Our research projects cover the following themes:
Constraint-based 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 routes and flux distributions, producing information about feasible functional states in biological cells. We have been working on elementary mode analysis and developed algorithms to enable a quantitative interpretation of the activity of specific paths of metabolic reactions (Schwartz & Kanehisa, 2005; Schwartz et al., 2007). We apply flux balance analysis in genome-scale metabolic networks to predict optimal conditions for growth and strategies to increase the production of specific compounds by microorganisms.
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).
We also more widely seek to understand fundamental principles that direct the organisation of metabolic systems and the distribution of metabolic flux, including the role of cycles (Kritz et al., 2010).
Systems biology of disease
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 helps predict the large-scale effects of disease perturbations and drug action on cellular systems.
We use network approaches to characterise global interactions between drugs, cellular pathways and disease factors (Nacher & Schwartz, 2008; Nacher & Schwartz, 2012). We apply logical and kinetic modelling to develop quantitative models of specific pathways in order to understand the mechanisms of genetic regulation in diseases such as cancer (Chen et al., 2010; Chen et al., 2012) and HIV infection.
Environmental systems biology
Biological organisms do not live in isolation but constant 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 for applications such as the effects of light and temperature changes on circadian rhythms (Tseng et al., 2012), thermodynamic effects on yeast metabolism, and the dynamic acclimation of photosynthesis.