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Abstract The DNA-binding protein from starved cells (Dps) plays a crucial role in maintaining bacterial cell viability during periods of stress. Dps is a nucleoid-associated protein that interacts with DNA to create biomolecular condensates in live bacteria. Purified Dps protein can also rapidly form large complexes when combined with DNA in vitro. However, the mechanism that allows these complexes to nucleate on DNA remains unclear. Here, we examine how DNA topology influences the formation of Dps–DNA complexes. We find that DNA supercoils offer the most preferred template for the nucleation of condensed Dps structures. More generally, bridging contacts between different regions of DNA can facilitate the nucleation of condensed Dps structures. In contrast, Dps shows little affinity for stretched linear DNA before it is relaxed. Once DNA is condensed, Dps forms a stable complex that can form inter-strand contacts with nearby DNA, even without free Dps present in solution. Taken together, our results establish the important role played by bridging contacts between DNA strands in nucleating and stabilizing Dps complexes.
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Online Decision-Making for Scalable Autonomous Systems
We present a general formal model called MODIA that can tackle a central challenge for autonomous vehicles (AVs), namely the ability to interact with an unspecified, large number of world entities. In MODIA, a collection of possible decision-problems (DPs), known a priori, are instantiated online and executed as decision-components (DCs), unknown a priori. To combine their individual action recommendations of the DCs into a single action, we propose the lexicographic executor action function (LEAF) mechanism. We analyze the complexity of MODIA and establish LEAF’s relation to regret minimization. Finally, we implement MODIA and LEAF using collections of partially observable Markov decision process (POMDP) DPs, and use them for complex AV intersection decision-making. We evaluate the approach in six scenarios within an industry-standard vehicle simulator, and present its use on an AV prototype.
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- Award ID(s):
- 1724101
- PAR ID:
- 10063272
- Date Published:
- Journal Name:
- International Joint Conference on Artificial Intelligence
- Page Range / eLocation ID:
- 4768 to 4774
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.24963/ijcai.2017/664
