PuSH - Publikationsserver des Helmholtz Zentrums München

Boe, R.H.* ; Ayyappan, V.* ; Schuh, L. ; Raj, A.*

Allelic correlation is a marker of trade-offs between barriers to transmission of expression variability and signal responsiveness in genetic networks.

Cell Syst. 13, 1016-1032.e6 (2022)
Postprint DOI PMC
Open Access Green
Genetic networks should respond to signals but prevent the transmission of spontaneous fluctuations. Limited data from mammalian cells suggest that noise transmission is uncommon, but systematic claims about noise transmission have been limited by the inability to directly measure it. Here, we build a mathematical framework modeling allelic correlation and noise transmission, showing that allelic correlation and noise transmission correspond across model parameters and network architectures. Limiting noise transmission comes with the trade-off of being unresponsive to signals, and within responsive regimes, there is a further trade-off between response time and basal noise transmission. Analysis of allele-specific single-cell RNA-sequencing data revealed that genes encoding upstream factors in signaling pathways and cell-type-specific factors have higher allelic correlation than downstream factors, suggesting they are more subject to regulation. Overall, our findings suggest that some noise transmission must result from signal responsiveness, but it can be minimized by trading off for a slower response. A record of this paper's transparent peer review process is included in the supplemental information.
Altmetric
Weitere Metriken?
[➜Einloggen]
Zusatzinfos bearbeiten [➜Einloggen]
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Allelic Correlation ; Network Modeling ; Noise Transmission ; Signal Processing ; Transcriptional Noise; Dynamic Proteomics; Cancer-cells; Noise; Consequences; Proteins; Origins
ISSN (print) / ISBN 2405-4712
e-ISSN 2405-4720
Zeitschrift Cell Systems
Quellenangaben Band: 13, Heft: 12, Seiten: 1016-1032.e6 Artikelnummer: , Supplement: ,
Verlag Elsevier
Verlagsort Maryland Heights, MO
Nichtpatentliteratur Publikationen
Institut(e) Institute of AI for Health (AIH)
Förderungen Federal Ministry of Education and Research, Germany (Bundesministerium fur Bildung und Forschung, BMBF)
NIH