| dc.description.abstract | Precision medicine aims to tailor medical interventions to individual patient characteristics, recognizing the inherent diversity in biological responses. At the core of this approach lies the imperative to move beyond one‐size‐fits‐all therapies and embrace a precise understanding of disease mechanisms and treatment responses. In this context, the integration of modeling and data analysis emerges as a promising tool for advancing precision medicine, offering a means for treatment selection and hypothesis generation. The principles of systems biology, with its emphasis on understanding complex biological systems as a whole, provide a guiding framework for comprehensively elucidating their intricate behaviors. Advancements in systems biology have already empowered the integration of diverse experimental and phenotypic data with prior knowledge for the creation of predictive models.
Computational modeling integrates a wide spectrum of mathematical formalisms, ranging from abstract to very detailed representations of biological reality. At the more abstract end of the spectrum, logical modeling stands out as a potent mathematical formalism that facilitates the representation and analysis of complex biological systems. The Boolean logic used in such models provides a relatively abstract yet effective means to capture the qualitative aspects of molecular interactions and cellular processes, where components such as genes, proteins, and signaling pathways are represented as Boolean variables. By transforming the continuous spectrum of molecular concentrations into discrete on/off states, logical models can efficiently capture the dynamic interplay of components within a cellular network. Logical modeling is particularly attractive as it requires minimal parameters and quantitative details and can make use of the abundant knowledge of causal interactions.
This thesis explores the synergy of experimental and computational approaches, focusing on the application of logical models for hypothesis generation and therapy design.
More specifically, the thesis aimed to address the following challenges in model building and analysis:
1. How can a disease be represented through cell type‐specific and multiscale modeling?
2. When does a model sufficiently represent a system?
3. How can continuous measurements be translated into discrete activities that Boolean models require?
4. How can model predictions be interpreted and aligned with biological observations?
5. How can logical models be used for hypothesis generation and therapy design?
Five papers stemming from these efforts showcase the use of logical models for hypothesis generation and as a tool for therapy design. Beyond these technical challenges, the research also contributed to our understanding of psoriasis and gastroenterological cancers. By the development of two psoriasis models, new insights into the development of the disease and its treatment were produced. Similarly, by creating a framework for model expansion, a colorectal cancer model provided a tool for drug synergy identification. Lastly, the mode of action of drug synergies was identified by a comprehensive multi‐omics analysis.
The above exemplifies the potential of the integration of computational modeling and experimental insights for advancing biological understanding at pre‐clinical levels. As these synergistic approaches continue to evolve, unlocking deeper insights into disease mechanisms and treatment strategies, their future potential holds the promise of guiding clinical interventions with greater precision and efficacy, ultimately being integrated as part of clinical practices and precision. | en_US |
