Cells, tissues, organs and organisms are incredible computing machines. Even a seemingly simple process, like a cell dividing itself equally in two is a complex signal processing challenge with potentially serious consequences to the health of the organism if mistakes are made.
Within the group we are interested in using computers to understand a variety of biological processes and we try to achieve this aim by using a combination of computer based simulations, mathematics and numerical algorithms. We also collaborate closely with web
We are interested in all aspects of growth and development, however our main projects can be grouped as follows:
Microtubules are polymers which appear in different alignments during growth. They guide cell wall cellulose microfibrils providing mechanical integrity to the cell and are important contributors to growth and form. We aim to understand the contribution of microtubules to growth and how their dynamics couple to larger scales of organisation.Microtubules (MTs) are subcellular filamentous polymers formed by heterodimers of -/β-tubulin (Figure 1). Dimer orientation polarises the filament into a plus and minus end which have different polymeric assembly/disassembly probabilities.MT-MT encounters lead to complex interactions,the most important of which are zippering where the plus end of the incident microtubule bends to face the direction of the target microtubule when the encounter angle is less than a critical angle (typically 40 degrees) and severing where the incident microtubule severs itʼs target.MT dynamics and interactions give rise to the ability of microtubules to create ordered structures vital to many cellular processes. They are associated with many aspects of differentiation and development, including nuclear positioning (Tran 2001), symmetric and asymmetric cell divisions (Kaltschmidt 2002), the mechanical properties of plant cell walls (Bausch and Kroy 2006), vesicle transport, establishment of polarity, anisotropic growth, organelle positioning and the sculpting of cell shape during development. The importance of MT self-organisation has been studied experimentally and their dynamics have been well characterised by a number of authors. However the complex behaviour of MTs makes it hard to understand by observation alone how the differentaspects of their dynamics contribute to the formation of ordered structures.
Figure 1: Schematic showing a single microtubule filament alpha and beta tubulin are indicated in red and green respectively. The plus and minus ends are also marked.
Grandison, S., Penfold, R., Vanden-Broeck, J.-M. 2005. Monte Carlo Simulation of an Imhonogeneous Dielectric Continuum Model for B-DNA. Physical Chemisty, Chemical Physics. 7:3486-3495
Within the group we are interested in using computers to understand a variety of biological processes and we try to achieve this aim by using a combination of computer based simulations, mathematics and numerical algorithms. We also collaborate closely with web
lab biologists across the Norwich Research Park as well as around the world.
We are interested in all aspects of growth and development, however our main projects can be grouped as follows:
Microtubule Dynamics
Microtubules are polymers which appear in different alignments during growth. They guide cell wall cellulose microfibrils providing mechanical integrity to the cell and are important contributors to growth and form. We aim to understand the contribution of microtubules to growth and how their dynamics couple to larger scales of organisation.Microtubules (MTs) are subcellular filamentous polymers formed by heterodimers of -/β-tubulin (Figure 1). Dimer orientation polarises the filament into a plus and minus end which have different polymeric assembly/disassembly probabilities.MT-MT encounters lead to complex interactions,the most important of which are zippering where the plus end of the incident microtubule bends to face the direction of the target microtubule when the encounter angle is less than a critical angle (typically 40 degrees) and severing where the incident microtubule severs itʼs target.MT dynamics and interactions give rise to the ability of microtubules to create ordered structures vital to many cellular processes. They are associated with many aspects of differentiation and development, including nuclear positioning (Tran 2001), symmetric and asymmetric cell divisions (Kaltschmidt 2002), the mechanical properties of plant cell walls (Bausch and Kroy 2006), vesicle transport, establishment of polarity, anisotropic growth, organelle positioning and the sculpting of cell shape during development. The importance of MT self-organisation has been studied experimentally and their dynamics have been well characterised by a number of authors. However the complex behaviour of MTs makes it hard to understand by observation alone how the differentaspects of their dynamics contribute to the formation of ordered structures.
Figure 1: Schematic showing a single microtubule filament alpha and beta tubulin are indicated in red and green respectively. The plus and minus ends are also marked.
Computational Fluid Mechanics
From how blood flows around our body, the the performance of aeroplanes and cars, fluids impact almost every aspect of our lives. Fluid dynamics is the study of motion of liquids and gasses in an attempt to understand their behaviour in more detail.
As well as being an important field of study in their own right the quest to understand fluids in more detail has provided the inspiration for many advances in computer science, mathematics and physics. Within the group we develop new mathematical and computational techniques to understand the behaviour of fluids in ever more complex domains. The majority of our work concentrates of free-surface flows. These are fluid mechanics problems where the shape that the fluid takes needs to be found as part of the solution. An everyday example of these sorts of flows might be the wake generated by a boat as it travels across a lake. However these sorts of flows can be found in a number of biological phenomena as well as in problems as diverse as microfluidics and printing.
References
Grandison, S., Penfold, R., Vanden-Broeck, J.-M. 2005. Monte Carlo Simulation of an Imhonogeneous Dielectric Continuum Model for B-DNA. Physical Chemisty, Chemical Physics. 7:3486-3495
Grandison, S., Papageorgiou, D. T., Vanden-Broeck, J.-M. 2006. Interfacial Capillary Waves in the Presence of Electric Fields. European Journal of Mechanics, B Fluids. 26(3):404-421
Grandison, S., Vanden-Broeck, J.-M., Papageorgiou, D. T., Miloh, T., Spivak, B. 2007. Axisymmetric Waves in Electrohydrodynamic Flows. Journal of Engineering Mathematics.(62) 133-148
Grandison, S.}, Morris, R. J. 2008. Biological Pathway Kinetic Rate Constants are Scaleinvariant. Bioinformatics. 24(6):741-743
Grandison, Gunasekaran, Cowtan, Mak, Lawson, Morris. 2009. Ligand Electron Density Shape Recognition Using 3D Zernike Descriptors. Lecture Notes in Bioinformatics. 5780, pp. 125-136
Winner of the IAPR Best Paper award at the PRIB conference 2009 Leggett, R.M., Grandison, S., Morris, R.M., Kelemen, G., and Moulton, V. (2009). Statistical analysis of the growth and morphology of the filamentous microbe Streptomyces coelicolor. Statistical Tools for Challenges in Bioinformatics, pp.107-110.
