Personal Information

Short bio: Professor Tagkopoulos' interests span a variety of topics related to biology and engineering. He is particularly interested in the modeling, simulation and experimental validation of biological hypotheses regarding the emergence of microbial behaviors in complex environments, the effect of environmental correlation-structure to genotypic and phenotypic characteristics, and the design and implementation of computational tools, for synthetic and systems biology. Professor Tagkopoulos is a faculty member of the department of Computer Science and UC Davis Genome Center. Prior to joining UC Davis he was a post-doctoral fellow in Princeton's Lewis-Sigler Institute for Integrative Genomics and a relationship manager in Credit Suisse's LOCuS fixed income derivatives group (DIT-SRA). He earned a Dipl.-Ing. in Electical and Computer Engineering from University of Patras, a MSc in Microelectronics from Columbia University and a PhD in Electrical Engineering from Princeton University in 2001, 2003 and 2008 respectively.

General background: I completed my PhD in the Department of Electrical Engineering at Princeton University in summer 2008 where I was also affiliated with the Lewis-Sigler Institute for Integrative Genomics. My research sofar intersect the fields of engineering and biology. During my PhD, I was working in the laboratory of Saeed Tavazoie.

My first experience with biological modeling and computational biology was during my M.Sc. in Columbia University, where I worked closely with C. Zukowski (EE) and D. Anastassiou (EE) on developing hardware with biological applications. At Princeton, I collaborated closely with R. Weiss (EE) on stem cell differentiation/pattern formation and worked with professor D. Tank (Molbio) on the development and implementation of a wireless implantable electrode. Additionally I worked on a few other bioinformatics projects (clustering and classification of biological data) with prof. SY Kung (EE) and D. Serpanos (EE).

My PhD thesis is focused on properties of evolving networks and emergence of anticipatory behavior. We showed on theoretical grounds and by using large scale simulations that the structured correlations of environmental variables (eg. temperature, oxygen, osmolarity, etc.) should allow free-living microbes to statistically predict future trajectories of their environments. Additionally, we showed that our predictions are valid in the case of bacterium E.coli.