Trade-offs between cost and information in cellular prediction.
Proc Natl Acad Sci U S A
; 120(41): e2303078120, 2023 10 10.
Article
em En
| MEDLINE
| ID: mdl-37792515
ABSTRACT
Living cells can leverage correlations in environmental fluctuations to predict the future environment and mount a response ahead of time. To this end, cells need to encode the past signal into the output of the intracellular network from which the future input is predicted. Yet, storing information is costly while not all features of the past signal are equally informative on the future input signal. Here, we show for two classes of input signals that cellular networks can reach the fundamental bound on the predictive information as set by the information extracted from the past signal Push-pull networks can reach this information bound for Markovian signals, while networks that take a temporal derivative can reach the bound for predicting the future derivative of non-Markovian signals. However, the bits of past information that are most informative about the future signal are also prohibitively costly. As a result, the optimal system that maximizes the predictive information for a given resource cost is, in general, not at the information bound. Applying our theory to the chemotaxis network of Escherichia coli reveals that its adaptive kernel is optimal for predicting future concentration changes over a broad range of background concentrations, and that the system has been tailored to predicting these changes in shallow gradients.
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Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Quimiotaxia
/
Escherichia coli
Tipo de estudo:
Health_economic_evaluation
/
Prognostic_studies
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Risk_factors_studies
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article