Publications by Colleges and Departments (MSU - Bozeman)
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Item Coding Code: Qualitative Methods for Investigating Data Science Skills(Informa UK Limited, 2023-11) Theobold, Allison S.; Wickstrom, Megan H.; Hancock, Stacey A.Despite the elevated importance of Data Science in Statistics, there exists limited research investigating how students learn the computing concepts and skills necessary for carrying out data science tasks. Computer Science educators have investigated how students debug their own code and how students reason through foreign code. While these studies illuminate different aspects of students’ programming behavior or conceptual understanding, a method has yet to be employed that can shed light on students’ learning processes. This type of inquiry necessitates qualitative methods, which allow for a holistic description of the skills a student uses throughout the computing code they produce, the organization of these descriptions into themes, and a comparison of the emergent themes across students or across time. In this article we share how to conceptualize and carry out the qualitative coding process with students’ computing code. Drawing on the Block Model to frame our analysis, we explore two types of research questions which could be posed about students’ learning. Supplementary materials for this article are available online.Item Ribosome Abundance Control in Prokaryotes(Springer Science and Business Media LLC, 2023-10) Shea, Jacob; Davis, Lisa; Quaye, Bright; Gedeon, TomasCell growth is an essential phenotype of any unicellular organism and it crucially depends on precise control of protein synthesis. We construct a model of the feedback mechanisms that regulate abundance of ribosomes in E. coli, a prototypical prokaryotic organism. Since ribosomes are needed to produce more ribosomes, the model includes a positive feedback loop central to the control of cell growth. Our analysis of the model shows that there can be only two coexisting equilibrium states across all 23 parameters. This precludes the existence of hysteresis, suggesting that the ribosome abundance changes continuously with parameters. These states are related by a transcritical bifurcation, and we provide an analytic formula for parameters that admit either state.Item Leveraging social networks for identification of people living with HIV who are virally unsuppressed(Wolters Kluwer Health, Inc., 2023-10) Cummins, Breschine; Johnson, Kara; Schneider, John A.; Del Vicchio, Natasha; Moshiri, Niema; Wertheim, Joel O.; Goyal, Ravi; Skaathun, BrittObjectives: This study investigates primary peer-referral engagement (PRE) strategies to assess which strategy results in engaging higher numbers of people living with HIV (PLWH) who are virally unsuppressed. Design: We develop a modeling study that simulates an HIV epidemic (transmission, disease progression, and viral evolution) over 6 years using an agent-based model followed by simulating PRE strategies. We investigate two PRE strategies where referrals are based on social network strategies (SNS) or sexual partner contact tracing (SPCT). Methods: We parameterize, calibrate, and validate our study using data from Chicago on Black sexual minority men to assess these strategies for a population with high incidence and prevalence of HIV. For each strategy we calculate the number of PLWH recruited who are undiagnosed or out-of-care and the number of direct or indirect transmissions. Results: SNS and SPCT identified 256.5 (95% C.I.: [234,279]) and 15 (95% C.I.: [7,27]) PLWH, respectively. Of these, SNS identified 159 (95% C.I.: [142,177]) PLWH out-of-care and 32 (95% C.I.: [21, 43]]) PLWH undiagnosed compared to 9 (95% C.I.: [3,18]) and 2 (95% C.I.: [0,5]) for SPCT. SNS identified 15.5 (95% C.I.: [6,25]) and 7.5 (95% C.I.: [2, 11]]) indirect and direct transmission pairs, while SPCT identified 6 (95% C.I.: [0,8]) and 5 (95% C.I.: [0,8]), respectively. Conclusions: With no testing constraints, SNS is the more effective strategy to identify undiagnosed and out-of-care PLWH. Neither strategy is successful at identifying sufficient indirect or direct transmission pairs to investigate transmission networks.Item Detecting punctuated evolution in SARS-CoV-2 over the first year of the pandemic(Frontiers Media SA, 2023-02) Surya, Kevin; Gardner, Jacob D.; Organ, Chris L.The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) evolved slowly over the first year of the Coronavirus Disease 19 (COVID-19) pandemic with differential mutation rates across lineages. Here, we explore how this variation arose. Whether evolutionary change accumulated gradually within lineages or during viral lineage branching is unclear. Using phylogenetic regression models, we show that ~13% of SARS-CoV-2 genomic divergence up to May 2020 is attributable to lineage branching events (punctuated evolution). The net number of branching events along lineages predicts ~5% of the deviation from the strict molecular clock. We did not detect punctuated evolution in SARS-CoV-1, possibly due to the small sample size, and in sarbecovirus broadly, likely due to a different evolutionary process altogether. Punctuation in SARS-CoV-2 is probably neutral because most mutations were not positively selected and because the strength of the punctuational effect remained constant over time, at least until May 2020, and across continents. However, the small punctuational contribution to SARS-CoV-2 diversity is consistent with the founder effect arising from narrow transmission bottlenecks. Therefore, punctuation in SARS-CoV-2 may represent the macroevolutionary consequence (rate variation) of a microevolutionary process (transmission bottleneck).Item Lifetime alcohol consumption patterns and young-onset breast cancer by subtype among Non-Hispanic Black and White women in the Young Women’s Health History Study(Springer Nature, 2023-10) Hirko, Kelly A.; Lucas, Darek R.; Pathak, Dorothy R.; Hamilton, Ann S.; Post, Lydia M.; Ihenacho, Ugonna; Carnegie, Nicole Bohme; Houang, Richard T.; Schwartz, Kendra; Velie, Ellen M.Purpose. The role of alcohol in young-onset breast cancer (YOBC) is unclear. We examined associations between lifetime alcohol consumption and YOBC in the Young Women’s Health History Study, a population-based case–control study of breast cancer among Non-Hispanic Black and White women < 50 years of age. Methods. Breast cancer cases (n = 1,812) were diagnosed in the Metropolitan Detroit and Los Angeles County SEER registry areas, 2010–2015. Controls (n = 1,381) were identified through area-based sampling and were frequency-matched to cases by age, site, and race. Alcohol consumption and covariates were collected from in-person interviews. Weighted multivariable logistic regression was conducted to calculate adjusted odds ratios (aOR) and 95% confidence intervals (CI) for associations between alcohol consumption and YOBC overall and by subtype (Luminal A, Luminal B, HER2, or triple negative). Results. Lifetime alcohol consumption was not associated with YOBC overall or with subtypes (all ptrend ≥ 0.13). Similarly, alcohol consumption in adolescence, young and middle adulthood was not associated with YOBC (all ptrend ≥ 0.09). An inverse association with triple-negative YOBC, however, was observed for younger age at alcohol use initiation (< 18 years vs. no consumption), aOR (95% CI) = 0.62 (0.42, 0.93). No evidence of statistical interaction by race or household poverty was observed. Conclusions. Our findings suggest alcohol consumption has a different association with YOBC than postmenopausal breast cancer—lifetime consumption was not linked to increased risk and younger age at alcohol use initiation was associated with a decreased risk of triple-negative YOBC. Future studies on alcohol consumption in YOBC subtypes are warranted.Item Using physical simulations to motivate the use of differential equations in models of disease spread(Informa UK Limited, 2023-09) Arnold, Elizabeth G.; Burroughs, Elizabeth A.; Burroughs, Owen; Carlson, Mary AliceThe SIR model is a differential equations based model of the spread of an infectious disease that compartmentalises individuals in a population into one of three states: those who are susceptible to a disease (S), those who are infected and can transmit the disease to others (I), and those who have recovered from the disease and are now immune (R). This Classroom Note describes how to initiate teaching the SIR model with two concrete physical simulations to provide students with first-hand experience with some of the nuanced behaviour of how an infectious disease spreads through a closed population. One simulation physically models disease spread by the exchange of fluids, using pH to simulate infection. A second simulation incorporates randomness through the use of a probability game to keep track of the state of each individual at each time step. Both simulations invite students to ask questions about what factors influence disease spread. The concrete experience from the physical simulations enables students to make connections to the abstract mathematical representation of the SIR model and discuss the sources of stochasticity present in the spread of an infectious disease.Item Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences(The Royal Society, 2023-07) Liu, Yuxuan; McCalla, Scott G.; Schaeffer, HaydenParticle dynamics and multi-agent systems provide accurate dynamical models for studying and forecasting the behaviour of complex interacting systems. They often take the form of a high-dimensional system of differential equations parameterized by an interaction kernel that models the underlying attractive or repulsive forces between agents. We consider the problem of constructing a data-based approximation of the interacting forces directly from noisy observations of the paths of the agents in time. The learned interaction kernels are then used to predict the agents’ behaviour over a longer time interval. The approximation developed in this work uses a randomized feature algorithm and a sparse randomized feature approach. Sparsity-promoting regression provides a mechanism for pruning the randomly generated features which was observed to be beneficial when one has limited data, in particular, leading to less overfitting than other approaches. In addition, imposing sparsity reduces the kernel evaluation cost which significantly lowers the simulation cost for forecasting the multi-agent systems. Our method is applied to various examples, including first-order systems with homogeneous and heterogeneous interactions, second-order homogeneous systems, and a new sheep swarming system.Item Estimating contact network properties by integrating multiple data sources associated with infectious diseases(Wiley, 2023-07) Goyal, Ravi; Carnegie, Nicole; Slipher, Sally; Turk, Philip; Little, Susan J.; De Gruttola, VictorTo effectively mitigate the spread of communicable diseases, it is necessary to understand the interactions that enable disease transmission among individuals in a population; we refer to the set of these interactions as a contact network. The structure of the contact network can have profound effects on both the spread of infectious diseases and the effectiveness of control programs. Therefore, understanding the contact network permits more efficient use of resources. Measuring the structure of the network, however, is a challenging problem. We present a Bayesian approach to integrate multiple data sources associated with the transmission of infectious diseases to more precisely and accurately estimate important properties of the contact network. An important aspect of the approach is the use of the congruence class models for networks. We conduct simulation studies modeling pathogens resembling SARS-CoV-2 and HIV to assess the method; subsequently, we apply our approach to HIV data from the University of California San Diego Primary Infection Resource Consortium. Based on simulation studies, we demonstrate that the integration of epidemiological and viral genetic data with risk behavior survey data can lead to large decreases in mean squared error (MSE) in contact network estimates compared to estimates based strictly on risk behavior information. This decrease in MSE is present even in settings where the risk behavior surveys contain measurement error. Through these simulations, we also highlight certain settings where the approach does not improve MSE.Item Resource allocation accounts for the large variability of rate-yield phenotypes across bacterial strains(eLife Sciences Publications, Ltd, 2023-05) Baldazzi, Valentina; Ropers, Delphine; Gouzé, Jean-Luc; Gedeon, Tomas; de Jong, HiddeDifferent strains of a microorganism growing in the same environment display a wide variety of growth rates and growth yields. We developed a coarse-grained model to test the hypothesis that different resource allocation strategies, corresponding to different compositions of the proteome, can account for the observed rate-yield variability. The model predictions were verified by means of a database of hundreds of published rate-yield and uptake-secretion phenotypes of Escherichia coli strains grown in standard laboratory conditions. We found a very good quantitative agreement between the range of predicted and observed growth rates, growth yields, and glucose uptake and acetate secretion rates. These results support the hypothesis that resource allocation is a major explanatory factor of the observed variability of growth rates and growth yields across different bacterial strains. An interesting prediction of our model, supported by the experimental data, is that high growth rates are not necessarily accompanied by low growth yields. The resource allocation strategies enabling high-rate, high-yield growth of E. coli lead to a higher saturation of enzymes and ribosomes, and thus to a more efficient utilization of proteomic resources. Our model thus contributes to a fundamental understanding of the quantitative relationship between rate and yield in E. coli and other microorganisms. It may also be useful for the rapid screening of strains in metabolic engineering and synthetic biology.Item Adversary decision-making using Markov models(SPIE, 2023-06) Andreas, Elizabeth; Dorismond, Jessica; Gamarra, MarcoThis study conducts three experiments on adversary decision-making modeled as a graph. Each experiment has the overall goal to understand how to exploit an adversary’s decision-making in order to obtain desired outcomes, as well as specific goals unique to each experiment. The first experiment models adversary decision-making using an Absorbing Markov chain (AMC). A sensitivity analysis of states (nodes in the graph) and actions (edges in the graph) is conducted which informs how downstream adversary decisions could be manipulated. The next experiment uses a Markov decision process (MDP). Assuming the adversary is initially blind to the rewards they will receive when they take an action, a Q´learning algorithm is used to determine the sequence of actions that maximizes the adversary rewards (called an optimum policy). This experiment gives insight in the possible decision-making of an adversary. Lastly, in the third experiment a two-player Markov game is developed, played by an agent (friend) and the adversary (foe). The agents goal is to decrease the overall rewards the adversary receives when it follows optimum policy. All experiments are demonstrated using specific examples.