Self-organisation in biology
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.
Through 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 self-assembled 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 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.
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 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.
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.
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 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).