| Summary: | Thorough analysis of the fundamental molecular processes involved in a biological system is often too complex to be performed intuitively. Systems Biology comes to the rescue of these perplexed situations by adopting principles of mathematics, employing computational methods and benefiting from high-throughput, large-scale experiments. In this context, we have utilized bottom-up approaches of System’s Biology to study cell cycle control and the implication of central cell cycle regulators in fate decisions.
In the first part of the thesis, harnessing the Quantitative model for CDK activity proposed by Stern and Nurse, we formulated a mathematical model with probabilistic features and applied single live cell fluorescent microscopy to collect additional data. Initially, we extracted publicly available data in non-tumorigenic cells regarding a biosensor that’s fluorescent intensity is a read out for CDK activity throughout the cell cycle, therefore providing us with single cell CDK activity measurements over time. By using this data, we tested if we could explain CDK activity build up during the G1 and S phase progression in a linear manner. Moreover, by considering the inherent noise of biological systems, each parameter implicated in the mathematical model was represented by a probability distribution. Also, by analysing some aspects of the model, and considering published knowledge we could estimate the timing of S phase onset in a population of cells. The second half of this project was on generating a cancer cell line stably expressing this CDK activity sensor and an additional marker of G1/S phase progression (Cdt1:GFP). Monitoring these cells over time by time-lapse fluorescent microscopy and employing image processing techniques we could obtain single cell quantitative data for CDK activity and Cdt1:GFP. Through this workflow, we could examine the CDK activity threshold at the onset of S-phase and the exact timing of this entry for distinct cells. Combining the simulated and experimental results we could observe a similar pattern in CDK activity build up, despite the difference in the timing of S phase.
In the second part of the thesis, the published proposed biological model of multiciliogenesis fate determination was examined in a mechanistic manner. Primarily, we constructed a simple continuous dynamic model based on mass action formalism. Each biological entity involved was represented by a production and degradation rate. By using data from experimental set-ups for each central regulator implicated in multiciliogenesis and employing global optimization algorithms we could compensate for the unknown parameter values. Restricted only to the interaction of Geminin and GemC1/Lynkeas, which are the primary factors of establishing the multiciliogenesis fate determination, we observed a missing determinant. This suggested either a misinterpretation of the structure of our mathematical equations or an extra element needed to explain the biological behaviour. However, driven by experiments in our laboratory, suggested an additional negative regulator on GemC1/Lynkeas. Adding this negative regulator to our system, we could capture the trend of GemC1/Lynkeas, thus highlightening a gap in understanding of the biological model.
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