Optimal Prediction by Cellular Signaling Networks.
Phys Rev Lett
; 115(25): 258103, 2015 Dec 18.
Article
en En
| MEDLINE
| ID: mdl-26722947
ABSTRACT
Living cells can enhance their fitness by anticipating environmental change. We study how accurately linear signaling networks in cells can predict future signals. We find that maximal predictive power results from a combination of input-noise suppression, linear extrapolation, and selective readout of correlated past signal values. Single-layer networks generate exponential response kernels, which suffice to predict Markovian signals optimally. Multilayer networks allow oscillatory kernels that can optimally predict non-Markovian signals. At low noise, these kernels exploit the signal derivative for extrapolation, while at high noise, they capitalize on signal values in the past that are strongly correlated with the future signal. We show how the common motifs of negative feedback and incoherent feed-forward can implement these optimal response functions. Simulations reveal that E. coli can reliably predict concentration changes for chemotaxis, and that the integration time of its response kernel arises from a trade-off between rapid response and noise suppression.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Transducción de Señal
/
Fenómenos Fisiológicos Celulares
/
Modelos Biológicos
Tipo de estudio:
Health_economic_evaluation
/
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Phys Rev Lett
Año:
2015
Tipo del documento:
Article
País de afiliación:
Alemania