Imperial College London

ProfessorMauricioBarahona

Faculty of Natural SciencesDepartment of Mathematics

Chair in Biomathematics
 
 
 
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Contact

 

m.barahona Website

 
 
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Location

 

6M31Huxley BuildingSouth Kensington Campus

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Summary

 

Overview

Multi-scale mathematical modelling of viral capsid assembly. Understanding the assembly pathways from the molecular description of proteins to the fully formed virus could help in the design of anti-viral therapies.

Broadly interested in applied mathematics in biological, physical and engineering systems


  • Dynamics of networks of interconnected nonlinear systems: graph theory and dynamics. Algebraic graph theory.
  • Data analysis: geometric dimensionality reduction and graph clustering. 
  • Stochastic dynamics and optimization
  • Multiscale dynamics and model reduction of bio-systems:  analysis of biomolecules and biomolecular assemblies
  • Social network analysis: Text and information flows.
  • Theory of synchronization: Coupled oscillators in biology and engineering, flocking phenomena
  • Algorithms for nonlinear signal analysis: Nonlinear detection, network reconstruction
  • Mathematical and computational biology: metabolic and genetic networks (deterministic and stochastic)
  • Network analysis for robust infrastructure
  • Applied PDEs in biology: nonlinear wave propagation in the arterial tree, chemotaxis.

 


    Research Staff

    Delvenne,D

    Anastassiou,CA

    Grima,DR

    Research Student Supervision

    August,E, Dynamics and motifs in biochemical reaction networks

    Hemberg,M, Stochastic analysis of biological networks

    Cooper,K, Clustering in Biological Networks

    Strelkowa,N, Stochastic Gene Regulatory Networks

    Yu,J, Design principles of metabolic networks

    Phoka,E, Network dynamics of neuronal assemblies from data

    Costa,JBRD, Graph rigidity for coarse-graining of protein structure and dynamics

    Lazaridis,E, Networks of bio-inspired (neuronal) analogue VLSI circuits

    Alley,S, Biophysical mechanisms for feedback control in membrane lipid metabolism