Search arXiv⌕ Search

arXiv · 1510.07410

A Bio-Synthetic Modulator Model for Diffusion-based Molecular Communications

Abstract

In diffusion-based molecular communication (DMC), one important functionality of a transmitter nano-machine is signal modulation. In particular, the transmitter has to be able to control the release of signaling molecules for modulation of the information bits. An important class of control mechanisms in natural cells for releasing molecules is based on ion channels which are pore-forming proteins across the cell membrane whose opening and closing may be controlled by a gating parameter. In this paper, a modulator for DMC based on ion channels is proposed which controls the rate at which molecules are released from the transmitter by modulating a gating parameter signal. Exploiting the capabilities of the proposed modulator, an on-off keying modulation scheme is introduced and the corresponding average modulated signal, i.e., the average release rate of the molecules from the transmitter, is derived in the Laplace domain. By making a simplifying assumption, a closed-form expression for the average modulated signal in the time domain is obtained which constitutes an upper bound on the total number of released molecules regardless of this assumption. The derived average modulated signal is compared to results obtained with a particle based simulator. The numerical results show that the derived upper bound is tight if the number of ion channels distributed across the transmitter (cell) membrane is small.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Hamidreza Arjmandi, Arman Ahmadzadeh, Robert Schober, Masoumeh Nasiri Kenari. 2016-04-19. A Bio-Synthetic Modulator Model for Diffusion-based Molecular Communications. https://arxiv.org/abs/1510.07410

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

A fully parallel densely connected probabilistic Ising machine with inertia for real-time applications

Ising machines---special-purpose hardware for heuristically solving Ising optimization problems---based on probabilistic bits (p-bits) have been established as a promising alternative to heuristic optimization algorithms run on conventional computers. However, it has---until now---been thought that Ising spins that are connected in probabilistic Ising machines (PIMs) cannot be updated in parallel without ruining the machine's solving ability. This has presented a major challenge to realizing the potential for probabilistic Ising machines to act as fast solvers for densely connected Ising problems. In this paper, we show that it is possible to circumvent this conventional wisdom. We introduce a modified form of Ising spin dynamics for PIMs, adding an inertia term, and verify in algorithm simulations, field-programmable gate array (FPGA) emulation, and in FPGA experiments that the modified dynamics enables fully parallel, synchronous updates and at the same time improves the achieved success probability. Our evaluations were performed with various types of abstract (Max-Cut and Sherrington-Kirkpatrick model) and application-derived (multiple-input and multiple-output, MIMO detection) dense Ising benchmark instances. Performing fully parallel updates results in a speed advantage that grows superlinearly with the number of spins, giving rise to large time-to-solution reductions for practical problem sizes. For both MC and the SK model at a problem size of 200, our approach achieved an average speedup of ~34x, with the best single-instance speedup reaching 150x. As an example of the practical utility of our approach in an application where speed is critical, we co-design the algorithm dynamics and hardware implementation for MIMO detection, achieving improved detection accuracy relative to the standard linear detector and higher throughput than the conventional sequential-update PIM.

cs.ET↗

Towards clinical adoption of voice and speech as measures of health: the need for harmonization

Speech and voice are multidimensional signals that capture both communicative intent and underlying physiological processes, providing a unique, non-invasive window into health. Analyzing these signals has the potential to yield digital biomarkers that (i) provide scalable, objective measurement tools for research and clinical care and (ii) reflect the presence or progression of diverse conditions, including neurological, psychiatric, respiratory, and cardiovascular disorders. Realizing this promise, however, requires the field to overcome pervasive reproducibility and generalizability issues due to heterogeneous data collection, processing, and analysis practices. A major source of this heterogeneity is how underlying acoustic measures themselves are defined and computed. In this paper, we outline key considerations across the speech biomarker discovery lifecycle, from data collection through machine learning modeling to clinical interpretation, needed to achieve reliable, reproducible, and clinically translatable results. Chief among these is the need for harmonization efforts to start from common, precisely specified measure definitions. As a first step, we therefore provide definitions, physiological correlates, and computational implementations for a minimal, clinically interpretable set of core speech measures spanning respiration, phonation, articulation, and fluency. We close by discussing ongoing standardization efforts and the open challenges that remain in advancing the adoption of speech- and voice-based digital biomarkers.

cs.ET↗

Whole-Blood Boundary Analysis of BioFET-Based ctDNA Detection for Intravascular Sensing in Intrabody Nanonetworks

Liquid biopsy can detect tumor-derived biomarkers such as circulating tumor DNA (ctDNA), but ultra-low-fraction assays remain costly, slow, and difficult to scale. This motivates interest in intravascular in vivo sensing in the context of intrabody nanonetworks, where nanosensors could support local biomarker monitoring. BioFET-based nanosensors are relevant here because they are label-free, highly miniaturizable, and have shown strong ctDNA sensitivity in controlled media. We examine whether this sensitivity still yields reliable ctDNA detection in whole blood using a reduced-order stochastic simulation model that links operating-point selection, Debye-screened charge transduction, stochastic finite-capacity binding, nonspecific adsorption, background fluctuations, and intrinsic electronic noise to blank-threshold detection. Monte Carlo evaluation with physiologically grounded parameters shows that short Debye length and several-nanometer charge-to-channel separation attenuate the current shift, while low-frequency noise and background fluctuations reduce the margin between target-present and blank responses. Under the tested quasi-static charge-gating regime, the simulated current shifts do not reliably exceed the blank-derived threshold at low ctDNA concentrations. The model therefore provides a whole-blood boundary analysis that identifies which interface configurations and operating conditions most strongly limit reliable BioFET-based intravascular ctDNA detection.

cs.ET↗