The style of genetic computing
Nicolas E. Buchler, Ulrich Gerland, and Terence Hwa

TL;DR
This paper presents a theoretical model demonstrating how bacterial transcription regulation can perform complex logic functions, highlighting the modularity and evolvability of genetic control systems and comparing bacterial and eukaryotic mechanisms.
Contribution
It introduces a quantitative model showing how cis-regulatory DNA sequences can implement diverse regulatory logic functions, revealing the programmable nature of transcription regulation.
Findings
Cis-regulatory systems can execute a wide range of control functions.
Bacterial transcription systems have limitations for complex control schemes.
Eukaryotic systems may overcome bacterial limitations.
Abstract
Cells receive a wide variety of cellular and environmental signals, which must be processed combinatorially to generate specific and timely genetic responses. We present here a theoretical study on the combinatorial control and integration of transcription signals, with the finding that cis-regulatory systems with specific protein-DNA interaction and glue-like protein-protein interactions, supplemented by distal activation or repression mechanisms, have the capability to execute a wide range of control functions encoded in the regulatory DNA sequences. Using a quantitative model based on the well-characterized bacterial transcription system, we show explicitly how various regulatory logic functions can be implemented, by selecting the strengths and relative positions of the relevant protein-binding DNA sequences in the cis-regulatory region. The architecture that emerges is naturally…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGene Regulatory Network Analysis · Bacterial Genetics and Biotechnology · DNA and Biological Computing
