Systems biology and bioinformatics

Biological systems are usually highly complex systems where cells interact with other cells, as well as with their spatial environment. Technological advances, such as in gene sequencing or brain imaging, allow researchers to collect enormous amounts of highly detailed information across different spatial and temporal scales.



Computational approaches are very well-suited to visualise, simplify and model such data; this allows us to gain insights into the underlying rules of phenomena such as cancer, neural development, ecological networks, and many others.

Our research

We develop AI methods (declarative, relational and rule-based machine learning, deep reinforcement learning) for prediction and control in biological networks, with application to targeted therapeutics. For example, we have recently developed AI methods (using deep reinforcement learning) that demonstrate successful control of gene regulatory networks, where the target states comprise a tiny fraction of the state space (metastatic melanoma network with 2^70 states).

Our relational learning and text-mining approach for automated discovery of food-webs is regarded as the first successful application of machine learning from large scale agricultural data and the learned trophic links were confirmed by subsequent empirical studies (using DNA analysis) and were published in high impact scientific journals.

We developed the high-performance software BioDynaMo to model complex biological systems, including biologically realistic neural development. This approach has been used to model brain development as well as multiple biomedically relevant use cases, such as cancer growth and fibrosis.

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School of Computer Science and Electronic Engineering
University of Surrey
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