Aging is a biological process characterized by the progressive functional decline of many interrelated physiological systems. In particular, aging is associated with the development of a systemic state of low-grade chronic inflammation (inflammaging), and with progressive deterioration of metabolic function. Systems biology has helped in identifying the mediators and pathways involved in these phenomena, mainly through the application of high-throughput screening methods, valued for their molecular comprehensiveness. Nevertheless, inflammation and metabolic regulation are dynamical processes whose behavior must be understood at multiple levels of biological organization (molecular, cellular, organ, and system levels) and on multiple time scales. Mathematical modeling of such behavior, with incorporation of mechanistic knowledge on interactions between inflammatory and metabolic mediators, may help in devising nutritional interventions capable of preventing, or ameliorating, the age-associated functional decline of the corresponding systems.
Abstract-In this work we examined the performance of two evolutionary algorithms, a genetic algorithm (GA) and particle swarm optimization (PSO), in the estimation of the parameters of a model for the growth kinetics of the yeast Debaryomyces hansenii. Fitting the model's predictions simultaneously to three replicates of the same experiment, we used the variability among replicates as a criterion to evaluate the optimization result. The performance of the two algorithms was tested using 12 distinct settings for their operating parameters and running each of them 20 times. For the GA, the crossover fraction, crossover function and magnitude of mutation throughout the run of the algorithm were tested; for the PSO, we tested swarms with 3 different types of convergence behavior -convergent with and without oscillations and divergent -and also varied the relative weights of the local and global acceleration constants. The best objective function values were obtained when the PSO fell in the zone of convergence with oscillations or zigzagging, and had a local acceleration larger than the global acceleration.
In this study we address the problem of finding a quantitative mathematical model for the genetic network regulating the stress response of the yeast Saccharomyces cerevisiae to the agricultural fungicide mancozeb. An S-system formalism was used to model the interactions of a five-gene network encoding four transcription factors (Yap1, Yrr1, Rpn4 and Pdr3) regulating the transcriptional activation of the FLR1 gene. Parameter estimation was accomplished by decoupling the resulting system of nonlinear ordinary differential equations into a larger nonlinear algebraic system, and using the Levenberg-Marquardt algorithm to fit the models predictions to experimental data. The introduction of constraints in the model, related to the putative topology of the network, was explored. The results show that forcing the network connectivity to adhere to this topology did not lead to better results than the ones obtained using an unrestricted network topology. Overall, the modeling approach obtained partial success when trained on the nonmutant datasets, although further work is required if one wishes to obtain more accurate prediction of the time courses.
BackgroundAntimicrobial resistance (AMR) is one of the most significant threats to public health globally. It will worsen without concerted efforts to spur the development of new antibiotics and ensure access and stewardship of existing ones. Pharmaceutical companies have a critical role to play in these efforts. The Access to Medicine Foundation developed the AMR Benchmark to measure how pharmaceutical companies are responding to AMR and to share and push best practices within the industry.MethodsThe AMR Benchmark assessed the AMR activities of 30 pharmaceuticalcompanies in 106 low- and middle-income countries. Survey data on company activities were collected across three research areas: research and development (R&D); manufacturing and production; and access and stewardship. For each research area, specific metrics were developed to evaluate company performance. These metrics were defined through consultation with experts working across the AMR field and represent a broad consensus on where companies can and should be taking action to limit AMR.ResultsThe Benchmark found that there are good practices in all research areas. Out of 276 R&D projects targeting infectious diseases, 175 target pathogens identified as priority by WHO/CDC. Of these, 88 are in preclinical stage, 87 are in clinical stage, and 54 target gram-negative bacteria. Out of 28 antibiotics in late clinical stage, only two have access and stewardship plans in place. Nearly half of companies evaluated are involved in AMR surveillance and eight companies set limits on antibiotic wastewater discharge. Lastly, four companies separate sales agent bonuses from antibiotics sales volumes to reduce overuse of antibiotics.ConclusionThe Benchmark identified the good ideas being implemented by companies to limit AMR and mapped opportunities to amplify current efforts. Although companies are taking some action, the R&D pipeline needs to be further strengthened and candidates reaching late clinical stage must be supported by concrete plans to ensure access and stewardship.
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