Arrival of the Fittest: why are biological systems so robust? | Arlindo L. Oliveira | in Digital Minds

arrivalIn his 2014 book, Arrival of the Fittest, Andreas Wagner addresses important open questions in evolution: how are useful innovations created in biological systems, enabling natural selection to perform its magic of creating ever more complex organisms? Why is it that changes in these complex systems do not lead only to non-working systems? What is the origin of variation upon which natural selection acts?

Wagner’s main point is that “Natural selection can preserve innovations, but it cannot create them. Nature’s many innovations—some uncannily perfect—call for natural principles that accelerate life’s ability to innovate, its innovability.”

In fact, natural selection can apply selective pressure, selecting organisms that have useful phenotypic variations, caused by the underlying genetic variations. However, for this to happen, genetic mutations and variations have to occur and, with high enough frequency, they have to lead to viable and more fit organisms.

In most man-made systems, almost all changes in the original design lead to systems that do not work, or that perform much worse than the original. Performing almost any change in a plane, in a computer or in a program leads to a system that either performs worst than the original, or else, that fails catastrophically. Biological systems seem much more resilient, though. In this book, Wagner explores several types of (conceptual) biological networks: metabolic networks, protein interaction networks and gene regulatory networks.

Each node in these networks corresponds to one specific biological function: in the first case, a metabolic network, where chemical entities interact; in the second case, a protein interaction network, where proteins interact to create complex functions; and in the third case, a gene regulatory network, where genes regulate the expression of other genes. Two nodes in such networks are neighbors if they differ in only one DNA position, in the genotype that encodes the network.

He concludes that these networks are robust to mutations and, therefore, to innovations. In particular, he shows that you can traverse these networks, from node to neighboring node, while keeping the biological function unchanged, only slightly degraded, or even improved. Unlike man-made systems, biological systems are robust to change, and nature can experiment tweaking them, in the process creating innovation and increasingly complex systems. This how the amazingly complex richness of life has been created in a mere four billion years.

In Digital Minds

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