From reductionism to realism: holistic mathematical modelling for complex biological systems

Area of Science:

• Mathematical biology
• Computational neuroscience

Background:

• Physics' reductionist approach excels at simple systems but struggles with biological complexity.
• Biological systems exhibit heterogeneity, polyfunctionality, and multi-scale interactions.
• Traditional complex systems modeling lacks predictive, empirically grounded biological insights.

Purpose of the Study:

• To propose a new mathematical modeling paradigm for biology.
• To address limitations of reductionist and traditional complex systems approaches.
• To leverage high-resolution data and high-performance computing for biological modeling.

Main Methods:

• Adopting a holistic mathematical modeling paradigm.
• Utilizing rich representational structures like annotated and multilayer networks.
• Employing agent-based models and simulation-based approaches.
• Focusing on inferring system dynamics from observations (inverse problem).

Main Results:

• Demonstrates the necessity of moving beyond reductionism in biological sciences.
• Highlights the potential of holistic modeling for neuroscience and other biological fields.
• Shows compatibility with the search for fundamental biophysical principles.

Conclusions:

• A shift towards holistic, complexity-embracing mathematical modeling is crucial for advancing biological sciences.
• This approach, integrating data and computation, will drive progress in mathematical biology.
• Neuroscience serves as a key case study for this paradigm shift.