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The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

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Published on: May 13, 2022

The time scale of evolutionary innovation.

Krishnendu Chatterjee¹, Andreas Pavlogiannis¹, Ben Adlam²

¹IST Austria, Klosterneuburg, Austria.

Plos Computational Biology

|September 12, 2014

Summary

Evolutionary innovations often require exponential time to discover new biological functions, especially for longer genetic sequences. However, a regeneration process can enable evolution to find these functions on polynomial time scales.

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Area of Science:

Evolutionary biology
Theoretical biology
Computational biology

Background:

Understanding the timescale of evolutionary innovations is a fundamental question in biology.
Previous studies focused on the fixation time of single mutations.
The time required for evolutionary trajectories to discover new functions remains largely unexplored.

Purpose of the Study:

To investigate the time scale required for populations to evolve new biological functions by exploring fitness landscapes.
To determine how the length of genetic sequences (L) influences the time needed for adaptation.
To identify mechanisms that permit evolution to operate on tractable (polynomial) time scales.

Main Methods:

Developed a theoretical framework to estimate evolutionary time as a function of sequence length (L).
Analyzed adaptation dynamics on various fitness landscapes.
Investigated the impact of a regeneration process on evolutionary timescales.

Main Results:

Adaptation on many fitness landscapes requires time that scales exponentially with sequence length (L).
This exponential scaling occurs even with broad selection gradients and uniformly distributed targets.
A specific regeneration process was shown to facilitate adaptation on polynomial time scales.

Conclusions:

Evolutionary search for new functions can be computationally intractable (exponential time) for longer sequences.
Regeneration processes represent a potential mechanism for accelerating evolutionary innovation.
This work provides insights into the efficiency and constraints of evolutionary trajectories.