Biology subjects

Radde, N. E.

Publications and source records attributed to Radde, N. E..

2 recordsLinked to original sources

bioRxiv2025

A Population Model Reveals Surprising Role of Stochastic Cell Division in Epigenetic Memory Systems

Epigenetic memory systems can store transient environmental signals in bacteria in form of DNA methylation patterns. A synthetic zinc finger protein (ZnF4) binds to the DNA in a methylation-dependent manner and represses the expression of the DNA methyltransferase CcrM. The ON-state of these systems is characterized by high CcrM expression, high methylation levels, and low ZnF4 binding, but the mechanisms ensuring long-term ON-state stability remain unclear. Measurements showed a gradual shift of cell populations from ON to OFF starting after about four days of cultivation. We use a hybrid modeling approach integrating flow-cytometry data and bulk methylation measurements to test the hypothesis that stochastic cell division is a key factor in this transition. Interestingly, model parameters cluster into two groups with opposite effects of cell division rates on ON-state stability. Experiments under varying growth conditions show that faster cell division increases memory stability -- an initially unexpected result. Model simulations provide a potential explanation for this observation and deepen our understanding about the mechanisms and timing of the ON/OFF switch in individual cells.

systems biology↗

bioRxiv2023

A provably convergent control closure scheme for the Method of Moments of the Chemical Master Equation

In this article, we introduce a novel moment closure scheme based on concepts from Model Predictive Control (MPC) to accurately describe the time evolution of the statistical moments of the solution of the Chemical Master Equation (CME). The Method of Moments, a set of ordinary differential equations frequently used to calculate the first nm moments, is generally not closed since lower-order moments depend on higher-order moments. To overcome this limitation, we interpret the moment equations as a nonlinear dynamical system, where the first nm moments serve as states and the closing moments serve as control input. We demonstrate the efficacy of our approach using three example systems and show that it outperforms existing closure schemes. For polynomial systems, which encompass all mass-action systems, we provide probability bounds for the error between true and estimated moment trajectories. We achieve this by combining convergence properties of a priori moment estimates from stochastic simulations with guarantees for nonlinear reference tracking MPC. Our proposed method offers an effective solution to accurately predict the time evolution of moments of the CME, which has wide-ranging implications for many fields, including biology, chemistry, and engineering.

systems biology↗