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)
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.
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Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
Typ der Hochschulschrift
Herausgeber
Schlagwörter
Allelic Correlation ; Network Modeling ; Noise Transmission ; Signal Processing ; Transcriptional Noise; Dynamic Proteomics; Cancer-cells; Noise; Consequences; Proteins; Origins
Keywords plus
Sprache
englisch
Veröffentlichungsjahr
2022
Prepublished im Jahr
0
HGF-Berichtsjahr
2022
ISSN (print) / ISBN
2405-4712
e-ISSN
2405-4720
ISBN
Bandtitel
Konferenztitel
Konferzenzdatum
Konferenzort
Konferenzband
Quellenangaben
Band: 13,
Heft: 12,
Seiten: 1016-1032.e6
Artikelnummer: ,
Supplement: ,
Reihe
Verlag
Elsevier
Verlagsort
Maryland Heights, MO
Tag d. mündl. Prüfung
0000-00-00
Betreuer
Gutachter
Prüfer
Topic
Hochschule
Hochschulort
Fakultät
Veröffentlichungsdatum
0000-00-00
Anmeldedatum
0000-00-00
Anmelder/Inhaber
weitere Inhaber
Anmeldeland
Priorität
Begutachtungsstatus
Institut(e)
Institute of AI for Health (AIH)
POF Topic(s)
30205 - Bioengineering and Digital Health
Forschungsfeld(er)
Enabling and Novel Technologies
PSP-Element(e)
G-540007-001
Förderungen
Federal Ministry of Education and Research, Germany (Bundesministerium fur Bildung und Forschung, BMBF)
NIH
Copyright
Erfassungsdatum
2022-12-08