Meta Logos Systems, LLC

Meta Logos Systems (MLS) was founded by the Sole Proprietor while working on his PhD in computer science at UCSC with specializations in Machine Learning, Bioinformatics, and Channel Current Cheminformatics (PhD Advisor David Haussler). Upon graduation from UCSC the founder took a joint appointment in New Orleans, partly with the Children’s Hospital Research Institute for Children (RIC), where he was Principal Investigator leading a team of 6 or 7 students, one postdoc, and one dedicated full time lab technician in operating a protein channel biochemistry wet-lab where the nanopore detector experiments were performed. The other part of the joint appointment was at the University of New Orleans (UNO) Computer Science Department, where he directed a team of 6 or 7 students, one system administrator, and one postdoc at performing a variety of signal processing software development tasks. A lot of the funding during this time (to pay salaries for up to 15 people, among other things) was NIH derived, as well as derived from NSF and state funding sources. During peak funding, there were funding sources for facilities at UNO, RIC, and MLS. Associated with each facility, were operationa nanopore detector facilities: 4 at RIC, 2 at UNO (see photos), and 1 at MLS. Extensive data was acquired at this time.

AI Agentic Biosensing

In the highly synergistic experimentation (2010-2020) involving nanopore detection and advanced machine-learning based signal analysis enhanced nanopore detection capabilities were shown to be possible. There was a critical constraint, however, that human experts were needed to manage the signal analysis pipeline at all times, to set it up, and to re-tune it occasionally. The expert signal analysis (with human agency) was a critical bottleneck to practical deployment of the nanopore detector device. Breakthroughs in AI/LLM ‘reasoning’ capabilities in 2017, and especially 2024 and 2025, have now laid the groundwork for an AI/ML-based nanopore detector (and, similarly, for almost any biosensor) signal analysis that can be managed by an AI/LLM.