Mathematical Sciences

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Mathematical research at MSU is focused primarily on related topics in pure and applied mathematics. Research programs complement each other and are often applied to problems in science and engineering. Research in statistics encompasses a broad range of theoretical and applied topics. Because the statisticians are actively engaged in interdisciplinary work, much of the statistical research is directed toward practical problems. Mathematics education faculty are active in both qualitative and quantitative experimental research areas. These include teacher preparation, coaching and mentoring for in-service teachers, online learning and curriculum development.

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Author: search.filters.author.Deckard, Anastasia

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    Experimental guidance for discovering genetic networks through hypothesis reduction on time series
    (Public Library of Science, 2022-10) Cummins, Breschine; Motta, Francis C.; Moseley, Robert C.; Deckard, Anastasia; Campione, Sophia; Gedeon, Tomáš; Mischaikow, Konstantin; Haase, Steven B.
    Large programs of dynamic gene expression, like cell cyles and circadian rhythms, are controlled by a relatively small “core” network of transcription factors and post-translational modifiers, working in concerted mutual regulation. Recent work suggests that system-independent, quantitative features of the dynamics of gene expression can be used to identify core regulators. We introduce an approach of iterative network hypothesis reduction from time-series data in which increasingly complex features of the dynamic expression of individual, pairs, and entire collections of genes are used to infer functional network models that can produce the observed transcriptional program. The culmination of our work is a computational pipeline, Iterative Network Hypothesis Reduction from Temporal Dynamics (Inherent dynamics pipeline), that provides a priority listing of targets for genetic perturbation to experimentally infer network structure. We demonstrate the capability of this integrated computational pipeline on synthetic and yeast cell-cycle data.
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    Highly-automated, high-throughput replication of yeast-based logic circuit design assessments
    (Oxford University Press, 2022-02) Goldman, Robert P; Moseley, Robert; Roehner, Nicholas; Cummins, Breschine; Vrana, Justin D; Clowers, Katie J; Bryce, Daniel; Beal, Jacob; DeHaven, Matthew; Nowak, Joshua; Higa, Trissha; Biggers, Vanessa; Lee, Peter; Hunt, Jeremy P.; Mosqueda, Lorraine; Haase, Steven B.; Weston, Mark; Zheng, George; Deckard, Anastasia; Gopaulakrishnan, Shweta; Stubbs, Joseph F.; Gaffney, Niall I.; Vaughn, Matthew W.; Maheshri, Narendra; Mikhalev, Ekaterina; Bartley, Bryan; Markeloff, Richard; Mitchell, Tom; Nguyen, Tramy; Sumorok, Daniel; Walczak, Nicholas; Myers, Chris; Zundel, Zach; Hatch, Benjamin; Scholz, James; Colonna-Romano, John
    We describe an experimental campaign that replicated the performance assessment of logic gates engineered into cells of Saccharomyces cerevisiae by Gander et al. Our experimental campaign used a novel high-throughput experimentation framework developed under Defense Advanced Research Projects Agency’s Synergistic Discovery and Design program: a remote robotic lab at Strateos executed a parameterized experimental protocol. Using this protocol and robotic execution, we generated two orders of magnitude more flow cytometry data than the original experiments. We discuss our results, which largely, but not completely, agree with the original report and make some remarks about lessons learned.
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