With the advent of Next Generation Sequencing and the Hi-C experiment, high quality genome-wide contact data are becoming increasingly available. These data represents an empirical measure of how a genome interacts inside the nucleus. Genome conformation is of particular interest as it has been experimentally shown to be a driving force for many genomic functions from regulation to transcription. Thus, the Three Dimensional-Genome Reconstruction Problem (3D-GRP) seeks to take Hi-C data and produces a complete physical genome structure as it appears in the nucleus for genomic analysis. We propose and develop a novel method to solve the Chromosome and Genome Reconstruction problem based on the Bat Algorithm (BA) which we called ChromeBat. We demonstrate on real Hi-C data that ChromeBat is capable of state-of-the-art performance. Additionally, the domain of Genome Reconstruction has been criticized for lacking algorithmic diversity, and the bio-inspired nature of ChromeBat contributes algorithmic diversity to the problem domain. ChromeBat is an effective approach for solving the Genome Reconstruction Problem.

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Background: With the advent of Next Generation Sequencing and the Hi-C experiment, high quality genome-wide contact data is becoming increasingly available. This data represents an empirical measure of how a genome interacts inside the nucleus. Genome conformation is of particular interest as it has been experimentally shown to be a driving force for many genomic functions from regulation to transcription. Thus, the Three-Dimensional Genome Reconstruction Problem seeks to take Hi-C data and produce the complete physical genome structure as it appears in the nucleus for genomic analysis. Results: We propose and develop a novel method to solve the Chromosome and Genome Reconstruction problem based on the Bat Algorithm which we called ChromeBat.We demonstrate on real Hi-C data that ChromeBat is capable of state of the art performance. Additionally, the domain of Genome Reconstruction has been criticized for lacking algorithmic diversity, and the bio-inspired nature of ChromeBat contributes algorithmic diversity to the problem domain. Conclusions: ChromeBat is an effective approach at solving the Genome Reconstruction Problem. The source code and usage guide can be found here: https://github.com/OluwadareLab/ChromeBat.

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How does information regarding an adversary's intentions affect optimal system design? This paper addresses this question in the context of graphical coordination games where an adversary can indirectly influence the behavior of agents by modifying their payoffs. We study a situation in which a system operator must select a graph topology in anticipation of the action of an unknown adversary. The designer can limit her worst-case losses by playing a security strategy, effectively planning for an adversary which intends maximum harm. However, fine-grained information regarding the adversary's intention may help the system operator to finetune the defenses and obtain better system performance. In a simple model of adversarial behavior, this paper asks how much a system operator can gain by fine-tuning a defense for known adversarial intent. We find that if the adversary is weak, a security strategy is approximately optimal for any adversary type; however, for moderately-strong adversaries, security strategies are far from optimal.