Develop scalable methods for identifying guided assembly protocols

Develop more robust and efficient numerical methods for identifying guided assembly protocols for larger networks than those tractable with the proposed Markov Chain Monte Carlo approach.

Background

The paper formulates guided assembly as a search over rooted assembly trees, whose leaves partition the target network's vertices and whose internal nodes specify successive assembly steps. Because the number of possible assembly trees grows super-exponentially with network size, exhaustive enumeration is infeasible.

The authors propose a Metropolis–Hastings-inspired Markov Chain Monte Carlo method that successfully identifies protocols for small networks, such as the C1-q complex, but explicitly state that it is not sufficiently robust or optimized for large networks. The unresolved problem is therefore to design improved computational methods capable of finding assembly protocols at larger scales.

References

While this method is able to correctly identify design protocols for small networks (like the C1-q complex), it is not robust enough nor fully optimized to identify design protocols for large networks. We leave the development of improved methods as future work.

— Design Principles for Reproducible Networks  (2609.03852 - Kolk et al., 3 Sep 2026) in Section 'Numerical Search for Assembly Protocols', subsection 'Space of Possible Assembly Trees'