Chiara Damiani


I am currently a post-doc researcher at the of Department of Informatics, Systems and Communication at University of Milan Bicocca and I am affiliated to the SYSBIO center for Systems Biology, which is an Italian “open access” Research Infrastructure (RI), directed by Prof. Lilia Alberghina, distributed throughout different locations, focused on Systems Biology, that integrates molecular analysis with math modeling and simulations.

I am currently working on a project that aims at understanding the links between metabolism and cancer via computational models. I adopt constrained-based modeling which, by relying on a steady state assumption, does not require any knowledge about the kinetic parameters of the enzymatic reactions and this is an invaluable advantage when dealing with genome-wide metabolic networks.  

I work in a tight connection with experimental biologists. My principal research interest is indeed to find computational solutions that can help biologists to answer to important questions.

I am characterized by the multidisciplinary approach typical of complex systems science, which I mainly learnt from Stuart Kauffman who was a pioneer in biocomplexity research. During my PhD, I had the opportunity to closely collaborate with him during six months where I pursued my research interest of investigating the influence of cell communication on the dynamics of gene regulatory networks, focusing the attention on the information storage, processing and transfer capabilities of gene regulatory networks by means of measures borrowed from information theory.

The mobility and heterogeneity of my work experience pushed forward my multidisciplinary approach: between my PhD and my current position I spent two years at the The Microsoft Research – University of Trento Centre for Computational and Systems Biology where I was in charge of developing new methods for computational biology. There I conceived and published:

– a novel sensitivity analysis methodology for stochastic models
– a novel causal approach to infer networks from metabolic concentration time series.

I was also testing the potentialities of a new programming language with the definition and simulation of complex molecular mechanistic models for drug discovery.
I collaborated with two prestigious Italian institutions: with the San Raffaele Scientific Institute on transcriptome data analysis and with the European Institute of Oncology on a stochastic model of cancer stem cells differentiation.