bioRxiv Science⌕ Search

Biology subjects

Liu, A. B.

Publications and source records attributed to Liu, A. B..

2 recordsLinked to original sources

Random adversarial threshold search enables automated DNA screening

Custom DNA synthesis underpins modern biology, but controlled genes in the wrong hands could threaten many lives and public trust in science. In .1992, a virology-trained mass murderer tried and failed to obtain physical samples of Ebola; today, viruses can be assembled from synthetic DNA fragments. Screening orders for controlled sequences is unreliable and expensive because current similarity search algorithms yield false alarms requiring expert human review. Exact-match search can achieve perfect specificity among short known sequences by detecting subsequences unique to controlled genes, but can be trivially evaded by incorporating mutations. Here we rescue exact-match search by additionally screening for predicted functional variants of pseudo-randomly chosen subsequences that arent found in known unregulated genes. To experimentally assess robustness, we protected nine windows from the M13 bacteriophage virus, then invited a "red team" to launch up to 21,000 attacks at each window and measure the fitness of their designed mutants. We identified defensible windows from regulated pathogens, built a test database that our experiments indicate will block 99.999% of functional attacks, and verified its sensitivity against redesigned enzymes. Exact-match search with functional variant prediction offers a promising way to safeguard biotechnology by automating DNA synthesis screening. SummarySearching for exact matches to pre-computed functional variants unique to controlled genes enables sensitive, secure, and automated DNA synthesis screening.

synthetic biology↗

Attribution of genetic engineering: a practical and accurate machine learning toolkit for 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 can reach 70% attribution accuracy distinguishing between over 1,300 labs. To make these models usable in practice, we introduce a framework for weighing predictions against other investigative evidence using calibration, and bring our model to within 1.6% of perfect calibration. Additionally, we demonstrate that simple models can accurately predict both the nation-state-of-origin and ancestor labs, forming the foundation of an integrated attribution toolkit which should promote responsible innovation and international security alike.

synthetic biology↗