Biological Control Systems Lab

We work in the area of systems medicine and systems biology, especially in systems dermatology, systems mycology and translational systems biology.

We apply engineering tools to extract the dynamical information from the data, reveal key regulatory mechanisms that determine the healthy or disease states, and to design effective personalised treatment strategies. Given that most diseases are caused by malfunctioning of intrinsic dynamical regulatory mechanisms across cellular, organ, and behavioural levels, we develop mechanistic models that describe dynamical behaviour of the system across different temporal and spatial scales.

We also develop methodologies to understand the original regulatory mechanisms in non-disease states, analyse and predict dynamical systems behaviour in disease states, and use them for accurate assessment and effective treatment.

We aim to establish a theoretical basis for biological control, to reinterpret the various phenomena in biological systems from the viewpoint of control, and to reveal the sophisticated design principles that are responsible for the plasticity of biological systems. Since the ability of biological control systems far exceeds that of many conventionally designed engineering systems, intrinsic understanding of the essential design principles of biological control systems may require new notions and a new theory for biological control.

Recent Publications

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(2023). Omics-Based Mathematical Modeling Unveils Pathogenesis of Periodontitis in an Experimental Murine Model. Journal of Dental Research.


(2023). A report and proposals for future activity from the inaugural Artificial Intelligence (AI) in Dermatology symposium held at the International Societies for Investigative Dermatology (ISID) 2023 meeting. JID Innovations.


(2023). Reliable detection of eczema areas for fully automated assessment of eczema severity from digital camera images. JID Innovations.

Cite DOI

(2022). Determining the clinical applicability of machine learning models through assessment of reporting across skin phototypes and rarer skin cancer types: A systematic review. Journal of the European Academy of Dermatology & Venereology.

Cite DOI

(2022). Reliable detection of eczema areas for fully automated assessment of eczema severity from digital camera images.

Cite DOI MedRxiv

Tools and Applications

Platform designed to collect eczema images from patients involved in a clinical trial.



R package to predict eczema.

Eczema Games

Eczema Games

Educational games to show the importance of proactive therapy for eczema.


We acknowledge financial support from Royal Society, NC3Rs, British Skin Foundation, and EPSRC.