Synchronization of Inhibitory Molecular Spike Oscillators

Molecular communication is the process of transmitting information by modulating the concentration of molecules over time. Molecular communication is suitable for autonomous nanomachines which are limited in size and capability and for interfacing with biological systems which perform functions controlled or influenced by molecules. Some functions may require nanomachines to perform sequential processes. Molecular communication can be used to synchronize multiple nanomachines and to coordinate the timing of the functionality. In this paper, transmitters self-oscillate by releasing a spike of negative autoregulating molecules when concentration of the molecule is below a threshold. When the concentration from a spike disperses and decreases below the threshold, the transmitter releases another spike of molecules. When the environment includes two transmitters, the oscillations of the two transmitters achieve in-phase or anti-phase synchronization depending on the distance between the transmitter and receiver. When there are multiple transmitters arranged in a circle, the oscillations of the transmitters produce in-phase or partially in-phase synchronization. Simulations were performed to characterize the period of oscillation and the phase difference in the oscillations of multiple transmitters.

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

[2]  Tadashi Nakano,et al.  Repeater design and modeling for molecular communication networks , 2011, 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[3]  M. Gracheva,et al.  Intercellular communication via intracellular calcium oscillations. , 2001, Journal of theoretical biology.

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

[5]  Tatsuya Suda,et al.  Molecular Communication: New Paradigm for Communication among Nanoscale Biological Machines , 2012 .

[6]  Vladimir K. Vanag,et al.  Synchronization of Chemical Micro-oscillators , 2010 .

[7]  Massimiliano Pierobon,et al.  Simulation-based evaluation of the diffusion-based physical channel in molecular nanonetworks , 2011, 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[8]  Massimiliano Pierobon,et al.  Diffusion-Based Noise Analysis for Molecular Communication in Nanonetworks , 2011, IEEE Transactions on Signal Processing.

[9]  T. Geisel,et al.  Delay-induced multistable synchronization of biological oscillators , 1998 .

[10]  N. Wingreen,et al.  Accuracy of direct gradient sensing by single cells , 2008, Proceedings of the National Academy of Sciences.

[11]  Eitan Altman,et al.  Bio-Inspired Models of Network, Information, and Computing Systems , 2012, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

[12]  W H Bossert,et al.  The analysis of olfactory communication among animals. , 1963, Journal of theoretical biology.

[13]  D. Sulzer,et al.  Analysis of exocytotic events recorded by amperometry , 2005, Nature Methods.

[14]  Ian F. Akyildiz,et al.  Automata modeling of Quorum Sensing for nanocommunication networks , 2011, Nano Commun. Networks.

[15]  Kazuhiro Oiwa,et al.  Molecular Communication: Modeling Noise Effects on Information Rate , 2009, IEEE Transactions on NanoBioscience.

[16]  A. Goldbeter Computational approaches to cellular rhythms , 2002, Nature.

[17]  Tatsuya Suda,et al.  Measuring Distance with Molecular Communication Feedback Protocols , 2010 .