Systems biology aims to understand how biological systems function through the interactions of their components. It uses both top-down and bottom-up approaches, with top-down identifying molecular interaction networks from large-scale omics data and bottom-up examining mechanisms arising from known component interactions. While high-throughput data has driven systems biology, the field also requires hypothesis-driven testing using computational models informed by both large and small-scale experimental data. Integrating different types of genome-wide and molecular-level data can provide insights into biological processes and diseases.

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decoding life: systems biology.
he molecular biosciences to come a long way towards characterizing the molecular constituents of life. Yet, the challenge for biology overall is to understand how organisms
namic interactions, systems biology addresses the missing links between molecules and physiology. Top-down systems biology identifies molecular interaction networks on
ed in genome-wide ‘omics’ studies. Bottom-up systems biology examines the mechanisms through which functional properties arise in the interactions of known components.
biology and discuss limitations of the top-down and bottom-up approaches, which, despite these limitations, have already led to the discovery of mechanisms and principles
ganism or the determination of the crystal structures of all of its proteins might constitute the biology of that system, and the processing of the data can require lots of
much to understanding how the interactions of the individual components lead to function and, thus, it does not constitute systems biology. A complete systems biological
on of an
ganisms made the falsification or verification of hypotheses in vivo virtually impossible. Functional genomics now enables the experimental analysis of complete sets of
anisms, however, average expression levels are determined over various cell types. Moreover, the number of proteins in most multicellular organisms is so large that their
te.
lenges for systems biology
ular (sub-) systems frequently take the form of top-down systems biology to identify correlations between the various variables of the systems. These are then formulated in
This rarely (if at all) leads to the formulation of relations between properties in terms of molecular mechanisms. Although the emphasis formally lies on inductive discovery
ological Systems from Systematic Measurements
ematic data. It is impossible to study a biological system as a whole without them. On one hand, the ability to make genome-wide (or proteome-wide or transcriptome-
he single greatest force driving the rise of systems biology. On the other hand, systems biology is not only about genome-scale measurements; it is about a philosophy
ental design and analysis (Ideker et al. 2001). Therefore, systems biology does not apply to genome-scale studies that are focused solely on discovery. Rather, it is a
s to perform predictive, hypothesis-driven science (Figure 2). Using genome-scale data to test hypotheses is nontrivial because it requires that the hypotheses
becomes possible with a genome-scale model of the system. Of course, systematic technologies are not the only means of measuring biological systems. It is critical that
validated by, detailed single-molecule measurements and literature.
tanding goal of human genetics. Despite several success stories [e.g., identification of the genetic basis of cystic fibrosis (Rommens et al. 1989), Tay-Sachs (Harding
, many diseases with quantifiably substantial genetic components continue to elude detailed genetic explanations (Culverhouse et al. 2002, Moore 2003). For this
easing role in this area through the computational integration of multiple types of genome-wide measurements (Adler et al. 2006, Ergün et al. 2007, Franke et al. 2006,
al. 2007, Oti et al. 2006, Tomlins et al. 2005, Yao et al. 2006).
network of combinatorial interactions among genes, collectively referred to as genetic interactions. Recently, several systems biology studies in yeast, fly, worm, and
des in our ability to map this genetic interaction network and its impact on function.
et al. 2008, Ulitsky et al. 2008b) have attempted to integrate genetic interaction networks with networks of physical interactions between proteins. As an example,
oods of a protein pair operating either within the same protein complex or between functionally related complexes on the basis of the strength of its genetic and
propriate pattern of physical and genetic interactions from known protein complexes curated in databases. Protein pairs with a strong genetic but weak physical
ween two functionally related complexes. An agglomerative clustering procedure was then used to merge the protein pairs into increasingly larger complexes and to
undles of many strong genetic interactions. Figure 4a shows three example complexes enriched for aggravating genetic interactions (i.e., synthetic lethality).
quitination. Protein-protein interactions are enriched among the proteins within each of the three complexes; in contrast, genetic interactions are enriched both within
ATION OF CELL FATE
c expression and regulatory control of hundreds of genes in response to both internal and external stimuli. To dissect the complex interplay among these regulatory
ave begun to combine classical experimental techniques with emerging high-throughput experimental techniques such as screens for RNAi, genome-wide mRNA
munoprecipitation (ChIP), and mass spectrometry–based proteomics (Chen et al. 2008, Kidder et al. 2008, Spooncer et al. 2008). How these vast amounts of data can be

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