Welcome to the Systems Biology Working Group atDMV Petri Dish!
Introduction
What Is Progress?
Knowledge Aggregation, Living Textbooks, and the Automation of Scientific Discovery
Our collective knowledge infrastructure — the textbooks, professional training resources, and literature syntheses that define what professionals across disciplines believe to be true — is quietly accruing a structural liability. Compounded confirmation bias, stacked citation-by-citation into the foundations of formal knowledge, means that breakthroughs can take decades to reach the classrooms, clinical workflows, and decision-making frameworks where they matter most. Meanwhile, the deepest friction is rarely acknowledged: before any field can build meaningful consensus on "why" or "how" a phenomenon occurs, it must first establish honest, consolidated agreement on "what" has actually been observed. That prior step is routinely skipped, assumed, or fragmented across siloed literature that never cross-pollinate.
By composing tools already at our disposal — large language models, classical NLP pipelines, public data repositories, and engineering-grade automation frameworks — it becomes possible to model knowledge itself, rather than merely imitate individual experts. One concrete expression of this is automating the writing of living textbooks: compressing the lag from bleeding-edge discovery, through replicated evidence, all the way to professional training resources. But the deeper aspiration reaches further — toward automating the discovery of scientific insights that have never previously been conceived, by systematically surfacing hypothesis combinations that no single siloed researcher would have had the cross-disciplinary vantage point to even ask. Drawing on ongoing systems biology and computational research — with ME/CFS research demoed as a use case for what siloed, fragmented knowledge infrastructure costs in practice — this talk maps the conceptual architecture, the real-world friction, and the data science toolkit for building it.
Mission
Advance ME/CFS research in a way that accounts for the incredible amount of complexity and heterogeneity in the data.
To repurpose "old data" (datasets which have already previously been generated within existing ME/CFS research) to find new insights into the condition.
To mine novel combinations of hypotheses and cohorts/datasets to find new discoveries and approaches to understanding ME/CFS.