The number of organisms whose complete genome has been sequenced has grown exponentially in the last few years. For each of these organisms, we know with remarkable accuracy the exact sequence of nucleotides in their DNA—in other words, we know their biochemical “blueprint.” Unfortunately, our understanding of life processes has not grown proportionally. For example, experimental evidence demonstrates that only 20% of the genes in yeast are essential for survival, but the reasons why these genes are essential are not known.
At a totally different scale, the scale of macro socio-economic systems, the increase in computational capabilities is also enabling us to acquire and process unprecedented amounts of information. As for processes at the molecular scale, our predictive power has not increased at the same pace. For example, nowadays it is relatively straightforward to obtain the number of passengers flying between any pair of cities in the world. Still, when a virus outbreak occurs, such as the SARS outbreak in 2003, little is known about which countries will be affected.
Cells and the air transportation system are examples of complex systems. In complex systems, individual components interact with each other—usually in nonlinear ways—giving rise to complex networks of interactions that are neither totally regular nor totally random. Partly because of the interactions themselves and partly
because of the interaction topology, complex systems cannot be properly understood by just analyzing their constituent parts—as Phil Anderson already pointed out in 1974, more is different.
Our group conducts research on complex systems across a wide variety of disciplines and develops models that provide insight into the emergence, evolution, and stability of these systems. The study of these problems requires an approach that emphasizes a holistic view of the system instead of using reductionist principles and solely focusing on the details or individual parts. The advantage of this approach is that we are not confined to a single discipline or field when choosing problems to investigate, giving us the opportunity to choose ones that are both scientifically interesting and possessing a broad impact to the community at large. This allows us to study problems as disparate as the growth of Escherichia coli in a bioreactor and what makes a good mentor.
To this end we develop and validate models that can be studied by means of computational experiments and verified using experimental or empirical data where available. We focus on the identification of the mechanisms determining the dynamics of a system and translate these mechanisms into a parsimonious set of rules that can be implemented and investigated computationally.