From acting on a target to understanding a system.

Why network pharmacology, and why now

The scientific case for reasoning about biological systems as networks rather than isolated targets isn't new — network pharmacology and systems biology have been active academic fields for well over a decade. What's changed recently is practical: the computational tools needed to reason over a network of realistic complexity, in a way that's still explainable to a human reviewer, have only become genuinely usable in the past few years. That's the gap Instructional Biology's platform is built to close — not the underlying science, which the field has been building toward for a long time, but the tooling that makes it tractable at the scale a real research program needs.

Two layers, working together

Our approach to the science splits into two connected halves. The first is representational: building a structured, evidence-weighted map of how biological signals relate to each other — the Signaling Cascade Knowledge Graph — so that a network-level hypothesis can be reasoned about explicitly instead of held informally in one researcher's intuition. The second is physical: making sure the biological material feeding that reasoning process hasn't already lost the structural information it's supposed to represent, which is the problem our bio-processing work is aimed at solving.

Neither half is sufficient alone. A perfect map built from degraded evidence is still built from degraded evidence. Perfectly preserved material with no structured way to reason across it just sits there as an expensive but unused resource. We think the science that matters right now is in the connection between the two — and that's where most of our own research effort is currently focused.