A major next step in hematopoietic stem cell (HSC) biology is to enhance our quantitative understanding of cellular and evolutionary dynamics involved in undisturbed hematopoiesis. Mathematical models have been and continue to be key in this respect, and are most powerful when parameterized experimentally and containing sufficient biological complexity. In this paper, we use data from label propagation experiments in mice to parameterize a mathematical model of hematopoiesis that includes homeostatic control mechanisms as well as clonal evolution. We find that nonlinear feedback control can drastically change the interpretation of kinetic estimates at homeostasis. This suggests that short-term HSC and multipotent progenitors can dynamically adjust to sustain themselves temporarily in the absence of long-term HSCs, even if they differentiate more often than they self-renew in undisturbed homeostasis. Additionally, the presence of feedback control in the model renders the system resilient against mutant invasion. Invasion barriers, however, can be overcome by a combination of age-related changes in stem cell differentiation and evolutionary niche construction dynamics based on a mutant-associated inflammatory environment. This helps us understand the evolution of e.g., TET2 or DNMT3A mutants, and how to potentially reduce mutant burden.

Oral drug absorption is the primary route for drug administration. However, this process hinges on multiple factors, including the drug\'s physicochemical properties, formulation characteristics, and gastrointestinal physiology. Given its intricacy and the exorbitant costs associated with experimentation, the trial-and-error method proves prohibitively expensive. Theoretical models have emerged as a cost-effective alternative by assimilating data from diverse experiments and theoretical considerations. These models fall into three categories: (i) data-driven models, encompassing classical pharmacokinetics, quantitative-structure models (QSAR), and machine/deep learning; (ii) mechanism-based models, which include quasi-equilibrium, steady-state, and physiologically-based pharmacokinetics models; and (iii) first principles models, including molecular dynamics and continuum models. This review provides an overview of recent modeling endeavors across these categories while evaluating their respective advantages and limitations. Additionally, a primer on partial differential equations and their numerical solutions is included in the appendix, recognizing their utility in modeling physiological systems despite their mathematical complexity limiting widespread application in this field.

Subaerial biofilms (SAB) are intricate microbial communities living on terrestrial surfaces, of interest in a variety of contexts including cultural heritage preservation, microbial ecology, biogeochemical cycling, and biotechnology. Here we propose a mathematical model aimed at better understanding the interplay between cyanobacteria and heterotrophic bacteria, common microbial SAB constituents, and their mutual dependence on local environmental conditions. SABs are modeled as thin mixed biofilm-liquid water layers sitting on stone. A system of ordinary differential equations regulates the dynamics of key SAB components: cyanobacteria, heterotrophs, polysaccharides and decayed biomass, as well as cellular levels of organic carbon, nitrogen and energy. These components are interconnected through a network of energetically dominant metabolic pathways, modeled with limitation terms reflecting the impact of biotic and abiotic factors. Daily cylces of temperature, humidity, and light intensity are considered as input model variables that regulate microbial activity by influencing water availability and metabolic kinetics. Relevant physico-chemical processes, including pH regulation, further contribute to a description of the SAB ecology. Numerical simulations explore the dynamics of SABs in a real-world context, revealing distinct daily activity periods shaped by water activity and light availability, as well as longer time scale survivability conditions. Results also suggest that heterotrophs could play a substantial role in decomposing non-volatile carbon compounds and regulating pH, thus influencing the overall composition and stability of the biofilm.

This review delves into the intricate traits of microbial communities encountered in spontaneously fermented foods (SFF), contributing to resistance, resilience, and functionality drivers. Traits of SFF microbiomes comprise of fluctuations in community composition, genetic stability, and condition-specific phenotypes. Synthetic microbial communities (SMCs) serve as a portal for mechanistic insights and strategic re-programming of microbial communities. Current literature underscores the pivotal role of microbiomes in SFF in shaping quality attributes and preserving the cultural heritage of their origin. In contrast to starter driven fermentations that tend to be more controlled but lacking the capacity to maintain or reproduce the complex flavors and intricacies found in SFF. SMCs, therefore, become indispensable tools, providing a nuanced understanding and control over fermented food microbiomes. They empower the prediction and engineering of microbial interactions and metabolic pathways with the aim of optimizing outcomes in food processing. Summarizing the current application of SMCs in fermented foods, there is still space for improvement. Challenges in achieving stability and reproducibility in SMCs are identified, stemming from non-standardized approaches. The future direction should involve embracing standardized protocols, advanced monitoring tools, and synthetic biology applications. A holistic, multi-disciplinary approach is paramount to unleashing the full potential of SMCs and fostering sustainable and innovative applications in fermented food systems.

UNASSIGNED: Lactate is not considered just a \"waste product\" of anaerobic glycolysis anymore. It has been proved to play a key role in several metabolic diseases, such as in the metabolic dysfunction-associated steatotic liver disease, obesity, and diabetes. The capability of simulating glucose-insulin-lactate interaction would be useful to design and test drugs targeting lactate metabolism in such pathological conditions. Minimal models are available, which describe and quantify glucose-lactate interaction but models to simulate postprandial glucose-insulin-C-peptide-lactate time courses are missing. The aim of this study is to fill this gap.
UNASSIGNED: Starting from the Padova Type 2 Diabetes Simulator (T2DS), we first added a description of glucose-lactate kinetics and then created a population of 100 in silico subjects to match glucose-insulin-C-peptide-lactate data of 44 adolescents with/without obesity who underwent a standard oral glucose tolerance test (OGTT) of 75 g.
UNASSIGNED: The developed model accurately predicts all molecules time courses, guaranteeing precise model parameter estimates (percent coefficient of variation [CV%] median [25th-75th percentile] = 19 [9-29]%). The generated in silico population shows good agreement with the clinical data in terms of area under the curve (AUC) (P = .6, .6, .9, .6 for glucose, insulin, C-peptide, and lactate, respectively) and parameter distributions (P > .1).
UNASSIGNED: We have developed a simulator to describe glucose, insulin, C-peptide, and lactate kinetics during an OGTT, which captures the behavior of a real population of adolescents with/without obesity both in terms of average and intersubject variability. Such simulator can be used to investigate the pharmacodynamics of drugs targeting lactate metabolic pathway in various pathological conditions.

Plant cell, tissue, and organ cultures (PCTOC) have been used as experimental systems in basic research, allowing gene function demonstration through gene overexpression or repression and investigating the processes involved in embryogenesis and organogenesis or those related to the potential production of secondary metabolites, among others. On the other hand, PCTOC has also been applied at the commercial level for the vegetative multiplication (micropropagation) of diverse plant species, mainly ornamentals but also horticultural crops such as potato or fruit and tree species, and to produce high-quality disease-free plants. Moreover, PCTOC protocols are important auxiliary systems in crop breeding crops to generate pure lines (homozygous) to produce hybrids for the obtention of polyploid plants with higher yields or better performance. PCTOC has been utilized to preserve and conserve the germplasm of different crops or threatened species. Plant genetic improvement through genetic engineering and genome editing has been only possible thanks to the establishment of efficient in vitro plant regeneration protocols. Different companies currently focus on commercializing plant secondary metabolites with interesting biological activities using in vitro PCTOC. The impact of omics on PCTOC is discussed.

In this paper we examine several definitions of vaccine efficacy (VE) that we found in the literature, for diseases that express themselves in outbreaks, that is, when the force of infection grows in time, reaches a maximum and then vanishes. The fact that the disease occurs in outbreaks results in several problems that we analyse. We propose a mathematical model that allows the calculation of VE for several scenarios. Vaccine trials usually needs a large number of volunteers that must be enrolled. Ideally, all volunteers should be enrolled in approximately the same time, but this is generally impossible for logistic reasons and they are enrolled in a fashion that can be replaced by a continuous density function (for example, a Gaussian function). The outbreak can also be replaced by a continuous density function, and the use of these density functions simplifies the calculations. Assuming, for example Gaussian functions, one of the problems one can immediately notice is that the peak of the two curves do not occur at the same time. The model allows us to conclude: First, the calculated vaccine efficacy decreases when the force of infection increases; Second, the calculated vaccine efficacy decreases when the gap between the peak in the force of infection and the peak in the enrollment rate increases; Third, different trial protocols can be simulated with this model; different vaccine efficacy definitions can be calculated and in our simulations, all result are approximately the same. The final, and perhaps most important conclusion of our model, is that vaccine efficacy calculated during outbreaks must be carefully examined and the best way we can suggest to overcome this problem is to stratify the enrolled volunteer\'s in a cohort-by-cohort basis and do the survival analysis for each cohort, or apply the Cox proportional hazards model for each cohort.

A new regression model is presented which offers flexibility, freedom from subjective determinations of linear range, and very wide applicability to measurement systems of industrial importance. This \"progressive decay\" model starts as a deceptively simple ordinary differential equation. We show here that its solution faithfully describes real but seemingly unconnected data from a plate-based assay for quantitation of RNA with RiboGreen® and dissolution data for a triple fixed-dose combination solid oral dosage form.

This study examined the effects of spread formulation and the structural/lubricant properties of six different commercial hazelnut and cocoa spreads on sensory perception. Rheology, tribology, and quantitative descriptive analysis (QDA) was assessed by also evaluating the correlation coefficients between the quality descriptor and the rheological and textural parameters. The viscosity was evaluated at different temperatures to better simulate conditions before and after ingestion. Tribological analysis was executed at 37°C to mimic the human oral cavity. The effect of saliva presence and the number of runs on tribological behaviors was investigated. Moreover, textural, calorimetric, and particle size distribution measurements were performed to reinforce the correlation between structural/thermal parameters (e.g., firmness, stickiness, sugar melting point) and sensory aspects. \"Visual viscosity,\" defined as a sensory attribute evaluated prior to consumption, negatively correlated with apparent viscosity measured at 20°C and 10 s-1, whereas \"body,\" defined during oral processing and related to creaminess, positively correlated with apparent viscosity measured at 37°C and 50 s-1. These attributes were mainly influenced by particulate microstructure and solid volume fraction within the formulation. Textural stickiness positively correlated with sensory \"adhesiveness\" and was related to fat composition and milk powder addition, while \"sweetness\" was related to sucrose content and sugar melting enthalpy. Tribological data provided meaningful information related to particle-derived attributes, as well as after-coating perception (fattiness/oiliness), thus better predicting food evolution during oral consumption.

BACKGROUND: Antibiotic-resistant Enterobacterales (ARE) are a public health threat worldwide. Dissemination of these opportunistic pathogens has been largely studied in hospitals. Despite high prevalence of asymptomatic colonization in the community in some regions of the world, less is known about ARE acquisition and spread in this setting. As explaining the community ARE dynamics has not been straightforward, mathematical models can be key to explore underlying phenomena and further evaluate the impact of interventions to curb ARE circulation outside of hospitals.
METHODS: We conducted a systematic review of mathematical modeling studies focusing on the transmission of AR-E in the community, excluding models only specific to hospitals. We extracted model features (population, setting), formalism (compartmental, individual-based), biological hypotheses (transmission, infection, antibiotic impact, resistant strain specificities) and main findings. We discussed additional mechanisms to be considered, open scientific questions, and most pressing data needs.
RESULTS: We identified 18 modeling studies focusing on the human transmission of ARE in the community (n=11) or in both community and hospital (n=7). Models aimed at (i) understanding mechanisms driving resistance dynamics; (ii) identifying and quantifying transmission routes; or (iii) evaluating public health interventions to reduce resistance. To overcome the difficulty of reproducing observed ARE dynamics in the community using the classical two-strains competition model, studies proposed to include mechanisms such as within-host strain competition or a strong host population structure. Studies inferring model parameters from longitudinal carriage data were mostly based on models considering the ARE strain only. They showed differences in ARE carriage duration depending on the acquisition mode: returning travelers have a significantly shorter carriage duration than discharged hospitalized patient or healthy individuals. Interestingly, predictions across models regarding the success of public health interventions to reduce ARE rates depended on pathogens, settings, and antibiotic resistance mechanisms. For E. coli, reducing person-to-person transmission in the community had a stronger effect than reducing antibiotic use in the community. For Klebsiella pneumoniae, reducing antibiotic use in hospitals was more efficient than reducing community use.
CONCLUSIONS: This study raises the limited number of modeling studies specifically addressing the transmission of ARE in the community. It highlights the need for model development and community-based data collection especially in low- and middle-income countries to better understand acquisition routes and their relative contribution to observed ARE levels. Such modeling will be critical to correctly design and evaluate public health interventions to control ARE transmission in the community and further reduce the associated infection burden.