Novel biological signal-processing framework precisely decodes complex cellular language

Cells naturally process external signals through intricate genetic circuits, enabling the adaptation to diverse environments. These circuits rely on complex, nonlinear interactions among genes and proteins to regulate behavior, maintain homeostasis, and respond to environmental cues. 

Synthetic biology leverages these mechanisms to engineer biosensors, metabolic pathways, and therapeutic systems. However, engineering traditional synthetic circuits struggle to process complex biological signals effectively, as non-orthogonal signal responses hinder precise regulatory control.

In a study published in Nature Communications, a team led by Prof. CHEN Ye from the Shenzhen Institutes of Advanced Technology of the Chinese Academy of Sciences, collaborating with SHAN Yang from Hunan Academy of Agricultural Sciences, developed a scalable biological signal-processing framework that uses synthetic operational amplifiers (OAs) to convert mixed cellular inputs into clean, orthogonal outputs, which enables precise, predictable control of complex biological systems.

Inspired by OAs in analog electronics, researchers conceptualized cellular sensing as an "encoding" step: Multiple signals generate a mixed, non-orthogonal transcriptional response. They then designed synthetic biological OAs as "decoders," and extracted target components from composite signals through weighted subtraction and amplification.

The OA circuits rely on engineered transcription factors and promoters which could be combined with a mathematical model to treat signal transformation as a matrix operation. This design can be generalized from two-dimensional signal separation to N dimensions, offering scalability for highly complex biological networks.

The framework enabled the construction of multiple inducer-free, growth-stage-responsive circuits in Escherichia coli, achieving regulatory signal amplification of up to 153/688-fold, and was subsequently validated through two representative applications. 

In dynamic control of biomanufacturing, it enabled Escherichia coli to sense its growth phase and autonomously switch gene expression, which promoted biomass accumulation during growth and produced target metabolites such as shikimic acid during production without costly inducers, improving the efficiency and reducing the cost. 

In signal decomposition, it resolved complex crosstalk among three quorum-sensing signals, which separated mixed inputs into three independent orthogonal outputs, showcasing the strong performance in high-dimensional, complex signal processing.

This study establishes a foundational framework for synthetic biology by decoding cellular states, enabling precise control of biological systems. This advance offers solutions to key biomanufacturing challenges and paves the way for smarter, more robust cellular computers in medicine, environmental remediation, and sustainable energy.