Designing molecular circuits for approximate maximum a posteriori demodulation of concentration modulated signals

Abstract

Motivated by the fact that living cells use molecular circuits (i.e. a set of chemical reactions) for information processing, this paper investigates the problem of designing molecular circuits for demodulation. In our earlier work, we use a Markovian approach to derive a demodulator for diffusion-based molecular communication. The demodulation filters take the form of an ordinary differential equation which computes the log-posteriori probability of a transmission symbol being sent. This work considers the realisation of these demodulation filters using molecular circuits assuming the transmission symbols are rectangular pulses of the same duration but different amplitudes, i.e. concentration modulation. This paper makes a number of contributions. First, we use time-scale separation and renewal theory to analytically derive an approximation of the demodulation filter from our earlier work. Second, we present a method to turn this approximation into a molecular circuit. By using simulation, we show that the output of the derived molecular circuit is approximately equal to the log-posteriori probability calculated by the exact demodulation filter if the log-posteriori probability is positive. Third, we demonstrate that a biochemical circuit in yeast behaves similarly to the derived molecular demodulation filter and is therefore a candidate for implementing the derived filter.

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• Designing molecular circuits for approximate maximum a posteriori demodulation of concentration modulated signals
• Detection of persistent signals and its relation to coherent feedforward loops
• Fluid Dynamics-Based Distance Estimation Algorithm for Macroscale Molecular Communication
• A Survey of Molecular Communication in Cell Biology: Establishing a New Hierarchy for Interdisciplinary Applications
• Using spatial partitioning to reduce the bit error rate of diffusion-based molecular communications
• Using biochemical circuits to approximately compute log-likelihood ratio for detecting persistent signals
• Using transcription-based detectors to emulate the behaviour of sequential probability ratio-based concentration detectors