National e-Science Centre
Edinburgh, UK
16-17 October 2008
The Informational Model and Immunology
during the 1950’s and 1960’s
Andrea Grignolio, Ph.D.
University of Bologna
Acume2 project and CIG
andrea.grignolio@unibo.it
ACUME 2 - European Thematic Network
Why Informational Models in Immunology ?
1. Because information models played a role in Clonal Selection Theory which, in turn, modernized
immunology
1-A How Antibodies
Are Made
1-B Immunological
Memory
1-C Absence of
Auto-antibodies
1-D The cell as a Place
of Ab Production
1-E Paradigm Shifting:
Neo-Lamarckian → Darwinian
2. Because of the influential role played by immunologists who used this notion
Frank Macfarlane Burnet
(1899-1985)
•Nobel laureate
1960
• Tolerance
40’s
• Self/Not-self
40’s
Niels Kay Jerne
David W. Talmage
(1911-1994)
(1919 - )
Nobel laureate 1984
• Pre-Selection Th.
1955
• Cellular Memory
1955
• Co-autor CST
1956
• Hemol. Plaque Tech 1963
• Overlapping reactiv. 1959
• Idyotipic Network Th 1974
Germline T. Coauthor 70’s
• Clonal Selection Th 1957
All the 3 proponents of the CST used informational models to explain their discovery!
The technical use of information
Anti-aircraft guns accuracy ,
automata and self-organized machines
Cybernetics
Norbert Wiener (1948)
p log p
Wire Telephone Transmissions
Shannon proposed a quantitative measure of the
complexity of linear code and in its mathematical
formula is expressed with analogy of entropy.
Wiener’s mathematical formula is similar to one
of Shannon, the main difference beeing that
Wiener used the concept of Negative Entropy
Information Theory
Claude Shannon (1948)
The technical use of information
Essays on the Use of Information Theory in Biology,
University of Illinois Press, 1953
Henry Quastler
Watson and Crick’s use of information
“… In a long molecule, many different
permutations are possible, and it therefore seems
likely that the precise sequence of the bases is the
code which carries the genetical information”
Antibody ?
The same problem was posed by
immunologists:
How about antibody synthesis ?
Burnet: memory, self and the Informational Model
“ … how a man who had a single attack of yellow fever
during his youth, could retain yellow-fever-antibodies in
his blood after 50 years?”
The problem of cellular memory suggested a model of
cellular communication …
as well as the self/notself discrimination (Immunol.
Identity)
Burnet’s First Mention in 1954
“In the fields that involve specific activities of proteins and
enzymes [ ....] there is an increasing tendency to describe
them in terms of replicating patterns which carry information
or instructions from one part of a cell or organism to another.
It is in line with the spirit of the times to believe that we shall
soon see the conscious development of a «communications
theory» of the living organism along these or analogous lines”
(Burnet, “How Antibodies Are Made?”, Scientific American, 191, 5, Nov., 1954, 74-78)
Frank Macfarlane Burnet (1899-1985)
Nobel laureate
1960
1956
Enzyme, Antigen and Virus.
A Study of Macromolecular Pattern in Action (Oxford U.P.)
Chapter V
§ 1. Information theory in biology
This monograph was originally conceived as an attempt to
develop something analogous to a communication theory
that would be applicable to the concepts of general biology.
However, it has not been found possible to make any serious
use of the already extensively developed concepts of
information in the strict sense. [...] The only extended account
of such an approach that I have been able to find is the
symposium edited by Quastler (1953).
• Generation of diversity (G.O.D.)
Frank Macfarlane Burnet
1956
Enzyme, Antigen and Virus.
A Study of Macromolecular Pattern in Action
Growing Antibody
Nucleic acids during
antibody formation
Nucleic acids needed to
create antibody diversity
The diagram suggests the increased versatility of a binary code. If the relationships of
A's and B's to immediately adjoining symbols are included, many more potentially
meaningful arrangements are available than if the binary symbols were in a simple
linear order.
Informational models in Burnet after CST
The lymphocyte —as a carrier of biological "information" , 1960
Immunological "information" , 1960, Nobel lecture
The fact mat around 4 ammo acid residues may be responsible for each
antibody pattern suggests use of the well-worn analogy between the 20
biological amino acids and the letters of the alphabet. If we adopt this
convention we can switch to a non-biological analogue of the random
process that gives rise during embryonic life to such a huge variety of
potential patterns. We imagine a computer set to produce at random 4
letter words from a 26 letter alphabet. If 10⁷ words are asked for we should
have a 99% probability of getting at least one example of every possible 4
letter word. As an example of eight consecutive words we might find:
TRES ABCD APQR CXAB OJBD THEY XPML FACE
Now suppose we have English speakers watching the output and striking
out all the English words, in this instance THEY and FACE. In the final
collection we have theoretically all the information required to construct all
English 4 letter words. Any combination which is not present is an English
word. In the same way 1, 2 and 3 letter words could be produced and
similarly sorted out into English words which are discarded and nonEnglish which remain. Our computer has another characteristic. Once the
selection has been completed all the remaining "words" are stored in the
memory and when any combination is asked for it can be produced in
unlimited numbers but only if it is in the memory. No English will be
produced.
Frank Macfarlane Burnet
Informational models in Jerne
For
the at
size
of the set
of possible
sentences
in do
a
Looking
languages,
we find
that all of
them make
language,
Chomskyof uses
thea word
“open-end-edness”,
with a vocabulary
roughly
hundred
thousand words,
and
I now These
think that
“open-ended”
description
or less.
vocabulary
sizesis the
are best
a hundred-fold
also
of the
of the
the size
antibody
repertoire.
smaller
than“completeness”
the estimates of
of the
antibody
Some
grammatical
rules
would
seem
to
be
required.
is
repertoire available to our immune system. But ifIt we
harder,
find an region
analogythat
to semantics:
does
considerhowever,
that thetovariable
characterizes
an
the
immune
system
distinguish
between
meaningful
and
antibody molecule is made up of two polypeptides, […] we
meaningless
antigens?
Perhaps
the distinction
may find a more
reasonable
analogy
between between
language
“self’
andimmune
“non-self’
is a valid
example.
It would
seem,
at
and the
system,
namely
by regarding
the
variable
first
sight,
that the
immune
response
a sentence
region
of a given
antibody
molecule
not astoa word
but as a
presented
by
an
invading
protein
molecule
is
merely
to
sentence or a phrase. The immense repertoire of the
select,
[…]
a suitable
image
of this antigenic
immune
system
then mirror
becomes
notofa part
vocabulary
of words,
sentence
but a lexicon of sentences which is capable of responding
to any sentence expressed by the multitude of antigens
which the immune system may encounter.
At this point, I shall make a quotation from Noam
Chomsky concerning linguistics: “Grammar is a device
that specifies the infinite set of well-formed sentences and
assigns to each of these one or more structural
descriptions. Perhaps we should call such a device a
generative grammar … which should, ideally, contain a
central syntactic component …, a phonological
component and a semantic component.” That is the end
of my quotation.
Jerne N.K., The Generative Grammar of the Immune System, Science, 229, Sept., 1984, 1057-59
Information Theory in Talmage
The number of families of different globulins that may be formed is much lager
than the number of different globulins that make up the information system. The
26 letters of our alphabet make up several hundred thousand English words. As
few as 500 different globulins may form 1011 different families containing 5
globulins and 1020 different families containing 10 globulins. In general the
number of different families of a given size (F) which may be formed from N
different globulins is given by the formula
N!
Number of families = ——————
(N - F)! F!
On the basis of the amount of information contained, 500 different globulins would
seem quite adequate to recognize or distinguish between almost all of the
different antigenic determinants that have been or could be synthesized. […]
Specificity may be represented mathematically by the statement that the
probability of two randomly selected families having a common member is low.
Universality implies that the probability of a randomly selected antigen having a
family size of zero is low. […] Specificity is represented by the probability of
cross-reaction between two randomly selected families which is given by the
formula
F2
Probability of cross-reaction = ———
N
Universality is indicated by the probability of no reaction with a randomly selected
antigenic determinant. This was calculated from the formula
F
Probability of no reaction = ( 1 — ————) N
N
Perhaps the major biological value of immunological specificity is the ability to
distinguish between self and not-self.
Talmage D.W., Cohen E.P., Antibody Production and Specificity, in Max Samter (ed. by),
Immunological Disease, Little Brown and Company, Boston, 1965, pp. 87-99
Instructive Theories of Antibody Formation
“A component hypothesis of all instructive theories of
antibody formation is that the antigens convey structural
information, like a template, on which to construct the
complementarily fitting antibody” (Schaffner 1993: 14)
Felix Haurowitz
Linus Pauling
The Antibody Repertoire Paradox
Repertoire
of antigens
Bacteria
Repertoire
of antibodies
Helminths
Flow of information
(to mold antibodies)
Protozoa
Viruses
Toxic molecules
Template Model of Enzyme Synthesis
Direction of adjoining enzyme’s building blocks, i.e.
amino acids
STRUCK
ADAPTIVE ENZYME
(labile surface)
COINS
NEW NUTRITIONAL
NEW ENZYME MOLECULE
MOLECULE (environ.
(able to digest the new molecule)
stimulus)
Throughout the 1940’s the metaphors of enzymes as templates, patterns, moulds,
struck-and-coin, lock-and-key, and phonographic negatives abounded in scientific
literature.
Burnet’s talk 1955
R.A. Fisher
Watson and Crick
•Sequential arrangement of bases/messages
•Few allele combinations generate
immune cell discrimination
•Permutational mechanism (code)
(gene → antibody → cell)
•Immaterial message (?)
THE CELL AS INFORMATION STORAGE
The approach of the physicists
Erwin Schrödinger
George Gamow
Gertrud and Henry
Quastler
Burnet
onH.,
Gamow
Erwin
Schrödinger,
Quastler
1953 What’s Life?, 1944
“It
often
been
asked
howdetermines
this tiny Content
speck
material,
of the
fertilized
egg, could
contain
Rnahascarries
the
code
which
the of
sequence
innucleus
whichCell”
the
amino
acid residues
are added.
“Chapter
III, §
3 “The
Informational
of a Bacterial
an elaborate
involving
future
development
of the
organism
This
is a vitalcode-script
feature and
one thatallisthe
very
difficult
to visualize.
Gamow
and […]
others (1954) pointed
Indeed,
of acids
atomstheinnumber
such a of
structure
not beis very much
large to
produce
an almost
put
that the
withnumber
20 amino
possibleneed
sequences
larger
than could
be
unlimited
number
of possible
For illustration,
think of the Morse code. The two
coded by the
sequence
of 4 basesarrangements.
in a polynucleotide
chain”
different signs of dot and dash in well-ordered groups of not more than four allow thirty different
G. Gamow, N. Metropolis, “Numerology of Peptide Chains”, Science, 120, 779, 1954
specifications…”
Information Theory in Biology, University of Illinois Press, Urbana, 1953, 251-262
THE CELL AS INFORMATION STORAGE
The approach of the neurophysiologists
Paul Alfred Weiss
John W. Pringle
John Zachary Young
(1898-1989)
(1912-1982)
(1907-1997)
Austrian developmental neurobiologist
British zoologist
British zoologist
(PhD in 1922 movements of butterfly wings),
who studied the anatomical
who discovered and studied
developed a theory of morphogenesis, → paved
mechanisms
in
the giant nerve fibres in
the
insect flight. He also did
squid. He also did research
research neurophysiology
on
way
for
the
concept
of
positional
information. He also studied mathematical
involved
models of pattern formation in embryology.
In
neurobiology
Weiss
discovered
the
→ MACROMOLECULAR PATTERN
learning,
demonstrating that memory
INDEPENDENT RESEARCH ON
fasciculation of the fasciculation of peripheral
nerves (axonal flow)
octopus
THE CELL AS A MEMORY STORAGE
OF EVOLUTIVE INFORMATION
stores are located in the
brain.
1956
Enzyme, Antigen and Virus.
A Study of Macromolecular Pattern in Action
… In another direction interesting quantitative analogies can be
drawn between the distribution of various numbers of some
twenty types of amino acid residues in a protein molecule and
the distribution of twenty-six letters in a paragraph of English.
In a certain sense it is reasonable to think of the biological
function of the protein as broadly analogous to the meaning of
a paragraph.
One feels that there may be a noteworthy generalization
awaiting the organic chemist who can show that the standard
24 amino acids [sic] represent an alphabet which, by
appropriate mutual arrangements, can provide specific
complementary patterns for all the configurations that are
possible in biologically acceptable molecules…
Frank Macfarlane Burnet
3. Because, at least in one case, IMs went well beyond their classic role of rhetorical instruments
serving as an actual tool for a discovery
Physicochemical approach of Memory
1) The idea that "information"
as the persistence of antigen matter
could be transmitted absent any
material embodiment
Early
Burnet’s
conception
that
2)
The
information
has
its
antibody function is carried by its whole
function on linearity (this allows
shape (three-dimensional carrier)
permutation)
Early Burnet’s conception of openness
of antibody repertoire to antigens
(environmental) modification
3) The concept of predetermined repertoire
From a limited (to environmental instruction)
number of items could be obtained a huge
repertoire of diversity by combinations
Heuristic Role of a Model !!!
General Idea → Experimental Data → Hypothesis 1
Hypothesis 2
In/Accurate
Hypothesis 3
Theory ● ● ● Presentation / Justification /
Popularization
The Clonal Selection Model
Self antigen
↓
Death
Antibodies
Memory cell
Information models after CST
• Burnet 1954 G.O.D. Analogy (Burnet, F.M., How Antibodies Are Made? Scientific American, 1954. 191(5):
p. 74-78.
•
Burnet, F.M., Enzyme antigen and virus; a study of macromolecular pattern in action. 1956, Cambridge [Eng]: University Press. viii, 193 p.
1958 Alphabetical analogy
1959 Alphabetical analogy
1960 Alphabetical analogy, Nobel lecture
• Jerne 1960 Alphabetical analogy (<1956 “Ivar”)
1974 (?) Network theory of Immune System
1984 linguistic use Nobel lecture
• Talmage 1965 Technical use (with E.P. Cohen)
1967 Technical use
Informational models in Jerne
Having observed that the machine translates a
foreign language into English, we might say: The machine
produces English, but recognizes only Foreign. We would
realize that both English and the Foreign language are
composed of the same alphabet, and that single letters cannot
be the units that are recognized by the machine. We would
probably conclude that an important feature of the machine
would have to be an ability to recognize single foreign words,
and that the mechanism by which it functions must, in some
form or other, include the consultation of a Foreign-English
dictionary. […]
Jerne N.K., Immunological Speculations, Annual Review of Microbiology, 14, 1960,
341-358
Informational models in Jerne
Grammar is a science that is more
than 2000 years old, whereas immunology
has become a respectable part of biology
only during the past hundred years. Though
both sciences still face exasperating
problems, this lecture attempts to establish
an analogy between linguistics and
immunology, between the descriptions of
language and of the immune system…
Jerne N.K., The Generative Grammar of the Immune System, Science, 229, Sept.,
1984, 1057-59
Principles, architectures…
• Degeneracy, modularity, protocols,
robustness, noise, redundancy…
• Fundamental dynamical principles
seemingly underlying many biological
phenomena …
• Cooperation & intersection of such
principles at different levels & scales
• Examples are i) immune components
acting under specific logics of
functioning; or ii) signal transduction
systems structured following peculiar
architecture…
Degeneracy
• Different structures, same output
• Plenty of examples:
• Genetic code, different base triplets
(codons) can give the same amino acid:
AUA, AUC, AUU give always isoleucine,
UCU, UCC, UCA, UCG, AGU, AGC
always give serine
• Same cell surface receptors can bind
different ligands
Degeneracy
• Protein fold (different polypeptides can fold to
be structurally and functionally equivalent)
• Genes (functionally equivalent alleles,
duplications, paralogs, etc., all exist)
• Protein functions (overlapping binding
functions and similar catalytic specificities are
seen)
• Metabolism (multiple, parallel biosynthetic and
catabolic pathways exist)
• Cells within tissues (no individual differentiated
cell is uniquely indispensable)
Degeneracy
• Intra- and intercellular signaling (parallel and
converging pathways of various hormones, growth
factors, second messengers, etc., transmit degenerate
signals)
• Immune responses (populations of antibodies and other
antigen-recognition molecules are degenerate)
• Connectivity in neural networks (there is enormous
degeneracy in local circuitry, long-range connections,
and neural dynamics)
• Behavioral repertoires (many steps in stereotypic
feeding, mating, or other social behaviors are either
dispensable or substitutable)
• Interanimal communication (there are large and
sometimes nearly infinite numbers of ways to transmit
the same message, a situation most obvious in
language)
Degeneracy
• Degeneracy in biological networks and neural
systems is generally defined as the ability of
elements that are structurally different to
perform the same function (to yield the same
output; many-to-one logic or one-to-many
logic)
• It is opposed to redundancy: the same function
is performed by identical elements (one-to-one
logic)
Degeneracy
• Many-to-one
• One-to-many
Immune response
• Cooperation of many cell types through
soluble molecular signals (cytokines)
• T lymphocytes and antibodies are key
elements, they recognize antigens (the
bad)
T Cell Receptor (TCR)
• Environmental sensor of the T cell
• The TCR is a molecule found on the
surface of T lymphocytes (or T cells)
responsible for recognizing antigens
bound to major histocompatibility complex
(MHC) molecules of Antigen Presenting
Cells (APCs).
Immune system T Cell Receptor
• Once recognized the antigen, T cells mount an
adequate, fine tuned response
• Given this exquisitely specific response to
every stimulus, T Cell Receptor was thought to
work with a one-to-one logic: one
receptor/one antigen to bind, but…
• Evidence shows that TCR can bind many
different ligands: it is degenerate, and still
maintains perfect tailored responses against
different dangerous antigen ligands
TCR degeneracy is a necessity
Cytokine signaling
• Cells secrete cytokines in the blood stream
• Cytokines are bound and recognized by
other cells that perform specific actions
• Cytokines are pleiotropic: one cytokine
type act on different cell types
• Cytokines are redundant: many different
cytokines act on the same cell type
stimulating the same effect
• NO one-to-one logic
Immune system integrated intercellular signalling network
TGF-β, RANK Ligand, MΦ derived Chemokine
Other 7 mediators
eB,D=10
Dendritic cell eD,D=11
eB,B=17
B lymphocyte
ACTH
CXCR3
Endorphins
Other 14 mediators
eB,M=3
eB,G=3
IL-10
MIP-1α, β
TNF-α
IL-6
IL-10
TNF-α
TGF-β
IL-8/CXCL-8
CD30L
eG,B=3
CD100/Sema4D
CD-27 Ligand
IL-11
Other 8 mediators
eD,B=17
TNF-α, TGF-β, Substance P
Other 14 mediators
IL-7
IL-10
TNF-α
eD,M=5
IL-10
IL-15
IL-16
MIP-1α, β
TNF-α
eD,G=3
GM-CSF
MIP-1α, β
TGF −β
IL-12
IL-16
TGF-β
eG,D=1
IL-12
IL-13
IL-15
Other 6 mediators
eM,D=5
eM,B=9
Granulocyte
eM,M=6
Mast cell
eM,G=1
TNF-α
Tieri et al., Bioinformatics, 2005
Eotaxin/CCL11
IL-15
MIP-1α, β
Other 3 mediators
Degeneracy
• Degeneracy also appears as a strategy to
conserve the ability to deliver the correct
message even if the carrier suffers some
disturbance (see robustness)
• In human communication there are many
different ways to transmit the same message
• Very important: at the same time, since
degenerated elements are structurally
different, they can still conserve the ability of
carry different messages
Degeneracy
• A degenerated system shows a certain
degree of redundant functionality,
maintaining at the same time the capability
–due to the diversity of the elements that
compose it– of yielding different outputs
• In other words, many different elements
can affect the output in a similar way
and, together, can still have independent
effects
Degeneracy
• On the contrary, a redundant system is not able
to yield different outputs, given the identical
nature of its elements
• Thus the advantage of degeneracy relies on the
contemporaneous ability of maintaining
performances (giving similar output) &
exploiting alternatives (giving different output)
• Degeneracy is prerequisite for natural selection
because selection pressures can only operate
on dissimilar organisms
Degeneracy
• The capability of exploiting alternative
routes (beyond robustness!) is useful
when facing unpredictable
perturbations, making the system
adaptable to unforeseen changes of the
surrounding environment
Modularity
• A general definition of a module is that of a
functional unit capable of maintaining its
intrinsic properties irrespective of what it
is connected to
• To connect diverse elements together
while still achieving predictable outcomes
• The use of modular components reduces
costs and makes the building process
much easier than it otherwise would be
(…recycling…)
Multiscale integration
Hunter & Borg, Integration from proteins
to organs: the Physiome Project, Nat.
Rev. Mol. Cell. Biol. 2003
Modularity
• Modularity can be considered at diverse
scales: amino acids, proteins, protein
complexes, signalling pathways,
organelles, cells, tissue, organs can all be
considered modules, building blocks, up to
the whole organism, itself a module into a
social system
• How is important modularity in biology?
Modularity
• From an evolutionary perspective there is
a growing awareness that modularity may
facilitate evolutionary change by
encouraging the ability to rewire modules
while maintaining modular function
• Rewiring to experiment new functions
and configurations with the same set of
pieces (LEGO keeps amusing people
since 1932)
Synthetic biology
• To apply modularity principles to design new
cellular circuits in the field of synthetic biology,
a new area of research that combines science
and engineering in order to design and build
("synthesize") novel biological functions and
systems
• Synthetic biology will depend on being able to
define reusable circuits such that they can be
connected together without the individual units
loosing functional cohesion http://parts.mit.edu/
Protocols
• Relatively few rules to organize a number
of components into a number of
(meaningful) combination
• Different protocols can act at different
scales
• Rules/protocols structured in a
nested/hierarchical way
• Autonomous dynamics
• Interdependence in the outcome
Protocols
• Grammar of a language is the corpus of
practices and rules of writing,
pronunciation, syntax, morphology
• Conservative in what one does
• Liberal in what one manages (Galloway,
MIT)
• Autonomy & interdependence:
Morphology, Syntax, Semantics,
Pragmatics
Unifying perspective
• A recent and appealing concept that can
take into account and contain many of
these principles is the bow tie
• Bow tie architectures seem able to sum up
and comprise many of these properties
into a unique organizing architecture
• Bow tie in biology is the description of a
general architecture consisting in:
• a large “fan in” (many different inputs)
• a “knot” composed by a smaller number
of elements, typically for control and
elaboration processes
• and a large “fan out” of products
Fan in
Variability
Knot
Stability
Fan out
Variability
few
many
many
Bow ties in metabolic networks
• Bacterial metabolic networks clearly
represent such structure, with
• many nutrients catabolized in
• …few carriers (ATP, NADH, NADPH...)
and precursors (i.e. intermediate
metabolites of glycolysis)…
• in turn synthesized in a larger quantity of
"building blocks" (amino acids, fatty
acids, sugars…)
Transcription & translation
• The transcription and translation (‘trans’)
processes also have a bow-tie architecture
• A few universal polymerase modules
that make up the ‘knot’ of the ‘trans’
bowtie machinery function efficiently with a
universal codon usage protocol, facilitating
the fan in of a large variety of genes and
the fan out of an even larger variety of
proteins.
• Nested together, the bow ties of core
metabolism and the trans machinery
create a larger ‘metabolism bow tie’ that
produces all cellular macromolecules
• Modularity and shared protocols also
facilitate the recycling of building blocks
within the system
Bow ties in technology
• In the power grid, several different
energy sources (dynamos, solar, wind
turbines…) are used to make a universal
50-60 Hz AC common carrier, which in
turn is widely disseminated to provide
power to a large and rapidly changing
variety of uses (all kinds of electrical
equipments)
The Internet
• Internet protocols (http, TCP, IP…) are
layered bow tie structures
• Any kind of heterogeneous information
(fan in) is diced & transmitted thru the
wires by protocols (core) and then
recomposed and delivered to the user in a
myriad of different formats (fan out)
• Each layer/protocol is responsible for a
given duty, but they need to work together
The Internet hourglass
Applications
Web
FTP
Mail
News
Video
Audio
ping
napster
Everything
Transport
on protocols
IP
TCP
SCTP
UDP
ICMP
IP
Ethernet 802.11
IP on
everything
Power lines
ATM Optical
Satellite Bluetooth
Link technologies
From Hari Balakrishnan
Common acronyms: http, TCP/IP
• hyper-text transport protocol: it deals
with the content of data, it is the
semantic layer, is devoted to maintaining
the meaning of data, and so their usability.
It is assured by the protocol that
interpretates the data delivered from
the net
• The next layer is represented by the TCP, the
transport control protocol, the transport
layer, dedicated to the correct transport of the
data: it assures that the communication flow is
correctly established and then closed, so that
data arrive undamaged and complete to the
destination
• another layer is the IP, internet protocol, the
out-and-out "data movement" layer, responsible
of the actual motion of the data from the
source to the user. Its duties concern not the
content (application layer) nor the coherence
(transport layer) of the data, but just of the way
they move through places
A pervasive architecture
• Bow ties are observed not only in biology
and technology…
• Money can be thought as a common
carrier that implements a bow-tie protocol
for the exchange of varied goods and
services
Goods & services
Goods & services
Efficient management of
complexity
High variability
Less constraints
High variability
Less constraints
More constraints
Less variability
General purpose
Robust
Uncertain
Flexible
Degenerate
Specialized
Fragile
Rigid
Efficient
General purpose
Robust
Uncertain
Flexible
Degenerate
Special purpose
enzymes
General purpose
polymerases
General purpose
polymerases
Degeneracy
• Many-to-one
• One-to-many
Bow tie
• Many-few-many
Bowtie, the drawbacks
• This robust design has inherent fragilities
• In a bow-tie structure, a chief source of fragility is that the
universal common currencies responsible for robustness
can be easily hijacked by parasites or used to amplify
pathological processes
• For example, tumor survival is enhanced by hijacking and
upregulating processes that are part of normal physiological
homeostasis
• On the Internet, the same hidden mechanisms that facilitate
the transparent delivery of any digital document also enable
the propagation of spam, viruses and ‘denial of service’
attacks
• As compared with a barter system, money greatly facilitates
trade and economic growth, but it increases the risk of
fragilities in the form of theft, counterfeiting, creative
accounting and financial market collapses
T Cell Receptor signaling system
• Degeneracy, modules, nested bowties…?
• They all seem to represent fundamental
features, keys aspects of functioning of the
various signalling systems
Protein universe
Proteins transported to proteasome
Bow tie
Immunoproteasome, i.p.
Peptides cut from i.p.
pMHC complexes
Bow tie
“Degeneracy” of a single TCR
T cell plasma membrane
Bow tie
T Cell
TCR-CD3 & co-receptors
Activated signalling pathway
Transcription factors
Gene expression
Cell responses
• The ubiquity of bow-tie structures in
advanced technologies supports the
large amount of biological evidence
indicating that these structures are
universal and fundamental organizing
principles, rather than frozen accidents of
evolution
• Is this an organizational framework on
which mathematical models can be
built...?
• Evolved bow tie structures facilitate
robust biologic functions, and based on
their design, also have inherent but
predictable fragilities
• Identification of large-scale
architectures such as the bow tie could
be a great help (or a prerequisite?) for
progress in the modeling and
understanding of complex (biological)
processes