Novel Theory, Software, and Biological Strategies for Targeting Multiple Levels of Biological Organization
TWCF Number
Project Duration
September 1 / 2018
- August 31 / 2020
Core Funding Area
Big Questions
North America
Amount Awarded
Grant DOI*

* A Grant DOI (digital object identifier) is a unique, open, global, persistent and machine-actionable identifier for a grant.

Michael Levin
Institution Tufts University

Scientists often assume that the best possible model of any physical system is the one that operates at the most fine-grained, detailed level. In the case of biology, this leads to a preference for chemical and cellular-level research, yielding a wealth of data about molecular states but few effective theories that explain high-level phenomena such as anatomical structure or the behavior of organisms.

In developmental biology, this reductionist tendency can be found in the belief that organisms’ phenotypes are merely the deterministic results of their genes and that the underlying mechanism must be described molecularly. However, cells are often harnessed towards large-scale anatomical goals that are not defined at the level of cells or molecules (Levin, 2014; Lobo et al., 2014). The concept that these large-scale, observable outcomes have “emerged” has also been met with skepticism. Thus, a new model is required to fill the gap between molecular-level research and seemingly unsubstantiable theories concerning the development of higher-level outcomes.

Recent empirical studies suggest that a framework drawing on information theory and causal modeling (Hoel 2017a; Hoel et al. 2013) may resolve these issues. For example, in their recent research into non-neuronal bioelectricity (Levin, 2017), Levin and Pezzulo demonstrated how cells and molecular structures encode and pursue such higher-level observable outcomes at a level outside traditional molecular or cellular research pathways.

Building on these efforts, the project team will combine novel theoretical research into emergence with actual on-the-bench lab work. As theory informs experiment and vice-versa, this project will create a valuable feedback loop with empirical results in addition to theoretical modeling.​

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