BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Zhou Fang (ETH Zürich) DTSTART:20231026T153000Z DTEND:20231026T160000Z DTSTAMP:20260421T123942Z UID:MoRN/80 DESCRIPTION:Title: A divide-and-conquer method for analyzing high-dimensional noisy gene expres sion networks\nby Zhou Fang (ETH Zürich) as part of Seminar on the Ma thematics of Reaction Networks\n\n\nAbstract\nIntracellular gene expressio n systems are inevitably random due to low molecular counts. Consequently\ , mechanistic models for gene expression should be stochastic\, and centra l to the analysis and inference of such models is solving the Chemical Mas ter Equation (CME)\, which characterizes the probability evolution of the randomly evolving copy-numbers of the reacting species. While conventional methods such as Monte-Carlo simulations and finite state projections exis t for estimating CME solutions\, they suffer from the curse of dimensional ity\, significantly decreasing their efficacy for high-dimensional systems . Here\, we propose a new computational method that resolves this issue th rough a novel divide-and-conquer approach. Our method divides the system i nto a leader system and several conditionally independent follower subsyst ems. The solution of the CME is then constructed by combining Monte Carlo estimation for the leader system with stochastic filtering procedures for the follower subsystems. We develop an optimized system decomposition\, wh ich ensures the low-dimensionality of the sub-problems\, thereby allowing for improved scalability with increasing system dimension. The efficiency and accuracy of the method are demonstrated through several biologically r elevant examples in high-dimensional estimation and inference problems. We demonstrate that our method can successfully identify a yeast transcripti on system at the single-cell resolution\, leveraging mRNA time-course micr oscopy data\, allowing us to rigorously examine the heterogeneity in rate parameters among isogenic cells cultured under identical conditions. Furth ermore\, we validate this finding using a novel noise decomposition techni que introduced in this study. This technique exploits experimental time-co urse data to quantify intrinsic and extrinsic noise components\, without r equiring supplementary components\, such as dual-reporter systems.\n LOCATION:https://researchseminars.org/talk/MoRN/80/ END:VEVENT END:VCALENDAR