Predicting the decision making chemicals used for bacterial growth

Abstract

Predicting the contribution of media components to bacterial growth was first initiated by introducing machine learning to high-throughput growth assays. A total of 1336 temporal growth records corresponding to 225 different media, which were composed of 13 chemical components, were generated. The growth rate and saturated density of each growth curve were automatically calculated with the newly developed data processing program. To identify the decision making factors related to growth among the 13 chemicals, big datasets linking the growth parameters to the chemical combinations were subjected to decision tree learning. The results showed that the only carbon source, glucose, determined bacterial growth, but it was not the first priority. Instead, the top decision making chemicals in relation to the growth rate and saturated density were ammonium and ferric ions, respectively. Three chemical components (NH 4 + , Mg 2+ and glucose) commonly appeared in the decision trees of the growth rate and saturated density, but they exhibited different mechanisms. The concentration ranges for fast growth and high density were overlapped for glucose but distinguished for NH 4 + and Mg 2+ . The results suggested that these chemicals were crucial in determining the growth speed and growth maximum in either a universal use or a trade-off manner. This differentiation might reflect the diversity in the resource allocation mechanisms for growth priority depending on the environmental restrictions. This study provides a representative example for clarifying the contribution of the environment to population dynamics through an innovative viewpoint of employing modern data science within traditional microbiology to obtain novel findings.

Experimental Evolution Expands the Breadth of Adaptation to an Environmental Gradient Correlated With Genome Reduction

“…However, the participation of components other than the target component in adaptive evolution has generally been neglected. A machine learning analysis of medium components showed that it was the trace elements (e.g., metal ions) rather than the major nutrients (e.g., glucose) that determined bacterial growth, which was sensitive to the concentration gradient ( Ashino et al, 2019 ). Thus, whether and how adaptation through experimental evolution is associated with correlated adaptation to environmental gradients must be addressed.…”

Clustering of Bacterial Growth Dynamics in Response to Growth Media by Dynamic Time Warping

“…The shape of a growth curve is unpredictable, even for defined strains growing under well-controlled conditions in the laboratory, because bacterial growth is attributed to not only the genotype but also the external factors, such as the composition of the medium. Our previous findings 2 of 11 verified that bacterial growth was either coordinated with genome reduction [14,15] or determined by the chemical composition of the growth medium [16]. Thus, model-based growth analyses were unsuitable to create a linkage between the growth dynamics and the growth condition.…”

Local adaptation mediated niche expansion in correlation with genetic richness

“…The stock solutions, that is, 1 M glucose, 0.615 M K2HPO4, 0.382 M KH2PO4, 0.203 M MgSO4, 0.0152 M thiamin/HCl, 0.0018 M FeSO4, and 0.766 M (NH4)2SO4, were sterilized using a sterile syringe filter with a 0.22-µm pore size hydrophilic PVDF membrane (Merck). The concentrations of most chemical compounds were altered one-by-one on a logarithmic scale to achieve a wide range of environmental gradients, as described previously 25 , which led to a total of alternative medium combinations (C1~28) were used for the fitness assay. The resultant concentrations of individual components in the ionic form are summarized in Table S2.…”