Publications by Colleges and Departments (MSU - Bozeman)
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Item 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.Item 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, JohnWe 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.Item Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline(MyJove Corporation, 2021-12) Moseley, Robert C.; Campione, Sophia; Cummins, Bree; Motta, Francis; Haase, Steven B.Developing gene regulatory network models is a major challenge in systems biology. Several computational tools and pipelines have been developed to tackle this challenge, including the newly developed Inherent Dynamics Pipeline. The Inherent Dynamics Pipeline consists of several previously published tools that work synergistically and are connected in a linear fashion, where the output of one tool is then used as input for the following tool. As with most computational techniques, each step of the Inherent Dynamics Pipeline requires the user to make choices about parameters that don’t have a precise biological definition. These choices can substantially impact gene regulatory network models produced by the analysis. For this reason, the ability to visualize and explore the consequences of various parameter choices at each step can help increase confidence in the choices and the results.The Inherent Dynamics Visualizer is a comprehensive visualization package that streamlines the process of evaluating parameter choices through an interactive interface within a web browser. The user can separately examine the output of each step of the pipeline, make intuitive changes based on visual information, and benefit from the automatic production of necessary input files for the Inherent Dynamics Pipeline. The Inherent Dynamics Visualizer provides an unparalleled level of access to a highly intricate tool for the discovery of gene regulatory networks from time series transcriptomic data.Item Using extremal events to characterize noisy time series(2020-02) Berry, Eric; Cummins, Bree; Nerem, Robert R.; Smith, Lauren M.; Haase, Steven B.; Gedeon, TomasExperimental time series provide an informative window into the underlying dynamical system, and the timing of the extrema of a time series (or its derivative) contains information about its structure. However, the time series often contain significant measurement errors. We describe a method for characterizing a time series for any assumed level of measurement error 𝜀 by a sequence of intervals, each of which is guaranteed to contain an extremum for any function that 𝜀-approximates the time series. Based on the merge tree of a continuous function, we define a new object called the normalized branch decomposition, which allows us to compute intervals for any level 𝜀. We show that there is a well-defined total order on these intervals for a single time series, and that it is naturally extended to a partial order across a collection of time series comprising a dataset. We use the order of the extracted intervals in two applications. First, the partial order describing a single dataset can be used to pattern match against switching model output (Cummins et al. in SIAM J Appl Dyn Syst 17(2):1589–1616, 2018), which allows the rejection of a network model. Second, the comparison between graph distances of the partial orders of different datasets can be used to quantify similarity between biological replicates.