A center on complex systems

The Center for Complexity & Biosystems (CC&B) is an interdisciplinary center for complex systems at the University of Milan.

Mission of the CC&B: The functioning of cellular processes, the organization of a biological organism, social dynamics and the behavior of many materials are complex phenomena emerging in a non trivial way from the interaction of an ensemble of elementary elements. To tackle the scientific and technological challenges posed by complex systems we need a new interdisciplinary approach that can help us to interpret the growing amount of data in all these fields of research. The CC&B is taking the challenge to study emergent properties of complex, biological and biomimetic materials, to understand how elementary cellular and bio-molecular processes result in the physiological and pathological behavior of an organism, and to analyze quantitatively dynamics of complex networks.

Data driven science at CC&B: Scientific research in the twenty-first century is more and more driven by enormous amounts of data. Extracting quantitative information from data is thus becoming a pressing challenge in all fields but particularly in biomedical research. Examples of the applications of CC&B research include (but are not restricted to):

  • Quantitative image analysis:
    Three dimensional reconstruction from confocal images.
    Cell tracking, migration assays/wound healing.
    Cell colony counting and statistics.

  • Pathways and genomics:
    Integrative analysis of genomic, transcriptomic, miRNA and proteomic data.
    Simulations of pathways and metabolic networks.

  • Computational modeling for biological systems:
    Molecular dynamics simulations of proteins.
    Meso-scale models of cellular processes.

  • Computational materials modeling:
    Simulations of micro and nano-mechanics of crystalline and amorphous materials.
    Mechanics of bio-inspired materials.

  • Statistical analysis of complex networks.
    Analysis and compression of web and social graphs.
    High performance parallel web crawling.
    Analysis of multilayer networks.

  • Machine and deep learning.
    Pattern recognition, classification, clustering.
    Data mining