000150550 001__ 150550
000150550 005__ 20251017144647.0
000150550 0247_ $$2doi$$a10.1103/PhysRevResearch.7.013010
000150550 0248_ $$2sideral$$a142627
000150550 037__ $$aART-2025-142627
000150550 041__ $$aeng
000150550 100__ $$aLe Treut, Guillaume
000150550 245__ $$aMarkov-bridge generation of transition paths and its application to cell-fate choice
000150550 260__ $$c2025
000150550 5060_ $$aAccess copy available to the general public$$fUnrestricted
000150550 5203_ $$aWe present a method to sample Markov-chain trajectories constrained to both the initial and final conditions, which we term Markov bridges. The trajectories are conditioned to end in a specific state at a given time. We derive the master equation for Markov bridges, which exhibits the original transition rates scaled by a time-dependent factor. Trajectories can then be generated using a refined version of the Gillespie algorithm. We illustrate the benefits of our method by sampling trajectories in the Müller-Brown potential. This allows us to generate transition paths which would otherwise be obtained at a high computational cost with standard kinetic Monte Carlo methods because commitment to a transition path is essentially a rare event. We then apply our method to a single-cell RNA sequencing dataset from mouse pancreatic cells to investigate the cell differentiation pathways of endocrine-cell precursors. By sampling Markov bridges for a specific differentiation pathway, we obtain a time-resolved dynamics that can reveal features such as cell types which behave as bottlenecks. The ensemble of trajectories also gives information about the fluctuations around the most likely path. For example, we quantify the statistical weights of different branches in the differentiation pathway to alpha cells.
000150550 536__ $$9info:eu-repo/grantAgreement/ES/MICINN AEI/PID2022-136374NB-C21
000150550 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000150550 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000150550 700__ $$aAncheta, Sarah
000150550 700__ $$aHuber, Greg
000150550 700__ $$aOrland, Henri
000150550 700__ $$0(orcid)0000-0001-7276-2942$$aYllanes, David
000150550 773__ $$g7, 1 (2025), 013010 [12 pp.]$$pPhys. rev. res.$$tPhysical Review Research$$x2643-1564
000150550 8564_ $$s2844255$$uhttps://zaguan.unizar.es/record/150550/files/texto_completo.pdf$$yVersión publicada
000150550 8564_ $$s3075061$$uhttps://zaguan.unizar.es/record/150550/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000150550 909CO $$ooai:zaguan.unizar.es:150550$$particulos$$pdriver
000150550 951__ $$a2025-10-17-14:34:35
000150550 980__ $$aARTICLE