• Computing optimal factories in metabolic networks with negative regulation

      Krieger, S.; Kececioglu, J.; Department of Computer Science, University of Arizona (Oxford Academic, 2022)
      MOTIVATION: A factory in a metabolic network specifies how to produce target molecules from source compounds through biochemical reactions, properly accounting for reaction stoichiometry to conserve or not deplete intermediate metabolites. While finding factories is a fundamental problem in systems biology, available methods do not consider the number of reactions used, nor address negative regulation. METHODS: We introduce the new problem of finding optimal factories that use the fewest reactions, for the first time incorporating both first- and second-order negative regulation. We model this problem with directed hypergraphs, prove it is NP-complete, solve it via mixed-integer linear programming, and accommodate second-order negative regulation by an iterative approach that generates next-best factories. RESULTS: This optimization-based approach is remarkably fast in practice, typically finding optimal factories in a few seconds, even for metabolic networks involving tens of thousands of reactions and metabolites, as demonstrated through comprehensive experiments across all instances from standard reaction databases. AVAILABILITY AND IMPLEMENTATION: Source code for an implementation of our new method for optimal factories with negative regulation in a new tool called Odinn, together with all datasets, is available free for non-commercial use at http://odinn.cs.arizona.edu. © The Author(s) 2022. Published by Oxford University Press.
    • Development and evaluation of the See Me Smoke-Free multi-behavioral mHealth app for women smokers

      Gordon, Judith; Armin, Julie; Hingle, Melanie D.; Giacobbi, Peter; Cunningham, James K.; Johnson, Thienne; Abbate, Kristopher; Howe, Carol L.; Roe, Denise J.; University of Arizona; et al. (Oxford Academic, 2017-06)
      Background: Women face particular challenges when quitting smoking, especially those with weight concerns. A multi-behavioral smoking cessation intervention addressing these concerns and incorporating guided imagery may assist women to engage in healthy lifestyle behaviors. An mHealth app can easily disseminate such an intervention. Purpose: The goals of this pilot study were to develop and test the feasibility and potential of the See Me Smoke-Free™ mHealth app to address smoking, diet and physical activity among women smokers. Methods: We used pragmatic, direct-to-consumer methods to develop and test program content, functionality, and the user interface, and conduct a pre-/post-test, 90-day pilot study. Results: We enrolled 151 participants. Attrition was 52%, leaving 73 participants. At 90 days, 47% of participants reported 7-day abstinence, and significant increases in physical activity and fruit consumption. Conclusions: Recruitment methods worked well, but similar to other mHealth studies, we experienced high attrition. This study suggests that a guided imagery mHealth app has the potential to address multiple behaviors. Future research should consider different methods to improve retention and assess efficacy.
    • Genome assembly of the JD17 soybean provides a new reference genome for comparative genomics

      Yi, X.; Liu, J.; Chen, S.; Wu, H.; Liu, M.; Xu, Q.; Lei, L.; Lee, S.; Zhang, B.; Kudrna, D.; et al. (Oxford Academic, 2022)
      Cultivated soybean (Glycine max) is an important source for protein and oil. Many elite cultivars with different traits have been developed for different conditions. Each soybean strain has its own genetic diversity, and the availability of more high-quality soybean genomes can enhance comparative genomic analysis for identifying genetic underpinnings for its unique traits. In this study, we constructed a high-quality de novo assembly of an elite soybean cultivar Jidou 17 (JD17) with chromosome contiguity and high accuracy. We annotated 52,840 gene models and reconstructed 74,054 high-quality full-length transcripts. We performed a genome-wide comparative analysis based on the reference genome of JD17 with 3 published soybeans (WM82, ZH13, and W05), which identified 5 large inversions and 2 large translocations specific to JD17, 20,984-46,912 presence-absence variations spanning 13.1-46.9 Mb in size. A total of 1,695,741-3,664,629 SNPs and 446,689-800,489 Indels were identified and annotated between JD17 and them. Symbiotic nitrogen fixation genes were identified and the effects from these variants were further evaluated. It was found that the coding sequences of 9 nitrogen fixation-related genes were greatly affected. The high-quality genome assembly of JD17 can serve as a valuable reference for soybean functional genomics research. © The Author(s) 2022. Published by Oxford University Press on behalf of Genetics Society of America.