The role of model integration in complex systems modelling

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Model integration, essential for simulating target systems in Systems Biology, is explored through the lens of cancer systems in this work. An extensive literature review identifies the various model forms and types in use, highlighting key challenges in model integration: paradigm (model formalism), focus (representation of real-world systems), and scale. To address these challenges, a two-tier model integration strategy is proposed, featuring a knowledge-driven approach to model semantics. The first step involves creating a novel description of models based on behavior rather than their mathematical or computational foundations. This is achieved by distilling abstract classes and properties that accurately capture model behavior and focus, allowing for integration with other models' behavioral descriptions. The second step decomposes this behavior into an agent-based system by translating models into local interaction rules. The text provides a comprehensive presentation of both theoretical and practical aspects of the method, enabling readers to apply it effectively in academic and professional contexts. Additionally, it includes a thorough bibliography of relevant research and literature, enhancing its value with a review of the current state of tumor modeling.