MATTIA MIOTTO

Education
Sapienza University of Rome, Department of Physics
- Ph.D. in Physics (January 2020). Thesis: Heterogeneity and noise in living systems: statistical physics perspectives
- M.Sc. in Physics (October 2016). Thesis: From associative memories to failsafe modules in biochemical networks
- B.Sc. in Physics (October 2014). Thesis: Hadrontherapy: a study of the biological effects
Rome, Italy

Research Interests and Track Record.

I am interested in the physics of living systems across multiple scales, from the molecular level to single cells to populations and ecosystems. My research questions revolve around the functional role of heterogeneities, the interplay between noise and information, the structure and functions of biomolecules, and the emergence of multi-cellular and population-level behavior. My background is in statistical physics applied to biological systems. During the PhD, I gained expertise on statistical physics tools to analyze and model the role of biological noise and heterogeneity both from theoretical and numerical points of view with particular emphasis on cancer cell data. When I started the post-doc, I decided to enlarge my research activity taking an active role in the experimental part. In these years, I contributed to devising experiments, and to analyzing and interpreting data.
I am currently the author of more than 45 articles published in peer-reviewed journals and/or pre-prints currently under review. According to Google Scholar, my articles
received 1091 citations in the last five years, resulting in an H-index of 19 and an i10-index of 29.

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Main research results
The following are the three main lines of research that I am conducting:
• Biological noise and state transitions in cancer cells. I am working at building up theoretical and experimental frameworks to quantity partition noise in cancer cell division and understand its role in driving cell state transitions. During my PhD, I developed RNAnet, a platform to measure the crosstalk in large scale miRNA-RNA interaction network. Analyzing for the first time RNA networks at systemic level of both normal
and tumoral cells, I proved that miRNAs mediate a weak but resilient and context-independent network of cross-regulatory interactions that interconnect the transcriptome, stabilize expression levels and support system-level responses.
• Statistical inference and populations dynamics. I contributed at a novel variational minimal maximum entropy principle and developing a maxent-based algorithm to evaluate statistical information on any system defined on a connected network and evaluates its configurational entropy. This innovative approach proved to be highly versatile, finding applications from the analysis of cancer cells images to the study of
prey-predator systems.
• Molecule stability and interactions. I proposed the first predictive method to assess the thermal stability of proteins, starting from their atomic structure. The tool, named Thermometer, is freely available as a web server and is based on my scientific finding that enhanced thermal resistance is provided by optimizing the electrostatic interaction network, at odd with binding affinity where evolution works optimizing van der Waals
interaction energy. In addition, working on SARS- CoV-2 interactions, I and co-workers were the first to computationally show that SARS-CoV-2 is able to binding to sialic acid molecules present on cell membranes and uses it as primary attachment factor [30]. Our predictions have been experimentally confirmed.