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
    
 
    
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        Sprache
        englisch
    
 
    
        Veröffentlichungsjahr
        2022
    
 
    
        Prepublished im Jahr 
        0
    
 
    
        HGF-Berichtsjahr
        2022
    
 
    
    
        ISSN (print) / ISBN
        2405-4712
    
 
    
        e-ISSN
        2405-4720
    
 
    
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	    Band: 13,  
	    Heft: 12,  
	    Seiten: 1016-1032.e6 
	    Artikelnummer: ,  
	    Supplement: ,  
	
    
 
  
        
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            Verlag
            Elsevier
        
 
        
            Verlagsort
            Maryland Heights, MO
        
 
	
        
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        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
    
 
    
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        Erfassungsdatum
        2022-12-08