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/.

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.

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.

MOTIVATION: The biological solution for synthesis or remediation of organic compounds using living organisms, particularly bacteria and yeast, has been promoted because of the cost reduction with respect to the non-living chemical approach. In that way, computational frameworks can profit from the previous knowledge stored in large databases of compounds, enzymes and reactions. In addition, the cell behavior can be studied by modeling the cellular context. RESULTS: We have implemented a Monte Carlo algorithm (DESHARKY) that finds a metabolic pathway from a target compound by exploring a database of enzymatic reactions. DESHARKY outputs a biochemical route to the host metabolism together with its impact in the cellular context by using mathematical models of the cell resources and metabolism. Furthermore, we provide the sequence of amino acids for the enzymes involved in the route closest phylogenetically to the considered organism. We provide examples of designed metabolic pathways with their genetic load characterizations. Here, we have used Escherichia coli as host organism. In addition, our bioinformatic tool can be applied for biodegradation or biosynthesis and its performance scales with the database size. AVAILABILITY: Software, a tutorial and examples are freely available and open source at http://soft.synth-bio.org/desharky.html

MOTIVATION: The rational design of biological networks with prescribed functions is limited to gene circuits of a few genes. Larger networks involve complex interactions with many parameters and the use of automated computational tools can be very valuable. We propose a new tool to design transcriptional networks with targeted behavior that could be used to better understand the design principles of genetic circuits. RESULTS: We have implemented a Simulated Annealing optimization algorithm that explores throughout the space of transcription networks to obtain a specific behavior. The software outputs a transcriptional network with all the corresponding kinetic parameters in SBML format. We provide examples of transcriptional circuits with logical and oscillatory behaviors. Our tool can also be applied to design networks with multiple external input and output genes. AVAILABILITY: The software, a tutorial manual, parameter sets and examples are freely available at http://synth-bio.yi.org/genetdes.html.