Decoding the evolution of anticipation and decision making in uncertain environments
International collaboration funded by the Human Frontier Science Program
Project description
In an international project involving three laboratories with expertise in microbial ecology (Dal Bello lab, Yale, USA), single-cell analyses (McGlynn lab, ELSI, Japan), and theoretical biology (Grilli research group, ICTP, Italy) , we aim to understand how microbes "learn" and anticipate environmental changes, a crucial ability for their survival. We'll investigate diverse microbial species in various environments—from stable to repetitive and random—to determine how much information cells can "remember" based on environmental predictability. We'll explore factors like genome size and growth rates, and use nanoSIMS enabled single-cell observations to track nutrient uptake and understand individuality within populations. By combining our experimental data with mathematical models and evolutionary experiments, we seek to develop a general understanding of learning mechanisms and their limits across the microbial world.
Open positions (anticipated start dates: December 2025)
Postdoctoral position, Yale, USA
Location
Dal Bello Lab, Department of Ecology and Evolutionary Biology and Microbial Sciences Institute, Yale University, New Haven, CT, USA
What you’ll be doing
The postdoc in the Dal Bello lab will primarily focus on experimental work, specifically conducting the high-throughput phenotyping of diverse natural microbial isolates to characterize their lag times and growth parameters across various environmental conditions. This includes setting up and monitoring well-mixed cultures, performing wash and transfer experiments, and collecting optical density data. They will also be responsible for preparing samples and performing the isotopic labeling for single-cell nanoSIMS analysis and will conduct long-term evolutionary experiments, including culturing, sampling, and freezing evolved bacterial populations, and performing population-level phenotyping.
Experience
We seek a motivated researcher with background in microbiology or related field passionate about gaining a quantitative, predictive understanding of bacterial ecology and evolution. Expertise in experimental microbial physiology and/or evolution is particularly valued.
How to apply
Inquiries about this position can be sent via email to Martina. Please include a brief description of your research interests, how they fit with the goals of the project, and the expectations for your postdoctoral training (research and career goals). Attach a CV with contact information for 2-3 references.
Postdoctoral position ICTP, Italy
Location
Grilli Research group, Quantitative Life Sciences section, The Abdus Salam International Centre for Theoretical Physics, Trieste, Italy
What you’ll be doing
The postdoc in JG group will be responsible for the quantitative analysis and theoretical modeling that integrates the experimental data. They will analyze the population-level and single-cell experimental results from MDB and SM to quantify adaptation and the relative role of phenotypic heterogeneity in variable environments. This will involve applying and extending existing frameworks of evolutionary dynamics and resource allocation to interpret experimental outcomes in terms of proteome partitioning and pre-allocation strategies. They will also develop the inverse mapping between pre-allocation profiles and environmental statistical properties to infer the internal representation of the environment encoded in the microbes' physiology. This postdoc's role is central to developing the theoretical understanding of microbial learning and anticipation.
Experience and skills
We seek a researcher with background in Physics, Applied Math or related field and strong motivation toward biological questions. Expertise in Quantitative Microbial Physiology and/or Evolutionary Dynamics is particularly valued.
How to apply
Inquiries about this position can be sent via email to Jacopo Grilli.
Postdoctoral position, Institute of Science Tokyo, Japan
Location
McGlynn Lab, Earth-Life Science Institute, Tokyo Institute of Technology
What you’ll be doing
The postdoc in the SM lab will be instrumental in the single-cell resolved analysis of isotope uptake using nanoSIMS. They will receive isotopically labeled samples from the MDB lab and perform detailed nanoSIMS analyses at the ARIM facility at the University of Tokyo. This involves meticulous sample preparation, instrument operation, and data acquisition to quantify individual cell activity by tracking nutrient uptake rates and assessing phenotypic heterogeneity within populations. They will also contribute to the methodological development of applying nanoSIMS to infer lag times, expanding its current uses. This postdoc's expertise is vital for providing the granular, single-cell insights necessary to understand the role of individuality in microbial anticipation.
Experience and skills
We seek a motivated researcher with background in microbiology or related field passionate about gaining a quantitative, predictive understanding of bacterial ecology and evolution. A background in experimental microbiology with knowledge of microscopy techniques is particularly valued.
How to apply
Please apply via this google form. Inquiries about this position can be sent via email to Shawn McGlynn.
More about the project
Abstract
All living organisms make decisions under uncertainty. Under steady growth, bacteria prepare for changes even in the absence of anticipatory cues by pre-expressing genes useful for potential future conditions. When conditions change, a phase of physiological adaptation precedes the resumption of growth. Its duration, called lag-time, is affected by the gene expression levels prior to the change. Since both growth rate and lag times are subject to natural selection, cells' ability to anticipate changes is expected to increase during evolution. Gene expression profiles, as determined by the gene regulatory network, encode an internal probabilistic model of environmental uncertainty that a bacterial lineage acquired ("learned") through evolution. Building on the correspondence between lag-times and anticipatory expression, this project aims to decode this internal model. We will characterize and compare the natural variation of growth rate and lag-times of E. coli K12, which has been adapted to laboratory conditions for decades, and a diverse pool of natural bacterial isolates. Using single-cell resolved secondary ion mass spectrometry (nanoSIMS), we will quantify phenotypic heterogeneity underlying these traits. We will evolve clonal populations of isolates under varying environmental conditions with different degrees of predictability. By characterizing lag in these different environments and the underlying phenotypic heterogeneity of the evolved lineages, we will decompose anticipation into pre-allocation and bethedging strategies. We will synergistically develop a theoretical framework grounded in data and based on quantitative physiology, evolutionary dynamics, and information theory. Our work will provide an understanding of the speed and the limits of adaptation in uncertain environments as a function of environmental and organismal complexity, an unsolved question across life sciences. Since nothing endures but change, our understanding of the evolution of anticipation will have impact beyond evolutionary microbiology (e.g., with applications in antibiotic resistance and the changing climate), setting the stage to an information-grounded and information-bounded view of evolution.