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