Coordinated

endothelial cell

networking,

a key component of angiogenesis,

is required to form and refine the

exquisite fractal network that emerges in the developing

ad mature lung to facilitate efficient gas exchange from birth onwards.

Real-time imaging of endothelial cells cultured within a specialized extracellular matrix (ECM)

microenvironment, designated the basement membrane, formed the biological  basis of this project.

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The angiogenetic networking behavior of lung mesenchymal cells, designated RFL-6 or MFLM-4 cells, which have the ability to transform into networking endothelial cells in response to basement membrane proteins was evaluated in contrasting conditions: with or without Prx-1, a gene that enhances networking. When Prx-1 is absent, cells formed clusters on basement membrane material. On the other hand, when Prx-1 was present, cells formed branched networks on this matrix. The network formations are also influenced by the topological geometries found within the underlying ECM. With the help of metalloproteinase, cell behaviors could either dissolve the existing ECM or regenreate and attach new structures to the matrix.

 

Thus the roles played by Prx-1 (code) and the ECM (environment) are highly influential

in creating these network morphologies by endothelial cells (components).

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This collaboration between code, environment and component has to operate on a series of scales which start with the single cells pairing, grouping and clustering, and eventually reconfiguring these clusters into larger networks which create the lung vasculature. By studying the relationship between these effectors, we can observe how inter-scalar relationships occur and move from the micro to the macro,

 

Our research is based on biological models and how they translate into computational models for biological and architectural research into conditional modeling and behavioral analysis. Cellular networking in lung tissue formation became the basis for our studies and how networking can become a model for designing responses to subtle variations in conditions like the environment. We used Generative Components (GC) to process the biological information operating via the rules and logics of a scale free network. Our intention was  to create a more flexible computational networking model that is defined by cnonditional relationships between cells and their environment by producing dynamic networking models. ​