Tensegrity, Dynamic Networks, and Complex Systems Biology: Emergence in Structural and Information Networks Within Living Cells

Abstract

The genomic revolution has led to the systematic characterization of all the genes of the genome and the proteins they encode. But we still do not fully understand how many cell behaviors are controlled, because many important biological properties of cells emerge at the whole-system level from the collective action of thousands of molecular components, which is orchestrated through specific regulatory interactions. In this chapter we present two distinct approaches based on the concept of molecular networks to understand two fundamental system properties of living cells: their ability to maintain their shape and mechanical stability, and their ability to express stable, discrete cell phenotypes and switch between them. We first describe how structural networks built using the principles of tensegrity architecture and computational models that incorporate these features can predict many of the complex mechanical behaviors that are exhibited by living mammalian cells. We then discuss how genome-wide biochemical signaling networks produce ``attractor'' states that may represent the stable cell phenotypes, such as growth, differentiation, and apoptosis, and which explain how cells can make discrete cell fate decisions in the presence of multiple conflicting signals. These network-based concepts help to bridge the apparent gap between emergent system features characteristic of living cells and the underlying molecular processes.