University of Tsukuba researchers used machine learning to discover that differentiation in decision-making factors for the lag, growth, and saturation phases of bacterial population growth safeguards the population from extinction. The large datasets produced in the study and the use of machine learning to handle complex modeling will be of interest to the biology community.

Microorganisms are everywhere. They live in soil, water, air, plants, animals, humans, and even inside us. We all know that bacteria and other microorganisms are essential for life. Yet, we don’t fully understand the relationship between the bacterial population’s growth and its environment. In order to better understand these relationships, scientists study the interactions between microorganisms and their environments.

The measurement of diversity in both genetics and the environment is critical for understanding community outcomes as an ecological cause and/or consequence, as well as the evolutionary and responsive strategies constrained by the environment. 

Microbial populations, as well as the environments in which they exist, are surprisingly complex. Studies to date have focused more on genetic diversity, such as metagenomics and microbial communities, than on environmental diversity, which makes it challenging to study their relationship with the environment. 

What is Population Dynamics?

To better understand bacteria, scientists have developed mathematical models that simulate their growth. These models are called population dynamics models. The goal is to predict how bacterial populations change over time, given certain conditions. Population dynamics models are useful tools for studying bacteria because they allow researchers to test hypotheses about how bacteria behave under various environmental conditions.

Japanese researchers have now discovered that machine learning can give them the means to do just that. In the research published this month in eLife, researchers from the University of Tsukuba demonstrated that machine learning could be used to analyze bacterial population growth to determine how it relates to changes in their environment.

Growth curves are frequently used to illustrate how microbe populations change over time. Lag time, growth rate, and saturated population size are typically the three parameters taken from these curves that are used to assess how well microbial populations fit with their environment (or carrying capacity). Within species, trade-offs between growth rate and lag time or population size have been observed, and these changes have been correlated with changes in saturated population size and growth rate among genetically diverse strains. These three parameters are likely related.

According to Professor Bei-Wen Ying, the study’s senior author, the two key questions that remained were: are these three parameters impacted by environmental diversity, and if so, how? To address these, the researchers investigated the bacterial growth strategy using data-driven approaches.

Using nearly a thousand combinations of growth media made up of 44 chemical compounds under controlled lab conditions, the researchers developed a sizable dataset that reflected the dynamics of Escherichia coli populations under a wide range of environmental conditions. They next employed machine learning to examine the big data for associations between the growth parameters and the different media combinations (ML). Without being explicitly programmed to do so, ML algorithms create a model from sample data in order to make predictions or decisions.

The analysis suggests that different growth phases for bacterial growth had different decision-making components, such as serine, sulfate, and glucose for growth delay (lag), growth rate, and maximum growth (saturation), respectively. Additional simulations and analyses revealed that branched-chain amino acids probably serve as pervasive coordinators for bacterial population growth circumstances.

According to Professor Ying, in situations where the bacteria encountered excess resources or starvation, the results also showed a typical and straightforward risk diversification strategy, which makes sense from an evolutionary and ecological perspective.

The research findings suggest that using data-driven approaches to investigate the world of microorganisms can provide new insights previously unattainable through traditional biological experiments. This study demonstrates that the ML-assisted approach, while still a developing technology in terms of biological reliability and accessibility, has the potential to open up new avenues for applications in the life sciences, particularly microbiology and ecology.

Story Source: Reference Paper | Reference Article

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Dr. Tamanna Anwar is a Scientist and Co-founder of the Centre of Bioinformatics Research and Technology (CBIRT). She is a passionate bioinformatics scientist and a visionary entrepreneur. Dr. Tamanna has worked as a Young Scientist at Jawaharlal Nehru University, New Delhi. She has also worked as a Postdoctoral Fellow at the University of Saskatchewan, Canada. She has several scientific research publications in high-impact research journals. Her latest endeavor is the development of a platform that acts as a one-stop solution for all bioinformatics related information as well as developing a bioinformatics news portal to report cutting-edge bioinformatics breakthroughs.


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