Computational Systems Biology (UPM-BBVA)

Target: The main scientific objectives of this lab are: 1) developing a theoretical activity concentrated on the advancement of complex network theory, with applications to biological, biomedical, social, and technological systems; 2) setting up a group with a strong specialization in biological data analysis with the ability to tackle specific challenges of relevance in systems biology and conceive and develop new toolboxes for the treatment of biomedical data;  3) implementing laboratory facilities (cultured neural networks and nonlinear electronics) to test novel concepts and ideas of systems biology, and making them ready for transfer into biomedical and industrial applications.

Description: The involved team worked on a basis of a permanent interaction of scientists, coming from disciplines as diverse as physics, applied mathematics, engineering, biology, computer science, and neuroscience, to elaborate computational modeling, numerical predictions, analytical and statistical testing, and experimental verification processes. Results made evident that this multi-disciplinarity approach in bio-computation, data analysis, nonlinear dynamics and modeling of complex networks, are the fundamental and needed premises for a real breakdown in the field, and as the unique way to determine relevant industrial, medical and biological applications in the forthcoming years.


Novel tools to study systems at their different scales (from a microscopic, to a mesoscopic, to a macroscopic level), as dynamical systems interacting by means of a complex topological wiring of connections. Merging the point of view of graph theory with the need of understanding the basic principle of functioning of biological and neural systems

Systematic analysis, recording and evaluation of brain computation capability from data out of clinical sources, EEG, MEG, EcoG from patients, especially but not only for what concerns epilepsy, Parkinson, Alzheimer and other neurodegenerative diseases.   This includes new methods and tools for network data representation. The analysis of magneto-encephalographic data with advanced nonlinear methods and complex network theory allowed to determine possibilities for early diagnostic of diseases, as well as the possibility to track and assess the risk of Mild Cognitive Impairment patients to develop into Alzheimer pathologies

Tools to characterize emerging phenomena in adaptively coupled networks, and, in particular to show how adaptation can be at the basis of emerging computation in biological networks with nonlinear units.

Infrastructure: The team set up two laboratories facilities: one of them for testing protocols for the study of morphogenesis and pattern formation in growing cultured neural networks of living neuron assemblies from invertebrate animals (obtaining already a collaboration agreement for studying the parallel dynamical organization of cultured neuronal networks with the University of Elche); the other being the establishment of a laboratory of nonlinear electronics.

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