Since our approach to tissue printing is supposed to work without a supporting scaffold the coheseiveness of the printed medium is crucial. Biotin binds strongly to Streptavidin as well as Avidin. Thus we are not worried about the individual bonding itself but rather about the interconnectedness of the individual cells and binding proteins.
Before diving into the details we introduce a few abbreviations to make reading and writing more easy and efficient. Since it is yet to decide weather Avidin or biotin will be attached to the cells or to the networking proteins (NP) we use the following nomenclature:

• B = biotinylated recombinant protein in solution
• A = recombinant Avidin (biotin binding) in solution
• CB = cell-bound biotinylated protein (BAP receptor)
• CA = cell-bound Avidin (biotin binding; eMA or scAvidin receptor)

In the worst case all CBS we dispense from the printhead would get immediately occupied with otherwise loose NP, which would leave us with lots of individual NP-coated cells. The other extreme case would be that that the cell hardly finds any protein to bond to. Again resulting in individual cells that are unconnected.

So we assumed that the problem is most likely a typical question of polymerization. While the bonds in our case are not covalent we decided it should be safe to assume they were, since they are very strong with a dissociation constant of 10^-15 M. So our most promising discoveries after browsing pertinent literature were the Carothers equation as well as the Flory-Stockmayer theory, which is a generalization of the former.

[describe Flory-Stockmayer theory]

[describe our assumtions to make it fit]

[describe results and limitations]

After we found that the results provided are not sufficient to optimize our process we were talking to some Professors in the field of polymer chemistry, biochemistry and biological modelling. [Most Answers were discouraging because ...] Ultimately we decided to pursue the advice of Dr. Hasenauer, who suggested to simulate the problem to understand it better. First with strong assumptions, later on reducing them stepwise to get more and more accurate results.

So our first approach assumed: every cell has x CBS every NP has y PBS there are 100 cells and z NP every pair of CBS and PBS have the same probability to bind. Regardless of spacial distance, obstruction, [some more fancy chemical words for this?] at some point either all protein or all cell binding sites will have bond. In a first experiment we fixed x to 30, compromising between a way higher number of binding sites, which could “consume” proteins, and a much lower amount of cells that could actually surround it due to spacial limitations. Furthermore we fixed y to 8 to get a first impression and run experiments with logarithmically varying z. To analyze the result we analyzed what we call a cell-graph. The cell-graph is obtained by drawing a node for each cell and connecting each node with an edge to the nodes of the respective other cells that this cell is connected to via a protein. On this graph we evaluated a couple of metrics from graph theory, resulting in the plots in

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Literaturreferenz

Literaturreferenz^[1]

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Textformatierung

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Links

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Bilder

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Introduction

Design

Experiments

Proof of concept

Demonstrate

Discussion

References

1. ↑ Schmidt, T. G., & Skerra, A. (2007). The Strep-tag system for one-step purification and high-affinity detection or capturing of proteins. Nature protocols, 2(6), 1528-1535.