Infectious disease · Immunology · Computation
We study how biological systems involved in infection and immunology can be represented, modelled and reasoned about computationally — and how those representations might ultimately support the design of new biological interventions.
01 / Method
Representation → Modelling → Reasoning → Design. Reliable reasoning depends on representations that preserve the biology; design depends on models that can be interrogated rather than treated as opaque prediction.
Our research →02 / Scope
Many mechanisms of infection, transmission and immunology recur across species, and emerging diseases often arise at the interfaces between humans, animals and their environments.
Research directions →03 / Current work
An experimental domain-specific language for representing biological systems and designs in structured, inspectable form, developed with interoperability with established biological standards in mind.
Open projects →Our approach
Biological systems are extraordinarily rich in mechanism, context and interaction. Much of what we know about them, however, remains distributed across scientific literature, experimental observations, databases and representations designed primarily for human interpretation.
Noviota is interested in a fundamental question: how can we represent biological systems well enough to reason about them computationally without losing the biological meaning that makes those systems useful?
Our research begins with representation. We investigate ways of describing biological entities, interactions, mechanisms, evidence and context in structured computational forms. From there, we are interested in what becomes possible when those representations can be modelled, interrogated and eventually used as substrates for biological design.
Infection studied as a dynamic biological system: the interactions between pathogen, host and intervention that determine whether exposure becomes disease.
Computational representations of innate and adaptive immunology — recognition, signalling, memory, immune evasion and response.
Expressing biological systems while preserving relationships, context, evidence and provenance — a foundation for modelling and reasoning.
Longer-term work on how computational representations and models might support the deliberate design of biological interventions.
One man’s ‘magic’ is another man’s engineering.
Robert A. Heinlein · Time Enough for Love · 1973
Insights
We publish research notes, technical work and longer-form writing as the programme develops. Topics include infectious disease, immunology, biological representation, computational reasoning, biological standards and biological engineering.
AI-designed bacteriophages offer an intriguing glimpse of the transition from predicting biology to designing it.
Read →Making biological information machine-readable is not the same as representing biology. As computation moves towards reasoning and design, what we preserve about mechanism, context, evidence and provenance becomes increasingly important.
Read →