Model‐guided combinatorial optimization of complex synthetic gene networks

• Published in: Molecular systems biology 2016-12, Vol.12 (12), p.899-n/a
• Main Authors: Schreiber, Joerg, Arter, Meret, Lapique, Nicolas, Haefliger, Benjamin, Benenson, Yaakov
• Format: Article
• Language: English
• Subjects: Automation; Binding sites; Biosensing Techniques; Biosensors; Canonical forms; Circuits; Combinatorial analysis; Composition; Computer engineering; EMBO26; EMBO41; Experimentation; Experiments; Gene Library; Gene Regulatory Networks; Genes, Synthetic; Genetic diversity; Genetic engineering; High-Throughput Screening Assays - methods; Interrogation; Knowledge management; library screening; MicroRNAs - genetics; miRNA; miRNA sensor; modeling; Models, Genetic; Networks; Optimization; Order parameters; Productivity; Screening; Synthetic Biology; synthetic gene circuit
• ISSN: 1744-4292; 1744-4292
• DOI: 10.15252/msb.20167265

Constructing gene circuits that satisfy quantitative performance criteria has been a long‐standing challenge in synthetic biology. Here, we show a strategy for optimizing a complex three‐gene circuit, a novel proportional miRNA biosensor, using predictive modeling to initiate a search in the phase space of sensor genetic composition. We generate a library of sensor circuits using diverse genetic building blocks in order to access favorable parameter combinations and uncover specific genetic compositions with greatly improved dynamic range. The combination of high‐throughput screening data and the data obtained from detailed mechanistic interrogation of a small number of sensors was used to validate the model. The validated model facilitated further experimentation, including biosensor reprogramming and biosensor integration into larger networks, enabling in principle arbitrary logic with miRNA inputs using normal form circuits. The study reveals how model‐guided generation of genetic diversity followed by screening and model validation can be successfully applied to optimize performance of complex gene networks without extensive prior knowledge. Synopsis A workflow for designing optimized gene circuits without extensive prior knowledge is presented. A mechanistic model guides the generation of a combinatorial circuit library, whose mechanistic characterization is in turn used to validate the model. A workflow is described to enable construction of complex gene circuits without extensive prior knowledge. A parametric model is analyzed to uncover favorable parameter regimes and guide the construction of a combinatorial circuit library. High‐throughput library characterization and low‐throughput detailed measurements are used to either validate or modify the model. The validated model guides construction of well‐functioning complex circuits computing XOR logic. Graphical Abstract A workflow for designing optimized gene circuits without extensive prior knowledge is presented. A mechanistic model guides the generation of a combinatorial circuit library, whose mechanistic characterization is in turn used to validate the model.