Seminars

Imagine searching for a single sentence in a library of three billion letters — and finding it in seconds. This is exactly what proteins do every time they locate their target on DNA to switch a gene on, repair damage, or copy the genome. This talk tells the physics story behind that search: how proteins cleverly combine random 3D diffusion with sliding and hopping along the DNA strand itself, turning an impossible search problem into a fast, efficient one. We'll explore how the environment inside a cell — DNA's twisting and bending, the crowding of countless other molecules, and DNA's packaging into chromatin — all shape and even accelerate this search. Along the way we'll see how proteins adapt their shape and motion to read the DNA landscape, how they cooperate with each other, and how damaged or unusual DNA changes the rules of the game.

Every cell in our body carries the same two meters of DNA, folded down to fit inside a nucleus a few microns wide — roughly like packing a thread the length of a football field into a tennis ball, without ever tangling it. Yet this folding isn't random: it determines which genes get switched on, and errors in it are linked to disease and developmental disorders. This talk builds that picture from the ground up. We'll start at the scale of a single gene, where physics-guided models — informed directly by experimental data — let us reconstruct the detailed 3D shape of specific genomic loci with striking precision. Then we'll zoom out to the scale of an entire chromosome, where a different kind of physics takes over. We'll see how physics principles reveal a hidden "backbone" that holds chromosomes together, a common blueprint for genome folding that holds across cell types and species, and how it shifts in predictable ways as cells develop, specialize, or go awry in disease.

In this thesis, we investigate lower bounds for the Weil height of algebraic numbers and the canonical height of points on elliptic curves. We establish three principal results. First, we show a connection between lower bounds of height of an algebraic number $\alpha$ to the low-lying zeros of the Dedekind zeta-function of $Q(\alpha)$. Second, we formulate a $p$-adic criterion for Lehmer’s conjecture. Finally, we prove the Bogomolov property for certain infinite extensions, referred to as asymptotically positive extensions and establish the corresponding Bogomolov property for the canonical height of points on elliptic curves defined over such extensions. https://zoom.us/j/98206472628 Meeting ID: 982 0647 2628 Passcode: 560304

Youth Astronomy and Space Congress

Youth Astronomy and Space Congress

Thermally activated escape processes are ubiquitous in nature and are commonly described by the Arrhenius law (AL), which states that the escape rate depends exponentially on the activation-energy barrier and the temperature. This simple yet powerful relation underpins numerous theoretical models and serves as a standard tool for extracting energy barriers from experiments on single-particle systems. This thesis develops a generalized Arrhenius framework for interacting diffusive systems using a hydrodynamic description. The resulting theory reveals how collective interactions modify thermally activated escape and provides a practical route to infer the number of metastable states, an important characteristic of complex energy landscapes, even when the underlying landscape is not explicitly known. The thesis further demonstrates how the escape time of a single particle can be systematically engineered through controlled modifications of the potential landscape. These findings are shown to be robust for both passive Brownian dynamics and thermally active run-and-tumble particles, thereby extending the applicability of the proposed framework to equilibrium and nonequilibrium settings. Overall, this thesis establishes new paradigms for understanding and controlling escape phenomena in systems governed by inter-particle interactions and nonequilibrium fluctuations, offering theoretical insights with broad relevance to statistical physics, and soft matter.

TBA

TBA

Gene regulatory networks (GRNs) orchestrate cellular behavior through complex combinatorial interactions among genes. Boolean models provide a tractable framework for studying GRN dynamics and have been widely used to analyze stability, multistability, and attractor-based representations of cellular phenotypes. While extensive research has established that biological GRNs are highly constrained in both network topology and regulatory logic, the specific effects of different classes of regulatory logic on global network dynamics have not been systematically characterized. The key objective of this thesis is to develop a systematic understanding of how different classes of Boolean functions (BFs) influence the dynamics of Boolean GRN models across varying network topologies and levels of abstraction, thereby informing principled choices of regulatory logic in Boolean modeling of biological systems. Using both random Boolean networks (RBNs) and reconstructed biological GRNs, we show that regulatory logic plays a central role in determining network stability and dynamical organization. First, we analyze ensembles of RBNs to disentangle the effects of regulatory logic from those of network topology. By systematically varying network size, connectivity, and degree distribution, and by employing multiple notions of dynamical stability, we show that biologically meaningful classes of BFs consistently steer network dynamics toward ordered or near-critical regimes. In contrast, networks governed by random BFs exhibit strong sensitivity to topological parameters and frequently transition toward chaotic behavior with increasing connectivity or size. Across diverse stability measures, biologically meaningful BFs display reduced variability and enhanced robustness. Second, we analyze the effect of BF classes on the stability of reconstructed Boolean GRNs using ensembles in which network structure and biological attractors are held fixed while regulatory logic is varied. Adopting a global state-space perspective, we systematically examine how different classes of BFs shape the structure of the state transition graph (STG). To characterize global dynamical organization, we adapt concepts originally developed in the study of cellular automata (CA) and introduce CA-inspired measures of bushiness and convergence. We find that biologically meaningful BFs lead to faster convergence and more contractive STG structures than random logic. Finally, we critically evaluate threshold majority rules (TMRs), a subclass of threshold functions frequently employed in GRN modeling due to their implementation simplicity. Through a comparative analysis with nested canalyzing functions (NCFs), we show that TMRs exhibit higher complexity and sensitivity, are underrepresented in empirical datasets of regulatory logic, and often fail to recover biological attractors and associated basin sizes. These results indicate that, despite their convenience, TMRs are poorly suited as a modeling choice for biological GRNs. Altogether, this thesis establishes regulatory logic as a key determinant of Boolean GRN dynamics and provides systematic guidance for selecting biologically grounded BFs in the modeling of GRNs. ********************************** Zoom Link to join the PhD defense: https://zoom.us/launch/jc/92590650237 Meeting ID: 907 155 2168 Passcode: 813471 **********************************