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Biological Graph Optimisation (BGO) is an emerging computational paradigm that models complex biological systems as graphs and applies advanced optimisation strategies to extract structure, function, and meaning from them. It sits at the intersection of systems biology, graph theory, high‑performance computing, and multi‑objective optimisation.
Biological systems are naturally graph‑structured:
Protein–Protein Interaction (PPI) Networks → proteins as nodes, interactions as edges
Gene Regulatory Networks (GRNs) → transcription factors and genes
Metabolic Networks → metabolites and reactions
Cell–Cell Communication Graphs → spatial and signalling relationships
Neural & Brain Networks → synaptic connectivity
Graphs allow us to capture:
Non‑linear dependencies
Emergent behaviour
Redundancy and robustness
Multi‑scale organisation
Biology is not linear — graphs are.
In BGO, we optimise graph properties under biological constraints.
🧩 Community / Module Detection
Identify functional protein complexes or signalling modules🔗 Pathway Extraction
Find biologically plausible paths between phenotypes🎯 Influential Node Selection
Disease drivers, biomarkers, drug targets📐 Graph Simplification
Reduce noise while preserving biological meaning🔄 Dynamic Rewiring
Compare healthy vs disease networks
Biological problems rarely have a single objective.
Pareto‑optimal fronts
Evolutionary algorithms (NSGA‑II, SPEA2)
Scalarisation methods
Constraint‑based optimisation
In biology, trade‑offs are the signal, not the noise.
Louvain / Leiden (modularity optimisation)
Markov Clustering (MCL)
Clique Percolation
Minimum Cut / Maximum Flow
Steiner Tree variants
Graph sparsification
Genetic Algorithms
Ant Colony Optimisation
Particle Swarm Optimisation
Simulated Annealing
Large biological graphs can contain millions of nodes and edges. Classical CPU approaches quickly become infeasible.
GPUs excel at:
Dense linear algebra
Massive parallel traversal
Monte‑Carlo style optimisation
Parallel community detection
Random walk simulations
Graph embeddings
Large‑scale matrix factorisation
CUDA / HIP
GraphBLAS
GPU‑accelerated sparse kernels
GPUs are ideal when many nodes can be processed simultaneously.
FPGAs offer:
Custom hardware pipelines
Ultra‑low latency
Deterministic execution
High energy efficiency
Streaming graph traversal
Motif detection
Pathway enumeration
Online optimisation
Many biological graphs are sparse
Repeated traversal patterns
Fixed optimisation kernels
FPGAs turn biological algorithms into hardware‑level logic.
Modern BGO systems increasingly use heterogeneous computing:
CPU → orchestration, logic, IO
GPU → large‑scale parallel optimisation
FPGA → streaming, motif/path detection
This enables:
Scalability
Real‑time analysis
Lower power consumption
Optimisation is meaningless without interpretation.
Common visual outputs:
Community‑coloured graphs
Pareto fronts
Pathway heatmaps
Temporal network evolution
(Figures typically include modular networks, Pareto trade‑off curves, and GPU/FPGA architecture diagrams.)
Cancer network rewiring
Drug target discovery
Synthetic lethality detection
Precision medicine
Neurodegenerative disease modelling
Explainable graph optimisation
Real‑time biological networks
AI‑guided multi‑objective optimisation
Hardware‑aware algorithm design
If you use concepts or implementations inspired by this work, please cite appropriately.
Contributions are welcome!
Algorithms
Benchmarks
Hardware backends
Visualisations
Biological Graph Optimisation is not just faster computation — it is a new way of thinking about life as a networked, optimisable system.