The Evolution of “Read-Write” Information in Biology: Computational and Experimental Models of Non-Genomic Information Imprinted on Living Tissues
TWCF Number
Project Duration
January 1 / 2016
- August 31 / 2017
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

Where does biological information come from?

The concept of information plays a prominent role in biology. Yet while information can both be read and written, most research in biology has focused only on the “read” aspect. For example, genetic information in DNA is actively read out by the cell to produce functional RNAs and proteins. The writing process for genomic information, by contrast, is underrepresented in biology research and will be investigated here.

We hypothesize that information may be written through feedback between the states and dynamics of biological systems. We will build theoretical and experimental tools to investigate this hypothesis by studying “read-write” behavior in model and real biological systems. Our project is organized into three aims:

1. To use simple computational models known as cellular automata to study how new information may be “written” into complex systems such as living organisms.

2.To use the well-developed theory of neural networks to study how biological systems can regenerate by storing information about self, and how new information may be “written” over the span of a single generation.

3. To use the results of the theoretical programs to perform experiments that test their predictions using real regenerating organisms, such as flatworms.

We expect this research to elucidate the fundamental mechanisms by which biological systems can acquire new information. Additionally, research on the burgeoning field of the origins of life will also be significantly impacted by our demonstration of “writing” information into distributed networks, a process that would have been critically important in early evolution before genes first emerged.

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