RT Journal Article
SR Electronic
T1 Stochastic modelling of cell differentiation networks from partially-observed clonal tracking data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.07.08.499353
DO 10.1101/2022.07.08.499353
A1 Del Core, L.
A1 Pellin, D.
A1 Grzegorczyk, M. A.
A1 Wit, E. C.
YR 2022
UL http://biorxiv.org/content/early/2022/07/10/2022.07.08.499353.abstract
AB Motivation Clarifying how hematopoietic stem cells differentiate into mature cell types is important for understanding how they attain specific functions and offers the potential for therapeutic manipulation. Over the past decades, clonal tracking has proven to be capable of unveiling population dynamics and hierarchical relationships in vivo. For this reason, clonal tracking studies are required for safety and long-term efficacy assessment in gene therapy. However, many standard clonal tracking studies consider only a subset of cell-types and are subject to noise.Results In this work, we propose a stochastic framework that investigates the dynamics of cell differentiation from typical clonal tracking data subject to measurement noise, false-negative errors, and systematically unobserved cell types. Our framework is based on stochastic reaction networks combined with extended Kalman filtering and Rauch-Tung-Striebel smoothing. Our tool can provide statistical support to biologists in gene therapy clonal tracking studies to better understand clonal reconstitution dynamics.Availability The stochastic framework is implemented in the package Karen which is available for download at https://github.com/delcore-luca/Karen. The code that supports the findings of this study is openly available at https://github.com/delcore-luca/CellDifferentiationNetworks.Contact l.del.core{at}rug.nlCompeting Interest StatementThe authors have declared no competing interest.