Our papers are the official record of our discoveries. They allow others to build on and apply our work. Each paper is the result of many months of research, so we make a special effort to make them clear, beautiful and inspirational, and publish them in leading journals.

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  • Eigenvalues of neutral networks: Interpolating between hypercubes

    TRT. ReevesRFR. FarrJBJ. BlundellAGA. GallagherTFT. Fink Discrete Mathematics

    Eigenvalues of neutral networks

    The principal eigenvalue of small neutral networks determines their robustness, and is bounded by the logarithm of the number of vertices.

  • Quantifying noise in mass spectrometry and yeast two-hybrid protein interaction detection experiments

    AAACA. CoolenNP Journal of the Royal Society Interface

    Protein interaction experiments

    Properties of protein interaction networks test the reliability of data and hint at the underlying mechanism with which proteins recruit each other.

  • Immune networks: multitasking capabilities near saturation

    EAAAABACA. CoolenDT Journal of Physics A

    Multitasking immune networks

    The immune system must simultaneously recall multiple defense strategies because many antigens can attack the host at the same time.

  • Immune networks: multi-tasking capabilities at medium load

    EAAAABACA. CoolenDT Journal of Physics A

    Multi-tasking in immune networks

    Associative networks with different loads model the ability of the immune system to respond simultaneously to multiple distinct antigen invasions.

  • PLoS ONE

    Networks for medical data

    Network analysis of diagnostic data identifies combinations of the key factors which cause Class III malocclusion and how they evolve over time.

  • EPL

    Robust and assortative

    Spectral analysis shows that disassortative networks exhibit a higher epidemiological threshold and are therefore easier to immunize.

  • Interface Focus

    What you see is not what you get

    Methods from tailored random graph theory reveal the relation between true biological networks and the often-biased samples taken from them.