Systems biology beyond networks: generating order from disorder through self-organization

Abstract

Erwin Schrödinger pointed out in his 1944 book "What is Life" that one defining attribute of biological systems seems to be their tendency to generate order from disorder defying the second law of thermodynamics. Almost parallel to his findings, the science of complex systems was founded based on observations on physical and chemical systems showing that inanimate matter can exhibit complex structures although their interacting parts follow simple rules. This is explained by a process known as self-organization and it is now widely accepted that multi-cellular biological organisms are themselves self-organizing complex systems in which the relations among their parts are dynamic, contextual and interdependent. In order to fully understand such systems, we are required to computationally and mathematically model their interactions as promulgated in systems biology. The preponderance of network models in the practice of systems biology inspired by a reductionist, bottom-up view, seems to neglect, however, the importance of bidirectional interactions across spatial scales and domains. This approach introduces a shortcoming that may hinder research on emergent phenomena such as those of tissue morphogenesis and related diseases, such as cancer. Another hindrance of current modeling attempts is that those systems operate in a parameter space that seems far removed from biological reality. This misperception calls for more tightly coupled mathematical and computational models to biological experiments by creating and designing biological model systems that are accessible to a wide range of experimental manipulations. In this way, a comprehensive understanding of fundamental processes in normal development or of aberrations, like cancer, will be generated.

Figure 1

Example of self-organization of inanimate matter: left panel shows an overview of the rock formation found at the Giant’s Causeway in County Antrim, Northern Ireland. The right panel shows a close-up photographed downwards onto the rock formation showing a regular, polygonal structure that emerged from volcanic activity.

Figure 2

Whole mounts of MCF10A cells (left panel) and MCF10A cells and RMF grown for three weeks within a matrix made of 1 mg col-I-50% Matrigel. Note that ducts only formed in the presence of fibroblasts. Scale bar 200 m (taken form [KM08]).

Figure 3

MCF10A + RMF co-culture in floating gel at the 10th day in culture. Whole mount picrosirius red staining; images were taken under polarized (left) and non-polarized light (right). (A) Acini in the lower layer of the gel (arrow). (B) Acini loosing the spherical symmetry (gray arrow) and interacting with neighboring structures through modified collagen fibers (white arrows). (C) Elongating structures interacting via modified collagen fibers (white arrow) and fusing with each other into tubular structures (black arrow). Notice the absence of modified collagen fibers nearby the non-elongating acini in lower left corner (gray arrow). (D) Bundle of thick collagen fibers formed between two structures along their elongation axis. (E) Collagen bundle (white arrow) formed along the elongation axis of a duct. (F) Tubular structure interacting with neighboring structure though collagen fibers (white arrow) and forming branching sprout. Scale bars 20 m (taken form [DM10]).

Figure 4

Schematic representation of contracted floating gel; i: periphery zone, ii: intermediate area, iii: central area. Circular and rectangular shapes denote the distribution of acinar and ductal structures respectively (taken from [DM10]).

Figure 5

The systems biology cycle for knowledge discovery shows how biological experiments are being tightly coupled with mathematical models through data analysis, modeling, simulation and validation. Only by completing a full cycle, actual knowledge is generated and our understanding of a biological system is furthered.