Explore the vast range of titles available.

Ferroptosis is a form of regulated cell death characterized by the accumulation of lipid hydroperoxides. There has been significant research on the pathways leading to the accumulation of oxidized lipids, but the downstream effects and how lipid peroxides cause cell death during ferroptosis remain a major puzzle. We evaluated key features of ferroptosis in newly developed molecular dynamics models of lipid membranes to investigate the biophysical consequences of lipid peroxidation, and generated hypotheses about how lipid peroxides contribute to cell death during ferroptosis.

Many cell models deal with constraints for life’s persistence, yet we lack principles for how dynamics interact with them and their origins in actual biology. Computational biology needs a theory of viability that confronts the life-death boundary to overcome this. We explore how geometric structures in a model’s state space offer organizing principles for cell fate, and how idealized models of emergent individuals may help explain life’s intrinsically generated limits.

The degree to which we can understand the multi-scale organization of cellular life is tied to how well our models can represent this organization and the processes that drive its evolution. This paper uses Vivarium—an engine for composing heterogeneous computational biology models into integrated, multi-scale simulations. Vivarium’s approach is demonstrated by combining several sub-models of biophysical processes into a model of chemotactic E. coli that exchange molecules with their environment, express the genes required for chemotaxis, swim, grow, and divide. This model is developed incrementally, highlighting cross-compartment mechanisms that link E. coli to its environment, with models for: (1) metabolism and transport, with transport moving nutrients across the membrane boundary and metabolism converting them to useful metabolites, (2) transcription, translation, complexation, and degradation, with stochastic mechanisms that read real gene sequence data and consume base pairs and ATP to make proteins and complexes, and (3) the activity of flagella and chemoreceptors, which together support navigation in the environment.

The BioModels Repository contains over 1000 manually curated mechanistic models from published literature, most often encoded in the Systems Biology Markup Language (SBML). This community-based standard formally specifies each model, but does not describe the computational experimental conditions to run a simulation and collect data. Therefore, it can be challenging to reproduce any figure or result from a publication with an SBML model alone. The Simulation Experiment Description Markup Language (SED-ML) provides a solution: a standard way to specify exactly how to run an experiment corresponding to a specific figure or result. BioModels was established years before SED-ML, and both systems evolved over time, both in content and acceptance. Hence, only about half of the entries in BioModels contained SED-ML files, and these files reflected the version of SED-ML that was available at the time. Additionally, almost all of these SED-ML files had at least one minor mistake that made them impossible to run. To make these models and their results more reproducible, we report here on our work updating, correcting and generating new SED-ML files for 1055 curated mechanistic models in BioModels. In addition, because SED-ML is implementation-independent, it can be used for verification, demonstrating that results hold across multiple simulation engines. We tested, corrected, and improved over 450 existing SED-ML files in the BioModels database, and created basic files for the rest of the entries. Then, we used a wrapper architecture for interpreting SED-ML, and report verification results across five different ODE-based biosimulation engines, after further improving the models, the wrappers, and the engines themselves. Our work with SED-ML and the BioModels collection aims to improve the utility of these models by making them more reproducible and credible. Improved reproducibility means these models are now even more fit for re-use, such as in new investigations and as components of multiscale models.

Antibiotic resistance poses mounting risks to human health, as current antibiotics are losing efficacy against increasingly resistant pathogenic bacteria. Of particular concern is the emergence of multidrug-resistant strains, which has been rapid among Gram-negative bacteria such as Escherichia coli . A large body of work has established that antibiotic resistance mechanisms depend on phenotypic heterogeneity, which may be mediated by stochastic expression of antibiotic resistance genes. The link between such molecular-level expression and the population levels that result is complex and multi-scale. Therefore, to better understand antibiotic resistance, what is needed are new mechanistic models that reflect single-cell phenotypic dynamics together with population-level heterogeneity, as an integrated whole. In this work, we sought to bridge single-cell and population-scale modeling by building upon our previous experience in “whole-cell” modeling, an approach which integrates mathematical and mechanistic descriptions of biological processes to recapitulate the experimentally observed behaviors of entire cells. To extend whole-cell modeling to the “whole-colony” scale, we embedded multiple instances of a whole-cell E . coli model within a model of a dynamic spatial environment, allowing us to run large, parallelized simulations on the cloud that contained all the molecular detail of the previous whole-cell model and many interactive effects of a colony growing in a shared environment. The resulting simulations were used to explore the response of E . coli to two antibiotics with different mechanisms of action, tetracycline and ampicillin, enabling us to identify sub-generationally-expressed genes, such as the beta-lactamase ampC, which contributed greatly to dramatic cellular differences in steady-state periplasmic ampicillin and was a significant factor in determining cell survival.

The adaptive response of cancer cells to hypoxia, a key microenvironmental factor in solid tumors, is orchestrated by Hypoxia-inducible factor 1 (HIF-1). Recent evidence indicate that oxygen tension in tumor is dynamic, with hypoxia being frequently unstable, or cycling. Cycling hypoxia is associated with specific phenotypic outcomes for the cancers. Transcriptomic analysis shows that for most genes, expression changes in cycling hypoxia lie expectedly in between the change caused by stable hypoxia, suggesting multi-cycle averaging of dosage in the oxygen tension, and likely HIF-1 induced transcription. However, a small subset of genes show an oscillation/cycling hypoxia specific response, suggesting that the transcriptional machinery of these genes may interpret cycling HIF-1 activity differently from stably high HIF-1 activity. Here, we model a gene regulatory circuit, the incoherent feed-forward loops (IFFLs) to illustrate that there are parameter regimes in such genetic circuits where oscillatory specific transcription is plausible. In these IFFL models, HIF-1 regulates gene transcription of a target gene directly, as well indirectly via another transcription factor with an opposite effect on gene transcription. This IFFL circuit is able to generate gene expression of certain target genes that is more extreme than either normoxia or stable hypoxia, and this nonlinear IFFL behavior can result from either the dynamic nature or even the intermediate, time averaged hypoxic signal Supplementary Information 1 ( Steady state analysis of IFFL circuits ). This gene circuit also allows us to search for plausible signaling intermediaries involved in the IFFL mediated cycling hypoxic response. Finally, we present experimental evidence suggesting that HIF-1 can form IFFLs with two key transcription factors p53, and Notch1, resulting in cycling hypoxia-specific gene expression linked to breast cancer progression and poor prognosis. Our work aims to draw attention to genetic circuits as plausible mechanisms where temporal fluctuations in the tumor microenvironment may directly inform downstream transcription. These ideas could identify hitherto unknown HIF-1 driven mechanism of cancer progression contributing to emergent tumor heterogeneity.

Can a bacterial cell model vet large datasets from disparate sources? Macklin et al. explored whether a comprehensive mathematical model can be used to verify or find conflicts in massive amounts of data that have been reported for the bacterium Escherichia coli , produced in thousands of papers from hundreds of labs. Although most data were consistent, there were data that could not accommodate known biological results, such as insufficient output of RNA polymerases and ribosomes to produce measured cell-doubling times. Other analyses showed that for some essential proteins, no RNA may be transcribed or translated in a cell's lifetime, but viability can be maintained without certain enzymes through a pool of stable metabolites produced earlier. Science , this issue p. eaav3751 Construction of a large-scale mechanistic model of Escherichia coli brings models and large datasets together to enhance knowledge in biology. The extensive heterogeneity of biological data poses challenges to analysis and interpretation. Construction of a large-scale mechanistic model of Escherichia coli enabled us to integrate and cross-evaluate a massive, heterogeneous dataset based on measurements reported by various groups over decades. We identified inconsistencies with functional consequences across the data, including that the total output of the ribosomes and RNA polymerases described by data are not sufficient for a cell to reproduce measured doubling times, that measured metabolic parameters are neither fully compatible with each other nor with overall growth, and that essential proteins are absent during the cell cycle—and the cell is robust to this absence. Finally, considering these data as a whole leads to successful predictions of new experimental outcomes, in this case protein half-lives.

Composition is a powerful principle for systems biology, focused on the interfaces, interconnections, and orchestration of distributed processes to enable integrative multiscale simulations. Whereas traditional models focus on the structure or dynamics of specific subsystems in controlled conditions, compositional systems biology aims to connect these models, asking critical questions about : What variables should a submodel expose through its interface? How do coupled models connect and translate across scales? How do domain-specific models connect across biological and physical disciplines to drive the synthesis of new knowledge? This approach requires robust software to integrate diverse datasets and submodels, providing researchers with tools to flexibly recombine, iteratively refine, and collaboratively expand their models. This article offers a comprehensive framework to support this vision, including: a conceptual and graphical framework to define interfaces and composition patterns; standardized schemas that facilitate modular data and model assembly; biological templates that integrate detailed submodels that connect molecular processes to the emergence of the cellular interface; and user-friendly software interfaces that empower research communities to construct and improve multiscale models of cellular systems. By addressing these needs, compositional systems biology will foster a unified and scalable approach to understanding complex cellular systems.

The extensive heterogeneity of biological data poses challenges to analysis and interpretation. Construction of a large-scale mechanistic model ofEscherichia colienabled us to integrate and cross-evaluate a massive, heterogeneous dataset based on measurements reported by various groups over decades. We identified inconsistencies with functional consequences across the data, including that the total output of the ribosomes and RNA polymerases described by data are not sufficient for a cell to reproduce measured doubling times, that measured metabolic parameters are neither fully compatible with each other nor with overall growth, and that essential proteins are absent during the cell cycle—and the cell is robust to this absence. Finally, considering these data as a whole leads to successful predictions of new experimental outcomes, in this case protein half-lives.