Computational representations of metabolism are increasingly common in medical, environmental, and bioprocess applications. Cellular growth is often an important output of computational biology analyses, and therefore, accurate measurement of biomass constituents is critical for relevant model predictions. There is a distinct lack of detailed macromolecular measurement protocols, including comparisons to alternative assays and methodologies, as well as tools to convert the experimental data into biochemical reactions for computational biology applications. Herein is compiled a concise literature review regarding methods for five major cellular macromolecules (carbohydrate, DNA, lipid, protein, and RNA) with a step-by-step protocol for a select method provided for each macromolecule. Additionally, each method was tested on three different bacterial species, and recommendations for troubleshooting and testing new species are given. The macromolecular composition measurements were used to construct biomass synthesis reactions with appropriate quality control metrics such as elemental balancing for common computational biology methods, including flux balance analysis and elementary flux mode analysis. Finally, it was demonstrated that biomass composition can substantially affect fundamental model predictions. The effects of biomass composition on in silico predictions were quantified here for biomass yield on electron donor, biomass yield on electron acceptor, biomass yield on nitrogen, and biomass degree of reduction, as well as the calculation of growth associated maintenance energy; these parameters varied up to 7%, 70%, 35%, 12%, and 40%, respectively, between the reference biomass composition and ten test biomass compositions. The current work furthers the computational biology community by reviewing literature regarding a variety of common analytical measurements, developing detailed procedures, testing the methods in the laboratory, and applying the results to metabolic models, all in one publicly available resource.

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Resource scarcity is a common stress in nature and has a major impact on microbial physiology. This review highlights microbial acclimations to resource scarcity, focusing on resource investment strategies for chemoheterotrophs from the molecular level to the pathway level. Competitive resource allocation strategies often lead to a phenotype known as overflow metabolism; the resulting overflow byproducts can stabilize cooperative interactions in microbial communities and can lead to cross-feeding consortia. These consortia can exhibit emergent properties such as enhanced resource usage and biomass productivity. The literature distilled here draws parallels between and laboratory studies and ties them together with ecological theories to better understand microbial stress responses and mutualistic consortia functioning.

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Early work by Klesges et al. (1983, 1986) suggested that mothers who frequently prompt their children to eat have children at greater risk for obesity. This is consistent with the hypothesis that controlling feeding practices override children's responsiveness to their internal fullness cues, increasing the risk of overeating and obesity (e.g., Johnson & Birch, 1994). Subsequent cross-sectional research on pressure to eat, however, has been inconsistent. Most studies have shown that maternal self-reports of pressure to eat are negatively associated with childhood obesity, and observational studies showed inconsistent relationships with child weight status. In the present study we examined the association between low-income, Latina mothers' pressure to eat and their preschool children's eating in the absence of hunger using both self-report and observational measures of feeding practices. A longitudinal design examined eating in the absence of hunger over 18 months; children's BMI at the initial timepoint was statistically controlled to address the tendency of mothers of underweight children to pressure their children to eat. At each timepoint, mothers completed the Child Feeding Questionnaire (Birch et al., 2001) and were observed feeding their child a meal in a laboratory setting. Eating in the absence of hunger (Fisher & Birch, 1999) was assessed at both timepoints as well. A cross-lagged panel model showed that observed maternal prompts to eat a different food at time one predicted kcal consumed in the absence of hunger at time two (controlling for kcal consumed in the absence of hunger at first timepoint: beta = 0.20, p < 0.05). Results suggest that pressure to eat alone may not be what contributes to eating in the absence of hunger, but that the nature of that pressure may be more important.