Large-scale topology, stability and biology of gene networks

• Experimental innovations in cell biology have provided a huge amount of genomescale data sets, settling the stage for understanding organisms on a system level. Recently, complex networks have been widely adopted and serve as a unifying language for widely different systems, including social, technological and biological systems. Still- in most biological cases-the number of interacting units vastly exceeds the number of measurements, hence large-scale models must still be very simple or non-specific. In this thesis we explore the limits of a linear (Lasso) network model on a genomic-scale for the Saccharomyces cerevisae organism and the limits of some analysis tools from the research field of complex networks. The former study (Paper I and Paper III) mainly regards validation issues, but also stipulate novel statistical system hypotheses, e.g., the system is significantly more stable than random, but still flexible to target stimuli. The latter study (Paper II) explores different heuristics in the search for communities (i.e., dense sub-graphs) within large complex networks and how the concept relates to functional modules.

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