An Information-Theoretic Characterization of the Optimal Gradient Sensing Response of Cells

Many cellular systems rely on the ability to interpret spatial heterogeneities in chemoattractant concentration to direct cell migration. The accuracy of this process is limited by stochastic fluctuations in the concentration of the external signal and in the internal signaling components. Here we use information theory to determine the optimal scheme to detect the location of an external chemoattractant source in the presence of noise. We compute the minimum amount of mutual information needed between the chemoattractant gradient and the internal signal to achieve a prespecified chemotactic accuracy. We show that more accurate chemotaxis requires greater mutual information. We also demonstrate that a priori information can improve chemotaxis efficiency. We compare the optimal signaling schemes with existing experimental measurements and models of eukaryotic gradient sensing. Remarkably, there is good quantitative agreement between the optimal response when no a priori assumption is made about the location of the existing source, and the observed experimental response of unpolarized Dictyostelium discoideum cells. In contrast, the measured response of polarized D. discoideum cells matches closely the optimal scheme, assuming prior knowledge of the external gradient—for example, through prolonged chemotaxis in a given direction. Our results demonstrate that different observed classes of responses in cells (polarized and unpolarized) are optimal under varying information assumptions

Frequency-Domain Model of Microfluidic Molecular Communication Channels with Graphene BioFET-based Receivers

Molecular Communication (MC) is a bio-inspired communication paradigm utilizing molecules for information transfer. Research on this unconventional communication technique has recently started to transition from theoretical investigations to practical testbed implementations, primarily harnessing microfluidics and sensor technologies. Developing accurate models for input-output relationships on these platforms, which mirror real-world scenarios, is crucial for assessing modulation and detection techniques, devising optimized MC methods, and understanding the impact of physical parameters on performance. In this study, we consider a practical microfluidic MC system equipped with a graphene field effect transistor biosensor (bioFET)-based MC receiver as the model system, and develop an analytical end-to-end frequency-domain model. The model provides practical insights into the dispersion and distortion of received signals, thus potentially informing the design of new frequency-domain MC techniques, such as modulation and detection methods. The accuracy of the developed model is verified through particle-based spatial stochastic simulations of pulse transmission in microfluidic channels and ligand-receptor binding reactions on the receiver surface

Sensing and molecular communication using synthetic cells: Theory and algorithms

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.Ph.D

Frequency-Domain Detection for Molecular Communication with Cross-Reactive Receptors

Molecular Communications (MC) is a bio-inspired communication paradigm that uses molecules as information carriers, requiring unconventional transceivers and modulation/detection techniques. Practical MC receivers (MC-Rxs) can be implemented using field-effect transistor biosensor (bioFET) architectures, where surface receptors reversibly react with ligands. The time-varying concentration of ligand-bound receptors is translated into electrical signals via field effect, which is used to decode the transmitted information. However, ligand-receptor interactions do not provide an ideal molecular selectivity, as similar ligand types, i.e., interferers, co-existing in the MC channel, can interact with the same type of receptors. Overcoming this molecular cross-talk in the time domain can be challenging, especially when Rx has no knowledge of the interferer statistics or operates near saturation. Therefore, we propose a frequency-domain detection (FDD) technique for bioFET-based MC-Rxs that exploits the difference in binding reaction rates of different ligand types reflected in the power spectrum of the ligand-receptor binding noise. We derive the bit error probability (BEP) of the FDD technique and demonstrate its effectiveness in decoding transmitted concentration signals under stochastic molecular interference compared to a widely used time-domain detection (TDD) technique. We then verified the analytical performance bounds of the FDD through a particle-based spatial stochastic simulator simulating reactions on the MC-Rx in microfluidic channels.Comment: Submitted to the IEEE for possible publication. arXiv admin note: text overlap with arXiv:2301.0104