi channel in this artificial setting.

Along the development of an organism, precursors cells within tissues become specified into distinct cell types. Research in the last decades has shown that many of such processes involve communication between cells, albeit others happen to be mainly cell-autonomous and random. Communication can happen through molecules which move across the tissue or by juxtacrine signaling. Here I will focus on patterning involving diffusion, presenting several theoretical approaches whose predictions are quantitatively compared to data in model organisms. First I will present a mechanism to drive self-confined expression through a diffusing activator. Afterwards I will introduce a new mechanism for diffusion to drive patterning in reaction-diffusion systems.