Biolab Mirror: Artificial Intelligence / Machine Learning (AI/ML) Digital Twin for Intent Characterization and Bio-Containment Integrity

STTR topic DTR27TZ02-NV001 (U.S. Department of Defense – DTRA), Phase I and II. Open to U.S. small businesses; STTR projects partner with a U.S. research institution. Opens: December 2, 2026. Closes: January 6, 2027. The proliferation of dual-use biological technologies and automated, high-containment laboratories globally presents a profound challenge to arms control verification and biosurveillance [3]. A state-sponsored facility operating under a declared, peaceful mandate (e.g., vaccine development, agricultural research) possesses all the necessary infrastructure, reagents, and personnel to rapidly pivot to unauthorized, non-compliant, or undeclared high-risk biological material production [2]. Current verification methods rely heavily on periodic physical inspections, voluntary declarations, and audits of digital LIMS. However, advanced adversaries can digitally spoof BMS, manipulate LIMS records, and conceal clandestine pathogen scaling through “population ghosting” [4][5]. The Department of War (DoW) requires a “Physics-to-Mesh Zero Trust” architecture [6]. This solicitation seeks proposals for a “Biolab Mirror”—a comprehensive Digital Twin that does not inherently trust digital control logs. Instead, the Mirror must continuously cross-validate digital telemetry against the unalterable laws of physics, chemistry, and biology (Conservation of Mass, Thermodynamics, Metabolic Rates). By establishing a rigorous physical baseline (e.g., correlating total water inflow with Effluent Decontamination System (EDS) out-flow, or matching documented animal feed procurement to autoclave waste mass and vivarium Carbon Dioxide (CO2) exhaust), the AI/ML engine must automatically flag the subtle “Coherence Failures” that betray clandestine pathogen scaling, undeclared animal models, or spoofed containment boundaries. SUPPLEMENTAL INFORMATION 1. Programmatic Metrics and Data Sourcing • Data Sourcing Strategy Synthetic Baseline Generation (Performer Sourced): The performer is…