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Distributed Implementation of Boolean Functions by Transcriptional Synthetic Circuits
M Ali Al-Radhawi1, Anh Phong Tran2, Elizabeth A Ernst3
1Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts 02115, United States.
ACS Synthetic Biology
|June 27, 2020
Summary
Synthetic biology enables complex genetic circuits, but scaling is limited. Distributing computation across cells using diffusible small molecules (DSMs) greatly expands circuit capabilities, realizing nearly all 4-input Boolean functions with minimal gates.
Area of Science:
- Synthetic biology and computational systems biology.
- Development of novel algorithms for genetic circuit design.
Background:
- Sophisticated synthetic genetic networks have been developed for logical functionalities since the early 2000s.
- Scaling these networks is limited by constraints like repressor toxicity and a lack of orthogonal repressors, restricting circuits to ~7 gates per cell.
- Distributing computation across multiple cell types communicating via diffusible small molecules (DSMs) offers a solution to scalability issues.
Purpose of the Study:
- To develop systematic methods for implementing distributed computation in synthetic genetic circuits for evaluating Boolean functions.
- To propose a fast algorithm for synthesizing distributed circuit realizations under constraints on gate count and DSMs.
- To explore the potential of DSMs in overcoming scalability limitations in genetic circuit design.
Main Methods:
- Developed a fast algorithm for synthesizing distributed genetic circuits for arbitrary Boolean functions.
- Employed an exact synthesis algorithm to find minimal circuits per cell, building a database of Boolean functions.
- Focused on circuits with up to 4 inputs, considering constraints of at most 7 gates per cell and limited orthogonal DSMs.
Main Results:
- Using a single DSM increases realizable circuits by at least 7.58-fold compared to centralized computation, with <= 7 gates per cell.
- Allowing two DSMs enables the realization of 99.995% of all possible 4-input Boolean functions under the same gate constraint.
- A computational toolbox implementing the proposed algorithm was created and made publicly available.
Conclusions:
- Distributed computation using DSMs significantly enhances the scalability and functional capacity of synthetic genetic networks.
- The proposed algorithmic approach provides an efficient method for designing complex distributed genetic circuits.
- This methodology is adaptable to existing genetic circuit design automation software, advancing the field of synthetic biology.
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