Research Projects

COPASI

COPASI is a free and open source software for modelling, simulation and analysis of biochemical networks. It is one of the most used simulators for SBML models.

A collaborative project ongoing since 2000, together with many people at Virgina Tech and University of Heidelberg.

Metabolic Reconstructions

A basic step in understanding cellular biology is to know all of its components and how they interact. A metabolic reconstruction is a network of molecules, reactions and genes involved in cellular metabolism.

We have been involved in several metabolic reconstructions, but particularly for yeast and human (Recon2).

Iron biochemistry

Iron is an essential element to life, but it is also a dangerous poison if not controlled properly.

Through modelling and simulation we study the metabolism and regulation of iron in mammalian cells. Our most recent work is about iron absorption in the intestine and regulation in the liver.

Optimisation and parameter estimation

We apply nonlinear optimisation methods to study biochemical networks. One of the main uses is for parameter estimation, where experimental data is used to calibrate computational models.

Digital organisms

Digital organisms are comprehensive computational models of whole organisms. We are researching ways in which to build these large computational models and have already developed several aspects of this (but much is still to be done!).

Standards for Systems Biology

Standards are extremely important for collaboration, interoperability and reproducibility. We have long been involved in developing and supporting standards for systems biology, namely with SBML and SED-ML. We developed SBRML and are leading NuML development

Yeast Systems Biology

We are involved in several studies about yeast biochemistry, gene regulation, stress response, etc. Altogether these form a systems view of this model organism.

We have built several models for yeast, our latest is on the pentose phosphate pathway (pre-print here).

Reverse Engineering

Methods to infer networks from observation data — reverse engineering — are required to make sense of large omic data. We are interested in this problem and have produced several algorithms for this purpose.

Multiscale Modelling

We are building a software package (ManyCell) for simulating populations of cells, where each cell's behaviour is based on a biochemical network. This type of two-scale simulation is achieved by combining two mechanisms: an agent-based simulator and an ODE simulator (COPASI).

See also our older projects.