Self-organization
in biology
T
hrough self-organization, a system
becomes ordered in space and/or
time, often leading to emergent
properties that qualitatively differ
from those of its individual units.
deducTions from reducTions
The reductionist approach — systematically
dismantling complex systems to examine
individual components — has been
successful for the sciences over the past few
centuries, from the isolation of the elements
in chemistry, the discovery of atomic and
subatomic particles in physics, to the
purification and study of proteins, DNA and
RNA in biology.
Although this reductionist approach in
biology will continue, there is increasing interest in determining the properties of systems of interacting biomolecules. How do
networks of proteins and genes integrate
and respond to signals? How do dynamic
organelle structures, such as the mitotic
spindle, form? What controls growth and
division? How does the genome create an
organism? Self-organization is central in
these processes1, at various sizes (Fig. 2).
Self-organized systems differ from selfassembled ones as they rely on a continuous
input of energy for maintenance and are far
from thermal equilibrium. Classical
thermodynamics — successful in the
physical sciences — does not apply. Instead
of self-assembling into the lowest energy
state, such as a crystal, energy-dissipating
components self-organize into highly
complex structures through which there is
a constant flux of energy and material.
Established theories, such as those of
dynamical systems and control (from
physics and engineering) can provide a
basis for understanding self-organization in
biology; however, the unique properties of
biological systems — their multiple components and energy-dissipation mechanisms,
and wide ranges in time and space — pose
practical and intellectual challenges.
Systems biology is seen as a highly
productive approach to solving complex
biological problems such as self-organization.
Although definitions vary, most agree that
systems biology is the application of
mathematical and theoretical approaches to
understand how the interaction of
metabolites, proteins, RNA, genes and cells
Fig. 1 | Cell
patterns
Purified
cell-division
proteins can
form spiral
waves.
can lead to often counterintuitive and
unexpected systems-level behaviour. Early
examples of systems biology include Turing’s
reaction-diffusion theory of morphogenesis
in which spatial patterns emerge from
simple molecular rules (Fig. 1), and the
voltage-dependent opening of ion channels
leading to an all-or-nothing change in the
electrical response of nerve cells.
A central concept in systems theory is
that positive feedback — mediated by
chemical, electrical or mechanical signals —
can lead to instabilities and switching, and
in turn to spatial and/or temporal patterns
or oscillations. Recent models have been
developed for the cell cycle, bacterial
chemotaxis2, cell differentiation in response
to growth factors, and the beating of the
heart and of flagella — all self-organized
systems3.
A
key question in biological self-organization is how small molecules
control size at the whole-cell level. recently, scientists at the
max Planck institute of molecular cell Biology and genetics have
discovered a new mechanism: motor proteins — tiny machines that use
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Research Perspectives of the Max Planck Society | 2010+
Technological advances
Systems biology has also come to mean the
modelling of systems at the whole-organism
level, informed by new ‘omics’ technologies.
It is now possible to measure protein levels
in a small number of cells using mass spectrometry, to quantify gene expression using
microarrays and next-generation sequencing,
and to use genome-wide RNA-interference
screens to accelerate the discovery of genes
involved in cellular processes such as membrane trafficking and motility4,5.
The huge data sets generated mean that
effective analysis lags behind; but important
discoveries show that general principles will
be uncovered. For example, within networks
of transcription factors, recurring elements
(or motifs) occur more often than random,
indicating an underlying, self-organizing
structure6.
Improvements in sample preparation
for electron cryomicroscopy, combined
with the rapid increase in the number of
solved protein structures, has enabled the
construction of atom-scale models of the
complex organelles, such as the axoneme7,
and the leading edge of a crawling cells.
This holds promise for modelling an entire cell at the atomic level — remarkable
considering that a bacterium contains 1012
atoms, not counting water.
Thirty years ago, Sulston and Horvitz
painstakingly reconstructed the entire
cellular development of a nematode worm;
what previously took a decade can now be
visualized in real time and applied to other
species, thanks to new microscope techniques
and image-processing algorithms8.
As an alternative to modelling, reconstitution or synthetic biology attempts to
recreate complex cellular processes from
purified components. Examples include
motor and transport systems, membrane
aTP as fuel — walk along and pace out the lengths of microtubules. after
reaching the end, they collectively depolymerize longer microtubules
faster than shorter ones, thereby providing feedback necessary to
control length (Varga, V. et al. Cell 138, 1174–1183, 2009).
Biology and Medicine
new theoretical approaches are required to understand self-organization
in biology; theory will drive discovery as it does in the physical sciences.
a huge amount of biological data will be generated over the coming years
from technological advances, not just in dna sequencing, proteomics and
other ‘omics’ disciplines, but in imaging of cells and tissues.
understanding biological self-organization may change the way we think
about development, cell differentiation and disease pathogenesis.
Fig. 2 | Levels of self-organization in biology, ranging from
proteins, through simple and complex macromolecular systems,
to cells and whole organisms.
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fusion, protein-translation machinery,
and DNA and RNA synthesis. Recent breakthroughs in reconstituting the cell-division
machinery in bacteria9 suggest it might be
possible to produce a self-replicating protocell, and perhaps even employ selection
strategies to replicate the natural evolution
of higher-order capabilities.
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Images courtesy of A. Hyman, MPI and M. Loose, Dresden University of Technology.(Technology)
daTa and Theory challenges
Huge amounts of data are anticipated from
genomics, proteomics, other omics such as
lipidomics and metabolomics, and light
and electron microscopy. Handling and interpreting data will require major advances
in bioinformatics and bioimformatics
(image bioinformatics).
Theory will be needed to make sense of
it all. Biological processes are usually analysed by reverse engineering: measuring the
individual components to define high-level
organizational rules. This is analogous, however, to reconstructing a computer program
by measuring the electric signals in individual transistors. New theoretical tools will be
needed to bridge multiple scales — from
single molecules to complexes, organelles,
cells, tissues and organisms.
Understanding biological self-organizing
principles might change the way we think
of cell differentiation and disease. Programming pluripotent stem cells and reprogramming cancer stem cells might be best
achieved using a systems-level approach.
For example, it might be possible to perturb
and destabilize cancer-regulatory networks
selectively by transient pharmacological
intervention. Other complex metabolic
disorders such as diabetes might also
benefit from a strategy in which a ‘magic
bullet’ is replaced by a gradual guidance of
metabolic networks back to a healthy
stasis.
In the next 10 years, we will be able to
‘zoom in and out’ of an organism and its
cells to see their arrangement and how they
got there during development, changing
the way we think about cell differentiation
and disease.
➟ For references see pages 38 and 39
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2010+ | Research Perspectives of the Max Planck Society
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