Ph.D. dissertation presentation,  A. D. Balomenos
Event Timing: Monday, February 8th, 2021 15:30-17:00 Athens time via  Zoom.
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𝗔𝗻𝗻𝗼𝘂𝗻𝗰𝗲𝗺𝗲𝗻𝘁
On Monday, February 8th, 2021, 15:30-17:00 Athens time, Ph.D. candidate 𝗠𝗿. 𝗔𝘁𝗵𝗮𝗻𝗮𝘀𝗶𝗼𝘀 𝗗. 𝗕𝗮𝗹𝗼𝗺𝗲𝗻𝗼𝘀 of the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, will present his Ph.D. dissertation titled “𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗼𝗳 𝗰𝗼𝗺𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗺𝗲𝘁𝗵𝗼𝗱𝘀 𝘁𝗼 𝗶𝗻𝘃𝗲𝘀𝘁𝗶𝗴𝗮𝘁𝗲 𝘁𝗵𝗲 𝘀𝘁𝗼𝗰𝗵𝗮𝘀𝘁𝗶𝗰 𝗱𝘆𝗻𝗮𝗺𝗶𝗰 𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝗿 𝗼𝗳 𝗺𝗶𝗰𝗿𝗼𝗯𝗶𝗮𝗹 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝗶𝗲𝘀”.

The Examination committee:

Ioannis Emiris, Professor, University of Athens
Dimitrios Gounopoulos, Professor, University of Athens
Konstantinos Koutsoumanis, Professor, Aristotle University of Thessaloniki
Elias S. Manolakos, Professor, University of Athens (research advisor)
Laurence Rahme, Professor Harvard Medical Scool, USA
Anastasia Tampakaki, Associate Professor, Agricultural University of Athens
Sergios Theodoridis, Emeritus Professor, University of Athens
𝗔𝗯𝘀𝘁𝗿𝗮𝗰𝘁
Methods and software tools for the analysis of cell movies that allow the accurate estimation of bacterial attributes at the single-cell level, the visualization and statistical modeling of the extracted cell attributes, and creating in silico experiments simulating microbial communities growth, remain currently a challenge in computational systems biology. Methods reported in the literature are lacking in many respects. They exhibit low success rates in cell segmentation of dense cell movies with thousands of cells, often require laborious parameterization, and are characterized as not user-friendly by biologists with no bioimage analysis expertise. Moreover, there is no end-to-end computational pipeline to automatically analyze cell movies, model the variability of extracted cell attributes, explore cell diversity visually, and facilitate the realistic simulation of microbial communities' behavior while considering the inherent stochasticity of the underlying biological phenomena.

This doctoral dissertation's main goal was to fill this gap by developing an end-to-end computational strategy with three stages for studying microbial communities' dynamic growth behavior. The first stage provides an automated bioimage analysis platform, which extracts, collects, and organizes the estimated cell attributes data hierarchically, without the need for human-user intervention. The second stage provides an information system for single-cell analytics and visualization. It allows the user to visualize in different ways and infer models for the cell attributes extracted by the image analysis stage at different community organization levels. The third stage supports the development of multiscale "digital twin" model realizations to investigate in silico microbial communities' growth. It can consider the individual cells' genetic "program", the microenvironment's conditions, and the simulated biological phenomena' inherent stochasticity for high fidelity purposes.

First, we developed a new cell tracking algorithm inspired by motion estimation for video compression that successfully associates cell instances in consecutive frames and can accurately construct dense and deep forests of lineage trees with many cell colonies and generations. Our method can map on the extracted trees cell instance attributes (e.g., cell surface, length, width) and cell life attributes (e.g., cell division time) with high fidelity. The very high bacterial matching accuracy it achieves (98.7%) for complex cell movies exceeds that of prior methods.

Our complete, end-to-end cell movies analysis methodology, codenamed BaSCA (Bacterial Single-Cell Analytics), covers cell segmentation, cell tracking, and lineage trees construction for complex cell movies. BaSCA was thoroughly evaluated using datasets generated by different labs and achieved an F-measure rate of 98%. The F-measure remains very high (over 96.7%) even for overcrowded cell movies with many merging colonies and thousands of bacteria in the field of view of the microscope.

The information extracted from the cell movie analysis is organized in a relational bio-database, allowing data mining at the single-cell level (single-cell analytics). Another innovative feature is creating different "views" in the data for the direct and friendly visualization of information (visual analytics). In this way, the system allows the user to select subpopulations (colonies, generations, relative cells in the trees) and perform statistical analyses and best distributions parameter estimation. Additionally, this work also contributed to an R package creation codenamed ViSCAR (Visualization and Single-Cell Analytics in R). Using Viscar, one can also correct inevitable segmentation and tracking errors introduced by the image analysis of dense cell movies.

Next, we had to enhance the capabilities of CellModeller, a popular open-source systems biology tool, to be able to recreate in silico with high fidelity the physical interaction of cells as they grow and divide to form a dense bacterial community while taking into account the genetic "program" of every individual cell, the underlying stochasticity of cell properties (e.g., division time), the microenvironment conditions, potential cell motion, etc. Thus, it became possible to generate in silico experiments of microbial communities growing in two dimensions based on stochastic atomic evolution models for each cell entering the simulation. We created a "digital twin" microbial community prototype to implement the proposed unified strategy for Salmonella's case (Salmonella enterica serovar Typhimurium). To that end, the regulatory networks of gene expression related to the mechanism of intercellular chemical communication (quorum sensing), the mechanism of virulence development of S. Typhimurium, and the interaction between the two were integrated into the individual-based cell models we developed. The proof of concept community level "digital twin" simulation model we developed also considers the inherent stochasticity that governs single-cells' growth and division, which affects the patterns of switching their phenotypic state from non-virulent to virulent. Using the digital twin, it is possible to investigate different scenarios in silico.

Overall the computational pipeline we have developed can automatically image-analyze cell movies from live-cell microscopy experiments, display efficiently and extract statistical models of single-cell attributes, and utilize them to create realistic synthetic movies. It can also stochastically simulate a microbial community's behavior in space and time. This was demonstrated with a digital twin of a community of pathogen S. Typhimurium cells, growing in a microenvironment that promotes stochastic phenotypic switching of the single-cells based on their own personalized genetic network "logic."
𝗣𝗿𝗼𝗰𝗲𝗱𝘂𝗿𝗲
Due to Covid-19 restrictions, the presentation will be held remotely by e-presence, namely, through an open to the public zoom call. The presentation will be in Greek.
Please fill out this form at least 24hours in advance to receive a link for the zoom meeting. Feel free to forward this invitation to interested parties.

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