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Vincent, N.

Publications and source records attributed to Vincent, N..

2 recordsLinked to original sources

Global change drivers and the risk of infectious disease

Anthropogenic change is contributing to the rise in emerging infectious diseases, but it remains unclear which global change drivers most increase disease and under what contexts. We amassed a dataset from the literature that includes 1,832 observations of infectious disease responses to global change drivers across 1,202 host-parasite combinations. We found that biodiversity loss, climate change, and introduced species were associated with increases in disease-related endpoints or harm (i.e., enemy release for introduced species), whereas urbanization was associated with decreases in disease endpoints. Natural biodiversity gradients, deforestation, forest fragmentation, and most classes of chemical contaminants had non-significant effects on these endpoints. Overall, these results were consistent across human and non-human diseases. Context-dependent effects of the global change drivers on disease were common and are discussed. These findings will help better target disease management and surveillance efforts towards global change drivers that increase disease. One-Sentence SummaryHere we quantify which global change drivers increase infectious diseases the most to better target global disease management and surveillance efforts.

ecology↗

Spatiotemporal patterning enabled by gene regulatory networks

Spatiotemporal pattern formation plays a key role in various biological phenomena including embryogenesis and neural network formation. Though the reaction-diffusion systems enabling pattern formation have been studied phenomenonlogically, the biomolecular mechanisms behind these processes has not been modelled in detail. Here, we study the emergence of spatiotemporal patterns due to simple synthetic commonly observed two- and three-node gene regulatory network motifs coupled with their molecular diffusion in one- and two-dimensional space. We investigate the patterns formed due to the coupling of inherent multistable and oscillatory behavior of toggle switch (two mutually repressing nodes), toggle switch with double self-activation, toggle triad (three mutually repressing nodes) and repressilator (three nodes repressing the other sequentially in a cyclic manner) with the effect of spatial diffusion of these molecules. We probe various parameter regimes corresponding to different regions of stability (monostable, multistable, oscillatory) and assess the impact of varying diffusion coefficients too. This analysis offers valuable insights into the design principles of pattern formation facilitated by these network motifs, and suggest mechanistic underpinnings of biological pattern formation.

biophysics↗