Computational Systems Biology

Computational Systems Biology

 

The focus of our group is the application of computational and mathematical methods in the area of modern biology to understand how biological systems function and evolve over time. Currently, we are working on two projects.

Evolution of bread wheat
Modern bread wheat (Triticum aestivum) is a hexaploid, composed of three diploid genomes (A, B and D) of seven chromosomes each, and is thought to be the product of hybridization between emmer (Triticum turgidum, tetraploid AB) and a wild goatgrass (Aegilops tauschii, diploid D) (Heun et al. 1997). Emmer itself is thought to be the product of hybridization between two diploid species: einkorn wheat (Triticum uratu, diploid A) and another goatgrass similar to Aegilops speltoides (diploid B). We are using computational techniques to understand the genetics of bread wheat and the hybridisation events that have lead the evolution of modern day bread wheat from different goat grasses and Eikorn (pasta wheat). This project is in collaboration with Harberd Lab at the Department of Plant Sciences, University of Oxford and uses the latest genomic science including high-throughput sequencing and associated statistical analysis to determine the precise nature and extent of the genetic variation that has led to the modern day bread wheat.

Rahnuma: Pathway analysis and network comparison tool
Rahnuma (http://portal.stats.ox.ac.uk:8080/rahnuma/) is a web-based tool that facilitates comparisons of metabolic networks between organisms and prediction of metabolic pathways between metabolites or groups of metabolites. Rahnuma provides an intuitive way to answer different biological questions focusing on differences between multiple organisms or evolution of different species by allowing pathway based metabolic network comparisons at organism as well as phylogenetic levels (Mithani et al. 2009). We are currently in the process of developing a newer version of the tool, Rahnuma 2.0, which will be more accessible and of greater value to a wide cross-section of the biological research community.

Research Publications

 

  • Local adaptation is associated with zinc tolerance in Pseudomonas endophytes of the metal-hyperaccumulator plant Noccaea caerulescens. 
    Fones H., McCurrach H., Mithani A., Smith A., Preston G.
    Proceedings of the Royal Society B. 283 20160648. (2016)
  • Microarray-based ultra-high resolution discovery of genomic deletion mutations.
    Belfield E., Brown C., Gan X., Jiang C., Baban D., Mithani A., Mott R., Ragoussis J., Harberd N.P.
    BMC Genomics. 15(1):224. (2014)
  • Genome-wide discovery of subgenome-specific base-identity in polyploids. 
    Mithani A., Belfield E.J., Brown C., Jiang C., Leach L.J., Harberd N.P. 
    BMC genomics. 14(1):653 (2013)
  • ROS-mediated vascular homeostatic control of root-to-shoot soil Na delivery in Arabidopsis.
    Jiang C., Belfield E., Mithani A., Visscher A., Ragoussis J., Mott R., Smith A.C., Harberd N.P. 
    The EMBO Journal. 14:31(22):4359-70 (2012)
  • Genome-wide analysis of mutations in mutant lineages selected following fast-neutron irradiation mutagenesis of Arabidopsis thaliana.
    Belfield E., Gan X., Mithani A., Brown C., Jiang C., Franklin K., Alvey E., Wibowo A., Jung M., Bailey K., Kalwani S., Ragoussis J., Mott R., Harberd N.P. 
    Genome Research. 22(7):1306-15 (2012)
  • In vitro plant regeneration induces a distinct genome-wide spectrum of mutations conferring variant phenotypes.
    Jiang C., Mithani A., Gan X., Belfield E., Klingler J.P., Zhu, J-K., Ragoussis J., Mott R., Harberd N.P.
    Current Biology 21(6):1385-1390 (2011)
  • Comparative analysis of metabolic networks provides insight into the evolution of pathogenic and non-pathogenic lifestyles inPseudomonas.
    Mithani A., Hein J., Preston G.M. 
    Molecular Biology and Evolution 28(1):483-499. (2011)
  • A bayesian approach to the evolution of metabolic networks on a phylogeny.
    Mithani A., Preston G.M., Hein J.
    PLoS Computational Biology 6(8):e1000868 (2010)
  • Rahnuma: hypergraph based tool for metabolic pathway prediction and network comparison.
    Mithani A., Preston G.M., Hein J.
    Bioinformatics 25(14):1831-1832 (2009)
  • A stochastic model for the evolution of metabolic networks with neighbor dependence.
    Mithani A., Preston G.M., Hein J.
    Bioinformatics 25(12):1528-1535. (2009)