›› RESEARCH
Big Data: The theoretical foundations of Big Data Science remain a wide open field. We investigate a new Big Data theory for high-throughput analytics and model-free Inference. Specifically, we explore the core principles of distribution-free and model-agnostic methods for scientific inference based on Big Datasets and we call it Compressive Big Data analytics (CBDA).
Systems Biology: mathematical modeling of immune response to pathogens (mathematical modeling of Mycobacterium tuberculosis) and host-microbiome interactions. Infection data are are from human, primate and mouse.
Mathematics: representing cell populations dynamics and interaction by various mathematical modeling techniques, e.g. deterministic Ordinary Differential Equation (ODE) and Delay Diffferential Equation (DDE) systems, as well as Stochastic models (Agent Based Models– ABM)).
Statistics: Parameter estimation and statistical techniques for uncertainty and sensitivity analysys in complex mathematical models (for example, statistical and computational techniques are used to model the effect of antigen dose on the early events in the immune response during Mycobacterium tuberculosis infection).
Bioinformatics: Data Mining and Biochemical System Network discovery
›› My updated CV
›› My Google Scholar Metrics and Publication List
›› WHO I AM
EDUCATION/TRAINING
• University of Rome I "La Sapienza", Rome, Italy. B.S. and M.S. (1997). Applied Statistics
• Ph.D.. University of Rome I "La Sapienza", Rome, Italy. 2002. Operations Research. Mentor: Angrea De Gaetano, MD PhD
• Postdoctoral Fellowship. University of Michigan Medical School, Dept. Microbiology and Immunology, Ann Arbor, MI. 2002-2005. Biomathematics. Mentor: Denise Kirschner PhD
• Postdoctoral Fellowship. Medical University of South Carolina, Charleston, SC. 2004. Bioinformatics. Mentor: Eberhard Voit PhD
POSITIONS
• Feb-June 2004, Postdoctoral Fellow, Biostatistics, Bioinformatics and Epidemiology, MUSC, Charleston, SC
• 2002-2005, Research Fellow, Microbiology and Immunology, University of Michigan, Ann Arbor, MI
• Jan-July 2005, Research Faculty, The Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, Atlanta, GA
• July 2005-2010, Research Investigator, Microbiology and Immunology, University of Michigan, Ann Arbor, MI
• Sept 2010-present, Research Assistant Scientist, Microbiology and Immunology, University of Michigan, Ann Arbor, MI
BIO
I am a Research Assistant Scientist in the Department of Microbiology and Immunology at the University of Michigan Medical School in Ann Arbor, Michigan. Rome (Italy) is my beloved hometown, where I was born on June the 1st 1970. I spent 3/4 of my life there and then in 2001 I decided to move to the United States to pursue my dreams and do science. I have a unique multi-disciplinary background, spanning from statistics and probability, to operations research, mathematics and systems biology. I have always tried to combine all these assets and skills throughout my research career, starting from my PhD thesis.
I got both a MS in Statistics (May 1997) and a PhD in Operations Research (Dec 2001) at the University of Rome "La Sapienza", Dept. of Statistic, Probability and Applied Statistic). I got my PhD in Operations Research at the Biomathematics Laboratory of the National Research Council, in Rome: it is located in the "Gemelli" area, which comprises the hospital (it's the hospital of the Pope), the university and many research facilities and institutions.
My PhD dissertation was based on parameter estimation of Ordinary Differential Equations (ODEs) systems by Least Squares (LS) approach and nonlinear programming algorithms. It merges both statistics and operations research, as well as biomathematics: in fact the mathematical models implemented in my PhD are related to glucose-insulin and lipids dynamics, including many simulations of growth and decay curves (Gompertz, Logistic, ....). Since then, I have been interested in biology and fascinated by immunology, which to me represents the perfect example of a continuously evolving complex system. Since then, I also proudly call myself a systems biologist.
I moved to Ann Arbor in July 2001 as a Research Fellow in Denise Kirschner lab, where I studied immunology for the first time in my life. Denise opened my mind to the wonderful world of immune system: it looked, and still looks, like sci fi to me. It's really amazing. My main research was on building mathematical models of the many faces of immune response to Mycobacterium tuberculosis infection in human.
Why TB? Well, my grandfather died of tuberculosis and I found out that the TB global burden is still enormous today: TB is the world's leading cause of death in humans from a single infectious agent, with a newly infected individual every second and approximately 35 deaths every 10 minutes (~2 million a year). I also realized that mathematical and computational modeling approaches can provide a unique opportunity to identify factors that are crucial to a successful outcome of infection in humans. These modeling tools not only offer an additional avenue for exploring immune dynamics at multiple biological scales, but also complement and extend knowledge gained via experimental tools. This gave me extra motivation and my research since then and for the past 10 years has mainly focused on questions related to host-pathogen interactions in infectious diseases, with a specific interest in the host immune response to Mtb at multiple spatial and time scales. All of these studies have been funded by the National Institutes of Health, through grants awarded to Dr. Kirschner, with myself as co-PI.
I moved to the Department of Biostatistics, Bioinformatics and Epidemiology at MUSC (Charleston, SC) for a short time in 2004, and later I joined the Department of Biomedical Engineering at Georgia Tech and Emory University in Atlanta. In both departments, I worked as a Research Scientist with Eberhard Voit on metabolic pathway network discovery (based on S-Systems approach, power-law formalism and biochemical system theory), applying algorhytms developed during my PhD years. I used non linear least squares as data fitting scheme for extracting structural information from time series of metabolite concentrations, or of gene or protein expression profiles. Biochemical system theory (BST) is the theoretical modeling framework, but the approach can be applied to general nonlinear systems of differential equations.
I then moved back to Ann Arbor in July 2005 as a faculty (Research Investigator) and now has a Assistant Research Scientist.
COMPRESSIVE BIG DATA ANALYTICS
Compressive Big Data Analytics – CBDA (Matlab, R, Python, Bash-Shell): 1) Predictive analytics on a variety of Big Data Repositories (structured data objects, PET-MRI, Microbiome, Gene Expression Profiles, text and more) 2) Deployment of R packages (CRAN and GitHub), 3) Theoretical foundations of Big Data Science as a new Big Data theory for high-throughput analytics and model-free Inference.
DATA PRIVACY
Statistical Obfuscation of Sensitive Big Data enabling Advanced Information Aggregation, Sharing, and Analytics (R, RShiny). The development of a statistical tool (DataSifter: US PATENT: ELECTRONIC MEDICAL RECORD DATASIFTER UM 7363; HDP Ref.: 2115-007363-USPS1, 10,776,516, Co-inventor) may provide the critical infrastructure at the right time to enable secure transdisciplinary team-based interrogation of Big Data including sensitive information.
HOST-MICROBIOME WORK
Based on the work with Eberhard Voit, I have recently devoted part of my research efforts on algorithms for data-driven model generation, fitting and selection (GFS algorithm, i.e., network inference) of gut microbiome data. I am currently working as a co-PI with Dr. Pat Schloss here at the University of Michigan (NIH RO1 grant funded) on Operational Taxonomic Units (OTU) microbiome data. The goal of this research project is to develop a dynamical model (ODE) that identifies, quantifies and predicts the overall architecture and dynamics of mice OTU data (relative abundance of microbial communities). This work will set the foundations for a more systematic and comprehensive study of host-microbiome interactions, where the effects of antibiotics is investigated in the context of microbiome diversity and stability, as well as susceptibility to bacterial (for example Clostridium difficile infection-, or CDI) or viral infections.
UNCERTAINTY AND SENSITIVITY ANALYSIS
A recurring central theme across all these modeling efforts is to choose how to perform network inference, parameter estimation and model validation. Accurate estimation of model parameters and ultimately the strength and quality of model predictions are challenged by an intrinsic biological and experimental variability in rates measured from in vivo or in vitro studies, where some interactions in the systems are not even currently measurable. As system biologists, we quantify the importance of each host mechanism involved directly and indirectly in the infection dynamics using statistical techniques known as uncertainty and sensitivity analyses. I recently published a highly cited review in Journal of Theoretical Biology which represents the first systematic study on uncertainty and sensitivity analyses techniques, focused on systems biology applications. The approach can be generalized to any type of model in which parameters are unknown/uncertain: discrete and continuos, deterministic/stochastic. Statistical sampling techniques (uncertainty) and generalized correlation indexes and variance decomposition methods (sensitivity) when combined guide our understanding as to how and what extent variability in parameter values affects infection outcomes. This approach can be used in a variety of mathematical and computational model settings and should be a necessary step for model validation, in any field, and especially needed in systems biology and complex systems. Some of these techniques are explicitly developed for deterministic models. However, we showed how they can be extended to stochastic models, such as agent-based models. Moreover, large complex systems can use uncertainty and sensitivity analyses techniques as a viable alternative for model fitting. A similar challenge is to apply classical model fitting approaches to ABM systems, where not only time courses need to be matched, but also shapes and patterns emerging from the model simulations. I plan to address most of the above questions in my future research.
HOBBIES
SOCCER: I love soccer and I coach kids K-12. My national team is Italy of course, but AS Roma has a special place in my heart.
MUSIC: I love music, both listening (particularly jazz) and playing (piano). I used to play jazz, R&B and soul in many bands in Italy.
›› CONTACT ME
If you want to share some thoughts, drop me a line at simeonem@umich.edu .
Albert Einstein
If we knew what it was we were doing, it would not be called research, would it?