MOLECULAR COMPUTING REFERENCES
“Research on bacterial signal transduction is shifting from a focus on individual genes and proteins in vitro to the study of
whole systems in vivo. Each component is now regarded as a node, the essential character of which can only be fully
appreciated in terms of its connections to other nodes. In this context, an individual E. coli cell is a network with about
108 nodes composed of the products of about 103 different genes. Analyses of the structure–function relationships
involved in paradigmatic signaling pathways such as Lac, Che, or PhoQ/P have revealed crucial elements of molecular
logic. The next task is to understand how these elements are connected to form a dynamic, adaptive cell.
How is information converted into knowledge, and how is knowledge sorted, evaluated and combined to guide action,
morphogenesis and growth?”
Melinda D. Baker and Jeffry B. Stock (2007) Signal Transduction: Networks and Integrated Circuits in Bacterial
Cognition Current Biology Vol 17 No 23, R1021-R1024.
Christopher M.Waters and Bonnie L. Bassler (2005) Quorum Sensing: Cell-toCell Communication in Bacteria. Annual Review of Cell and Developmental
Biology. Vol. 21: 319-346
Nadell CD, Bucci V, Drescher K, Levin SA, Bassler BL, Xavier JB. (2013) Cutting through the
complexity of cell collectives. Proc R Soc B 280: 20122770. http://dx.doi.org/10.1098/rspb.2012.2770
Rutherford ST, Bassler BL. (2012) Bacterial quorum sensing: its role in virulence and
possibilities for its control. Cold Spring Harb Perspect Med. 2 pii: a012427.
Bassler BL. (2010) Small cells--big future. Mol Biol Cell. 21: 3786-3787.
Mehta P, Goyal S, Long T, Bassler BL, Wingreen NS. (2009) Information processing and
signal integration in bacterial quorum sensing. Mol Syst Biol. 5: 325.
Ng WL, Bassler BL. (2009) Bacterial quorum-sensing network architectures. Annu Rev
Genet. 43: 197-222.
Alexei Kurakin, “Self-Organization vs. Watchmaker: Stochastic Dynamics of
Cellular Organization” Biological Chemistry, 2005, 386: 247–254; p. 250.
“Defying the ideas of design and clockwork determinism, a leitmotiv of the latest experimental
research are the ubiquitous observations of self-organization and stochasticity that appears to emerge as
general principles underlying the dynamics and organization of life at all scales. Stochastic molecular
motors, stochastic enzymes, stochastic self-organization of cytoskeleton structures, sub-cellular and
sub-nuclear compartments, stochastic self-organization of macromolecular complexes mediating
transcription, DNA repair and chromatin structure/function, stochastic gene expression and stochastic
cellular responses are poorly compatible with the familiar notions of design, programs, instructions and
codes, and their systematic appearance is a call for active efforts to loosen the grip of the conventional
mechanistic models and concepts in a search for an alternative and more adequate description of life
systems.” (3) Kurakin, above
“In installment five, I began exploring an alternative view of life as an emergent phenomenon within
an overall framework of physical emergence that has been developed by investigators in condensedmatter physics over the past half century.” Barham
”No one has discussed the implications of the just-in-time self-organization of cellular structures with
greater emphasis, eloquence, and profundity than Alexei Kurakin.”
L.Landweber, L.Kari. The evolution of cellular computing: nature's solution to a
computational problem. Biosystems 52(1999)
L.Kari, L.F.Landweber. Computational power of gene rearrangement. Proc. DNA
Computing 5, DIMACS Series, 54(2000)
L.Kari, J.Kari, L.Landweber. Reversible molecular computation in ciliates. In Jewels
are Forever, Springer-Verlag (1999)
Mark J. Schnitzer Biological computation: Amazing algorithms Nature 416, 683-683
doi:10.1038/416683a
W
hen can a physical system be said to perform a computation? In the broadest sense, every physical
system performs a computation by realizing a solution to the dynamic equations that govern its physical
behaviour. However, physical computing is more interesting in the narrower domain in which a set of
physical variables represents the values of another set of mathematical ones. A physical variable might be a
voltage within a digital computer, the height of a stack of poker chips, or a chemical concentration in a
biological cell. These variables are then dynamically transformed by physical processes, in a way that
represents algorithmic manipulation of the mathematical variables. Mark J. Schnitzer
Ramiz Daniel, Jacob R. Rubens, Rahul Sarpeshkar & Timothy K. Lu (15 May 2013)
Synthetic analog computation in living cells Nature 497, 619-623
doi:10.1038/nature12148
Yaakov Benenson, Tamar Paz-Elizur, Rivka Adar, Ehud Keinan, Zvi Livneh &
+ et al. (22 November 2001) Programmable and autonomous computing machine
made of biomolecules Nature 414, 430-434 doi:10.1038/35106533
Lila Kari, Grzegorz Rozenberg (October 2008). "The Many Facets of
Natural Computing". Communications of the ACM 51: pp.72–83.
Leandro Nunes de Castro (March 2007). "Fundamentals of Natural
Computing: An Overview". Physics of Life Reviews 4: pp.1–36.
based on template guided recombination – this is state transition
table of an automata. However, while classical automata are
deterministic (or indeterministic in a weak sense of choice between
known states) biological systems are indeterministic in a strong sense,
that is their transition can be to states which are not known in advance
(new states for the system). As this kind of transition does not happen
very often, such indeterminism leads to development. It is based on the
fact that living organisms are open systems, and environment is an
unlimited source of inputs.
Nakagawa, H., Sakamoto, K., Sakakibara, Y. Development of an in vivo
computer based on Escherichia Coli. In LNCS 3892, pages 203-212,
Springer, 2006:
“Other approaches to cellular computing include developing an in vivo
programmable and autonomous finite-state automaton with E. coli,[45]
and designing and constructing in vivo cellular logic gates and genetic
circuits that harness the cell's existing biochemical processes (see for
example [46][47]).”
“Our
fundamental
idea
to
develop
a
programmable
and
autonomous finite-state automata on E. coli is that we first encode
an input string into one plasmid, encode state-transition functions
into the other plasmid, and introduce those two plasmids into an
E. coli cell by electroporation. Second, we execute a protein-synthesis
process in E. coli combined with four-base codon techniques to
simulate a computation (accepting) process of finite automata, which
has been proposed for in vitro translation-based computations in [8].
This approach enables us to develop a programmable in vivo
computer by simply replacing a plasmid encoding a state-transition
function with others. Further, our in vivo finite automata are
autonomous because the protein-synthesis process is autonomously
executed in the living E. coli cell. We show some successful
experiments to run an in vivo finite-state automaton on E. coli.”
Zabet NR, Hone ANW, Chu DF Design principles of transcriptional logic
circuits. In Artificial Life XII Proceedings of the Twelfth International
Conference on the Synthesis and Simulation of Living Systems, pages
186-193. MIT Press, August 2010.
Zabet NR Towards Modular, Scalable and Optimal Design of Transcriptional
Logic Systems. 2010.
At a minimum, one must distinguish between “self-assembly” (or “self-ordering”),
which may be described in “downhill” (exergonic) terms, and “self-organization,”
which cannot be adequately so described. On this important topic, see:
Julianne D. Halley and David A. Winkler, “Consistent Concepts of Self-Organization and
Self-Assembly,” Complexity, 2008, 14(2): 10–17.
Shapiro, J. A. (2011). Evolution: A View from the 21st Century. Upper Saddle River,
New Jersey, FT Press Science. ISBN 978-0132780933.
Living cells and organisms are cognitive (sentient) entities that act and interact
purposefully to ensure survival, growth, and proliferation. They possess
corresponding sensory, communication, information processing, and decisionmaking capabilities. (p. 143)
One of the most profound lessons from the past six decades of molecular cell biology is
that all aspects of cell functioning and cellular biochemistry are subject to regulation. (p.69)
Without an elaborate sensory apparatus to pick up signals about chemicals in the
environment (nutrients, poisons, signals emitted by other cells) or to keep track of
intracellular events (DNA replication, organelle growth, oxidative damage), a cell’s
opportunity to proliferate or contribute to whole-organism development would be
severely restricted. Life requires cognition at all levels [40, 41].” Shapiro p. 7
“The selected cases just described are examples where molecular biology has
identified specific components of cell sensing, information transfer, and decisionmaking processes. In other words, we have numerous precise molecular descriptions
of cell cognition, which range all the way from bacterial nutrition to mammalian cell
biology and development. The cognitive, informatic view of how living cells operate
and utilize their genomes is radically different from the genetic determinism
perspective articulated most succinctly, in the last century, by Francis Crick’s famous
“Central Dogma of Molecular Biology.” “ p. 24
Bray, D. (2009). Wetware: A Computer in Every Living Cell New Haven, CT,
Yale University Press. ISBN 978-0300141733.
LUCA CARDELLI and GIANLUIGI ZAVATTARO Turing
universality of the Biochemical Ground Form, Mathematical
Structures in Computer Science / Volume 20 / Special Issue 01 / February
2010, pp 45-73
Zavattaro’s research was partially funded by ‘Progetto Strategico’ CompReNDe: Compositional and executable
Representations of Nano Devices.
BEN-JACOB & ROGLIC
Roglic, Darko in Information and Computation
SUPER-RECURSIVE
FEATURES
OF
EVOLUTIONARY
PROCESSES
AND
THE
MODELS
COMPUTATIONAL EVOLUTION
Ben-Jacob, E. (1998) Bacterial wisdom, Godel’s theorem and creative genomic webs. Physica A 248, 57-76
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Dynamic Environment Phil. Trans. R. Soc. Lond. A, 361:1283-1312
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Trends in Microbiology, Vol 12/8 pp 366-372
FOR
Ben Jacob, E. and Shapira, Y. (2005) Meaning-Based Natural Intelligence Vs. Information Based Artificial
Intelligence. Cradle of Creativity
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359 ; 495-524,
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Journal B