Chemical Reactions-Based Microfluidic Transmitter and Receiver Design for Molecular Communication

The design of communication systems capable of processing and exchanging information through molecules and chemical processes is a rapidly growing interdisciplinary field, which holds the promise to revolutionize how we realize computing and communication devices. While molecular communication (MC) theory has had major developments in recent years, more practical aspects in designing components capable of MC functionalities remain less explored. This paper designs chemical reactions-based microfluidic devices to realize binary concentration shift keying (BCSK) modulation and demodulation functionalities. Considering existing MC literature on information transmission via molecular pulse modulation, we propose a microfluidic MC transmitter design, which is capable of generating continuously predefined pulse-shaped molecular concentrations upon rectangular triggering signals to achieve the modulation function. We further design a microfluidic MC receiver capable of demodulating a received signal to a rectangular output signal using a thresholding reaction and an amplifying reaction. Our chemical reactions-based microfluidic molecular communication system is reproducible and its parameters can be optimized. More importantly, it overcomes the slow-speed, unreliability, and non-scalability of biological processes in cells. To reveal design insights, we also derive the theoretical signal responses for our designed microfluidic transmitter and receiver, which further facilitate the transmitter design optimization. Our theoretical results are validated via simulations performed through the COMSOL Multiphysics finite element solver. We demonstrate the predefined nature of the generated pulse and the demodulated rectangular signal together with their dependence on design parameters.

[1]  U. Alon An introduction to systems biology : design principles of biological circuits , 2019 .

[2]  Ian F. Akyildiz,et al.  End-to-End Propagation Noise and Memory Analysis for Molecular Communication over Microfluidic Channels , 2014, IEEE Transactions on Communications.

[3]  Baojun Wang,et al.  Engineering modular and orthogonal genetic logic gates for robust digital-like synthetic biology , 2011, Nature communications.

[4]  Massimiliano Pierobon,et al.  Capacity of a Diffusion-Based Molecular Communication System With Channel Memory and Molecular Noise , 2013, IEEE Transactions on Information Theory.

[5]  Y. Tung,et al.  Generation of nitric oxide gradients in microfluidic devices for cell culture using spatially controlled chemical reactions. , 2013, Biomicrofluidics.

[6]  Andrew W. Eckford,et al.  A Comprehensive Survey of Recent Advancements in Molecular Communication , 2014, IEEE Communications Surveys & Tutorials.

[7]  H. Berg Random Walks in Biology , 2018 .

[8]  Andrew W. Eckford,et al.  Molecular MIMO: From Theory to Prototype , 2016, IEEE Journal on Selected Areas in Communications.

[9]  G. Whitesides The origins and the future of microfluidics , 2006, Nature.

[10]  Massimiliano Pierobon,et al.  Parity-Check Coding Based on Genetic Circuits for Engineered Molecular Communication Between Biological Cells , 2018, IEEE Transactions on Communications.

[11]  D. Di Carlo Inertial microfluidics. , 2009, Lab on a chip.

[12]  Drew Endy,et al.  A survey of enabling technologies in synthetic biology , 2013, Journal of biological engineering.

[13]  Andrea J. Goldsmith,et al.  On the capacity of diffusion-based molecular timing channels , 2016, 2016 IEEE International Symposium on Information Theory (ISIT).

[14]  Andrew W. Eckford,et al.  Tabletop Molecular Communication: Text Messages through Chemical Signals , 2013, PloS one.

[15]  Robert Schober,et al.  Improving Receiver Performance of Diffusive Molecular Communication With Enzymes , 2013, IEEE Transactions on NanoBioscience.

[16]  Adam Noel,et al.  Molecular communication with a reversible adsorption receiver , 2015, 2016 IEEE International Conference on Communications (ICC).

[17]  Jehoshua Bruck,et al.  Programmability of Chemical Reaction Networks , 2009, Algorithmic Bioprocesses.

[18]  Baojun Wang,et al.  Recognizing and engineering digital-like logic gates and switches in gene regulatory networks. , 2016, Current opinion in microbiology.

[19]  Massimiliano Pierobon,et al.  A Microfluidic Feed Forward Loop Pulse Generator for Molecular Communication , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[20]  Thomas F. Stocker,et al.  Introduction to Climate Modelling , 2011 .

[21]  Guy Karlebach,et al.  Modelling and analysis of gene regulatory networks , 2008, Nature Reviews Molecular Cell Biology.

[22]  Vahid Jamali,et al.  Modeling Duct Flow for Molecular Communication , 2017, 2018 IEEE Global Communications Conference (GLOBECOM).

[23]  S. Shen-Orr,et al.  Network motifs: simple building blocks of complex networks. , 2002, Science.

[24]  K. Oh,et al.  Generalized serial dilution module for monotonic and arbitrary microfluidic gradient generators. , 2009, Lab on a chip.

[25]  Adam Noel,et al.  Modeling and Simulation of Molecular Communication Systems With a Reversible Adsorption Receiver , 2015, IEEE Transactions on Molecular, Biological and Multi-Scale Communications.

[26]  J. Verwer,et al.  Numerical solution of time-dependent advection-diffusion-reaction equations , 2003 .

[27]  Chien-Chung Peng,et al.  Generation of oxygen gradients in microfluidic devices for cell culture using spatially confined chemical reactions. , 2011, Lab on a chip.

[28]  Ron Weiss,et al.  Genetic circuit building blocks for cellular computation, communications, and signal processing , 2003, Natural Computing.

[29]  Kai Mesa,et al.  Essential Cell Biology , 2015, The Yale Journal of Biology and Medicine.

[30]  Xiujun Li,et al.  Microfluidic Devices for Biomedical Applications , 2013 .

[31]  U Alon,et al.  The incoherent feed-forward loop accelerates the response-time of the gal system of Escherichia coli. , 2006, Journal of molecular biology.

[32]  Ian F. Akyildiz,et al.  Interference effects on modulation techniques in diffusion based nanonetworks , 2012, Nano Commun. Networks.

[33]  Titus H. Klinge,et al.  Modular and Robust Computation with Deterministic Chemical Reaction Networks , 2016 .

[34]  Laura Galluccio,et al.  Communications and Switching in Microfluidic Systems: Pure Hydrodynamic Control for Networking Labs-on-a-Chip , 2013, IEEE Transactions on Communications.

[35]  Y. Koucheryavy,et al.  The internet of Bio-Nano things , 2015, IEEE Communications Magazine.

[36]  U. Alon Network motifs: theory and experimental approaches , 2007, Nature Reviews Genetics.

[37]  Urbashi Mitra,et al.  Interference Mitigation in Large-Scale Multiuser Molecular Communication , 2019, IEEE Transactions on Communications.

[38]  J. G. Wendel The Non-Absolute Convergence of Gil-Pelaez' Inversion Integral , 1961 .

[39]  Adam Noel,et al.  3D Stochastic Geometry Model for Large-Scale Molecular Communication Systems , 2016, 2016 IEEE Global Communications Conference (GLOBECOM).

[40]  Raghupathy Sivakumar,et al.  Time-Elapse Communication: Bacterial Communication on a Microfluidic Chip , 2013, IEEE Transactions on Communications.

[41]  Hui Yang,et al.  Micro-optics for microfluidic analytical applications. , 2018, Chemical Society reviews.

[42]  Ian F. Akyildiz,et al.  Nanonetworks: A new communication paradigm , 2008, Comput. Networks.

[43]  Murat Kuscu,et al.  Modeling convection-diffusion-reaction systems for microfluidic molecular communications with surface-based receivers in Internet of Bio-Nano Things , 2018, PloS one.

[44]  Stamatios Giannoukos,et al.  A Chemical Alphabet for Macromolecular Communications. , 2018, Analytical chemistry.