When one model is not enough: combining epistemic tools in systems biology

Area of Science:

• Philosophy of Science
• Systems Biology
• Epistemology

Background:

• The philosophy of scientific modeling has shifted towards understanding diverse model functions and their epistemic goals.
• Existing research highlights the importance of model diversity for scientific theories but needs to explore how combined epistemic means generate new scientific entities.

Purpose of the Study:

• To analyze how a combination of different epistemic means can generate and stabilize new entities in science.
• To apply Rheinberger's practice-oriented account of knowledge production to understand modeling practices.
• To investigate the role of multiple representational means in knowledge generation within systems biology.

Main Methods:

• Drawing on Rheinberger's historical epistemology and practice-oriented account of knowledge production.
• Analyzing modeling practices through a case study of network modeling in systems biology.
• Examining the application of engineering approaches to biological systems.

Main Results:

• The conceptual repertoire of historical epistemology provides insights into modeling practice.
• Multiple representational means are integral to the dynamics of knowledge generation.
• The diversity of constraints across different models, when used together, fosters knowledge production.

Conclusions:

• The interlocking use of diverse epistemic means, despite their varied constraints, creates potential for knowledge production.
• Network modeling in systems biology exemplifies how combining different representational approaches drives scientific discovery.
• Understanding the functional diversity of models is crucial for advancing scientific theories and generating new knowledge.