Pathways and biological systems modelling group

Research projects

Our research projects cover the following themes:


Metabolic networks

Constraint-based modelling techniques enable a quantitative analysis of large systems of metabolic reactions without the need for kinetic data. Relying on the topology and stoichiometry of metabolic networks, they define the set of possible metabolic paths and flux distributions, producing information about feasible functional states in biological cells. We are developing algorithms based on elementary mode analysis to quantify the activity of specific metabolic paths (Schwartz & Kanehisa, 2005; Schwartz et al., 2007) and predict flux distributions in perturbed conditions such as cancer (Schwartz et al., 2015).

The construction of genome-scale kinetic models is the next grand challenge for systems biology. We are developing the GRaPe software enabling the construction of large kinetic models based on generic rate equations and the integration of experimental 'omics' data, and apply this technique to the modelling of yeast and bacterial metabolism (Adiamah et al., 2010; Adiamah & Schwartz, 2012).

More widely, we seek to understand fundamental principles that direct the organisation of cellular systems including the role of cycles (Kritz et al., 2010) and the hierarchical structure of cellular functions (Stoney et al., 2015).


Network medicine and disease models

Although investments by pharmaceutical companies have risen continuously, the number of newly approved drugs has remained almost constant in the last decade. The traditional approach of drug development generally targets a single gene or gene product. However, many diseases are multifactorial and computational modelling is needed to predict large-scale effects of disease perturbations and drug action on cellular systems.

We use network approaches to analyse global interactions between drugs, cellular pathways and diseases (Nacher & Schwartz, 2012; Nacher et al., 2014). We develop new algorithms to integrate molecular interaction networks with 'omics' and phenotypic data in order to identify dysregulated pathways in disease (Soul et al., 2015). We use logical and kinetic modelling to develop quantitative models of specific pathways in order to understand dysregulated mechanisms in disorders such as cancer (Chen et al., 2013; Hetmanski et al., 2016), skeletal diseases (Dunn et al., 2016) and HIV infection (Oyeyemi et al., 2015).


Biotechnology and environment

Biological organisms do not live in isolation but constantly interact with their environment and have to adjust their intracellular activity to fluctuating conditions. Systems biology models need to be coupled to environmental factors in order to reflect these dynamic processes of adaptation. We work on the integration of environmental factors into intracellular models in several systems of importance to biotechnology, including effects of light and temperature on circadian rhythms (Tseng et al., 2012), and thermodynamic effects on yeast metabolism (Paget et al., 2014). We study how changes in nutrient availability and environmental conditions affect the growth of organisms, e.g. by modelling photosynthesis in algae (Chapman et al., 2015).