Sensing and molecular communication using synthetic cells: Theory and algorithms
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Molecular communication (MC) is a novel communication paradigm in which molecules are used to encode, transmit and decode information. MC is the primary method by which biological entities exchange information and hence, cooperate with each other. MC is a promising paradigm to enable communication between nano-bio machines, e.g., biosensors with potential applications such as cancer and disease detection, smart drug delivery, toxicity detection etc. The objective of this research is to establish the fundamentals of diffusion-based molecular communication and sensing via biological agents (e.g., synthetic bacteria) from a communication and information theory perspective, and design algorithms for reliable communication and sensing systems. In the first part of the thesis, we develop models for the diffusion channel as well as the molecular sensing at the receiver and obtain the maximum achievable rate for such a communication system. Next, we study reliability in MC. We design practical nodes by employing synthetic bacteria as the basic element of a biologically-compatible communication system and show how reliable nodes can be formed out of the collective behavior of a population of unreliable bio-agents. We model the probabilistic behavior of bacteria, obtain the node sensing capacity and propose a practical modulation scheme. In order to improve the reliability, we also introduce relaying and error-detecting codes for MC. In the second part of the thesis, we study the molecular sensing problem with potential applications in disease detection. We establish the rate-distortion theory for molecular sensing and investigate as to how distortion can be minimized via an optimal quantizer. We also study sensor cell arrays in which sensing redundancy is achieved by using multiple sensors to measure several molecular inputs simultaneously. We study the interference in sensing molecular inputs and propose a probabilistic message passing algorithm to solve the pattern detection over the molecular inputs of interest.