Reinforcement learning in synthetic gene circuits.

Synthetic gene circuits allow programming in DNA the expression of a phenotype at a given environmental condition. The recent integration of memory systems with gene circuits opens the door to their adaptation to new conditions and their re-programming. This lays the foundation to emulate neuromorphic behaviour and solve complex problems similarly to artificial neural networks. Cellular products such as DNA or proteins can be used to store memory in both digital and analog formats, allowing cells to be turned into living computing devices able to record information regarding their previous states. In particular, synthetic gene circuits with memory can be engineered into living systems to allow their adaptation through reinforcement learning. The development of gene circuits able to adapt through reinforcement learning moves Sciences towards the ambitious goal: the bottom-up creation of a fully fledged living artificial intelligence.

Model-based design of RNA hybridization networks implemented in living cells.

Synthetic gene circuits allow the behavior of living cells to be reprogrammed, and non-coding small RNAs (sRNAs) are increasingly being used as programmable regulators of gene expression. However, sRNAs (natural or synthetic) are generally used to regulate single target genes, while complex dynamic behaviors would require networks of sRNAs regulating each other. Here, we report a strategy for implementing such networks that exploits hybridization reactions carried out exclusively by multifaceted sRNAs that are both targets of and triggers for other sRNAs. These networks are ultimately coupled to the control of gene expression. We relied on a thermodynamic model of the different stable conformational states underlying this system at the nucleotide level. To test our model, we designed five different RNA hybridization networks with a linear architecture, and we implemented them in Escherichia coli. We validated the network architecture at the molecular level by native polyacrylamide gel electrophoresis, as well as the network function at the bacterial population and single-cell levels with a fluorescent reporter. Our results suggest that it is possible to engineer complex cellular programs based on RNA from first principles. Because these networks are mainly based on physical interactions, our designs could be expanded to other organisms as portable regulatory resources or to implement biological computations.

Dynamic signal processing by ribozyme-mediated RNA circuits to control gene expression.

Organisms have different circuitries that allow converting signal molecule levels to changes in gene expression. An important challenge in synthetic biology involves the de novo design of RNA modules enabling dynamic signal processing in live cells. This requires a scalable methodology for sensing, transmission, and actuation, which could be assembled into larger signaling networks. Here, we present a biochemical strategy to design RNA-mediated signal transduction cascades able to sense small molecules and small RNAs. We design switchable functional RNA domains by using strand-displacement techniques. We experimentally characterize the molecular mechanism underlying our synthetic RNA signaling cascades, show the ability to regulate gene expression with transduced RNA signals, and describe the signal processing response of our systems to periodic forcing in single live cells. The engineered systems integrate RNA-RNA interaction with available ribozyme and aptamer elements, providing new ways to engineer arbitrary complex gene circuits.

A new frontier in synthetic biology: automated design of small RNA devices in bacteria.

RNA devices provide synthetic biologists with tools for manipulating post-transcriptional regulation and conditional detection of cellular biomolecules. The use of computational methods to design RNA devices has improved to the stage where it is now possible to automate the entire design process. These methods utilize structure prediction tools that optimize nucleotide sequences, together with fragments of known independent functionalities. Recently, this approach has been used to create an automated method for the de novo design of riboregulators. Here, we describe how it is possible to obtain riboregulatory circuits in prokaryotes by capturing the relevant interactions of RNAs inside the cytoplasm using a physicochemical model. We focus on the regulation of protein expression mediated by intra- or intermolecular interactions of small RNAs (sRNAs), and discuss the design of riboregulators for other functions. The automated design of RNA devices opens new possibilities for engineering fully synthetic regulatory systems that program new functions or reprogram dysfunctions in living cells.

Exploring the Dynamics and Mutational Landscape of Riboregulation with a Minimal Synthetic Circuit in Living Cells.

The regulation of gene expression, triggered by conformational changes in RNA molecules, is widely observed in cellular systems. Here, we examine this mode of control by means of a model-based design and construction of a fully synthetic riboregulatory device. We present a theoretical framework that rests on a simple energy model to predict the dynamic response of such a system. Following an equilibrium description, our framework integrates thermodynamic properties­anticipated with an RNA physicochemical model­with a detailed description of the intermolecular interaction. The theoretical calculations are confirmed with an experimental characterization of the action of the riboregulatory device within living cells. This illustrates, more broadly, the predictability of genetic robustness on synthetic systems, and the faculty to engineer gene expression programs from a minimal set of first principles.

RiboMaker: computational design of conformation-based riboregulation.

MOTIVATION: The ability to engineer control systems of gene expression is instrumental for synthetic biology. Thus, bioinformatic methods that assist such engineering are appealing because they can guide the sequence design and prevent costly experimental screening. In particular, RNA is an ideal substrate to de novo design regulators of protein expression by following sequence-to-function models. RESULTS: We have implemented a novel algorithm, RiboMaker, aimed at the computational, automated design of bacterial riboregulation. RiboMaker reads the sequence and structure specifications, which codify for a gene regulatory behaviour, and optimizes the sequences of a small regulatory RNA and a 5'-untranslated region for an efficient intermolecular interaction. To this end, it implements an evolutionary design strategy, where random mutations are selected according to a physicochemical model based on free energies. The resulting sequences can then be tested experimentally, providing a new tool for synthetic biology, and also for investigating the riboregulation principles in natural systems. AVAILABILITY AND IMPLEMENTATION: Web server is available at http://ribomaker.jaramillolab.org/. Source code, instructions and examples are freely available for download at http://sourceforge.net/projects/ribomaker/.

Computational design of genomic transcriptional networks with adaptation to varying environments.

Transcriptional profiling has been widely used as a tool for unveiling the coregulations of genes in response to genetic and environmental perturbations. These coregulations have been used, in a few instances, to infer global transcriptional regulatory models. Here, using the large amount of transcriptomic information available for the bacterium Escherichia coli, we seek to understand the design principles determining the regulation of its transcriptome. Combining transcriptomic and signaling data, we develop an evolutionary computational procedure that allows obtaining alternative genomic transcriptional regulatory network (GTRN) that still maintains its adaptability to dynamic environments. We apply our methodology to an E. coli GTRN and show that it could be rewired to simpler transcriptional regulatory structures. These rewired GTRNs still maintain the global physiological response to fluctuating environments. Rewired GTRNs contain 73% fewer regulated operons. Genes with similar functions and coordinated patterns of expression across environments are clustered into longer regulated operons. These synthetic GTRNs are more sensitive and show a more robust response to challenging environments. This result illustrates that the natural configuration of E. coli GTRN does not necessarily result from selection for robustness to environmental perturbations, but that evolutionary contingencies may have been important as well. We also discuss the limitations of our methodology in the context of the demand theory. Our procedure will be useful as a novel way to analyze global transcription regulation networks and in synthetic biology for the de novo design of genomes.

De novo automated design of small RNA circuits for engineering synthetic riboregulation in living cells.

A grand challenge in synthetic biology is to use our current knowledge of RNA science to perform the automatic engineering of completely synthetic sequences encoding functional RNAs in living cells. We report here a fully automated design methodology and experimental validation of synthetic RNA interaction circuits working in a cellular environment. The computational algorithm, based on a physicochemical model, produces novel RNA sequences by exploring the space of possible sequences compatible with predefined structures. We tested our methodology in Escherichia coli by designing several positive riboregulators with diverse structures and interaction models, suggesting that only the energy of formation and the activation energy (free energy barrier to overcome for initiating the hybridization reaction) are sufficient criteria to engineer RNA interaction and regulation in bacteria. The designed sequences exhibit nonsignificant similarity to any known noncoding RNA sequence. Our riboregulatory devices work independently and in combination with transcription regulation to create complex logic circuits. Our results demonstrate that a computational methodology based on first-principles can be used to engineer interacting RNAs with allosteric behavior in living cells.

Full design automation of multi-state RNA devices to program gene expression using energy-based optimization.

Small RNAs (sRNAs) can operate as regulatory agents to control protein expression by interaction with the 5' untranslated region of the mRNA. We have developed a physicochemical framework, relying on base pair interaction energies, to design multi-state sRNA devices by solving an optimization problem with an objective function accounting for the stability of the transition and final intermolecular states. Contrary to the analysis of the reaction kinetics of an ensemble of sRNAs, we solve the inverse problem of finding sequences satisfying targeted reactions. We show here that our objective function correlates well with measured riboregulatory activity of a set of mutants. This has enabled the application of the methodology for an extended design of RNA devices with specified behavior, assuming different molecular interaction models based on Watson-Crick interaction. We designed several YES, NOT, AND, and OR logic gates, including the design of combinatorial riboregulators. In sum, our de novo approach provides a new paradigm in synthetic biology to design molecular interaction mechanisms facilitating future high-throughput functional sRNA design.

T7 phage-assisted evolution of riboswitches using error-prone replication and dual selection.

Leveraging riboswitches, non-coding mRNA fragments pivotal to gene regulation, poses a challenge in effectively selecting and enriching these functional genetic sensors, which can toggle between ON and OFF states in response to their cognate inducers. Here, we show our engineered phage T7, enabling the evolution of a theophylline riboswitch. We have replaced T7's DNA polymerase with a transcription factor controlled by a theophylline riboswitch and have created two types of host environments to propagate the engineered phage. Both types host an error-prone T7 DNA polymerase regulated by a T7 promoter along with another critical gene-either cmk or pifA, depending on the host type. The cmk gene is necessary for T7 replication and is used in the first host type for selection in the riboswitch's ON state. Conversely, the second host type incorporates the pifA gene, leading to abortive T7 infections and used for selection in the riboswitch's OFF state. This dual-selection system, termed T7AE, was then applied to a library of 65,536 engineered T7 phages, each carrying randomized riboswitch variants. Through successive passage in both host types with and without theophylline, we observed an enrichment of phages encoding functional riboswitches that conferred a fitness advantage to the phage in both hosts. The T7AE technique thereby opens new pathways for the evolution and advancement of gene switches, including non-coding RNA-based switches, setting the stage for significant strides in synthetic biology.

Fine-tuning tomato agronomic properties by computational genome redesign.

Considering cells as biofactories, we aimed to optimize its internal processes by using the same engineering principles that large industries are implementing nowadays: lean manufacturing. We have applied reverse engineering computational methods to transcriptomic, metabolomic and phenomic data obtained from a collection of tomato recombinant inbreed lines to formulate a kinetic and constraint-based model that efficiently describes the cellular metabolism from expression of a minimal core of genes. Based on predicted metabolic profiles, a close association with agronomic and organoleptic properties of the ripe fruit was revealed with high statistical confidence. Inspired in a synthetic biology approach, the model was used for exploring the landscape of all possible local transcriptional changes with the aim of engineering tomato fruits with fine-tuned biotechnological properties. The method was validated by the ability of the proposed genomes, engineered for modified desired agronomic traits, to recapitulate experimental correlations between associated metabolites.

Computational design of synthetic regulatory networks from a genetic library to characterize the designability of dynamical behaviors.

The engineering of synthetic gene networks has mostly relied on the assembly of few characterized regulatory elements using rational design principles. It is of outmost importance to analyze the scalability and limits of such a design workflow. To analyze the design capabilities of libraries of regulatory elements, we have developed the first automated design approach that combines such elements to search the genotype space associated to a given phenotypic behavior. Herein, we calculated the designability of dynamical functions obtained from circuits assembled with a given genetic library. By designing circuits working as amplitude filters, pulse counters and oscillators, we could infer new mechanisms for such behaviors. We also highlighted the hierarchical design and the optimization of the interface between devices. We dissected the functional diversity of a constrained library and we found that even such libraries can provide a rich variety of behaviors. We also found that intrinsic noise slightly reduces the designability of digital circuits, but it increases the designability of oscillators. Finally, we analyzed the robust design as a strategy to counteract the evolvability and noise in gene expression of the engineered circuits within a cellular background, obtaining mechanisms for robustness through non-linear negative feedback loops.

Theoretical and experimental analysis of the forced LacI-AraC oscillator with a minimal gene regulatory model.

Oscillatory dynamics have been observed in multiple cellular functions and synthetic constructs; and here, we study the behavior of a synthetic oscillator under temporal perturbations. We use a minimal model, involving a single transcription factor with delayed self-repression and enzymatic degradation, together with a first-order perturbative approach, to derive an analytical expression for the power spectrum of the system, which characterizes its response to external forces and molecular noise. Experimentally, we force and monitor the dynamics of the LacI-AraC oscillator in single cells during long time intervals by constructing a microfluidics device. Pulse dynamics of IPTG with different periods serve to perturb this system. Due to the resonance of the system, we predict theoretically and confirm experimentally the dependence on the forcing frequency of the variability in gene expression with time and the synchronization of the population to the input signal. The reported results show that the engineering of gene circuits can provide test cases for dynamical models, which could be further exploited in synthetic biology.

qSanger: Quantification of Genetic Variants in Bacterial Cultures by Sanger Sequencing.

Genetic variations such as mutations and recombinations arise spontaneously in all cultured organisms. Although it is possible to identify nonneutral mutations by selection or counterselection, the identification of neutral mutations in a heterogeneous population usually requires expensive and time-consuming methods such as quantitative or droplet polymerase chain reaction and high-throughput sequencing. Neutral mutations could even become dominant under changing environmental conditions enforcing transitory selection or counterselection. We propose a novel method, which we called qSanger, to quantify DNA using amplitude ratios of aligned electropherogram peaks from mixed Sanger sequencing reads. Plasmids expressing enhanced green fluorescent protein and mCherry fluorescent markers were used to validate qSanger both in vitro and in cotransformed Escherichia coli via quantitative polymerase chain reaction and fluorescence quantifications. We show that qSanger allows the quantification of genetic variants, including single-base natural polymorphisms or de novo mutations, from mixed Sanger sequencing reads, with substantial reduction of labor and costs compared to canonical approaches.

Scarless Recombineering of Phage in Lysogenic State.

We present a scarless recombineering-based method for introducing multiple point mutations into the genome of a temperate phage. The method uses the λ Red recombineering system to promote exogenous ssDNA oligos to anneal on the prophage lagging strand during host genome replication. DNA repair is suppressed by inducing the expression of a dominant-negative mutant protein of the methyl-directed mismatch repair system. Screening for recombinant cells without a selection marker is feasible due to its high recombination frequency, estimated as more than 40% after six cycles. The method enables scarless editing of the genome of a bacteriophage in 4-5 days.

Lambda Red Recombineering of Bacteriophage in the Lysogenic State.

We present a recombineering-based method for editing the genome of a temperate phage. The method uses the lambda Red recombination system to edit the genome of a lysogenized host with a prophage compatible with bacteriophage lambda. Linear DNA is used as the recombination substrate and antibiotic resistance is used as the basis for selection of recombinants. The method enables the genetic manipulation of a prophage in 3-5 days.

Pseudotyping Bacteriophage P2 Tail Fibers to Extend the Host Range for Biomedical Applications.

Bacteriophages (phages) represent powerful potential treatments against antibiotic-resistant bacterial infections. Antibiotic-resistant bacteria represent a significant threat to global health, with an estimated 70% of infection-causing bacteria being resistant to one or more antibiotics. Developing novel antibiotics against the limited number of cellular targets is expensive and time-consuming, and bacteria can rapidly develop resistance. While bacterial resistance to phage can evolve, bacterial resistance to phage does not appear to spread through lateral gene transfer, and phage may similarly adapt through mutation to recover infectivity. Phages have been identified for all known bacteria, allowing the strain-selective killing of pathogenic bacteria. Here, we re-engineered the Escherichia coli phage P2 to alter its tropism toward pathogenic bacteria. Chimeric tail fibers formed between P2 and S16 genes were designed and generated through two approaches: homology- and literature-based. By presenting chimeric P2:S16 fibers on the P2 particle, our data suggests that the resultant phages were effectively detargeted from the native P2 cellular target, lipopolysaccharide, and were instead able to infect via the proteinaceous receptor, OmpC, the natural S16 receptor. Our work provides evidence that pseudotyping P2 is feasible and can be used to extend the host range of P2 to alternative receptors. Extension of this work could produce alternative chimeric tail fibers to target pathogenic bacterial threats. Our engineering of P2 allows adsorption through a heterologous outer-membrane protein without culturing in its native host, thus providing a potential means of engineering designer phages against pathogenic bacteria from knowledge of their surface proteome.

Integral control of plant gravitropism through the interplay of hormone signaling and gene regulation.

The interplay between hormone signaling and gene regulatory networks is instrumental in promoting the development of living organisms. In particular, plants have evolved mechanisms to sense gravity and orient themselves accordingly. Here, we present a mathematical model that reproduces plant gravitropic responses based on known molecular genetic interactions for auxin signaling coupled with a physical description of plant reorientation. The model allows one to analyze the spatiotemporal dynamics of the system, triggered by an auxin gradient that induces differential growth of the plant with respect to the gravity vector. Our model predicts two important features with strong biological implications: 1), robustness of the regulatory circuit as a consequence of integral control; and 2), a higher degree of plasticity generated by the molecular interplay between two classes of hormones. Our model also predicts the ability of gibberellins to modulate the tropic response and supports the integration of the hormonal role at the level of gene regulation.

Model-based redesign of global transcription regulation.

Synthetic biology aims to the design or redesign of biological systems. In particular, one possible goal could be the rewiring of the transcription regulation network by exchanging the endogenous promoters. To achieve this objective, we have adapted current methods to the inference of a model based on ordinary differential equations that is able to predict the network response after a major change in its topology. Our procedure utilizes microarray data for training. We have experimentally validated our inferred global regulatory model in Escherichia coli by predicting transcriptomic profiles under new perturbations. We have also tested our methodology in silico by providing accurate predictions of the underlying networks from expression data generated with artificial genomes. In addition, we have shown the predictive power of our methodology by obtaining the gene profile in experimental redesigns of the E. coli genome, where rewiring the transcriptional network by means of knockouts of master regulators or by upregulating transcription factors controlled by different promoters. Our approach is compatible with most network inference methods, allowing to explore computationally future genome-wide redesign experiments in synthetic biology.

Engineering of a Promoter Repressed by a Light-Regulated Transcription Factor in Escherichia coli.

Light-regulated gene expression systems allow controlling gene expression in space and time with high accuracy. Contrary to previous synthetic light sensors that incorporate two-component systems which require localization at the plasma membrane, soluble one-component repression systems provide several advantageous characteristics. Firstly, they are soluble and able to diffuse across the cytoplasm. Secondly, they are smaller and of lower complexity, enabling less taxing expression and optimization of fewer parts. Thirdly, repression through steric hindrance is a widespread regulation mechanism that does not require specific interaction with host factors, potentially enabling implementation in different organisms. Herein, we present the design of the synthetic promoter PEL that in combination with the light-regulated dimer EL222 constitutes a one-component repression system. Inspired by previously engineered synthetic promoters and the Escherichia coli lacZYA promoter, we designed PEL with two EL222 operators positioned to hinder RNA polymerase binding when EL222 is bound. PEL is repressed by EL222 under conditions of white light with a light-regulated repression ratio of five. Further, alternating conditions of darkness and light in cycles as short as one hour showed that repression is reversible. The design of the PEL-EL222 system herein presented could aid the design and implementation of analogous one-component optogenetic repression systems. Finally, we compare the PEL-EL222 system with similar systems and suggest general improvements that could optimize and extend the functionality of EL222-based as well as other one-component repression systems.