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Combining Antibiotics: Can Two Antibiotics Outperform One?
We use mathematical tools from control theory to study when and how infections can be successfully treated. This is key to avoiding therapies that sound good but just won’t work. Our focus is on a big question in antimicrobial resistance: are two antibiotics better than one.
A recent study suggested that combinations aren’t always more effective [cite]. But using nonlinear control theory, we find the opposite—drug combinations can control a wider range of infections. However, for this to work, we first need to understand the starting point: not all infections can be treated the same way, and knowing where we begin is crucial to choosing the right strategy.
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. 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. The peacock evolved to showcase long, colorful feathers as this enhances reproductive fitness, however, this trait has rendered it more susceptible to numerous predators. Yet, the collateral sensitivity trad-off is non trivial, and we need to predict effective treatments while optimizing resources. This can be accomplished by leveraging mathematical models and computational biology, both of which serve as powerful tools in advancing our understanding of this biological process .
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].
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