Multicellular Computing Site Map
Multicellular architectures: parallels
between life and computing
The Four Architectural Principles
Four principles of
architectural strategies for managing cooperation between cells emerged
more than 500 million years ago. They are rare in single cell
organisms yet nearly
multicellular organisms. And they each evolved before or
coincident with the
of multicellular life. Now they are re-emerging in the web of
-- Although there is a tendency to think of computers as general
purpose, most computers are in fact quite specialized already
multicellular computing -- code transfer should also be
and dealing with interpreted
code complicate matters further.
parts of the system communicate by modifying their
local environment. The modifications become persistent cues for
other elements in the system
-- self is defined by the
body-as-stigmergy-structure, not by the identities of the cells or an
Stigmergy in computing
cues left in persistent stores, e.g., databases, network structures and
Web Services, organize multicellular computing
Apoptosis in Computing
multicellular computing, the
individual computer must be willing to sacrifice itself for the good of
the larger organism. But old single-cell computing attitudes that
each computer is supreme will die slowlly.
How the four
principles are intertwined
-- the four principles co-evolved and
are interdependent both as abstract architecural principles and as
concrete implimentations within each cell.
to multicellular computing is happening.
The Four Principles can smooth the transition.
Background Issues of the
Underlying Problems of Complexity
A brief statement of the
-- Complexity in the digital world is beyond our control, yet computing
becomes ever more central to business and society
-- Dynamic complex systems inevitably
become even more complex. But why? Turns out that's a deep
-- Dynamic elements in a system that adapt
to other dynamic elements create positive feedback loops and complex
Out of control
complexity is out of control, it takes control
The need for
-- both life and computing use encapsulation to limit
unwanted dynamic interactions
-- Information processing, complexity, encapsulation and
the evolution toward multicellularity
-- Cells process
information in order to survive and thrive. How comparable are
their capabilities to computers?
Background Issues of Emergence and Evolution
-- Complex systems inevitably evolve
multiple levels of complexity which are difficult to understand, and
even more difficult to predict
Scale and Emergence
-- It took more than a dozen intermediate
of emergence to evolve multicellular life, and they all
still play a role in everyday living systems.
-- The evolution of computing systems is
far shorter than the evolution of life, but now the two are merging!
tradeoff between the
richness of possible interactions and the number of elements required
in a system for emergence to generate new surprises.
Examples of Emergence
familiar examples in nature: hurricanes, flocks of birds, and sand dunes
evolution of multicellular systems
-- From "training wheels"
full-blown multicellular life
Evolution, co-evolution and
-- No matter how hard we try to engineer
complex computing systems, they stubbornly insist upon evolving.
Contact: sburbeck at mindspring.com
Last revised 6/6/2012