Methods & technologies

Methods are selected by the questions they can answer

This is a capability and study-design framework, not a claim that every method is currently available or in active use. Final selection depends on verified expertise, host resources and research design.

Question-led selectionCapability under verificationValidation by design

Method architecture

Thirteen method families across measurement and inference

Each card separates what a method measures, why it may be relevant and the class of question it can address.

Genomic Measurement

Next Generation Sequencing

Measures: Nucleic-acid sequence at scale across genomes and transcriptomes.
Relevance: Provides quantitative material for genomic and transcriptomic analysis.
Research question: Which sequence-level features track biological variation?

Genomic Measurement

Genomics

Measures: Genome-wide variation and structural organisation.
Relevance: Supports analysis of inherited and acquired genomic variation.
Research question: How might genomic variation shape aging trajectories?

Genomic Measurement

Transcriptomics

Measures: RNA abundance across genes, isoforms and conditions.
Relevance: Captures regulatory state and cellular response.
Research question: Which regulatory programmes vary across age and state?

Molecular Measurement

Proteomics

Measures: Protein abundance, modification and complex composition.
Relevance: Connects molecular composition more directly to cellular function.
Research question: Which proteome changes precede functional decline?

Resolution

Single-Cell Analysis

Measures: Molecular state at the resolution of individual cells.
Relevance: Resolves heterogeneity hidden in bulk measurements.
Research question: Which cell populations contribute to tissue-level phenotypes?

Resolution

Spatial Omics

Measures: Molecular profiles that retain tissue coordinates.
Relevance: Preserves the spatial context in which mechanisms operate.
Research question: How is molecular variation distributed within tissue architecture?

Structure & Physics

Structural Biology

Measures: Three-dimensional structures of biomolecules and complexes.
Relevance: Provides a mechanistic basis for structure–function reasoning.
Research question: How does structural change alter functional capacity?

Structure & Physics

Molecular Biophysics

Measures: Stability, dynamics, kinetics and interaction energetics.
Relevance: Quantifies the physical basis of molecular behaviour.
Research question: Which biophysical parameters distinguish resilient states?

Computation

Bioinformatics

Measures: Derived quantities from large-scale biological datasets.
Relevance: Makes high-dimensional data reproducibly analysable.
Research question: Which analytical pipelines yield stable, interpretable signals?

Computation

Network Biology

Measures: Topology and dynamics of molecular interaction networks.
Relevance: Places individual measurements into a systems context.
Research question: How does network organisation buffer perturbation?

Computation

Machine Learning

Measures: Statistical structure and predictive relationships in data.
Relevance: Can detect complex patterns when validity and interpretability are controlled.
Research question: Can predictive models remain mechanistically interpretable?

Inference

Biostatistics

Measures: Effect size, uncertainty and inferential validity.
Relevance: Defines the evidence required to support a scientific claim.
Research question: What design supports a defensible causal conclusion?

Inference

Data Integration

Measures: Joint structure across heterogeneous data modalities.
Relevance: Connects separate measurement layers into one analytical framework.
Research question: How can layers be integrated without inflating false discovery?

Measurement validity

Does the assay measure the biological quantity required by the hypothesis?

Inference validity

Can the analysis distinguish uncertainty, prediction and causal interpretation?

Independent validation

Can central findings be challenged with orthogonal data or methodology?