Confronting risks of mirror life.

Broad discussion is needed to chart a path forward.

Developing transmissible vaccines for animal infections.

Intrinsically safe designs and a staged transparent development process will be essential.

FLIGHTED: Inferring Fitness Landscapes from Noisy High-Throughput Experimental Data.

Machine learning (ML) for protein design requires large protein fitness datasets generated by high-throughput experiments for training, fine-tuning, and benchmarking models. However, most models do not account for experimental noise inherent in these datasets, harming model performance and changing model rankings in benchmarking studies. Here we develop FLIGHTED, a Bayesian method of accounting for uncertainty by generating probabilistic fitness landscapes from noisy high-throughput experiments.

CRISPR-mediated germline mutagenesis for genetic sterilization of Anopheles gambiae males.

Rapid spread of insecticide resistance among anopheline mosquitoes threatens malaria elimination efforts, necessitating development of alternative vector control technologies. Sterile insect technique (SIT) has been successfully implemented in multiple insect pests to suppress field populations by the release of large numbers of sterile males, yet it has proven difficult to adapt to Anopheles vectors. Here we outline adaptation of a CRISPR-based genetic sterilization system to selectively ablate male sperm cells in the malaria mosquito Anopheles gambiae.

A multiplexed, confinable CRISPR/Cas9 gene drive can propagate in caged Aedes aegypti populations.

Aedes aegypti is the main vector of several major pathogens including dengue, Zika and chikungunya viruses. Classical mosquito control strategies utilizing insecticides are threatened by rising resistance. This has stimulated interest in new genetic systems such as gene drivesHere, we test the regulatory sequences from the Ae.

A framework for identifying fertility gene targets for mammalian pest control.

Fertility-targeted gene drives have been proposed as an ethical genetic approach for managing wild populations of vertebrate pests for public health and conservation benefit. This manuscript introduces a framework to identify and evaluate target gene suitability based on biological gene function, gene expression and results from mouse knockout models. This framework identified 16 genes essential for male fertility and 12 genes important for female fertility that may be feasible targets for mammalian gene drives and other non-drive genetic pest control technology.

CRISPR-mediated germline mutagenesis for genetic sterilization of males.

Rapid spread of insecticide resistance among anopheline mosquitoes threatens malaria elimination efforts, necessitating development of alternative vector control technologies. Sterile Insect Technique (SIT) has been successfully implemented in multiple insect pests to suppress field populations by the release of large numbers of sterile males, yet it has proven difficult to adapt to vectors. Here we outline adaptation of a CRISPR-based genetic sterilization system to selectively ablate male sperm cells in the malaria mosquito .

A framework for identifying fertility gene targets for mammalian pest control.

Fertility-targeted gene drives have been proposed as an ethical genetic approach for managing wild populations of vertebrate pests for public health and conservation benefit.This manuscript introduces a framework to identify and evaluate target gene suitability based on biological gene function, gene expression, and results from mouse knockout models.This framework identified 16 genes essential for male fertility and 12 genes important for female fertility that may be feasible targets for mammalian gene drives and other non-drive genetic pest control technology.

Analysis of the first genetic engineering attribution challenge.

The ability to identify the designer of engineered biological sequences-termed genetic engineering attribution (GEA)-would help ensure due credit for biotechnological innovation, while holding designers accountable to the communities they affect. Here, we present the results of the first Genetic Engineering Attribution Challenge, a public data-science competition to advance GEA techniques. Top-scoring teams dramatically outperformed previous models at identifying the true lab-of-origin of engineered plasmid sequences, including an increase in top-1 and top-10 accuracy of 10 percentage points.

Direct and indirect impacts of synthetic biology on biodiversity conservation.

The world's biodiversity is in crisis. Synthetic biology has the potential to transform biodiversity conservation, both directly and indirectly, in ways that are negative and positive. However, applying these biotechnology tools to environmental questions is fraught with uncertainty and could harm cultures, rights, livelihoods, and nature.

Measuring the tolerance of the genetic code to altered codon size.

Translation using four-base codons occurs in both natural and synthetic systems. What constraints contributed to the universal adoption of a triplet codon, rather than quadruplet codon, genetic code? Here, we investigate the tolerance of the genetic code to tRNA mutations that increase codon size. We found that tRNAs from all 20 canonical isoacceptor classes can be converted to functional quadruplet tRNAs (qtRNAs).

Insidious Insights: Implications of viral vector engineering for pathogen enhancement.

Optimizing viral vectors and their properties will be important for improving the effectiveness and safety of clinical gene therapy. However, such research may generate dual-use insights relevant to the enhancement of pandemic pathogens. In particular, reliable and generalizable methods of immune evasion could increase viral fitness sufficient to cause a new pandemic.

Systematic molecular evolution enables robust biomolecule discovery.

Evolution occurs when selective pressures from the environment shape inherited variation over time. Within the laboratory, evolution is commonly used to engineer proteins and RNA, but experimental constraints have limited the ability to reproducibly and reliably explore factors such as population diversity, the timing of environmental changes and chance on outcomes. We developed a robotic system termed phage- and robotics-assisted near-continuous evolution (PRANCE) to comprehensively explore biomolecular evolution by performing phage-assisted continuous evolution in high-throughput.

Low-N protein engineering with data-efficient deep learning.

Protein engineering has enormous academic and industrial potential. However, it is limited by the lack of experimental assays that are consistent with the design goal and sufficiently high throughput to find rare, enhanced variants. Here we introduce a machine learning-guided paradigm that can use as few as 24 functionally assayed mutant sequences to build an accurate virtual fitness landscape and screen ten million sequences via in silico directed evolution.

Enabling high-throughput biology with flexible open-source automation.

Our understanding of complex living systems is limited by our capacity to perform experiments in high throughput. While robotic systems have automated many traditional hand-pipetting protocols, software limitations have precluded more advanced maneuvers required to manipulate, maintain, and monitor hundreds of experiments in parallel. Here, we present Pyhamilton, an open-source Python platform that can execute complex pipetting patterns required for custom high-throughput experiments such as the simulation of metapopulation dynamics.

Bidirectional contact tracing could dramatically improve COVID-19 control.

Contact tracing is critical to controlling COVID-19, but most protocols only "forward-trace" to notify people who were recently exposed. Using a stochastic branching-process model, we find that "bidirectional" tracing to identify infector individuals and their other infectees robustly improves outbreak control. In our model, bidirectional tracing more than doubles the reduction in effective reproduction number (R) achieved by forward-tracing alone, while dramatically increasing resilience to low case ascertainment and test sensitivity.

The biosecurity benefits of genetic engineering attribution.

Biology can be misused, and the risk of this causing widespread harm increases in step with the rapid march of technological progress. A key security challenge involves attribution: determining, in the wake of a human-caused biological event, who was responsible. Recent scientific developments have demonstrated a capability for detecting whether an organism involved in such an event has been genetically modified and, if modified, to infer from its genetic sequence its likely lab of origin.

A machine learning toolkit for genetic engineering attribution to facilitate biosecurity.

The promise of biotechnology is tempered by its potential for accidental or deliberate misuse. Reliably identifying telltale signatures characteristic to different genetic designers, termed 'genetic engineering attribution', would deter misuse, yet is still considered unsolved. Here, we show that recurrent neural networks trained on DNA motifs and basic phenotype data can reach 70% attribution accuracy in distinguishing between over 1,300 labs.