Biology subjects

Koeppl, H.

Publications and source records attributed to Koeppl, H..

4 recordsLinked to original sources

bioRxiv2021

Automated Design of Robust Genetic Circuits: Structural Variants and Parameter Uncertainty

Genetic design automation methods for combinational circuits often rely on standard algorithms from electronic design automation in their circuit synthesis and technology mapping. However, those algorithms are domain-specific and are hence often not directly suitable for the biological context. In this work we identify aspects of those algorithms that require domain-adaptation. We first demonstrate that enumerating structural variants for a given Boolean specification allows us to find better performing circuits and that stochastic gate assignment methods need to be properly adjusted in order to find the best assignment. Second, we present a general circuit scoring scheme that accounts for the limited accuracy of biological device models including the variability across cells and show that circuits selected according to this score exhibit higher robustness with respect to parametric variations. If gate characteristics in a library are just given in terms of intervals, we provide means to efficiently propagate signals through such a circuit and compute corresponding scores. We demonstrate the novel design approach using the Cello gate library and 33 logic functions that were synthesized and implemented in vivo recently (1). We show that an average 1.3-fold and a peak 6.5-fold performance increase can be achieved by simply considering structural variants and that an average 1.8-fold and a peak 30-fold gain in the novel robustness score can be obtained when selecting circuits according to it. Graphical TOC Entry O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=82 SRC="FIGDIR/small/456094v1_ufig1.gif" ALT="Figure 2"> View larger version (20K): org.highwire.dtl.DTLVardef@3ef8aorg.highwire.dtl.DTLVardef@635957org.highwire.dtl.DTLVardef@7e747eorg.highwire.dtl.DTLVardef@122db0_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2021

The Functional Nanopore (FuN) Screen: A Versatile Genetic Assay to Study and Engineer Protein Nanopores in Escherichia coli

Nanopores comprise a versatile class of membrane proteins that carry out a range of key physiological functions and are increasingly developed for different biotechnological applications. Yet, a capacity to study and engineer protein nanopores by combinatorial means has so far been hampered by a lack of suitable assays that combine sufficient experimental resolution with throughput. Addressing this technological gap, the Functional Nanopore (FuN) screen now provides a quantitative and dynamic read-out of nanopore assembly and function in E. coli. The assay is based on genetically-encoded fluorescent protein sensors that resolve the nanopore-dependent influx of Ca2+ across the inner membrane of E. coli. Illustrating its versatile capacity, the FuN screen is first applied to dissect the molecular features that underlie the assembly and stability of nanopores formed by the S2168 holin. In a subsequent step, nanopores are engineered by recombining the transmembrane module of S2168 with different ring-shaped oligomeric protein structures that feature defined hexa-, hepta- and octameric geometries. Library screening highlights substantial plasticity in the ability of the S2168 transmembrane module to oligomerize in alternative geometries while the functional properties of the resultant nanopores can be fine-tuned through the identity of the connecting linkers. Overall, the FuN screen is anticipated to facilitate both fundamental studies and complex nanopore engineering endeavors with many potential applications in biomedicine, biotechnology and synthetic biology.

synthetic biology↗

bioRxiv2021

Functionalizing cell-free systems with CRISPR-associated proteins: Application to RNA-based circuit engineering

Cell-free systems have become a compelling choice for the prototyping of synthetic circuits. Many robust protocols for preparing cell-free systems are now available along with toolboxes designed for a variety of applications. Thus far the production of cell-free extracts has often been decoupled from the production of functionalized proteins. Here, we leveraged the most recently published protocol for E. coli-based cell extracts with the endogenous production of two CRISPR-associated proteins, Csy4 and dCas9. We found pre-expression did not affect the resulting extract performance, and the final concentrations of the endonucleases matched the level required for synthetic circuit prototyping. We demonstrated the benefits and versatility of dCas9 and Csy4 through the use of RNA circuitry based on a combination of single guide RNAs, small transcriptional activator RNAs and toehold switches. For instance, we show that Csy4 processing increased fourfold the dynamic range of a previously published AND-logic gate. Additionally, blending the CRISPR-enhanced extracts enabled us to reduce leakage in a multiple inputs gate, and to extend the type of Boolean functions available for RNA-based circuits, such as NAND-logic. Finally, the use of dual transcriptional and translational reporters for the engineering of RNA-based circuits, allowed us to gain better insight into their underlying mechanisms. We hope this work will facilitate the adoption of advanced processing tools for RNA-based circuit prototyping in a cell-free environment.

synthetic biology↗

bioRxiv2019

Cell-free prototyping of AND-logic gates based on heterogeneous RNA activators

RNA-based devices controlling gene expression bear great promise for synthetic biology, as they offer many advantages like short response times and light metabolic burden compared to protein-circuits. However, little work has been done regarding their integration to multi-level regulated circuits. In this work, we combined a variety of small transcriptional activator RNAs (STARs) and toehold switches to build highly effective AND-gates. To characterise the components and their dynamic range, we used an Escherichia coli (E. coli) cell-free transcription-translation (TX-TL) system dispensed via nanoliter droplets. We analysed a prototype gate in vitro as well as in silico, employing parameterised ordinary differential equations (ODEs), where parameters were inferred via parallel tempering, a Markov chain Monte Carlo (MCMC) method. Based on this analysis, we created nine additional AND-gates and tested them in vitro. The functionality of the gates was found to be highly dependent on the concentration of the activating RNA for either the STAR or the toehold switch. All gates were successfully implemented in vivo, offering a dynamic range comparable to the level of protein circuits. This study shows the potential of a rapid prototyping approach for RNA circuit design, using cell-free systems in combination with a model prediction.\n\nAbbreviationsTX-TL (transcription-translation), ODEs (ordinary differential equations), STARs (small transcriptional activator RNAs), MCMC (Markov chain Monte Carlo).

synthetic biology↗