Molecular communication (MC) is a novel communication paradigm envisioned to enable revolutionary future medical and biological use cases such as in-body networks for the diagnosis and treatment of diseases. MC is based on the transport of molecules for information exchange and represents a very energy-efficient and bio-compatible communication mechanism on the centimeter to nanometer scale. The communication nodes can be very small as they will be based on artificial cells or other types of tiny nano-machines.
In order to realize complex applications, such as targeted drug delivery or the detection and localization of infections and tumors, nano-machines must cooperate and communicate. The specific properties and mechanisms in biological environments and at very small scales lead to several challenges:
- Novel channel models and conditions based on diffusion and flow of molecules.
- Extremely slow speeds compared to electromagnetic waves.
- Highly stochastic behavior of the molecules.
- Low capability of future nano-machines, not able to conduct complex computations or sophisticated algorithms.
Therefore, research on MC networks is crucial to enable a future internet of bio-nano things (IoBNT) integrating classical and molecular networks.
In this thesis, the student will work on the topic of chemical reaction networks (CRNs), which represent a possible substrate for computations and programmability in biological systems. A CRN is built from a number of coupled chemical reactions and is designed to turn a certain concentration of input molecules into a concentration of output molecules.
The student will be tasked with implementing a CRN that approximates a real signal processing algorithm, namely successive interference cancellation (SIC). SIc could be used, for example, to realize non-orthogonal multiple access schemes in a larger MC network.
The CRN will be designed conceptually and implemented using Python. Then, the CRN will be evaluated rigorously using both deterministic solvers based on differential equations, as well as stochastic simulations that take into account individual random molecule interactions.