Centre for Innovation in Computing at Lassonde

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Andrew Eckford
A
Associate Professor
Electrical Engineering and Computer Science Department

Associate Professor, EECS Department
Member, ICAL
Member, Centre for Vision Research
Status-Only Assistant Professor, ECE Department, University of Toronto

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2021 – 2022 Research Highlights

Information and Communication Theory in Biology and Biotechnology

In this research, we consider communication among biological organisms, and at scales that allow manipulation of biological organisms. In the first thread of this program, we seek to understand how microorganisms communicate with each other, and the fitness that they gain from this communication. As one example of this phenomenon, bacteria use quorum sensing to coordinate their actions in forming colonies or exploiting shared resources. We apply information-theoretic tools, such as Kelly betting, to understand the fidelity of this communication and the fitness gained from it in the presence of noise. In the second thread of this program, we consider biotechnological applications of microscale and nanoscale communication. Using advanced techniques such as molecular communication, we consider how tiny devices might coordinate their activities, much as microorganisms do through quorum sensing, enabling futuristic new therapies and other biomedical applications.

Research Highlights

  1. Y. Fang, A. Noel. A. W. Eckford, N. Yang, and J. Guo, “Characterization of cooperators in quorum sensing with 2D molecular signal analysis,” IEEE Transactions on Communications, vol. 69, no. 2, pp. 799-816, Feb. 2021.
  2. D. Jing, Y. Li, and A. W. Eckford, “Power control for ISI mitigation in mobile molecular communication,” IEEE Communications Letters, vol. 25, no. 2, pp. 460-464, Feb. 2021.
  3. H. Elayan, A. W. Eckford, and R. Adve, “Information rates of controlled protein interactions using terahertz communication,” IEEE Transactions on Nanobioscience, vol. 20, no. 1, pp. 9-19, Jan. 2021.
  4. S. Moffett, N. Wallbridge, C. Plummer, and A. W. Eckford, “The fitness value of information with delayed phenotype switching: Optimal performance with imperfect sensing,” Physical Review E, vol. 102, no. 5, 052403, Nov. 2020.