Systems Biological Analysis of mTOR-Dependent Molecular Mechanisms of Autophagy
Hajdú Bence
Molecular Medicine Division
Dr. Várnai Péter
2025-12-10 13:00:00
Pathobiochemistry
Dr. Ligeti Erzsébet
Dr. Mészáros-Kapuy Orsolya
Dr. Cserző Miklós
Dr. Csikász-Nagy Attila
Dr. Hegedűs Tamás
Dr.Wittmann Mariann
Dr. Fekete János Tibor
In this PhD thesis, we contributed significant insights into the dynamical regulation of
ULK1-mediated autophagy induction through innovative systems biology approaches.
We developed an experimental-computational pipeline that utilizes noisy western blot
time-series data for parameter estimation in dynamic models. This methodology enabled us
to construct a chemical reaction network model incorporating PP2A, ULK1, and mTORC1
interactions that successfully predicted cellular responses to treatments not used in our
parameterization.
We made important discoveries about autophagy oscillations, observing periodic activation
at specific concentrations of rapamycin and okadaic acid. Through phase plane
analysis, we demonstrated that models without time delay could not reproduce these dynamics.
We proved this by incorporating an intermediary regulatory component mediating
some of AMPK’s effects on both ULK1 and mTORC1, establishing that AMPK had to
exert part of its regulatory influence through this intermediary protein for the system to
exhibit oscillations.
To extend our compuatational domain, we developed a comprehensive autophagyapoptosis
model by extensively modifying an existing framework from Liu et al. We
applied state-of-the-art computational methods and tools to address the complex challenges
of biological systems modeling. We repurposed the Optima++ computational framework
for biochemical reaction networks, using Optima++ for the first time outside of combustion
kinetics. Our approach combined local sensitivity analysis using the SUE impact measure
to identify influential parameters, which we then optimized using the advanced FOCTOPUS
algorithm. We established a new standard for utilizing multiple data sources in
biochemical reaction network parameter optimization through the standardized ReSpecTh
XML format, enabling systematic compilation and integration of experimental data from
diverse literature sources.
These mechanistic insights provide a foundation for understanding autophagy dysregulation
in diseases such as cancer, neurodegeneration, and metabolic disorders, potentially
informing future therapeutic interventions targeting this crucial cellular process.
