Living systems exhibit a number of remarkable properties including self-organisation, adaptation, reproduction and evolution. To understand how these properties arise we need to move beyond a focus on the composition of biological systems, to consider their internal relationships or organisation. I am interested in approaching biology from a systemic perspective, i.e. how do the properties of biological systems emerge from underlying dynamic processes and their patterns of organisation? In doing this, I use theory and computation, drawing on traditions of systemic theory, incorporating approaches from complex systems as well as contemporary systems biology.
The lessons we learn from the study of biological systems are of great relevance to other complex systems and I am involved in offering biological perspectives in a wide range of interdisciplinary contexts, spanning the sciences, social science and the humanities. These include the use of bio-inspired algorithms in engineering, the understanding of complex phenomenon in social systems, and in the relevance of complexity to forms of narrative.
I work in the York Centre for Complex Systems Analysis (YCCSA) – an interdisciplinary research environment facilitating collaborative interactions across many different departments at York including Maths, Chemistry, Computer Science, Electronics, Environment and Management. I welcome interest from prospective research students and postdoctoral fellows from a variety of backgrounds who are interested in the challenge of working on complex systems in a dynamic collaborative interdisciplinary environment.
Fig 1: Schematic architecture of an abstract artificial biochemical network, showing the coupling of a Artifical Genetic Network (AGN) to an Artificial Metabolic Network (AMN). Such networks can be (computationally) evolved to perform complex engineering control tasks.
Fig 2: Characterisation of the ligand-target encounter complex by Brownian Dynamics simulation: Regions of diffusive binding of a peptide to the MDM2 protein (yellow). (Top) probability density (Bottom) structural ensemble
|Gina Allison||PhD Student - BBSRC White Rose Doctoral Training Programme in Mechanistic Biology||
Assembly dynamics of the DNA partition protein ParF
(co-supervisor with Dr. Daniela Barillà)
|PhD Student – Wellcome Trust CIDCATS PhD Training Programme||
A framework for complex systems immunology(co-supervisor Prof. Susan Stepney, Computer Science)
I am a generalist by nature, and in my teaching I tend to emphasise concepts and their relationships over detailed factual knowledge. I aim to engage students, ideally through communicating my own interest and enthusiasm in a topic, but also by providing new perspectives and thinking tools. I believe in the importance of learning how to learn, and enjoy challenging students to embrace different conceptual frameworks.
I traditionally lecture in topics concerning biological systems, covering theoretical and computational aspects in both scientific and engineering approaches. These include introduction to graph theory and networks, systemic thinking and approaches, (bioinformatics) analysis of complex biological data, and modelling and simulation in systems and synthetic biology.
I like to offer opportunities for students to explore more philosophical issues relating to Biology. I challenge students to read books, to critique papers, sometimes to watch and review related videos/movies, and to form and discuss opinions. I like to expose new and challenging ideas, and to use tutorials as a space for synthesis and reflection. I learn a lot from tutorials myself, it is through the soil of discussion that good ideas can emerge, be clarified and develop.
I offer opportunities for research projects in two main areas: one is more computational e.g. computer simulation of biomolecules, biological data analysis and visualisation, network construction and analysis; the other is around science communication e.g. the analysis of corpora of texts, construction of dialogues, production of animations etc. I like to work up projects with my students, often in relation to their proposed next step after their degree. I adapt my supervisory style according to the needs of the student. I enjoy seeing students develop and mature when facing the challenges and uncertainties of research: it's a deep learning experience.
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