Network physics of attractive colloidal gels: Resilience, rigidity, and phase diagram.

Colloidal gels exhibit solid-like behavior at vanishingly small fractions of solids, owing to ramified space-spanning networks that form due to particle-particle interactions. These networks give the gel its rigidity, and with stronger attractions the elasticity grows as well. The emergence of rigidity can be described through a mean field approach; nonetheless, fundamental understanding of how rigidity varies in gels of different attractions is lacking. Moreover, recovering an accurate gelation phase diagram based on the system's variables has been an extremely challenging task. Understanding the nature of colloidal clusters, and how rigidity emerges from their connections is key to controlling and designing gels with desirable properties. Here, we employ network analysis tools to interrogate and characterize the colloidal structures. We construct a particle-level network, having all the spatial coordinates of colloids with different attraction levels, and also identify polydisperse rigid fractal clusters using a Gaussian mixture model, to form a coarse-grained cluster network that distinctly shows main physical features of the colloidal gels. A simple mass-spring model then is used to recover quantitatively the elasticity of colloidal gels from these cluster networks. Interrogating the resilience of these gel networks shows that the elasticity of a gel (a dynamic property) is directly correlated to its cluster network's resilience (a static measure). Finally, we use the resilience investigations to devise [and experimentally validate] a fully resolved phase diagram for colloidal gelation, with a clear solid-liquid phase boundary using a single volume fraction of particles well beyond this phase boundary.

Molecular Interaction Networks and Cardiovascular Disease Risk: The Role of Food Bioactive Small Molecules.

Diet is a well-known modifiable risk factor for cardiovascular diseases, which are the leading cause of death worldwide. However, our current understanding of the human diet is still limited in terms of fully capturing the role of dietary compounds in the intraspecies and interspecies biochemical networks that determine our health. This is due, in part, to a lack of detailed information on the presence of small molecules in food (molecular weight ≤1000 daltons), their amounts, and their interactions with known protein targets. As a result, our ability to develop a mechanistic understanding of how food chemicals impact our health is limited. In recent years, the Foodome project has tackled several aspects of this challenging universe, leveraging the expertise of a diverse group of scientific communities, from computer science to epidemiology. Here, we review the most recent efforts of the Foodome project in mapping the chemical complexity of food and predicting its effect on human health. Leveraging the network medicine framework applied to Amla-a medicinal plant-we offer a rationale for future research on the mechanism of action of food bioactive small molecules, whose designing principles could inspire next-generation drug discovery and combinations.