Q&A: Systems biology
© BioMed Central Ltd 2009
Published: 26 January 2009
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© BioMed Central Ltd 2009
Published: 26 January 2009
Systems biology is the study of complex gene networks, protein networks, metabolic networks and so on. The goal is to understand the design principles of living systems.
Stas Shvartsman at Princeton tells a story that provides a good answer to this question. He likens biology's current status to that of planetary astronomy in the pre-Keplerian era. For millennia people had watched planets wander through the nighttime sky. They named them, gave them symbols, and charted their complicated comings and goings. This era of descriptive planetary astronomy culminated in Tycho Brahe's careful quantitative studies of planetary motion at the end of the 16th century. At this point planetary motion had been described but not understood.
Then came Johannes Kepler, who came up with simple theories (elliptical heliocentric orbits; equal areas in equal times) that empirically accounted for Brahe's data. Fifty years later, Newton's law of universal gravitation provided a further abstraction and simplification, with Kepler's laws following as simple consequences. At that point one could argue that the motions of the planets were understood.
Systems biology begins with complex biological phenomena and aims to provide a simpler and more abstract framework that explains why these events occur the way they do. Systems biology can be carried out in a 'Keplerian' fashion – look for correlations and empirical relationships that account for data – but the ultimate hope is to arrive at a 'Newtonian' understanding of the simple principles that give rise to the complicated behaviors of complex biological systems.
Note that Kepler postulated other less-enduring mathematical models of planetary dynamics. His Mysterium Cosmographicum showed that if you nest spheres and Platonic polyhedra in the right order (sphere-octahedron-sphere-icosahedron-sphere-dodecahedron-sphere-tetrahedron-sphere-cube-sphere), the sizes of the spheres correspond to the relative sizes of the first six planets' orbits. This simple, abstract way of accounting for empirical data was probably just a happy coincidence. Happy coincidences are a potential danger in systems biology as well.
In a limited sense, yes. Some 'emerging properties', as discussed below, disappear when you reduce a system to its individual components.
However, systems biology stands to gain a lot from reductionism, and in this sense systems biology is anything but the antithesis of reductionism. Just as you can build up to an understanding of complex digital circuits by studying individual electronic components, then modular logic gates, and then higher-order combinations of gates, one may well be able to achieve an understanding of complex biological systems by studying proteins and genes, then motifs (see below), and then higher-order combinations of motifs.
Systems of two proteins or genes can do things that individual proteins/genes cannot. Systems of ten proteins or genes can do things that systems of two proteins/genes cannot. Those things that become possible once a system reaches some level of complexity are termed emergent properties.
Three proteins connected in a simple negative-feedback loop (A → B → C -| A) can function as an oscillator; two proteins (A → B-|A)can not. Two proteins connected in a simple negative-feedback loop can convert constant inputs into pulsatile outputs; a one-protein loop (A -| A) cannot. So pulse generation emerges at the level of a two-protein system and oscillations emerge at the level of a three-protein system.
Biological networks are often depicted graphically: for example, you could draw a circle for protein A, a circle for protein B, and a line between them if A regulates B or vice versa. The circles are the nodes in the graph of the A/B system. Nodes can represent genes, proteins, protein complexes, individual states of a protein, and so on.
A line connecting two nodes is an edge. The edge can be directed: for example, if A regulates B, we write an arrow – a directed edge – from A to B, whereas if B regulates A we write an arrow from B to A. Or the edge can be undirected; for example, it represents a physical interaction between A and B.
As defined by Uri Alon, a motif is a statistically over-represented subgraph of a graphical representation of a network. Motifs include things like negative feedback loops, positive feedback loops, and feed-forward systems.
No. They are completely different. In a positive-feedback system, A activates B and B turns around to activate A. A transitory stimulus that activates A could lock the system into a self-perpetuating state where both A and B are active. In this way, the positive-feedback loop can act like a toggle switch or a flip-flop. A positive-feedback loop behaves much like a double-negative feedback loop, where A and B mutually inhibit each other. That system can act like a toggle switch too, except that it toggles between A on/B off and A off/B on states, rather than between A off/B off and A on/B on states. Good examples of this type of system include the famous lambda phage lysis/lysogeny toggle switch, and the CDK1/Cdc25/Wee1 mitotic trigger.
In a feed-forward system, A impinges upon C directly, but A also regulates B, which regulates C. A feed-forward system can be either 'coherent' or 'incoherent', depending upon whether the route through B does the same thing to C as the direct route does. There is no feedback – A affects C, but C does not affect A – and the system cannot function as a toggle switch. A good example of feed-forward regulation is the activation of the protein kinase Akt by the lipid second messanger PIP3 (PIP3 binds Akt, which promotes Akt activation, and PIP3 also stimulates the kinase PDK1, which phosphorylates Akt and further contributes to Akt activation). Since both routes contribute to Akt activation, this is an example of coherent feed-forward regulation. Uri Alon's classic analysis of motifs in Escherichia coli gene regulation identified numerous coherent feed-forward circuits in that system.
Some level of comfort in doing simple algebra and calculus is a must. Beyond that, probably the most useful math is nonlinear dynamics. The Strogatz textbook mentioned below is a great introduction to nonlinear dynamics.
Systems biologists often model biological processes with ordinary differential equations (ODEs), but the fact is that almost none of them can be solved exactly. (The one that can be solved exactly describes exponential approach to a steady state, and it's something every biologist should work out at some point in his or her training.) Most often, systems biologists solve their ODEs numerically, often with canned software packages like Matlab or Mathematica.
Ideally, a model should not only reproduce known biology and predict unknown biology, it should also be 'robust' in important respects.
Robustness is important to systems biologists because of the attractiveness of the idea that a biological system must function reliably in the face of myriad uncertainties. Maybe robustness, more than efficiency or speed, is what evolution must optimize to create successful biological systems.
Modeling can provide some insight into the robustness of particular networks and circuits. Just as a biological system must be robust with respect to insults the system is likely to encounter, a successful model should also be robust with respect to parameter choice. If a model 'works', but only for a precisely chosen set of parameters, the system it depicts may be too finicky to be biologically useful, or to have been 'found' in evolution.
ODE models assume that each dynamical species in the model – each protein, protein complex, RNA, or whatever – is present in large numbers. This is sometimes true in biological systems. For example, regulatory proteins are often present at concentrations of 10 to 1,000 nM. For a four picoliter eukaryotic cell, this corresponds to 24,000 to 2,400,000 molecules per cell. This is probably large enough to warrant ODE modeling. However, genes and some mRNAs are present at concentrations of one or two molecules per cell. At such low numbers, each individual transcriptional event or mRNA degradation event becomes a big deal, and the appropriate type of modeling is stochastic modeling.
Sometimes systems are too complicated, or have too many unknown parameters to warrant ODE modeling. In these cases, Boolean models and probabilistic Bayesian models can be particularly useful.
Sometimes it is important to see how dynamical behaviors propagate through space, in which case either partial differential equation (PDE) models or stochastic reaction/diffusion models may be just the ticket.
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