The ICREA-Complex Systems Lab, lead by Ricard Solé, is part of the Biology Department of Universitat Pompeu Fabra/PRBB and member of the Institut de Biologia Evolutiva. We are an interdisciplinary team exploring the evolution of complex systems, both natural and artificial, searching for their common laws of organization. We do both theoretical and experimental work, closely working in collaboration with the Santa Fe Institute. We study the origins and evolution of complex systems and the boundaries of such complexity (and how to break them) using methods from statistical physics, synthetic/systems biology and network theory.
We are developing theoretical models of network evolution, with special interest in the open-ended nature of complexity, its hierarchical organization and the presence of catastrophes and breakpoints in large-scale dynamics. See our paper on Origins of hierarchy in networks, PNAS. Read more here
Both technology and biology share a number of relevant traits. Our Lab explores the similarities and differences between them, with special attention to the origins of innovation and the physics of the underlying landscapes. See our paper on Technological innovation. Read more here
One potential path to restore the balance of endangered ecosystems and fight against climate change could be Terraforming our own planet. We explore (mathematically and experimentally) the potential scenarios that could allow to redesign our biosphere using synthetic biology as a major engineering approach. Read more here
Synthetic biology, evolutionary robotics and artificial life allow us to re-create major innovations of biological evolution while searching for new ones. We want to make a new synthesis of major transitions in human made, simulated, natural and synthetic systems and look for novel types of artificial transitions. Read more here
We are exploring how to create new forms of multicellular computation and how to build a complex biological computer. By evolving bio-inspired hardware and software, we also search for robust solutions to complex problems. See our paper on Distributed biological computation. Read more here
We study the architecture and evolution of language and brain networks. Our goal is to develop theoretical models of language emergence and change and explain the origins of their complexity. See our paper on Language networks. Read more here
Both cancer populations and RNA viruses display high levels of genetic instability. We study how this unstable state contributes to adaptation and, perhaps, to new forms of therapy based on the presence of lethal thresholds. Read more here