Dr. Sergi Valverde is complex systems scientist and head of the Evolution of Networks group at the Institute of Evolutionary Biology (CSIC) . Born in Barcelona, studied computer science and physics at the School of Informatics (FIB) of the Universitat Politecnica de Catalunya (UPC) and worked for a while as professional game developer (1997-2002). Sergi's scientific career was mentored by ICREA Professor Ricard Sole (Complex Systems Lab, 2003-2016) and was very fortunate to travel to the Santa Fe Institute for many years. Sergi was an assistant professor of "Complexity Science" and "Evolutionary Algorithms" at the Degree of Biomedical Engineering of the University Pompeu Fabra (2009-2020). He actively collaborates with the Node Lab at the Centre for Mathematical Research (Dr. Josep Sardanyés), the University of Tennessee at Knoxville (Prof. R. Alex Bentley), the Universitat Politecnica de Valencia (Prof. Santiago F. Elena, Evolutionary Systems Virology Group) and the University Pompeu Fabra (Prof. Jordi Garcia-Ojalvo), and serves as board member of Complexitat.cat , the Catalan Network for the Study of Complex Systems. His published research deals with complex networks, collective intelligence, and computational models of evolutionary and ecological processes. Currently, Sergi is working in an evolutionary theory of technological innovation.
We want to understand the origins of innovation in cultural and technological evolution, with an emphasis in information networks, and how these cultural and tecnological innovations interact with natural ecosystems. Software is the invisible, and yet the most important, technology for our society and we need quantitative, testable theories of software evolution and development. In this context, we pioneered the study of software systems modeled as complex networks. We think there are important lessons about the information theoretical approach to biology (and evolution) to be learned from the complex networks approach to software.
Technological evolution is a crucial component of culture and allows us expanding our capacities beyond any other species ever did and we have a quite good record of it. We are still far from having a quantitative theory of technological change, particularly when comparing with evolutionary theory since Darwin. How optimality principles, tinkering and constraints shape network organization? Are there selection forces similar to those present in nature? Is history as relevant in understanding technological evolution? To what extent is innovation the result of tinkering and recombination? My last two projects (e.g., Spanish National Grant FIS2016-77447-R) exemplify this type of research.
In the current rapidly changing and degrading ecosystems, understanding how environmental heterogeneity influences species adaptation is more important than ever. A main concern is not only empirical data, but also a mathematical theory that explains how ecosystems respond to external perturbations. What is the role played by spatial embedding in distributed growth and dynamical processes on top of networks? Previous studies focused on pairwise species interactions. Indeed, the theory of complex networks and complex systems has been the main methodological framework developed (and used everyday) by the ETL. The group consolidated a full line of scientific research focusing on evolutionary models of ecological networks, in particular, the study of host-phage bipartite interaction networks.
The network approach (although useful) does not fully capture environmental dependencies (and anthropogenic changes) in high-order ecosystem features, like modularity and nestedness. Habitat-mediated interactions necessitate an extension of networks, which are properly represented with an hypergraph. In a recent publication (Valverde et al., 2020) we have extended hypergraph modeling to real plant-virus infections. Our theoretical and empirical study of these habitat-mediated interactions suggests how the organization of ecosystems is flexible and adapts to environmental changes (see the short talk below).
Artificial life addresses the main goal of biology, i.e., understanding life, by developing artificial systems that exhibit life-like properties. For example, can we develop artificial systems that exhibit creativity and innovation? We are embracing these questions by the exploration of standard systems like Avida and developing our physically-based artificial life systems (like Chimera, see the video below or read the paper here ). In the future, we plan to extend this computational approach to model the evolution of large-scale ecosystems.