A number of computational tools have been developed by researchers working at the Centre for Mathematical Biology, some of which are described below.
Chaste (Cancer, Heart and Soft Tissue Environment) is a general purpose simulation package aimed at multi-scale, computationally demanding problems arising in biology and physiology. Current functionality includes tissue and cell level electrophysiology, discrete tissue modelling, and soft tissue modelling. The package is being developed by a team composed mainly of researchers based at the Centre for Mathematical Biology and at the Computational Biology Group at Oxford University Computing Laboratory. Development draws on expertise from software engineering, high performance computing, mathematical modelling and scientific computing. While Chaste is a generic extensible library, software development to date has focused on two distinct areas: continuum modelling of cardiac electrophysiology (Cardiac Chaste); and discrete modelling of cell populations (Systems Biology Chaste), with specific application to tissue homeostasis and carcinogenesis (Cancer Chaste). More information on Chaste is available at the official website, from which it can also be downloaded.
- J.M. Pitt-Francis, P. Pathmanathan, M.O. Bernabeu, R. Bordas, J. Cooper, A.G. Fletcher, G.R. Mirams, P.J. Murray, J. Osborne, A. Walter, S.J. Chapman, A. Garny, I.M.M. van Leeuwen, P.K. Maini, B. Rodriguez, S.L. Waters, J.P. Whiteley, H.M. Byrne and D.J. Gavaghan (2009). Chaste: a test-driven approach to software development for biological modelling. Comp. Phys. Comm. 180:2452-2471. (eprints)
DifFUZZY is a fuzzy (soft) clustering technique developed to handle data sets that are "curved", elongated or those which contain clusters of different dispersion. DifFUZZY can be used to study high dimensional data sets, such as microarray and other high-throughput bioinformatics data. The algorithm has been implemented in Matlab and C++ and is available to download here. DifFUZZY consists of three main steps. It first identifies the core clusters (the data points that are closely packed together or interconnected when applying a small neighbourhood of each data point), then the undecided data points, and finally, it assigns membership values to the undecided points. These steps involve a suitably chosen diffusion process on the graph generated the from data points. Further details of this algorithm are given in the reference below.
- O. Cominetti, A. Matzavinos, S. Samarasinghe, D. Kulasiri, S. Liu, P.K. Maini and R. Erban (2010). DifFUZZY: A fuzzy clustering algorithm for complex data sets. Int. J. of Comput. Intelligence in Bioinformatics and Systems Biology 1:402-417. (eprints)
STOCHSIMGPU: Parallel stochastic simulation for the Systems Biology Toolbox 2 for MATLAB
The importance of stochasticity in biological systems is becoming increasingly recognised and the computational cost of biologically realistic stochastic simulations urgently requires development of efficient software. We present a new software tool STOCHSIMGPU, which exploits graphics processing units (GPUs) for parallel stochastic simulations of biological/chemical reaction systems and show that significant gains in efficiency can be made. It is integrated into MATLAB and works with the Systems Biology Toolbox2 (SBTOOLBOX2) for MATLAB. The GPU-based parallel implementation of the Gillespie stochastic simulation algorithm (SSA), the logarithmic direct method (LDM), and the next reaction method (NRM) is approximately 85 times faster than the sequential implementation of the NRM on a central processing unit (CPU). Using our software does not require any changes to the user's models, since it acts as a direct replacement of the stochastic simulation software of the SBTOOLBOX2. The software is open source under the GPLv3 and is available to download here. The website also contains supplementary information.
- G. Klingbeil, R. Erban, M. Giles and P. K. Maini (2011). STOCHSIMGPU: Parallel stochastic simulation for the Systems Biology Toolbox 2 for MATLAB. Bioinformatics 27:1170-1171. (eprints)
Bacterial motility analysis tool
- G. Rosser, A. G. Fletcher, P.K. Maini and R. E. Baker (2013). The effect of sampling rate on observed statistics in a correlated random walk. J. R. Soc. Interface 10:20130273. (eprints)