Chilam – Laboratory for the Simulation of Complex Adaptive Systems

Research project that identifies and analyzes the causal relationships between factors that influence human behavior linked to obesity with the aim of building a mental model, which encompasses concepts such as beliefs, perception of oneself and the world.

Infectious diseases (ID) represent one of the main threats to public health worldwide. Globally more than 70% of IDs originated from zoonotic pathogens circulating in wildlife and domestic animals. Zoonoses are generated by a wide spectrum of microorganisms including: bacteria, viruses, fungi, protozoa and helminths. Many factors can establish and accelerate the origin of zoonoses. […]

In spite of a huge investment in both human and financial capital, the incidence of obesity and metabolic diseases, such as type 2 diabetes, is increasing worldwide. Although such diseases are rightly considered to be “complex”, the implications for how they should be studied and analyzed have not been fully appreciated. Their complexity is manifest […]

In short, a user will be able to quickly create their own predictive model focused on a prediction linked to a decision in a friendly environment where no specialized knowledge, neither modeling nor machine learning is required.

SPECIES is an interactive tool for analyzing ecological niches and the potential distribution of species. Furthermore, it can be used to infer networks between species that can highlight potential interaction between species. SPECIES harbors information of 2 to 3 billion biological organisms distributed worldwide. This information represents a large source to understand biodiversity and its […]