Engineering orthogonal dual transcription factors for multi-input synthetic promoters

Synthetic biology has seen an explosive growth in the capability of engineering artificial gene circuits from transcription factors (TFs), particularly in bacteria. However, most artificial networks still employ the same core set of TFs (for example LacI, TetR and cI). The TFs mostly function via repression and it is difficult to integrate multiple inputs in promoter logic. Here we present to our knowledge the first set of dual activator-repressor switches for orthogonal logic gates, based on bacteriophage λ cI variants and multi-input promoter architectures. Our toolkit contains 12 TFs, flexibly operating as activators, repressors, dual activator–repressors or dual repressor–repressors, on up to 270 synthetic promoters. To engineer non cross-reacting cI variants, we design a new M13 phagemid-based system for the directed evolution of biomolecules. Because cI is used in so many synthetic biology projects, the new set of variants will easily slot into the existing projects of other groups, greatly expanding current engineering capacities

Liposome-based liquid handling platform featuring addition, mixing, and aliquoting of femtoliter volumes

This paper describes the utilization of giant unilamellar vesicles (GUVs) as a platform for handling chemical and biochemical reagents. GUVs with diameters of 5 to 10 µm and containing chemical/biochemical reagents together with inert polymers were fused with electric pulses (electrofusion). After reagent mixing, the fused GUVs spontaneously deformed to a budding shape, separating the mixed solution into sub-volumes. We utilized a microfluidic channel and optical tweezers to select GUVs of interest, bring them into contact, and fuse them together to mix and aliquot the reaction product. We also show that, by lowering the ambient temperature close to the phase transition temperature Tm of the lipid used, daughter GUVs completely detached (fission). This process performs all the liquid-handing features used in bench-top biochemistry using the GUV, which could be advantageous for the membrane-related biochemical assays

Drug-inducible control of lethality genes: a low background destabilizing domain architecture applied to the Gal4-UAS system in Drosophila

Destabilizing domains (DDs) are genetic tags that conditionally control the level of abundance of proteins-of-interest (POI) with specific stabilizing small-molecule drugs, rapidly and reversibly, in a wide variety of organisms. The amount of the DD-tagged fusion protein directly impacts its molecular function. Hence, it is important that the background levels be tightly regulated in the absence of any drug. This is especially true for classes of proteins that function at extremely low levels, such as lethality genes involved in tissue development and certain transcriptional activator proteins. Here, we establish the uninduced background and induction levels for two widely used DDs (FKBP and DHFR) by developing an accurate quantification method. We show that both DDs exhibit functional background levels in the absence of a drug, but each to a different degree. To overcome this limitation, we systematically test a double architecture for these DDs (DD-POI-DD) that completely suppresses the protein’s function in an uninduced state, while allowing tunable functional levels upon adding a drug. As an example, we generate a drug-stabilizable Gal4 transcriptional activator with extremely low background levels. We show that this functions in vivo in the widely used Gal4-UAS bipartite expression system in Drosophila melanogaster. By regulating a cell death gene, we demonstrate that only the low background double architecture enables tight regulation of the lethal phenotype in vivo. These improved tools will enable applications requiring exceptionally tight control of protein function in living cells and organisms

A Top-Performing Algorithm for the DREAM3 Gene Expression Prediction Challenge

A wealth of computational methods has been developed to address problems in systems biology, such as modeling gene expression. However, to objectively evaluate and compare such methods is notoriously difficult. The DREAM (Dialogue on Reverse Engineering Assessments and Methods) project is a community-wide effort to assess the relative strengths and weaknesses of different computational methods for a set of core problems in systems biology. This article presents a top-performing algorithm for one of the challenge problems in the third annual DREAM (DREAM3), namely the gene expression prediction challenge. In this challenge, participants are asked to predict the expression levels of a small set of genes in a yeast deletion strain, given the expression levels of all other genes in the same strain and complete gene expression data for several other yeast strains. I propose a simple -nearest-neighbor (KNN) method to solve this problem. Despite its simplicity, this method works well for this challenge, sharing the “top performer” honor with a much more sophisticated method. I also describe several alternative, simple strategies, including a modified KNN algorithm that further improves the performance of the standard KNN method. The success of these methods suggests that complex methods attempting to integrate multiple data sets do not necessarily lead to better performance than simple yet robust methods. Furthermore, none of these top-performing methods, including the one by a different team, are based on gene regulatory networks, which seems to suggest that accurately modeling gene expression using gene regulatory networks is unfortunately still a difficult task

A unified design space of synthetic stripe-forming networks

Synthetic biology is a promising tool to study the function and properties of gene regulatory networks. Gene circuits with predefined behaviours have been successfully built and modelled, but largely on a case-by-case basis. Here we go beyond individual networks and explore both computationally and synthetically the design space of possible dynamical mechanisms for 3-node stripe-forming networks. First, we computationally test every possible 3-node network for stripe formation in a morphogen gradient. We discover four different dynamical mechanisms to form a stripe and identify the minimal network of each group. Next, with the help of newly established engineering criteria we build these four networks synthetically and show that they indeed operate with four fundamentally distinct mechanisms. Finally, this close match between theory and experiment allows us to infer and subsequently build a 2-node network that represents the archetype of the explored design space

Statecharts for Gene Network Modeling

State diagrams (stategraphs) are suitable for describing the behavior of dynamic systems. However, when they are used to model large and complex systems, determining the states and transitions among them can be overwhelming, due to their flat, unstratified structure. In this article, we present the use of statecharts as a novel way of modeling complex gene networks. Statecharts extend conventional state diagrams with features such as nested hierarchy, recursion, and concurrency. These features are commonly utilized in engineering for designing complex systems and can enable us to model complex gene networks in an efficient and systematic way. We modeled five key gene network motifs, simple regulation, autoregulation, feed-forward loop, single-input module, and dense overlapping regulon, using statecharts. Specifically, utilizing nested hierarchy and recursion, we were able to model a complex interlocked feed-forward loop network in a highly structured way, demonstrating the potential of our approach for modeling large and complex gene networks