Articles|Systems Biology

Big Picture Biology

By Brona McVittie

“Biology is traditionally a very descriptive science,” says Rainer Spang (University of Regensburg, Germany). While “physics is predictive and uses mathematics to deduce new laws of nature,” Rainer admits that the same has never been true of biology. Biologists observe life and try to make sense of what they see, whereas physicists - dealing with energy, forces and miniscule particles - have much more heavily relied on theory and the predictive power of mathematics. Over the past few centuries technological advance has allowed biologists to probe deeper and deeper into the basis of biological life.

By the early 1950s, physicists and chemists had helped biologists to understand the most fundamental of all biological molecules: DNA. This revolutionary discovery identified the double-helix as the basis for life in all known biological species. And the theory of how DNA replicates itself, passing from generation to generation, was proven. The stuff of genes was no longer a mystery and the field of molecular biology exploded with a frenzy of activity.

The existence of genes had been acknowledged many years prior, but without an understanding of their physical nature. Now biologists had a tangible entity to explore, with the understanding that each gene corresponds to a DNA sequence encoding a functional molecule with a specific job inside the cell. And a few decades later, thanks to the development of sequencing technology (see Sequences within Cells), the Human Genome Project described the full complement of genes common to our species.

Understanding what a gene does has traditionally involved its ablation or knock-out. Thus, the function of genes has largely been inferred from the consequences of their absence. For example, the 'eyeless' gene - first identified in the fruit fly, Drosophila melanogaster - got its name following the observation that without it, flies cannot develop eyes. While this method is necessarily simplistic, the main drawback is that many genes do not simply have one job, but can play a role in more than one cellular system. So knocking them out can have multiple consequences at the level of the cell, creating a messy picture.

Systems biology aims to aggregate the results of this reductionist approach, encompassing a trend in the biosciences towards the study of whole biological networks and systems. It is necessarily multidimensional: combining our knowledge of life at the level of molecules, cells and the whole organism; and multidisciplinary: availing of techniques from physics, chemistry, mathematics, computing, engineering science and biology. However, while this moves the focus towards the bigger picture perspective, scientific investigation still requires clearly defined parameters.

“I think it's important to define the system in systems biology,” comments Henning Walczak (Imperial College London, UK). “I mean, rather than covering the entire ocean at a depth of one or two feet, we need to explore a clearly defined tract within that ocean in full depth.” The ApoNET project seeks to understand the relationships between two cellular systems, “TNF: the gene-induction inflammatory system; and TRAIL: the apoptotic system,” Henning explains. “From this core, we'd like to expand into other systems. But we need to do it this way, rather than trying to cover the entire ocean.”

“And we want to be able to predict things in biology,” adds Rainer, whose work involves developing computer algorithms and statistical tools that help biologists like Henning make sense of the data from their experiments on cells (see The Dominion of Data). Predictive power in biology holds huge promise for medicine, where a knowledge of mutations that cause tumours could allow us to make useful predictions regarding the best way to treat them. Rainer believes that within 5-10 years we will move much closer to affordable 'personalised medicine', which treats each individual patient differently based on their genetics and biology.

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