Each year, the manufacturers of influenza vaccines have to select the virus strain against which to target their product. If the selection hits the mark and the chosen strain is dominant during the influenza season, the vaccine will be effective. If not, the effect will remain weaker.
In cancer therapy, the medication may initially work well, killing off cancer cells. Then the process stops and the treatment loses its effect. A resistant sub-type of the cancer cells has gained the upper hand.
The ability to predict how viruses, bacteria and cancerous cells will evolve in the future would make solving such problems easier. This is precisely the objective of Ville Mustonen, professor of bioinformatics.
“Once we learn to predict the evolution of pathogens, we will be able to have an influence on its direction.”
Traditionally, evolutionary research has assumed a retrospective approach, striving to understand how organisms have developed into their current form. By utilising mathematical methods, a new research approach aims to predict what they will evolve into in the near future.
This research is focused on the evolution of quickly evolving cells and bacteria. For example, predicting human evolution would not make much sense, since decades pass between the birth of each generation, which makes testing the reliability of predictions impossible.
A CHALLENGE FOR YEAST
For now, evolutionary predicting is only in its early stages, with groundwork being laid. Mustonen's group has studied rapid evolutionary dynamics in collaboration with French and Swedish experimental research groups focused on evolution.
“With the use of drugs we put yeast cells to a challenge to see how evolution progresses and populations develop.”
Next, Mustonen would like to investigate what kind of impact bacteriophages, or viruses that eliminate bacteria, have on bacterial evolution. For this purpose, Mustonen has applied for funding together with Teppo Hiltunen, an Academy of Finland research fellow in the field of microbiology.
The evolution of resistance is another fascinating research topic. Resistance is a prevalent problem related to an increasing number of pathogens. If the extermination of an entire pathogen population fails, part of it develops immunity.
UNCOVERING POWER RELATIONS
Mustonen is outlining a concept to generate models for predicting the evolution of asexually reproducing organisms. The concept could be applied to bacteria and viruses, as well as cancer cells.
In the process of developing the computational model, the prior behaviour of the population is examined. First, the sub-types found are measured and subsequently modelled, which determines their competence in the environment. Once the power relations of the strains are known, a prediction can be made.
However, the goal is not only to passively observe a pathogen population, but also to mould its surroundings through the use of drugs; these will change the competence of the sub-types since certain types tolerate drugs better than others. The next step is to understand how the population changes in a stressful situation and to test various cell lines against drugs.
Mustonen's professorship at the University of Helsinki is divided between the Faculty of Science and the Faculty of Biological and Environmental Sciences. His doctoral dissertation was in the field of statistical physics, but evolutionary research work with biological data had already attracted him more than a decade ago. As data volumes have grown, biology has increasingly turned into a quantitative science.
The amount of data is growing quicker than computing power, and old algorithms are no longer able to process it all. New and improved computational methods are therefore needed.
Despite the deluge of data, biological datasets are not always readily available to researchers. They may also be expensive or poorly applicable to certain research questions.
“You can’t start by expecting data to arrive clean and provide easy answers to the questions we wish to ask. It is an intensive process comprised of several stages.”
As regards cancer, the aim is to find a way to compute how the disease will react to treatment.
Some years ago, Mustonen was simulating a situation where the genetic code of cancer cells would have been monitored in real time during treatment. As research material, he used biopsied cancer cell specimens collected at five different stages from a leukaemia patient who by then had already passed away. The specimens clearly indicated how, at first, the drug had affected the dominant cancer cell type. Later, however, another sub-type became dominant, offering resistance to the effects of the drug.
“All cancer types are heterogeneous: through mutation, they have generated sub-types that may react differently to drugs. Physicians might benefit from knowing that a change in dominance has occurred, making it necessary to resort to another therapy, if such an option exists.”
In the future, predicting evolution may become part of the routine in planning patient care. However, this requires solutions to many open challenges.
Evolutionary processes are yet to be understood outside of laboratory conditions. For the time being, tests have primarily been conducted using uniformly composed populations in standardised environments, while in reality populations are comprised of several sub-types living in changing conditions.
“Even if a drug works on a cell line in the laboratory, it may not have the desired effect on a patient. In actual real-world conditions, more research and facts are required.”
This article was published in Finnish in the Y/08/18 issue of Yliopisto magazine.