Computational Biology

The Computational Biology group uses computational and mathematical approaches as tools to understand how biological systems work. Our interests range from studying neurons in the brain to modelling the function of the heart. We aim to understand the evolution of populations and swarm behaviour in insect colonies, including the performance of biologically-inspired paradigms for optimisation. Understanding the short-term and long-term behaviour of biological systems often requires close collaborations across disciplines, and involves mathematics, computation and simulation. The interdisciplinary nature of research within the group attracts funding from a range of sources such as Arthritis Research, BBSRC, EPSRC, the European Union (FP7), the Royal Society, Procter and Gamble, and the Wellcome Trust.

A simulation of the human heart

A simulation of skin cells

A closeup of a bee colony

The application areas are grouped as follows:

Individual Based Modeling represents biological entities such as cells as computational agents in order to simulate complex biological systems. Each cell is regarded an autonomous agent with its own actions and interactions. Simulating a system of acting and interacting cells allows to understand the dynamic behaviour of the system, and it has an intuitive appeal to biological collaborators. The computational infrastructure for implementing individual based models is provided by the FLAME framework. Recent applications of individual-based modeling include modelling behaviour of sperm in the reproductive tract, signalling between and interaction of skin and bladder cells, and inflammation in the lining of lung tissue.

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Computational Neuroscience aims at using computational models in order to understand how the brain works. Dr Eleni Vasilaki studies models of synaptic plasticity: the ability of the synapse, the connection between two neurons, to change in strength in response to use of the synapse in transmitting electrical signals. She develops models of synaptic plasticity that are compatible with electrophysiology and behavioral experiments, aiming to understand the principles that shape the network of neural connections in the brain at a fundamental and computational level. Further she works with electrical engineers aiming to fabricate neuromorphic devices on new, low energy, non-CMOS technology.

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Data Driven Models make use of genetic and transcriptomic data from biological experiments to infer how cells and biological systems operate. We use techniques from machine learning and statistics to build models of how different mutations in a cell affect the functioning of the cell. The group is embedded within the Sheffield Institute for Translational Neuroscience (SITraN) which focuses on applications in neurological disorders such as Motor Neurone Disease.

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Behavioural and Evolutionary Theory is the main focus of the Behavioural and Evolutionary Theory Lab, headed by Professor James Marshall. The lab comprises an interdisciplinary collection of individuals interested in how and why behaviours evolve. They are interested in behaviours and behavioural mechanisms, and their evolutionary function. To this end they apply a range of theoretical approaches, from mathematics and statistics, decision theory, computer science, and physics. Particular topics of interest are currently the evolution of social behaviour, such as altruism and cooperation, and optimal decision-making mechanisms in groups, such as social insects, and in individuals.

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Theory of Bio-Inspired Optimisation covers general-purpose optimisation paradigms that draw inspiration from biological systems. Evolutionary algorithms mimic mutation, recombination, and selection to artificially evolve candidate solutions for optimisation and design problems. Swarm intelligence paradigms such as ant colony optimisation or particle swarm optimisation are based on the collective intelligence of animal swarms. We work on providing a theoretical foundation for understanding the working principles of these heuristic algorithms through quantifying how quickly they find satisfactory solutions for various problems. This exposes how performance depends on algorithmic parameters and design choices, and helps to design better bio-inspired optimisation algorithms.

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Computational Physiology aims at developing computational tools that can be used to gain mechanistic insights into physiology, as well as being used in clinical practice to guide interventions and treatment. A major recent development has been the INSIGNEO institute for in-silico medicine, a joint initiative between the University of Sheffield and the Sheffield Teaching Hospitals NHS Foundation Trust, which provides a focus for work in this area. The Theoretical Mechanobiology Group, headed by Paul Watton, focuses on developing mathematical/computational models to represent and simulate the interaction of mechanics with biology.

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