Nonlinear signaling on biological networks: the role of stochasticity and spectral clustering
Signal transduction within biological cells is governed by networks of interacting proteins. Communication between these proteins is mediated by signaling molecules which bind to receptors and induce stochastic transitions between different conformational states. Signaling is typically a cooperative process which requires the occurrence of multiple binding events so that reaction rates have a nonlinear dependence on the amount of signaling molecule. It is this nonlinearity that endows biological signaling networks with robust switchlike properties which are critical to their biological function. In this study, we investigate how the properties of these signaling systems depend on the network architecture. Our main result is that these nonlinear networks exhibit bistability where the network activity can switch between states that correspond to a low and high activity level. We show that this bistable regime emerges at a critical coupling strength that is determined by the spectral structure of the network. In particular, the set of nodes that correspond to large components of the leading eigenvector of the adjacency matrix determines the onset of bistability. Above this transition, the eigenvectors of the adjacency matrix determine a hierarchy of clusters, defined by its spectral properties, which are activated sequentially with increasing network activity. We argue further that the onset of bistability occurs either continuously or discontinuously depending upon whether the leading eigenvector is localized or delocalized. Finally, we show that at low network coupling stochastic transitions to the active branch are also driven by the set of nodes that contribute more strongly to the leading eigenvector..
Media Type: 
Electronic Article 

Year of Publication: 
2017 

Publication: 
2017 
Contained In: 
arXiv.org  (2017) vom: 16. Feb. To Main Record  year:2017 

Language: 
English 

Contributors: 
HernandezHernandez, Gonzalo [Author] 

Links: 

doi: 
10.1103/PhysRevE.95.032313 

funding: 


Supporting institution / Project title: 

PPN (CatalogueID): 
XAR007621639 

LEADER  01000caa a22002652 4500  

001  XAR007621639  
003  DE627  
005  20230429064451.0  
007  cr uuuuuuuu  
008  200409s2017 xx o 00 eng c  
024  7  a 10.1103/PhysRevE.95.032313 2 doi  
035  a (DE627)XAR007621639  
035  a (arXiv)1702.05065  
040  a DE627 b ger c DE627 e rakwb  
041  a eng  
082  0  a 530  
082  0  a 570  
245  1  0  a Nonlinear signaling on biological networks: the role of stochasticity and spectral clustering 
264  1  c 2017  
336  a Text b txt 2 rdacontent  
337  a Computermedien b c 2 rdamedia  
338  a OnlineRessource b cr 2 rdacarrier  
520  a Signal transduction within biological cells is governed by networks of interacting proteins. Communication between these proteins is mediated by signaling molecules which bind to receptors and induce stochastic transitions between different conformational states. Signaling is typically a cooperative process which requires the occurrence of multiple binding events so that reaction rates have a nonlinear dependence on the amount of signaling molecule. It is this nonlinearity that endows biological signaling networks with robust switchlike properties which are critical to their biological function. In this study, we investigate how the properties of these signaling systems depend on the network architecture. Our main result is that these nonlinear networks exhibit bistability where the network activity can switch between states that correspond to a low and high activity level. We show that this bistable regime emerges at a critical coupling strength that is determined by the spectral structure of the network. In particular, the set of nodes that correspond to large components of the leading eigenvector of the adjacency matrix determines the onset of bistability. Above this transition, the eigenvectors of the adjacency matrix determine a hierarchy of clusters, defined by its spectral properties, which are activated sequentially with increasing network activity. We argue further that the onset of bistability occurs either continuously or discontinuously depending upon whether the leading eigenvector is localized or delocalized. Finally, we show that at low network coupling stochastic transitions to the active branch are also driven by the set of nodes that contribute more strongly to the leading eigenvector.  
700  1  a HernandezHernandez, Gonzalo e verfasserin 4 aut  
700  1  a Myers, Jesse e verfasserin 4 aut  
700  1  a AlvarezLacalle, Enric e verfasserin 4 aut  
700  1  a Shiferaw, Yohannes e verfasserin 4 aut  
773  0  8  i Enthalten in t arXiv.org g (2017) vom: 16. Feb. 
773  1  8  g year:2017 g day:16 g month:02 
856  4  0  u http://dx.doi.org/10.1103/PhysRevE.95.032313 z lizenzpflichtig 3 Volltext 
856  4  0  u https://arxiv.org/abs/1702.05065 z kostenfrei 3 Volltext 
912  a GBV_XAR  
912  a SSGOLCPHA  
951  a AR  
952  j 2017 b 16 c 02  
953  2 045F a 530  
953  2 045F a 570 