Principles of Fungal Metabolism, Growth and Bacterial Interactions
Authors:
William Cannon1,3* ([email protected], PI), Austin Hansen1, Yi-Syuan Guo2,3, Tomas Rush2, Dale Pelletier2,
Scott Retterer2, Mark Alber1, Bruce Palmer3, Connah Johnson3, Andrew Myers4, Ann Almgren4
Institutions:
1University of California–Riverside; 2Oak Ridge National Laboratory; 3Pacific Northwest National Laboratory; 4Lawrence Berkeley National Laboratory
Goals
The goals of this project are to elucidate fundamental principles of species interactions using hybrid machine learning/simulation models of fungal-bacterial interactions and dynamics. These hybrid data analytic/simulation models are used to carry out virtual experiments and develop fundamental understanding of the interactions between fungi and bacteria, specifically the mycorrhizal fungus Laccaria bicolor and Pseudomonas sp. helper. At the same time, researchers carry out experiments aimed at developing and testing quantitative assays to measure the same interactions, and whose data inform views of biology. Researchers are:
• Evaluating the fundamental physical operating principles of cells and using these principles to develop physics-informed machine learning models of metabolism.
• Evaluating how fungal lifestyles impact growth in sparse nutrient environments.
• Computationally and experimentally investigating pseudomonas chemotaxis in response to L. bicolor produced metabolites.
Abstract
The exchange of metabolites between microbes is an emergent property that evolves because the exchanged metabolites allow for increased growth of both species by reducing the thermodynamic cost of growth. Instead of each species producing every metabolite needed, metabolite exchange allows each microbe to specialize and efficiently produce a metabolite, such as trehalose, in exchange for one that it cannot produce as cheaply, such as thiamine. In order to evaluate the benefits of such microbial trade, physics-based models are needed that are capable of modeling the thermodynamic costs and benefits. The long-term goal of this project is to understand fundamental principles of fungal-bacterial interactions through physics-based, simulation and machine learning models of metabolism, protein expression and gene expression, and to couple these models to the mycelial growth and bacterial chemotaxis.
In this regard, the maximization of entropy, such as is done through metabolic exchange discussed above, has been alluded to historically or directly stated as the goal or an emergent property of biological systems by both physicists and ecologists, e.g., Lotka 1922a; Lotka 1922b; Odum and Pinkerton 1955; Prigogine 1978; Vallino 2009. Yet, the concept has been underdeveloped regarding application to systems such as metabolism, and because of the abstract nature of the concept, it has gained insufficient recognition as an operational principle among microbiologists and cell biologists. There are several issues regarding the application of the physical principles to biological systems the team addresses in this project. This project first demonstrates how one can determine a species phenotype based on knowledge of the genome and the environmental conditions by using physical principals. Specifically, using statistical thermodynamics, this study shows how a cell’s most probable state (phenotype) can be determined (Cannon et al. 2023), and how physicochemical constraints can be used to predict the internal regulation of the cell (Britton et al. 2020). The team demonstrates these concepts using a sophisticated model of metabolism (King et al. 2023).
To scale these concepts beyond the cell to interactions between fungi and bacteria, this group has developed realistic 2D and 3D models of fungal and bacterial growth in which each cell can contain sophisticated metabolisms and exchange nutrients with other species. These models require sophisticated, large-scale computing. Researchers have teamed with the DOE Exascale Computing project to implement Adaptive Mesh Refinement (AMR) using high performance computing in these models. The AMReX (AMR for the Exascale) project at Lawrence Berkeley National Laboratory supports the development of block-structured AMR algorithms for solving systems of partial differential equations on exascale architectures. The team has now developed large-scale, hybrid central processing unit-graphics processing unit simulations for growth of bacterial colonies (Palmer et al. 2023), growth of fungal mycelia, and the integrated growth and metabolic exchange between fungi and bacteria. By the end of the year, these methods will enable sophisticated computer experiments that can be used to complement experimental field work and omics measurements.
References
Britton, S., et al. 2020. “Enzyme Activities Predicted by Metabolite Concentrations and Solvent Capacity in the Cell,” Journal of the Royal Society, Interface 17(171), 20200656. DOI:10.1098/rsif.2020.0656.
Cannon, W. R., et al. 2023. “Probabilistic and Entropy Production Modeling of Chemical Reaction Systems: Characteristics and Comparisons to Mass Action Kinetic Models,” arXiv 2310.06135. DOI:10.48550/arXiv.2310.06135.
King, E., et al. 2023. “An Approach to Learn Regulation to Maximize Growth and Entropy Production Rates in Metabolism,” Frontiers in Systems Biology 3. DOI:10.3389/ fsysb.2023.981866.
Lotka, A. J. 1922a. “Natural Selection as a Physical Principle,” Proceedings of the National Academies of Sciences of the United States of America 8(6), 151–4. DOI:10.1073/pnas.8.6.151.
Lotka, A. J. 1922b. “Contribution to the Energetics of Evolution,” Proceedings of the National Academies of Sciences of the United States of America 8(6), 147–51. DOI:10.1073/ pnas.8.6.147.
Odum, H.T., and R. T. Pinkerton. 1955. “Time’s Speed Regulator: The Optimum Efficiency for Maximum Power Output in Physical and Biological Systems,” American Scientist 43(2).
Palmer, B. J., et al. 2023. “BMX: Biological Modelling and Interface Exchange,” Scientific Reports 13(1), 12235. DOI:10.1038/s41598-023-39150-1.
Prigogine, I. 1978. “Time, Structure, and Fluctuations,” Science 201(4358), 777–85. DOI:10.1126/science.201.4358.77.
Vallino, J. J. 2009. “Ecosystem Biogeochemistry Considered as a Distributed Metabolic Network Ordered by Maximum Entropy Production,” Philosophical Transactions of the Royal Society of London Biological Sciences 365, 1417–27. DOI:10.1098/rstb.2009.0272.
Funding Information
This project is supported by the DOE’s BER program under contract 78460. The AMReX developers (Almgren, Myers) were supported by the DOE Exascale project.