Distributed Algorithms for Microbiological Systems
Recent advances in the design of genetically engineered synthetic bacteria have high potential impact on new solutions towards biological computing by bacteria such as bio-sensing applications, bio-production, and intelligent drugs. This research project proposes innovative solutions to the problem of building distributed circuits in bacteria from an algorithmic, theoretical perspective that contributes to real-world implementable solutions. We envision the following expected outcomes: New robust algorithms and improved understanding of design decisions: The mathematical analysis of distributed algorithms serves two main purposes: it establishes correctness and performance bounds and allows one to systematically assess algorithmic design decisions. Since biological implementations and their environment are inherently noise- and fault-prone, robustness to these effects is key for the distributed algorithms. Concrete requirements are robustness to variations of model parameters (e.g., due to the variability among bacterial cells) and to a fraction of the bacteria not following the algorithm (e.g., due to loss-of-function mutations). New building blocks: Any algorithm crucially relies on available primitives/building blocks. For distributed algorithms communication primitives are central. We will design, implement, and experimentally verify a new phage-based system to transfer DNA from one bacterium to another.
Recent advances in the design of genetically engineered synthetic bacteria have high potential impact on new solutions towards biological computing by bacteria such as bio-sensing applications, bio-production, and intelligent drugs. This research project proposes innovative solutions to the problem of building distributed circuits in bacteria from an algorithmic, theoretical perspective that contributes to real-world implementable solutions. We envision the following expected outcomes: New robust algorithms and improved understanding of design decisions: The mathematical analysis of distributed algorithms serves two main purposes: it establishes correctness and performance bounds and allows one to systematically assess algorithmic design decisions. Since biological implementations and their environment are inherently noise- and fault-prone, robustness to these effects is key for the distributed algorithms. Concrete requirements are robustness to variations of model parameters (e.g., due to the variability among bacterial cells) and to a fraction of the bacteria not following the algorithm (e.g., due to loss-of-function mutations). New building blocks: Any algorithm crucially relies on available primitives/building blocks. For distributed algorithms communication primitives are central. We will design, implement, and experimentally verify a new phage-based system to transfer DNA from one bacterium to another.

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