tion and information fluxes alongside the existing thermodynamic and statistical principles of physical science. It is at this interface of biology with physics and information theory that the fundamental principles governing living matter are likely to be discovered.

Biological complexity is built on specific interactions between molecules, and these interactions are linked to each other and held in balance through complex networks. These networks underpin the regulation and signaling that govern intracellular function and multicellular behavior all the way to the development of the organism, and their multilayered complexity makes studying the systems challenging.

On the smallest level, interactions are mediated by molecular forces (hydrogen bonding, electrostatics, hydrophobicity), which form the physicochemical basis of molecular recognition between polynucleotide and polypeptide structures. Although we understand the basic laws governing these forces, using these laws to reliably predict specific, complex intermolecular interactions and tracking the effect of the intermolecular interaction to the behavior of a whole organism remain a challenge.

To deal with some of these challenges, ab initio approaches are now frequently complemented by data-driven bioinformatics that analyze and compare empirical data to untangle the interactions between numerous related interacting pairs of molecules. This combination of approaches weaves together ideas and methods from computer science, statistics, physics, and biology, and researchers use them to reveal the patterns (called “code” by some) underpinning the interaction (see Figure 4-1).

Bioinformatic studies provide supramolecular-level descriptions in which, instead of the basic interatomic forces, one works with interaction profiles—that is, the strengths of interactions with different possible partners that de facto define biological molecules. Indeed, characterization of such interaction profiles allows an understanding of interactions at the level of the whole organism and bioinformatic approaches are now extensively used for identifying regulatory targets of transcription factors—proteins that control gene expression. This approach is delivering ingenious quantitative tools that enable us to extract enhanced knowledge from existing biological data in new ways.

The story does not end with describing the specific interactions, however. As mentioned above, the interactions between biomolecules are the building blocks of molecular and genetic networks, and the networks must also be studied and understood. For example, a covalent modification of a specific protein via the enzymatic activity of another (e.g., phosphorylation catalyzed by a kinase) might trigger enzymatic activity of the target protein or cause its re-localization within the cell. In genetic networks, regulation of gene expression and protein synthesis are controlled through the action of transcription factors and recently discov-

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