71 research outputs found
Prediction of Cellular Burden with Host--Circuit Models
Heterologous gene expression draws resources from host cells. These resources
include vital components to sustain growth and replication, and the resulting
cellular burden is a widely recognised bottleneck in the design of robust
circuits. In this tutorial we discuss the use of computational models that
integrate gene circuits and the physiology of host cells. Through various use
cases, we illustrate the power of host-circuit models to predict the impact of
design parameters on both burden and circuit functionality. Our approach relies
on a new generation of computational models for microbial growth that can
flexibly accommodate resource bottlenecks encountered in gene circuit design.
Adoption of this modelling paradigm can facilitate fast and robust design
cycles in synthetic biology
Analytic derivation of bacterial growth laws from a simple model of intracellular chemical dynamics
The social patterning of risk factors for noncommunicable diseases in five countries: evidence from the modeling the epidemiologic transition study (METS)
Socio-demographic and clinical factors contributing to smoking cessation among men: a four-year follow up study of the Korean Health Panel Survey
Evolution of socioeconomic inequalities in smoking: results from the Portuguese national health interview surveys
Association of risk factors with smoking during pregnancy among women of childbearing age: an epidemiological field study in Turkey
Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.Peer reviewe
Higher self-reported prevalence of hypertension among Moluccan-Dutch than among the general population of the Netherlands: results from a cross-sectional survey
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