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1. Population collapse and adaptive rescue during long‐term chemostat fermentation

   https://doi.org/10.1002/bit.26898  

   Rai, Navneet; Huynh, Linh; Kim, Minseung; Tagkopoulos, Ilias (January 2019, Biotechnology and Bioengineering)

   Abstract Microbial fermentation is an essential process for research and industrial applications, yet our understanding of cellular dynamics during long‐term fermentation is limited. Here, we report a reproducible phenomenon of abrupt population collapse followed by a rapid population rescue that was observed during long‐term chemostat cultivations, for various strains ofEscherichia coliin minimal media. Through genome resequencing and whole‐genome transcriptional profiling of replicate runs over time, we identified that changes in the tRNA and carbon catabolic genes are the genetic basis of this phenomenon. Since current fermentation models are unable to capture the observed dynamics, we present an extended model that takes into account critical biological processes during fermentation, and we further validated carbon source predictions through forward experimentation. This study extends the predictability of current models for microbial fermentation and adds to our system‐level knowledge of cellular adaptation during this crucial biotechnological process. 

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2. Right but wrong: How students' mechanistic reasoning and conceptual understandings shift when designing agent‐based models using data

   https://doi.org/10.1002/sce.21890  

   Fuhrmann, Tamar; Rosenbaum, Leah; Wagh, Aditi; Eloy, Adelmo; Wolf, Jacob; Blikstein, Paulo; Wilkerson, Michelle (January 2025, Science Education)

   Abstract When learning about scientific phenomena, students are expected tomechanisticallyexplain how underlying interactions produce the observable phenomenon andconceptuallyconnect the observed phenomenon to canonical scientific knowledge. This paper investigates how the integration of the complementary processes of designing and refining computational models using real‐world data can support students in developing mechanistic and canonically accurate explanations of diffusion. Specifically, we examine two types of shifts in how students explain diffusion as they create and refine computational models using real‐world data: a shift towards mechanistic reasoning and a shift from noncanonical to canonical explanations. We present descriptive statistics for the whole class as well as three student work examples to illustrate these two shifts as 6th grade students engage in an 8‐day unit on the diffusion of ink in hot and cold water. Our findings show that (1) students develop mechanistic explanations as they build agent‐based models, (2) students' mechanistic reasoning can co‐exist with noncanonical explanations, and (3) students shift their thinking toward canonical explanations after comparing their models against data. These findings could inform the design of modeling tools that support learners in both expressing a diverse range of mechanistic explanations of scientific phenomena and aligning those explanations with canonical science. 

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3. Multiple Model Systems and Representation of Biological Phenomena

   Yoshida, Yoshinari (November 2021, Integrated HPS Conference Proceedings)

   Schickore, Jutta (Ed.)

   Biologists often study certain biological systems as models of a phenomenon of interest even if they already know that the phenomenon occurs through diverse mechanisms and hence none of those systems can sufficiently represent it by itself. To understand this modeling practice, the present paper provides an account of how multiple model systems can be used to study a phenomenon whose underlying mechanisms are diverse. Even if generalizability of results from a single model system is significantly limited, generalizations concerning particular aspects of mechanisms often hold across certain ranges of biological systems, which enables multiple model systems to jointly represent such a phenomenon. Comparing mechanisms that operate in different biological systems as examples of the same phenomenon also facilitates characterization and investigation of individual mechanisms. I also compare my account with two existing accounts of the use of multiple model systems and argue that my account is distinct from and complementary to them. 

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4. Strong El Niño Events Lead to Robust Multi‐Year ENSO Predictability

   https://doi.org/10.1029/2023GL106988  

   Lenssen, N.; DiNezio, P.; Goddard, L.; Deser, C.; Kushnir, Y.; Mason, S_J; Newman, M.; Okumura, Y. (June 2024, Geophysical Research Letters)

   Abstract The El Niño‐Southern Oscillation (ENSO) phenomenon—the dominant source of climate variability on seasonal to multi‐year timescales—is predictable a few seasons in advance. Forecast skill at longer multi‐year timescales has been found in a few models and forecast systems, but the robustness of this predictability across models has not been firmly established owing to the cost of running dynamical model predictions at longer lead times. In this study, we use a massive collection of multi‐model hindcasts performed using model analogs to show that multi‐year ENSO predictability is robust across models and arises predominantly due to skillful prediction of multi‐year La Nina events following strong El Niño events. 

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5. Examining Student Testing and Debugging Within a Computational Systems Modeling Context

   https://doi.org/10.1007/s10956-023-10049-w  

   Bowers, Jonathan; Eidin, Emanuel; Stephens, Lynn; Brennan, Linsey (May 2023, Journal of Science Education and Technology)

   Abstract Interpreting and creating computational systems models is an important goal of science education. One aspect of computational systems modeling that is supported by modeling, systems thinking, and computational thinking literature is “testing, evaluating, and debugging models.” Through testing and debugging, students can identify aspects of their models that either do not match external data or conflict with their conceptual understandings of a phenomenon. This disconnect encourages students to make model revisions, which in turn deepens their conceptual understanding of a phenomenon. Given that many students find testing and debugging challenging, we set out to investigate the various testing and debugging behaviors and behavioral patterns that students use when building and revising computational system models in a supportive learning environment. We designed and implemented a 6-week unit where students constructed and revised a computational systems model of evaporative cooling using SageModeler software. Our results suggest that despite being in a common classroom, the three groups of students in this study all utilized different testing and debugging behavioral patterns. Group 1 focused on using external peer feedback to identify flaws in their model, group 2 used verbal and written discourse to critique their model’s structure and suggest structural changes, and group 3 relied on systemic analysis of model output to drive model revisions. These results suggest that multiple aspects of the learning environment are necessary to enable students to take these different approaches to testing and debugging. 

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