Jeffrey J. Tabor

Robust multicellular computing using genetically encoded NOR gates and chemical /`wires/'

Computation underlies the organization of cells into higher-order structures, for example during development or the spatial association of bacteria in a biofilm. Each cell performs a simple computational operation, but when combined with cell–cell communication, intricate patterns emerge. Here we study this process by combining a simple genetic circuit with quorum sensing to produce more complex computations in space. We construct a simple NOR logic gate in Escherichia coli by arranging two tandem promoters that function as inputs to drive the transcription of a repressor. The repressor inactivates a promoter that serves as the output. Individual colonies of E. coli carry the same NOR gate, but the inputs and outputs are wired to different orthogonal quorum-sensing ‘sender’ and ‘receiver’ devices. The quorum molecules form the wires between gates. By arranging the colonies in different spatial configurations, all possible two-input gates are produced, including the difficult XOR and EQUALS functions. The response is strong and robust, with 5- to >300-fold changes between the ‘on’ and ‘off’ states. This work helps elucidate the design rules by which simple logic can be harnessed to produce diverse and complex calculations by rewiring communication between cells.

Synthetic biology: engineering Escherichia coli to see light.

We have designed a bacterial system that is switched between different states by red light. The system consists of a synthetic sensor kinase that allows a lawn of bacteria to function as a biological film, such that the projection of a pattern of light on to the bacteria produces a high-definition (about 100 megapixels per square inch), two-dimensional chemical image. This spatial control of bacterial gene expression could be used to ‘print’ complex biological materials, for example, and to investigate signalling pathways through precise spatial and temporal control of their phosphorylation steps.

A Synthetic Genetic Edge Detection Program

Edge detection is a signal processing algorithm common in artificial intelligence and image recognition programs. We have constructed a genetically encoded edge detection algorithm that programs an isogenic community of E. coli to sense an image of light, communicate to identify the light-dark edges, and visually present the result of the computation. The algorithm is implemented using multiple genetic circuits. An engineered light sensor enables cells to distinguish between light and dark regions. In the dark, cells produce a diffusible chemical signal that diffuses into light regions. Genetic logic gates are used so that only cells that sense light and the diffusible signal produce a positive output. A mathematical model constructed from first principles and parameterized with experimental measurements of the component circuits predicts the performance of the complete program. Quantitatively accurate models will facilitate the engineering of more complex biological behaviors and inform bottom-up studies of natural genetic regulatory networks.

Refactoring and Optimization of Light-Switchable Escherichia coli Two-Component Systems

Light-switchable proteins enable unparalleled control of molecular biological processes in live organisms. Previously, we have engineered red/far-red and green/red photoreversible two-component signal transduction systems (TCSs) with transcriptional outputs in E. coli and used them to characterize and control synthetic gene circuits with exceptional quantitative, temporal, and spatial precision. However, the broad utility of these light sensors is limited by bulky DNA encoding, incompatibility with commonly used ligand-responsive transcription factors, leaky output in deactivating light, and less than 10-fold dynamic range. Here, we compress the four genes required for each TCS onto two streamlined plasmids and replace all chemically inducible and evolved promoters with constitutive, engineered versions. Additionally, we systematically optimize the expression of each sensor histidine kinase and response regulator, and redesign both pathway output promoters, resulting in low leakiness and 72- and 117-fold dynamic range, respectively. These second-generation light sensors can be used to program the expression of more genes over a wider range and can be more easily combined with additional plasmids or moved to different host strains. This work demonstrates that bacterial TCSs can be optimized to function as high-performance sensors for scientific and engineering applications.

Optogenetic characterization methods overcome key challenges in synthetic and systems biology

Systems biologists aim to understand how organism-level processes, such as differentiation and multicellular development, are encoded in DNA. Conversely, synthetic biologists aim to program systems-level biological processes, such as engineered tissue growth, by writing artificial DNA sequences. To achieve their goals, these groups have adapted a hierarchical electrical engineering framework that can be applied in the forward direction to design complex biological systems or in the reverse direction to analyze evolved networks. Despite much progress, this framework has been limited by an inability to directly and dynamically characterize biological components in the varied contexts of living cells. Recently, two optogenetic methods for programming custom gene expression and protein localization signals have been developed and used to reveal fundamentally new information about biological components that respond to those signals. This basic dynamic characterization approach will be a major enabling technology in synthetic and systems biology.

Engineering Escherichia coli to see light

We have designed a bacterial system that is switched between different states by red light. The system consists of a synthetic sensor kinase that allows a lawn of bacteria to function as a biological film, such that the projection of a pattern of light on to the bacteria produces a high-definition (about 100 megapixels per square inch), two-dimensional chemical image. This spatial control of bacterial gene expression could be used to ‘print’ complex biological materials, for example, and to investigate signalling pathways through precise spatial and temporal control of their phosphorylation steps.

Non-transcriptional regulatory processes shape transcriptional network dynamics.

Information about the extra- or intracellular environment is often captured as biochemical signals that propagate through regulatory networks. These signals eventually drive phenotypic changes, typically by altering gene expression programmes in the cell. Reconstruction of transcriptional regulatory networks has given a compelling picture of bacterial physiology, but transcriptional network maps alone often fail to describe phenotypes. Cellular response dynamics are ultimately determined by interactions between transcriptional and non-transcriptional networks, with dramatic implications for physiology and evolution. Here, we provide an overview of non-transcriptional interactions that can affect the performance of natural and synthetic bacterial regulatory networks.

Engineering stochasticity in gene expression

Stochastic fluctuations (noise) in gene expression can cause members of otherwise genetically identical populations to display drastically different phenotypes. An understanding of the sources of noise and the strategies cells employ to function reliably despite noise is proving to be increasingly important in describing the behavior of natural organisms and will be essential for the engineering of synthetic biological systems. Here we describe the design of synthetic constructs, termed ribosome competing RNAs (rcRNAs), as a means to rationally perturb noise in cellular gene expression. We find that noise in gene expression increases in a manner proportional to the ability of an rcRNA to compete for the cellular ribosome pool. We then demonstrate that operons significantly buffer noise between coexpressed genes in a natural cellular background and can even reduce the level of rcRNA enhanced noise. These results demonstrate that synthetic genetic constructs can significantly affect the noise profile of a living cell and, importantly, that operons are a facile genetic strategy for buffering against noise.

Light-Activated Nuclear Translocation of Adeno-Associated Virus Nanoparticles Using Phytochrome B for Enhanced, Tunable, and Spatially Programmable Gene Delivery

Gene delivery vectors that are activated by external stimuli may allow improved control over the location and the degree of gene expression in target populations of cells. Light is an attractive stimulus because it does not cross-react with cellular signaling networks, has negligible toxicity, is noninvasive, and can be applied in space and time with unparalleled precision. We used the previously engineered red (R)/far-red (FR) light-switchable protein phytochrome B (PhyB) and its R light dependent interaction partner phytochrome interacting factor 6 (PIF6) from Arabidopsis thaliana to engineer an adeno-associated virus (AAV) platform whose gene delivery efficiency is controlled by light. Upon exposure to R light, AAV engineered to display PIF6 motifs on the capsid bind to PhyB tagged with a nuclear localization sequence (NLS), resulting in significantly increased translocation of viruses into the host cell nucleus and overall gene delivery efficiency. By modulating the ratio of R to FR light, the gene delivery efficiency can be tuned to as little as 35% or over 600% of the unengineered AAV. We also demonstrate spatial control of gene delivery using projected patterns of codelivered R and FR light. Overall, our successful use of light-switchable proteins in virus capsid engineering extends these important optogenetic tools into the adjacent realm of nucleic acid delivery and enables enhanced, tunable, and spatially controllable regulation of viral gene delivery. Our current light-triggered viral gene delivery prototype may be broadly useful for genetic manipulation of cells ex vivo or in vivo in transgenic model organisms, with the ultimate prospect of achieving dose- and site-specific gene expression profiles for either therapeutic (e.g., regenerative medicine) or fundamental discovery research efforts.

Post-translational tools expand the scope of synthetic biology.

Synthetic biology is improving our understanding of and ability to control living organisms. To date, most progress has been made by engineering gene expression. However, computational and genetically encoded tools that allow protein activity and protein-protein interactions to be controlled on their natural time and length scales are emerging. These technologies provide a basis for the construction of post-translational circuits, which are capable of fast, robust and highly spatially resolved signal processing. When combined with their transcriptional and translational counterparts, synthetic post-translational circuits will allow better analysis and control of otherwise intractable biological processes such as cellular differentiation and the growth of tissues.