RobustCircuit Project Z1

Project Z1 is designed to tackle two core challenges resulting from the collaborative project design of RobustCircuit:

• Molecular, subcellular and cellular dynamics should be of consistent data types and quality to be quantitatively comparable between projects.

• When manipulation of imprecise parameters without collateral effects is challenging in biological experiments, computational modeling is needed to allow to specifically modulate imprecise parameters and provide testable hypotheses for the generation of robust outcomes.

Question: How do imprecise molecular, subcellular and cellular dynamics and properties enable higher level robustness in neural circuit assemblies?

Hypothesis: Imprecise processes provide pools of variation to ensure flexible and robust neural circuit assemblies through competition and selection

Project Summary

The goal of RobustCircuit is to understand common principles underlying the requirements of imprecise processes at lower scales (from molecules to cells) to yield robust outcomes at higher scales (from cells to behavior) in neural circuit assembly.  Live imaging is the principle means to obtain quantitative data on imprecise and robust processes at the levels of molecular, subcellular and cellular dynamics.  Seven of the eight projects utilize intravital and ex vivo live imaging to obtain statistically powerful data on imprecisions, including noise, in subcellular dynamics.  Based on such quantitative data, computational modeling allows one to make predictions for the roles imprecisions play in creating robust systems.  The Z1 project is therefore designed to tackle two core challenges resulting from our collaborative project design:

Challenge 1: Molecular, subcellular and cellular dynamics should be of the same data type and quality to be quantitatively comparable between projects.  Solution: Objective 1 is devised to provide a common and standardized approach to 4D live imaging acquisition and comparative tracking method adaptations and analyses.

Challenge 2: The manipulation of imprecise parameters without collateral effects is challenging in biological experiments.  In contrast to the biological experiment, computational modeling allows to specifically modulate imprecise parameters and provide testable hypotheses for the generation of robust outcomes.  Solution: Objective 2 is devised to provide modeling approaches that can be adapted from an established stochastic modeling framework previously tested by RobustCircuit PIs for the generation of robust synapse formation based on stochastic subcellular dynamics.

References

1. Kiral, F.R., Dutta, S.B., Linneweber, G.A., Poppa, C., Duch, C., von Kleist, M., Hassan, B.A., and Hiesinger, P.R. (2021). Brain Connectivity inversely scales with developmental temperature in Drosophila, Cell Rep. 37(12):110145.
2. Kiral, F.R., Linneweber, G.A., Mathejczyk, T., Georgiev, S.V., Wernet, M.F., Hassan, B.A., von Kleist, M., Hiesinger, P.R. (2020). Autophagy-dependent filopodial kinetics restrict synaptic partner choice during Drosophila brain wiring. Nat Commun 11(1):1325. doi: 10.1038/s41467-020-14781-4.
3. Ziesche, R.F., Arlt, T., Finegan, D.P., Heenan, T. M. M., Tengattini, A., Baum, D., et al., (2020). 4D imaging of lithium-batteries using correlative neutron and X-ray tomography with a virtual unrolling technique. Nat Commun 11, 777.
4. Ozel, M.N., Kulkarni, A., Hasan, A., Brummer, J., Moldenhauer, M., Daumann, I.M., Wolfenberg, H., Dercksen, V.J., Kiral, F.R., Weiser, M., Prohaska, S., von Kleist, M.*, Hiesinger, P.R.* Serial synapse formation through filopodial competition for synaptic seeding factors, Developmental Cell 50(4):447-461.e8. * co-corresponding authors
5. Baum, D., Weaver, J.C., Zlotnikov, I., Knötel, D., Tomholt, L., Dean, M.N. (2019) High-throughput segmentation of tiled biological structures using Random-Walk Distance Transforms. Integrative and Comparative Biology, 59: 1700-1712
6. von Kleist M., Schütte C., Zhang W. (2018), Statistical analysis of the first passage path ensemble of jump processes. Journal of Statistical Physics, 170, 809-43