Effects of Extruder Dynamics and Noise on Simulated Chromatin Contact Probability Curves

Abstract Loop extrusion by SMC complexes is a key mechanism underlying chromatin folding during both interphase and mitosis. Despite this shared mechanism, computational models of loop extrusion often rely on fundamentally different assumptions: interphase models typically use dynamic extruders with finite lifetimes, while mitotic models employ static extruders placed according to loop size distributions. In this work, we investigate whether these modeling paradigms are interchangeable or yield intrinsically incompatible results. Using publicly available Hi-C data from mitotic chicken cells, we systematically compare dynamic and static loop extrusion models implemented in the Polychrom framework. We evaluate how key parameters—such as extruder lifetime, extrusion velocity, and spatial noise—affect simulated contact probability curves P(s) and loop size distributions. Our results reveal that while both model types can be tuned to approximate the general shape of P(s), they produce distinct internal structures and divergent relationships between loop size and contact decay. We also show that increased extruder lifetimes lead to excessive nested loop formation, which alters both loop statistics and P(s) derivatives. Introducing spatial exclusion constraints between extruders partially restores consistency with static models. These findings highlight that differences in extruder behavior and polymer noise levels can significantly impact chromatin model outcomes and must be carefully accounted for when interpreting or comparing simulation results across biological conditions. Author summary Chromatin organization plays a crucial role in gene regulation and cellular function, yet our understanding of its three-dimensional structure relies heavily on computational modeling and the interpretation of complex experimental data. In this study, we use coarse-grained modeling approaches to simulate chromatin folding and systematically investigate how different analysis metrics and data processing methods influence the conclusions drawn from such models. By comparing widely used metrics and exploring the effects of normalization and noise, we highlight potential pitfalls and biases that can arise in chromatin modeling studies. Our findings provide practical recommendations for researchers in the field, aiming to improve the robustness and reproducibility of computational analyses of chromatin architecture. This work will help guide future studies toward more reliable interpretations of chromatin structure and its biological implications.