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Resource allocation is essential to selection and implementation of innovative projects in science and technology. Current "winner-take-all" models for grant applications require significant researcher time in writing extensive project proposals, and rely on the availability of a few time-saturated volunteer experts. Such processes usually carry over several months, resulting in high effective costs compared to expected benefits. We devised an agile "community review" system to allocate micro-grants for the fast prototyping of innovative solutions. Here we describe and evaluate the implementation of this community review across 147 projects from the "Just One Giant Lab's OpenCOVID19 initiative" and "Helpful Engineering" open research communities. The community review process uses granular review forms and requires the participation of grant applicants in the review process. Within a year, we organised 7 rounds of review, resulting in 614 reviews from 201 reviewers, and the attribution of 48 micro-grants of up to 4,000 euros. The system is fast, with a median process duration of 10 days, scalable, with a median of 4 reviewers per project independent of the total number of projects, and fair, with project rankings highly preserved after the synthetic removal of reviewers. Regarding potential bias introduced by involving applicants in the process, we find that review scores from both applicants and non-applicants have a similar correlation of r=0.28 with other reviews within a project, matching traditional approaches. Finally, we find that the ability of projects to apply to several rounds allows to foster the further implementation of successful early prototypes, as well as provide a pathway to constructively improve an initially failing proposal in an agile manner. Overall, this study quantitatively highlights the benefits of a frugal, community review system acting as a due diligence for rapid and agile resource allocation in open research and innovation programs, with implications for decentralised communities., Competing Interests: No competing interests were disclosed., (Copyright: © 2023 Graham CLB et al.)

International development and aid are often conducted through the allocation of funding determined by decisions of non-locals, especially in the west for those in the global south. In addition, such funding is often disassociated from local expertise, therefore providing little long-term developmental impact and generating distrust. This is particularly true for conservation, as well as environmental and educational programmes. We hypothesize that by granting local people the educational tools and the necessary funding to develop their own projects through the use of an applicant-driven peer-review approach, it is possible to relocalize the decision-making process to the programme participants, with the potential to generate and select more relevant projects with developmental outcomes of higher quality. Here we created an online curriculum for antimicrobial resistance (AMR) education that was followed by 89 participants across Ghana, Tanzania, Nigeria and Uganda. We then created an open research programme that facilitated the creation of eight de novo projects on AMR. Finally, we organized an applicant-driven grant round to allocate funding to the 'Neonatal Sepsis in Nigeria' project to conduct a pilot study and awareness campaign. This work opens perspectives for the design of frugal educational programmes and the funding of context-specific, community-driven projects aimed at empowering local stakeholders in the global South., Competing Interests: The authors declare no conflict of interest, (© 2023 The Authors.)

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., (© The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The discovery and study of a vast number of regulatory RNAs in all kingdoms of life over the past decades has allowed the design of new synthetic RNAs that can regulate gene expression in vivo. Riboregulators, in particular, have been used to activate or repress gene expression. However, to accelerate and scale up the design process, synthetic biologists require computer-assisted design tools, without which riboregulator engineering will remain a case-by-case design process requiring expert attention. Recently, the design of RNA circuits by evolutionary computation and adapting strand displacement techniques from nanotechnology has proven to be suited to the automated generation of DNA sequences implementing regulatory RNA systems in bacteria. Herein, we present our method to carry out such evolutionary design and how to use it to create various types of riboregulators, allowing the systematic de novo design of genetic control systems in synthetic biology.

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., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

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.

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.

Automatic design is based on computational modeling and optimization methods to provide prototype designs to targeted problems in an unsupervised manner. For biological circuits, we need to produce quantitative predictions of cell behavior for a given genotype as consequence of the different molecular interactions. Automatic design techniques aim at solving the inverse problem of finding the sequences of nucleotides that better fit a targeted behavior. In the post-genomic era, our molecular knowledge and modeling capabilities have allowed to start using such methodologies with success. Herein, we describe how the emergence of this new type of tools could enable novel synthetic biology applications. We highlight the essential elements to develop automatic design procedures for synthetic biology pointing out their advantages and bottlenecks. We discuss in detail the experimental difficulties to overcome in the in vivo implementation of designed networks. The use of automatic design to engineer biological networks is starting to emerge as a new technique to perform synthetic biology, which should not be neglected in the future., (Copyright © 2012 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.)

We review the current status of expression of heterologous systems for bioenergy and bioproduction in bacteria using a model-based approach. As an aim for synthetic biology, it requires mathematical models of genetic modules that could be characterized independently of their context. This fastens the design of metabolic circuits using a combinatorial design approach, where given pathways could be optimized for maximal bioproduction, while being nontoxic for the chassis. We show how recent characterization of genetic parts, such as promoters, RBS or sRNAs could be used to fine-tune the expression of individual genes to achieve that goal. We also present lists of enzymes that are used for bioproduction, enlarging such set of biological parts.