ICTP-SAIFR Activities

I will discuss the derivation of the Emergent Second Law recently identified in Freitas and Esposito, Nat. Commun. 13, 5084 (2022). For stochastic systems with a well defined macroscopic limit, this result provides a link between the deterministic dynamics that emerges in such a limit, and the fluctuations that are observed at steady state. I will also discuss ongoing work about the generalization of this result to evolving non-equilibrium states.

The genome is organized within a nucleus where chromosomes fold into an ensemble of conformations. This spatial arrangement within the nucleus is critical for regulating gene expression and other DNA-templated processes. Chromosome conformation capture techniques such as Hi-C provide information about the genome architecture by creating 2D contact maps. Recently, measuring efforts were expanded to several organisms, cell lines, tissues, and cell cycle phases, in which obtaining high-quality maps is still challenging. These contact maps serve as an essential input for top-down theoretical models. Aided by the under-development chromatin folding and structure theory, we create a framework using polymer theory enriched with the Maximum Entropy Approach to learn and understand the chromosome spatial organization. To enhance the training of the energy functions, we included a combined machine learning minimization method that allows us to speed up the modeling, even for large systems. We use our developed platform, Open-MiChroM, to perform fast simulations and training. The models generated are precise compared to the experimental Hi-Cs, and the 3D structures ensemble is consistent with the crystal liquid theory for chromosomes. Additionally, we are able to predict important features of chromosome organization, such as the phase separation between chromatin types and the formation of chromosome territories. This novel modeling allows the exploration of a broad spectrum of 3D genome organizations on different organisms, cell lines, and cell phases.

Stochastic thermodynamics consists of a family of formalisms able to describe non-equilibrium processes in a general way and from a thermodynamic standpoint. It allows to obtain powerful results of universal nature, and has been employed in recent years to a wide variety of problems in different areas. I will introduce the basic concepts of the field and review recent developments. In particular, I will discuss a recent extension of the Second Law of thermodynamics to non-equilibrium steady states (Freitas and Esposito, Nat. Commun. 13, 5084 (2022)). I will also discuss the application of stochastic thermodynamics to non-linear electronic circuits (Freitas et. al. Phys. Rev. X 11, 031064 (2021)), and population dynamics models

Despite eight years since the initial detection of gravitational waves, the astrophysical origin of these phenomena remains elusive. Recent years have witnessed a growing interest in a novel gravitational wave formation pathway: the active galactic nuclei (AGN) channel.  I will begin by providing an overview of the current status of gravitational wave detections, the main astrophysical mechanisms driving the pairing and coalescence of black holes, and the observational signatures crucial for distinguishing between various formation scenarios. I will then describe the key features of the AGN channel, discuss our ongoing efforts in modeling compact objects within accretion disks in AGNs, and highlight the primary challenges associated with modeling black hole-gas disk interactions.  Partially based on: arXiv:2403.00060, arXiv:2312.13281

This seminar presents ongoing research on the qualitative dynamics of reaction-diffusion processes, cornerstone complex systems in physics and biology. These equations, fundamental to modeling phenomena ranging from chemical reactions to ecological and cellular processes, encapsulate the intricate balance between transport mechanisms and local interactions. A central aim in the study of such systems is to decipher the macroscopic or qualitative behaviors that emerge from these complex interactions, seeking to understand how patterns, waves, and structures develop on larger scales. However, despite the valuable analytical insights offered by perturbation or renormalization techniques, these approaches can struggle in highly nonlinear or multi-scale regimes and do not easily generalize to new parameters. Addressing these challenges, this work leverages recent advancements in data-driven dynamical systems theory to uncover the low-dimensional dynamics governing macroscopic features of interest. By employing machine learning techniques to derive low-dimensional representations, this approach clarifies the emergence of qualitative structures which are often obscured in the high-dimensional data of the original systems. This method not only facilitates a deeper understanding of the system's dynamics but also opens new avenues for control and parameter identification. Preliminary results demonstrate the efficacy of this methodology in shedding light on the behavior of the Gray Scott model and Min protein dynamics, both examples of reaction-diffusion systems with significant theoretical and biological implications. The seminar will detail the theoretical underpinnings, methodological developments, and early outcomes of this research, highlighting its potential to advance our understanding of reaction-diffusion systems as they arise in biology