Analog computation with transcriptional networks

TL;DR

This paper proves that controlling transcription factor production alone suffices for complex analog computations in synthetic transcriptional networks, eliminating the need to control degradation rates.

Contribution

It introduces a systematic method to engineer analog dynamics in transcriptional networks by controlling production rates alone, with a Python compiler for exact implementation.

Findings

Demonstrates equivalence of production-only control to full control systems for analog computation.

Provides examples including oscillations, chaos, sorting, memory, and control systems.

Offers a Python package to convert polynomial ODEs into transcriptional networks.

Abstract

Transcriptional networks represent one of the most extensively studied types of systems in synthetic biology. Although the completeness of transcriptional networks for digital logic is well-established, *analog* computation plays a crucial role in biological systems and offers significant potential for synthetic biology applications. While transcriptional circuits typically rely on cooperativity and highly non-linear behavior of transcription factors to regulate *production* of proteins, they are often modeled with simple linear *degradation* terms. In contrast, general analog dynamics require both non-linear positive as well as negative terms, seemingly necessitating control over not just transcriptional (i.e., production) regulation but also the degradation rates of transcription factors. Surprisingly, we prove that controlling transcription factor production (i.e., transcription rate) without explicitly controlling degradation is mathematically complete for analog computation, achieving equivalent capabilities to systems where both production and degradation are programmable. We demonstrate our approach on several examples including oscillatory and chaotic dynamics, analog sorting, memory, PID controller, and analog extremum seeking. Our result provides a systematic methodology for engineering novel analog dynamics using synthetic transcriptional networks without the added complexity of degradation control and informs our understanding of the capabilities of natural transcriptional circuits. We provide a compiler, in the form of a Python package that can take any system of polynomial ODEs and convert it to an equivalent transcriptional network implementing the system *exactly*, under appropriate conditions.