Computer Science and Engineering, Department of


First Advisor

Massimiliano Pierobon

Date of this Version





Part of this work is published by IEEE details are given below:

M. Pierobon, Z. Sakkaff, J. L. Catlett, and N. R. Buan, “Mutual Information Upper Bound of Molecular Communication Based on Cell Metabolism,” in Proceedings of the 17th IEEE International workshop on Signal Processing advances in Wireless Communications (SPAWC 2016), Edinburg, UK, July 2016

A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science, Major: Computer Science, Under the Supervision of Professor Massimiliano Pierobon, Lincoln, Nebraska: December, 2016

Copyright 2016 Zahmeeth Sayed Sakkaff.


Synthetic biology is providing novel tools to engineer cells and access the basis of their molecular information processing, including their communication channels based on chemical reactions and molecule exchange. Molecular communication is a discipline in communication engineering that studies these types of communications and ways to exploit them for novel purposes, such as the development of ubiquitous and heterogeneous communication networks to interconnect biological cells with nano and biotechnology-enabled devices, i.e., the Internet of Bio-Nano Things. One major problem in realizing these goals stands in the development of reliable techniques to control the engineered cells and their behavior from the external environment. A possible solution may stem from exploiting the natural mechanisms that allow cells to regulate their metabolism, the complex network of chemical reactions that underlie their growth and reproduction, as a function of chemical compounds in the environment.

In this thesis, molecular communication concepts are applied to study the potential of cell metabolism, and its regulation, to channel information from the outside environment into the cell as function of chemical compounds in the environment, and quantify how much information of the internal state of the metabolic network can be perceived from the outside environment. For this, cell metabolism is characterized in this work through two abstractions, namely, as a molecular communication encoder and a modulator, respectively. The former models the cell metabolism as a binary encoder of the mechanisms underlying the regulation of the cell metabolic network state in function of the chemical composition of the external environment. The latter models the metabolic network inside the cell as a digital modulator of metabolite exchange/growth according to the information contained in its state. Based on these abstractions, the aforementioned potential of cell metabolism is quantified with the information theoretic mutual information parameter obtained through the use of a well-known and computationally efficient metabolic simulation technique.

Numerical results are obtained through simulation of cell metabolism based on the standard processes of Genome Scale Modeling (GEM) and Flux Balance Analysis (FBA). These preliminary proof-of-concept results are based on the following three main cellular species: Escherichia coli (E. coli), the “standard" organism in microbiology, and two important human gut microbes studied in our collaborators' lab, namely, the Bacteroides thetaiotaomicron (B. theta) and the Methanobrevibacter smithii (M. smithii), which provide a direct connection of this work to future practical applications.

Adviser: Massimiliano Pierobon