Growing up, the traditional view I had of a scientist was that of an individual wearing a lab coat, goggles, and gloves, either holding a test tube over a Bunsen burner or growing cells on a Petri dish Jurassic Park-style. As I have moved from high school to undergrad to graduate school, my perspective has certainly changed quite a bit, especially as I have been involved in more computational projects. Suddenly, I have found myself amongst scientists intent on understanding organism growth who never actually look away from their computer screens during the entire workday (except for a little stretch, lunch or coffee of course!). What is the place of in silico experiments in biological fields? In this series, I present several examples where real-life problems (in the biological fields or adjacent to those) can be tackled with the help of computers. These will not necessarily be directly related to my work but I hope they inspire you as much as they have inspired me!

End Malaria

According to the World Health Organization (WHO), malaria was the cause of hundreds of thousands of deaths in 2018 alone. We are currently in the year 2023, and only seven years from the deadline set by the WHO as part of its Sustainable Development Goals to decrease the incidence and mortality rates of malaria by at least 90%. While several factors contribute to the spread of malaria (such as inadequate water drainage) and must be urgently addressed, it is important to note that malaria itself is caused by unicellular parasites called Plasmodium; if we could kill the parasites over a wide range, we would have eliminated malaria from our planet. Extensive experiments have been and are still being conducted to develop drugs against the bug; however, new strains of the pathogen are emerging that are resistant to the intended effects of these very same drugs. As a way to supplement or even extend these in vivo studies, researchers currently employ computer simulations in various capacities. Today, I will focus on how simulations can be used to guide the implementation of antimalarial strategies.

Let’s begin by considering that different Plasmodium species with different growth strategies can be more common in different parts of the world. For example, the deadly Plasmodium falciparum is most commonly found on the African continent. On the other hand, Plasmodium vivax, more prevalent in Latin America and Southeast Asia, has a dormant liver phase; in other words, following an initial infection, the parasite may remain undetected in the unfortunate patient for years, re-activating suddenly due to any number of reasons. Given these complex factors, mathematical simulations can be used to evaluate the efficacy of different disease control measures in different parts of the world (as has been done here and here). In particular, consider that resistant Plasmodium strains may emerge as a result of administering strong doses of antimalarial drugs without consideration of the complexities of the malarial infection in individual patient cases (as suggested here). A pertinent question can then be: to what extent should our resources be devoted towards drug administration versus implementing preventative measures? Plasmodium may look like it is outwitting drug therapeutic programmes, but through in silico efforts, we may strategically win this fight!

This is only one example of how computer simulations may help in making malaria a thing of the past. The growth of individual Plasmodium parasites can also be modelled on the computer and factors affecting their growth simulated rapidly unlike the equivalent traditional workflow used in biological laboratories. This exciting area of research will be the topic of my next post so stay tuned!