Synthetic nonlinear computation for genetic circuit design.

Area of Science:

• Synthetic biology
• Systems biology
• Biocomputation

Background:

• Biological systems exhibit inherent nonlinearity across molecular, intracellular, and intercellular scales.
• Nonlinear signal processing is crucial for biological functions like cooperative binding, feedback regulation, and population dynamics.
• Existing computation frameworks in synthetic biology aim to harness these nonlinearities for biosensing and therapeutics.

Purpose of the Study:

• To review and categorize existing works on nonlinear computations in synthetic biology.
• To highlight the implementation of nonlinear computations across different biological scales.
• To provide a framework for understanding biological signal processing.

Main Methods:

• Literature review of studies implementing nonlinear computations in synthetic biology.
• Categorization of reviewed works based on the scale of implementation: cis-transcription, biochemical circuit, and cellular networks.
• Analysis of how molecular, intracellular, and intercellular mechanisms contribute to nonlinear signal processing.

Main Results:

• Nonlinear computations are observed at multiple scales, from molecular cooperative binding to complex cellular network dynamics.
• Cis-transcription level mechanisms, like cooperative binding, act as molecular weight variables.
• Intracellular networks utilize feedback and feedforward loops for behaviors such as multistability and adaptation.
• Intercellular networks generate population dynamics through distributed biochemical networks.

Conclusions:

• Nonlinear computation is a fundamental principle in synthetic biology, enabling sophisticated signal processing.
• Understanding and engineering nonlinearities across scales is key for advancing biosensing and therapeutic applications.
• This review provides a structured overview of nonlinear computation strategies in synthetic biology.