Translational systems biology using an agent-based approach for dynamic knowledge representation: An evolutionary...

Area of Science:

• Biomedical Research
• Computational Biology
• Systems Biology

Background:

• Translating basic research into clinical therapeutics is a major challenge, especially for complex diseases like cancer, sepsis, and wound healing.
• Understanding these diseases requires recognizing their dynamic nature and integrating diverse information sources.
• Existing discovery procedures need augmentation to handle complex pathophysiological processes.

Purpose of the Study:

• To introduce Translational Systems Biology as an approach to address the challenge of translating basic research into clinical applications.
• To highlight the utility of computational models in representing and investigating dynamic biological systems.
• To propose agent-based modeling as a key method for dynamic hypothesis representation.

Main Methods:

• Developing computational models to capture the behavior of mechanistic hypotheses.
• Utilizing agent-based modeling to represent dynamic biological processes.
• Integrating disparate information sources and knowledge to augment discovery procedures.

Main Results:

• Computational models provide a tool to visualize "thought experiments," filling knowledge gaps in disease processes.
• Agent-based models offer a framework for translating mechanistic knowledge dynamically.
• Transparent representation of hypotheses facilitates community-wide knowledge integration.

Conclusions:

• Translational Systems Biology, particularly agent-based modeling, is vital for describing and communicating complex biological knowledge.
• This approach can form the basis of "knowledge ecologies" for hypothesis selection and knowledge development.
• Dynamic hypothesis representation enhances the investigation of systems processes and the development of effective therapeutics.