Model selection for assessing the effects of doxorubicin on triple-negative breast cancer cell lines.

Doxorubicin is a chemotherapy widely used to treat several types of cancer, including triple-negative breast cancer. In this work, we use a Bayesian framework to rigorously assess the ability of ten different mathematical models to describe the dynamics of four TNBC cell lines (SUM-149PT, MDA-MB-231, MDA-MB-453, and MDA-MB-468) in response to treatment with doxorubicin at concentrations ranging from 10 to 2500 nM. Each cell line was plated and serially imaged via fluorescence microscopy for 30 days following 6, 12, or 24 h of in vitro drug exposure. We use the resulting data sets to estimate the parameters of the ten pharmacodynamic models using a Bayesian approach, which accounts for uncertainties in the models, parameters, and observational data. The ten candidate models describe the growth patterns and degree of response to doxorubicin for each cell line by incorporating exponential or logistic tumor growth, and distinct forms of cell death. Cell line and treatment specific model parameters are then estimated from the experimental data for each model. We analyze all competing models using the Bayesian Information Criterion (BIC), and the selection of the best model is made according to the model probabilities (BIC weights). We show that the best model among the candidate set of models depends on the TNBC cell line and the treatment scenario, though, in most cases, there is great uncertainty in choosing the best model. However, we show that the probability of being the best model can be increased by combining treatment data with the same total drug exposure. Our analysis points to the importance of considering multiple models, built on different biological assumptions, to capture the observed variations in tumor growth and treatment response.

Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil.

COVID-19 is affecting healthcare resources worldwide, with lower and middle-income countries being particularly disadvantaged to mitigate the challenges imposed by the disease, including the availability of a sufficient number of infirmary/ICU hospital beds, ventilators, and medical supplies. Here, we use mathematical modelling to study the dynamics of COVID-19 in Bahia, a state in northeastern Brazil, considering the influences of asymptomatic/non-detected cases, hospitalizations, and mortality. The impacts of policies on the transmission rate were also examined. Our results underscore the difficulties in maintaining a fully operational health infrastructure amidst the pandemic. Lowering the transmission rate is paramount to this objective, but current local efforts, leading to a 36% decrease, remain insufficient to prevent systemic collapse at peak demand, which could be accomplished using periodic interventions. Non-detected cases contribute to a ∽55% increase in R0. Finally, we discuss our results in light of epidemiological data that became available after the initial analyses.

Ecological modeling and pest population management: a possible and necessary connection in a changing world / Modelagem ecológica e manejo de populações de pragas: uma conexão possível e necessária para um mundo em transformação

Ecological modeling is an important tool for investigating dynamic behavior patterns in populations, trophic interactions, and behavioral ecology. However, the ecological patterns that reflect population oscillation trends are often not clearly visible without analytical instruments such as ecological models. Thus, ecological modeling plays a fundamental role in describing demographic processes that are important for population dynamics. Ecological models, besides making possible the visualization of ecological patterns, may also reveal patterns of population persistence in many trophic systems, including prey-predator or host-parasitoid relationships, interactions that are commonly present in integrated pest management programs. In this forum, we present the main ecological aspects important for model building and implementation of integrated pest management programs for insects. Particularly, in this study, we analyze the combination between host-parasitoid models and the concept of economic threshold level on a spatio-temporal scale. As a conclusion about the model combination, spatial structure is essential for models of this nature, since its introduction into the system significantly alters the economic threshold-level values.

A modelagem ecológica é uma ferramenta importante para a investigação de padrões de comportamento dinâmico em populações, interações tróficas e também em ecologia comportamental. Contudo, os padrões ecológicos que refletem tendências de oscilação populacional muitas vezes não são claramente visíveis sem instrumentos analíticos, como os modelos ecológicos. Dessa forma, a modelagem ecológica exerce papel fundamental na descrição de processos demográficos importantes para a dinâmica populacional. Os modelos ecológicos, além de tornarem possível a visualização de padrões ecológicos, podem também revelar padrões de persistência populacional nos diversos sistemas tróficos, incluindo as relações presa-predador ou hospedeiro-parasitóide, interações comumente presentes em programas de manejo integrado de pragas. Neste fórum apresentamos os principais aspectos ecológicos importantes para a construção de modelos e implementação de programa de manejo de pragas em insetos. Em particular, analisamos a combinação entre modelos hospedeiro-parasitóide e o conceito de nível de dano em escala espaço-temporal. Como conclusão sobre a combinação de modelos, evidencia-se que a estrutura espacial é essencial para modelos desta natureza, já que sua introdução no sistema altera significativamente os valores de nível de dano econômico.

Ecological modeling and pest population management: a possible and necessary connection in a changing world.

Ecological modeling is an important tool for investigating dynamic behavior patterns in populations, trophic interactions, and behavioral ecology. However, the ecological patterns that reflect population oscillation trends are often not clearly visible without analytical instruments such as ecological models. Thus, ecological modeling plays a fundamental role in describing demographic processes that are important for population dynamics. Ecological models, besides making possible the visualization of ecological patterns, may also reveal patterns of population persistence in many trophic systems, including prey-predator or host-parasitoid relationships, interactions that are commonly present in integrated pest management programs. In this forum, we present the main ecological aspects important for model building and implementation of integrated pest management programs for insects. Particularly, in this study, we analyze the combination between host-parasitoid models and the concept of economic threshold level on a spatio-temporal scale. As a conclusion about the model combination, spatial structure is essential for models of this nature, since its introduction into the system significantly alters the economic threshold-level values.