Biology meets physics: Reductionism and multi-scale modeling of morphogenesis

- We examine the tyranny of scales problem in physics and biology.
- Multi-scale modeling points to limitations of reductionist approaches.
- The role of physics approaches in biological multi-scale modeling is examined.
- Biomechanical forces can play direct causal roles by altering biological processes.
- Boundary conditions reveal important aspects of scale-dependent system behavior.

A common reductionist assumption is that macro-scale behaviors can be described “bottom-up” if only sufficient details about lower-scale processes are available. The view that an “ideal” or “fundamental” physics would be sufficient to explain all macro-scale phenomena has been met with criticism from philosophers of biology. Specifically, scholars have pointed to the impossibility of deducing biological explanations from physical ones, and to the irreducible nature of distinctively biological processes such as gene regulation and evolution. This paper takes a step back in asking whether bottom-up modeling is feasible even when modeling simple physical systems across scales. By comparing examples of multi-scale modeling in physics and biology, we argue that the “tyranny of scales” problem presents a challenge to reductive explanations in both physics and biology. The problem refers to the scale-dependency of physical and biological behaviors that forces researchers to combine different models relying on different scale-specific mathematical strategies and boundary conditions. Analyzing the ways in which different models are combined in multi-scale modeling also has implications for the relation between physics and biology. Contrary to the assumption that physical science approaches provide reductive explanations in biology, we exemplify how inputs from physics often reveal the importance of macro-scale models and explanations. We illustrate this through an examination of the role of biomechanical modeling in developmental biology. In such contexts, the relation between models at different scales and from different disciplines is neither reductive nor completely autonomous, but interdependent.