Breaking down the microbiology world one bite at a time
Conversations with microbes
Microbe A: Hey, ‘B’! Look, who is here.
Microbe B: Ooh, they look like us! Do you think we have a competition?
Microbe C: I think they are not our competitors. Instead, they are mimicking the competition amongst us.
Microbe A: What do you mean?
Microbe C: As ‘B’ mentioned, look how similar they are to us. Yet, there is something artificial about them… Looks like someone made them look like us and act like us.
Microbe B: Can you explain more?
Microbe C: You know how we enjoy making resources for each other. You make acetate, ‘A’ makes propionate, and I love using both of these compounds to make butyrate.
Microbe A: Yes! And we love you for that!
Microbe C: Thanks! But, sometimes we want the same resources, and we have to compete for them.
Microbe B: That is not a pleasant interaction.
Microbe C: The friendly banter, the caring of each other, our life in our environment. This other artificial community is trying to mimic our care for each other and also our friendly banter.
Microbe A: Why would they do that? Why don’t they make their own community?
Microbe B (with a smirk): You know, the other day I heard these giant people talking about how they want to understand our interactions. Huh! Looks like they still haven’t figured it out.
Microbe A: Hah! Meaning that we are good at hiding our exchange.
Microbe B. Sure we are! Hey, C, do you think the giant people have anything to do with this artificial community?
Microbe C: Bingo! I think it has everything to do with the giant people. They are still trying to figure us out. I think they are on the right track.
Microbe A: How so?
Microbe C: You know, my giant friend has written about it. Let’s find out!
While microbes may not use words to talk to each other, they still have conversations in the form of sharing resources and competing against each other for nutrients. A minimal set of nutrients is required for essential interactions between microbes. Scientists have proved that each microorganism has a definite nutritional requirement. Therefore, they can design a “cocktail of microbes”, if they know the nutritional requirement of each microbe in that cocktail. This is a time-consuming job, because researchers need to find an exact set of nutrients in an environment and match it to the microbes. Given the plethora of nutrients available in natural environments (gut, sediments, soil, etc.), an infinite number of laboratory experiments can be performed, which makes a researcher’s life tough. To make their job easier, computational algorithms and simulations can be used to predict the combination of nutrients and microbes that are worth exploring in laboratories.
Microbe A: Makes a lot of sense. What kind of algorithms do the giants use?
Microbe C: They use something called genetic algorithms, which are based on basic laws of evolution. Let’s see if they found something.
Researchers (Pacheco AR, Segrè D) decided to use a combination of mathematical simulations and genetic algorithms to search amongst the plethora of nutrients, and find the right combination of microorganisms. First, they found the parameters for the nutrient search by running simulations on around 6,000 combinations of microbial communities (13 microbial species) and environmental compositions. At the end of each simulation, the number of different microbes was counted. The researchers noted that each simulation led to a different microbial composition, and 6 microbes survived in at least 1 environment. To test how sensitive these microbes are, the researchers simulated changes in the environments by varying two or more nutrients. They observed that both big and small changes in the environment lead to extreme variation in the abundance of the microbes. This means that even small variations in the environment could lead to the disruption of a microbiome.
After determining a set of environments that can sustain most of the microbes, researchers used this knowledge to determine a set of nutrients that could lead to the survival of specific microbial communities. They applied a genetic algorithm to select certain environmental compositions (P), and calculated a score for each environment based on the distribution of the counts of each of the 13 microbial species (Figure 1). The researchers selected top n environments and then used those compositions to generate the remaining P-n environments (Figure 1). They applied the genetic algorithm again to this new set of environments and assigned the score. The researchers repeated this process until they reached the final set of environments that allowed the most even distribution of the microbes, meaning that all microbes were present in equivalent amounts.
To test the efficiency of this algorithm, scientists applied it to compositions with unequal amounts of microbes. For example, an uneven distribution of microbes is usually found in a natural environment. For such an environment, researchers found that fewer runs of algorithms were required. The nutrient composition of such an environment was dominated by a set of highly abundant nutrients, meaning that the availability of only a few nutrients drives the abundance of the microbes. This could also mean that microbes exchanged a lot of nutrients amongst themselves, which increased the abundance of the neighboring microbes.
Microbe A: Huh! Looks like the giant people are soon going to decipher our secret.
Microbe C: Yes, It looks like they are on the right track. But I feel that they should validate their algorithm with us. Maybe, we should all meet for coffee sometime!
Microbes all together: Agreed!
Link to the original post: Alan R. Pacheco and Daniel Segre, An evolutionary algorithm for designing microbial communities via environmental modification, Journal of the Royal Society Interface, June 2021
Featured image: Made by author
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