Flapjack: a data management and analysis tool for genetic circuit characterization

Yanez Feliu, G.; Earle Gomez, B.; Berrocal, V. C.; Munoz Silva, M.; Nunez, I. N.; Matute, T. F.; Arce Medina, A.; Vidal, G.; Vidal Cespedes, C.; Dahlin, J.; Federici, F.; Rudge, T. J.

doi:10.1101/2020.10.30.362244

bioRxiv · 10.1101/2020.10.30.362244

Flapjack: a data management and analysis tool for genetic circuit characterization

Yanez Feliu, G.·Earle Gomez, B.·Berrocal, V. C.·Munoz Silva, M.·Nunez, I. N.·Matute, T. F.·Arce Medina, A.·Vidal, G.·Vidal Cespedes, C.·Dahlin, J.·Federici, F.·Rudge, T. J.

View original source ↗Export metadata ↓

Abstract

Characterization is fundamental to the design, build, test, learn (DBTL) cycle for engineering synthetic genetic circuits. Components must be described in such a way as to account for their behavior in a range of contexts. Measurements and associated metadata, including part composition, constitute the test phase of the DBTL cycle. These data may consist of measurements of thousands of circuits, measured in hundreds of conditions, in multiple assays potentially performed in different labs and using different techniques. In order to inform the learn phase this large volume of data must be filtered, collated, and analyzed. Characterization consists of using this data to parameterize models of component function in different contexts, and combining them to predict behaviors of novel circuits. Tools to store, organize, share, and analyze large volumes of measurement and metadata are therefore essential to linking the test phase to the build and learn phases, closing the loop of the DBTL cycle. Here we present such a system, implemented as a web app with a backend data registry and analysis engine. An interactive frontend provides powerful querying, plotting and analysis tools, and we provide a REST API and Python package for full integration with external build and learn software. All measurements are associated to circuit part composition via SBOL. We demonstrate our tool by characterizing a range of genetic components and circuits according to composition and context.

Explore related subjects

synthetic biology Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Yanez Feliu, G., Earle Gomez, B., Berrocal, V. C., Munoz Silva, M., Nunez, I. N., Matute, T. F., Arce Medina, A., Vidal, G., Vidal Cespedes, C., Dahlin, J., Federici, F., Rudge, T. J.. 2020-10-31. Flapjack: a data management and analysis tool for genetic circuit characterization. https://doi.org/10.1101/2020.10.30.362244

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Programmable interphyla communication for Staphylococcus aureus by transferability of homoserine lactone sensors between Bacillales bacteria

Staphylococcus aureus is a Gram-positive opportunistic pathogen of the order Bacillales and a frequent member of human-associated microbiomes. Despite its clinical relevance, engineering S. aureus and the communities it inhabits remains limited by a shortage of well-characterized genetic tools for precise control of gene expression and intercellular signaling. Biosensors for homoserine lactones (HSLs) are widely used in Gram-negative bacteria, as HSLs are small, passively diffusible signaling molecules for and their sensors are tightly regulated. Here, we show that synthetically designed HSL sensors using LuxR-family regulators are highly functional in S. aureus. Notably, synthetic designs previously built for the Gram-positive model Bacillus subtilis, a distantly related Bacillales, transferred functionally and establish repurposing the genetic toolbox as an effective strategy for importing these functions into S. aureus. Sensor performance correlated strongly between the two Bacillales, and activation in S. aureus reached 55-fold, exceeding the dynamic range of reported inducible systems. We further demonstrate tightly regulated, programmable interphyla communication between Gram-negative Escherichia coli and S. aureus via HSL signaling. These results provide a new toolset for engineering interbacterial communication and open a route to extending programmable HSL signaling to other tough-to-engineer bacteria.

synthetic biology↗

bioRxiv2026

Creation of genomic chimeras via megabase scale genome expansion

An organism's physiology is controlled by its genomic content. Building genomic chimeras would enable melding of the diverse functions and properties of life. However, prior arts in genome synthesis are limited to reconstituting pre-existing functions within a singular genome rather than combining diverse genomic functions across multiple distinct genomes. Existing methods are also prohibitively expensive, labor intensive, and time consuming, and thus fundamentally not scalable for creating large synthetic chimeric genomes. To address all these limitations, we invented Additive Conjugative-CAST Engineering (ACE) combining conjugation with CRISPR-associated transposition (CAST) to deliver and integrate up to half a genome per step from a donor cell into a precisely defined position in the recipient's genome. We have demonstrated ACE's capability to integrate at least a 2-megabase donor genomic segment in a single step and at least 3 megabase in two consecutive steps. Importantly, we confirmed ACE's generality through the creation of multiple genomic chimeras across species, genus, and at least order barriers. With ACE, we further demonstrate such chimeric organisms, or what we call genome expanded organisms (GEOs), can be stably forged from at least three starting bacterial strains, and can be stably maintained to express all their acquired genomic parts in a single chimera. ACE offers a new paradigm of creating artificial lifeforms to combine desired properties and potentially create novel functions beyond the constraints of nature. ACE also offers a unique perspective to probe unexplored plasticity in genome architecture and expression patterns of novel, massively expanded genomes.

synthetic biology↗

bioRxiv2026

Functional analysis of small-molecule and protein binding domains using monomeric streptavidin-based two-component biosensors

Protein biosensors are important analytical and diagnostic tools that convert molecular recognition into measurable activity. Most common two-component biosensors require two non-competing recognition domains, yet binding affinity and specificity alone do not reliably predict their compatibility with the rest of the biosensor. We report a modular platform that exploits monomeric streptavidin (mSA2) as a surrogate recognition domain, enabling rapid functional evaluation of candidate binders in the context of biosensor scaffold without requiring a second ligand-specific binding protein. We established a chromatography-free protocol for recovering functional mSA2-based biosensor components from Escherichia coli. Using biotinylated small molecules methotrexate and vitamin D3, as well as heart failure protein biomarkers galectin-3 and S100A7 we demonstrate ligand-dependent reporter biosensor activation. The developed platform provides a scalable approach for rapid binder evaluation and biosensor prototyping, streamlining biosensor development using binding domains generated by directed evolution or computational protein design.

synthetic biology↗