Parallels between Biology and Computing
Biological systems are complex evolving
information processing systems that exploit multicellularity
as a way to manage ever-growing complexity. For similar reasons,
now driven inexorably toward multicellular architectures.
As computing becomes an ever more pervasive part of our modern
naturally look to understand it in terms of what we know of
complex phenomena such as biology. And vice versa. On the one
computing professionals the way to the exceedingly sophisticated
found in biological organisms: collaborations between cells,
even between organisms collaborating/competing in an ecology. On
computing systems are recent and are human designed. We can
stages of evolution in computing and thereby perhaps get insight
the evolution of computing that may have had parallels
in biology in the mists of prehistory.
Parallels between biology and computing systems include:
The most recent, and most obvious parallel is that both rely on
to enable complexity to grow beyond the limits manageable in a
cell or single computer. Multicellullarity itself evolves from
single-cell architectures because of its inherent advantages. The
multicellular systems have few types of specialized cells.
become more sophisticated and capable by exploiting more cells and
types of cells.
At present we can only compare the simplest biological examples
the most complex computing examples because multicellular
computing is in its infancy.
Examples in Biology
Simple Metazoans (i.e., multicellular organisms) include:
- Placozoa -- Thought
to be the
simplest Metazoan organism.
They are millimeter-scale discs that contain a few thousand
The cell types are: dorsal cells with flagella, ventral “gland
ventral flagellar cells, and central “fiber cells ” that have
the properties of both nerve cells and muscle cells.
- Hydra -- A
milimeter-scale (5-20mm), multi-tentacled Cnidaria (the family
It is estimated to contain 50,000 - 70,000 cells. It has at
cell types including simple nerves, epidermis (outer layer),
gastrodermis (inner layer), germ cells, and Nematocysts
Cnideria Cyanea) -- Number of cells >
10 million. At least 22 cell types including neurons, sensors,
endocrine, and Nematocysts used for capturing prey.
Examples in Computing
Wikipedia, large public database services (e.g., EMBL/EBI Hinxton
GenBank), various "clouds" (Google's Cloud, Amazon's EC2), Social
Twitter), Massively Multiplayer Online Role Playing Games (World
Second Life) and instant messaging, chat and VOIP systems (e.g.,
The largest and most familiar, and those about which more data is
- Ebay -- Estimated 54K,
At least 5 functional types of servers: database,
LDAP, web servers, application servers, networking switches
- Facebook -- An
estimated 180,000 servers as of mid 2013. There are at least 4
servers, web servers, database servers, and application
- Google -- uses at
million servers and very likely more than 2 million servers.
Their system uses at least 9 specialized types of server:
servers, indexing, spelling, documents, http, search,
Note that Facebook and EBay, while among the largest
computing systems, are comparable to Placozoa, the smallest
And Google, by far the largest multicellular computing system is
simpler than a jellyfish. Moreover, a jellyfish is self
self sustaining, and self reproducing whereas Google is none of
Neither biological nor computing systems advance just by
more and more "cells" of one type. They seem to prefer to specialize and exploit more
types. The chart below, based on 2009 data, has far too
few data points to support a
firm conclusion, yet it indicates a functional relationship,
both realms, between the
number of "cells" and the number of "cell" types.
Contact: sburbeck at mindspring.com
Last revised 7/24/2013