Research
I am currently a Posdoctoral Researcher in the Department of Mathematics and Statistic Sciences at the University of Idaho, United States. More info here.
My research is focused in understanding the evolutionary mechanisms that shape the structure and diversity of bacterial populations, in particular, the dynamics of public health relevant traits, such as antibiotic resistance. By the use of computational biology, integrating multi-scale experimental data and mathematical modeling.
The Silent Pandemic of Antibiotic Resistance
Bacteria that attack us have been bombarded with antibiotics for almost 90 years. They can undergo 6 or 7 generations each day (like E. coli), thus accumulating more than 200,000 generations since then. If they were human generations, it would represent 4.5 million years of hominid evolution, placing us at the emergence of Australopithecus. Nowadays, we face the challenge of superbugs, bacteria resistant to nearly all existing antibiotics. This is the silent pandemic, a pressing global health crisis causing a high death toll [cite].
But not all hope is lost since we count with a biological mechanism to combat antibiotic resistance — known as collateral sensitivity [cite]. This phenomenon occurs when bacteria that have developed resistance to one antibiotic become more susceptible to another. Think in collateral sensitivity akin to the peacock's tail problem: it evolved to showcase long, colorful feathers as this enhances reproductive fitness, however, this trait has rendered it more susceptible to numerous predators. Yet to exploit the collateral sensitivity trad-off in our favor is a non trivial problem. We need to predict effective treatments while optimizing resources, and this can be accomplished by leveraging mathematical models and computational biology to this end.
Tech Solutions for Water Scarcity
At present, world’s urban populations face water scarcity. Population growth, urbanization, and socioeconomic developments are expected to increase water demand by 50% to 80% over the next three decades and population facing water scarcity is projected to increase from 933 million in 2016 to at least 1.700 billion people in 2050, with India as the most severely affected country [cite]. In parallel water quality will be deteriorated as well. Climate change will affect spatial distribution, air temperature and rainfall, affecting the dilution of contaminants with strong influence on ecological status of freshwater.
This dire prognosis has rushed industrial developments and academic interest on technological strategies related to the assessment of water quality; i.e., real-time collection and analysis of relevant data to determine the physical, chemical and biological profile of a freshwater resource. Usually monitoring these parameters exploits highly reliable instrumentation restricted to few and widely available locations leaving relevant regions unreachable [cite]. To overcome these challenges the use of Unmanned Surface Vehicles (USV) has been proposed with remarkable results [cite].
