Dynamic modeling and analysis of cancer cellular network motifs

With the advent of high-throughput biology, we now routinely scan cells and organisms at practically all levels, from genome to protein, metabolism, signaling and other cellular functions. This methodology allowed biological studies to move from a reductionist approach, such as isolation of specific pathways and mechanisms, to a more integrative approach, where biological systems are seen as a network of interconnected components that provide specific outputs and functions in response to stimuli. Recent literature on biological networks demonstrates two important concepts that we will consider in this review: (i) cellular pathways are highly interconnected and should not be studied separately, but as a network; (ii) simple, recurrent feedback motifs within the network can produce very specific functions that favor their modular use. The first theme differs from the traditional approach in biology because it provides a framework (i.e., the network view) in which large datasets are analyzed with an unbiased view. The second theme (feedback motifs) shows the importance of locally analyzing the dynamic properties of biological networks in order to better understand their functionality. We will review these themes with examples from cell signaling networks, gene regulatory networks and metabolic pathways. The deregulation of cellular networks (metabolism, signaling etc.) is involved in cancer, but the size of the networks and resulting non-linear behavior do not allow for intuitive reasoning. In that context, we argue that the qualitative classification of the 'building blocs' of biological networks (i.e. the motifs) in terms of dynamics and functionality will be critical to improve our understanding of cancer biology and rationalize the wealth of information from high-throughput experiments. From the examples highlighted in this review, it is clear that dynamic feedback motifs can be used to provide a unified view of various cellular processes involved in cancer and this will be critical for future research on personalized and predictive cancer therapies.