Bhasin, H. | Search bioRxiv

bioRxiv2026

BioSecBench-Function: A Verifiable Benchmark for Reasoning about Biological Function from Experimental Data

Inferring biological function from experimental data is central to understanding emerging pathogens and developing effective countermeasures, yet interpreting these data remains slow and expert-intensive. AI agents could help accelerate this process by reasoning across sequence, structural, and biophysical evidence. We present BioSecBench-Function, a verifiable benchmark for recovering biosecurity-relevant function from real biological data. The benchmark comprises 111 evaluations built from published datasets and graded deterministically against ground truth. We organize evaluations along two dimensions: threat axis (spanning seven biosecurity-relevant question types) and biological question (indicating whether the solution depends primarily on sequence, structure, or biophysical assay data). Across 7,326 runs from twenty-two model-harness configurations, Opus 5 under Claude Code led on endpoint pass rate at 50.3%, and Grok 4.6 under Grok Build led on overall pass rate at 44.1% when refusals counted as failures. Performance varied substantially across both model-harness configurations and task categories. Refusal rates differed sharply by provider, and cost was a poor predictor of accuracy: several configurations exceeded 40% pass rate at low cost. BioSecBench-Function provides a standard for measuring whether agents can be trusted to interpret what a new pathogen or variant does when the next outbreak arrives.

biophysics↗

bioRxiv2024

Long non-coding RNAs regulate the expression of cell surface receptors in plants

Plants are exposed to a variety of growth, developmental, and environmental cues during their lifespan. To survive and thrive, plants have developed sophisticated ways of responding to these signals that involve regulation at the transcriptional, post-transcriptional, translational, and post-translational levels. Leucine-rich repeat receptor-like kinases are the largest family of receptor-like kinases in plants and respond to a range of external and internal stimuli. They act as crucial regulators of plant growth, development, and immunity. To fully understand LRR-RLK function, it is essential to understand how their expression is regulated under different conditions. While there have been numerous studies on post-translational regulation of LRR-RLKs through phosphorylation and ubiquitination, there is little known about the mechanisms of transcriptional and post-transcriptional regulation of LRR-RLKs. In this study, we show that natural antisense transcript long non-coding RNAs are central regulators of LRR-RLK expression at the transcriptional and post-transcriptional levels. LRR-RLK genes are almost universally associated with cis-NATs and we confirm cis-NAT expression in planta using strand-specific RT-PCR. We leverage several well-studied LRR-RLKs to demonstrate that cis-NATs regulate LRR-RLK expression and function. For cis-NATs to fine-tune LRR-RLK expression, their expression and regulatory activity must be tightly controlled and cell autonomous. Using a combination of GUS reporter assays and tissue-specific promoters, we provide evidence that cis-NATs have these characteristics, positioning them as key regulators of LRR-RLK function. We also demonstrate that the association of LRR-RLK genes with cis-NATs is conserved across much of plant evolution, suggesting that this previously unexplored regulatory mechanism serves an important and ancient purpose.

plant biology↗