Biology – Quantitative Biology – Molecular Networks
Scientific paper
2010-02-12
Biology
Quantitative Biology
Molecular Networks
Article 17 pages, S1: 12 pages
Scientific paper
Living cells are continually exposed to environmental signals that vary in time. These signals are detected and processed by biochemical networks, which are often highly stochastic. To understand how cells cope with a fluctuating environment, we therefore have to understand how reliably biochemical networks can transmit time-varying signals. To this end, we must understand both the noise characteristics and the amplification properties of networks. In this manuscript, we use information theory to study how reliably signalling cascades employing autoregulation and feedback can transmit time-varying signals. We calculate the frequency-dependence of the gain-to-noise ratio, which reflects how reliably a network transmits signals at different frequencies. We find that the gain-to-noise ratio may differ qualitatively from the power spectrum of the output, showing that the latter does not directly reflect signaling performance. Moreover, we find that auto-activation and auto-repression increase and decrease the gain-to-noise ratio for all of frequencies, respectively. Positive feedback specifically enhances information transmission at low frequencies, while negative feedback increases signal fidelity at high frequencies. Our analysis not only elucidates the role of autoregulation and feedback in naturally-occurring biological networks, but also reveals design principles that can be used for the reliable transmission of time-varying signals in synthetic gene circuits.
Ronde Wiet de
ten Wolde Pieter Rein
Tostevin Filipe
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