Application deadline: February 15, 2021.
Martina dal Bello (MIT), Jonathan Friedman (Hebrew U), Josh Goldford (MIT), Ben Good (Stanford), Akshit Goyal (MIT), Tami Liberman (MIT), Pankaj Mehta (BU), Julian Regalado Perez (U. Copenhagen), Joshua Weitz (Georgia Tech)
Guest Lecturers (preliminary list):
The summer course is closely linked to the concurrent KITP program The Ecology and Evolution of Microbial Communities. Course participants will attend the program's daily research seminars as part of the course curriculum. Students and lecturers will also have frequent opportunities for less formal interactions. Confirmed program participants include Devaki Bhaya (Carnegie Inst.), Daniel Fisher (Stanford), Susan Holmes (Stanford), Terry Hwa (UCSD), Jay Lennon (Indiana U), Sergey Maslov (UIUC), Pankaj Mehta (Boston U.), Andrew Murray (Harvard), Diane Newman (Caltech), Alex Petroff (Clark U), Paul Rainey (Max Planck), Alvaro Sanchez (Yale), Daniel Segrè (Boston U.), Wenying Shou (UCL), Kim Sneppen (Bohr Inst.), Alfred Spormann (Stanford), Ned Wingreen (Princeton)
Microbes form diverse and complex communities in both environmental and host-associated niches. These communities are ubiquitous and have global impacts on processes as diverse as biogeochemical nutrient cycling in terrestrial and marine ecosystems; the formation of intimate associations with varied hosts, and the suppression or emergence of disease. This hands-on research course will integrate laboratory projects and mathematical modelling to provide students with an understanding of the new tools and analytical approaches used to address questions in microbiome ecology and evolution. Students will gain experience developing models and reproducible analysis workflows to ask sharp, quantitative research questions focused on the discovery of the principles underlying microbial community structure, function and evolution.
Online Course Structure
Because of campus and lab access restrictions we have redesigned the course converting it to a “dry-lab” form. The course will run in remote mode via Zoom. Our course will be based on “paper projects” that will involve in-depth study (including data re-analysis) of published work under the guidance of its authors. The first week will focus on tutorials building up basic skills required/advantageous for one or more “paper projects”. Weeks 2 and 3 are allocated for two-week long paper projects. Three projects will run in parallel with students assigned to different groups for their duration. Weeks 4 and 5 will offer a different set of one-week projects/active journal clubs. The basic expectation is for the projects to include six (or more) hours of Zoom time “in class" per week, for example on Monday, Wednesday and Friday. The students will be expected to listen to the concurrently running Eco/Evo program lectures, Zoom among themselves or work independently the rest of the time.
"Dry-lab" Projects (partial list)
Ben Good (Week 2-3): G Garud, B. H. Good, O. Hallatschek, and K. S. Pollard. Evolutionary dynamics of bacteria in the gut microbiome within and across hosts. PLoS Biology 17(1):e3000102 (2019).
This module will introduce approaches for tracking genetic variation within species in complex metagenomic samples. We will explore these questions in the context of the human gut microbiome, by reproducing key results from our recent study (Garud, Good et al, PLoS Bio 2019). We will start from the raw sequencing reads, and slowly build up approaches for detecting within-host evolution and strain replacement events in longitudinally sequenced fecal samples. Our goal will be to understand some of the main challenges involved in working with metagenomic datasets, and how they both constrain and enhance the evolutionary inferences that can be drawn from them.
1. Garud & Good et al (2019). Evolutionary dynamics of bacteria in the gut microbiome within and across hosts. PLoS Biology 17(1):e3000102.
2. Roodgar & Good et al (biorXiv). Longitudinal linked read sequencing reveals ecological and evolutionary responses of a human gut microbiome during antibiotic treatment. https://www.biorxiv.org/content/10.1101/2019.12.21.886093v2
We will try to introduce all of the necessary concepts as we go, but students looking for additional background may find the following resources helpful.
(1) A. Murat Eren, Microbial ‘Omics: An Introduction. https://merenlab.org/momics/
(2) Nayfach et al (2016). An integrated metagenomics pipeline for strain profiling reveals novel patterns of bacterial transmission and biogeography. Genome Res, 26(11):1612-1625.
(3) B.H. Good. Lecture Notes on Quantitative Evolutionary Dynamics and Genomics. https://bgoodlab.github.io/courses/apphys23x7/.
Jonathan Friedman & P. Blainey (Week 2-3): Positive interactions are common among culturable bacteria. bioRxiv 2020.06.24.169474
This module will be on (pairwise) interactions between bacteria. I will discuss how to infer interactions from species abundance data, and the participants will analyze data to study how interactions change across environmental conditions, and as species coevolve. The specifics necessarily depend on the participants' background, what was covered in the first-week tutorials, and the amount of time they have to dedicate to working on this. This is a nice introductory paper to inferring interactions, and the data we will analyze is from our preprint using the kChip to study interactions across carbon sources, and paper about species coevolution.
Seppe Kuehn (Week 2-3): Predicting metabolite dynamics from genomic structure in denitrifying bacterial communities.
Project will follow and extend our recent preprint https://www.biorxiv.org/content/10.1101/2020.09.29.315713v2
The goal of this project is to address the ‘structure-function’ problem in communities. How does the genomic structure determine metabolic function? We pose this question as a prediction problem: can we predict the dynamic flux of metabolites through a consortium from the genomes of each member of the community? We use denitrification as a model process - which is anaerobic respiration utilizing oxidized nitrogen compounds as electron acceptors. We will infer consumer resource model parameters from time series measurements of metabolites (data provided). We will use these models to predict metabolite dynamics in consortia of multiple strains and then compare those predictions with data. Finally, we will take a statistical approach using regularized regression to map the genes each strain possesses to their associated consumer resource model parameters - completing a map from genomes to community-level metabolic function. Tutorials on consumer resource models and python will be sufficient to participate in this project. We will direct students towards open questions which emerge from the study above and provide raw data
Joshua Weitz (Week 4): Viral Strategies and Dynamics from Lysis to Latency
Objective: Viral infections can lead to the lysis of microbes and the release of organic matter back into the environment. This organic matter can then be taken up by other organisms, promoting new growth (e.g., by heterotrophic bacteria). Yet, not all viral infections end in lysis. Temperate bacteriophage can integrate their genomes into the chromosome of the bacterial host forming a lysogen. The integrated phage genome (or ‘prophage’) can be stably passed from mother to daughter cells. Over time, the prophage can also induce, reinitiating the lytic pathway, leading to the lysis of the bacterial host and the release of virions back into the environment. Notably, both the ‘decision’ to lyse or integrate upon infection and the ‘decision’ to induce from the lysogenic state are modulated by viral, cellular, and environmental factors. The aim of this short course is to develop a foundational theoretical framework as well as practical computational skills to analyze such decisions in a dynamical systems context. To do so, the short course will provide students with the necessary ecological, theoretical, and computational foundations to explore problems independently. The course will include background information on nonlinear dynamical systems, local stability analysis, invasion analysis, as well as core computational methods for analyzing the joint dynamics of viruses and their microbial hosts. Students are expected to work independently or in small groups to analyze how the invasion fitness of viral strategies depends on ecological context and how distinct viral strategies interact.
Tami Lieberman and Shijie Zhao (Week 5): Adaptive Evolution within Gut Microbiomes.
More details to follow
Martina Dal Bello et al. (Week 4) Resource-diversity relationships in bacterial communities reflect the network structure of microbial metabolism https://www.biorxiv.org/content/10.1101/2020.09.12.294660v2
More details to follow
Akshit Goyal, Otto Cordero & Sergey Maslov (Week 5?)- Predicting community metabolites and interactions using models and data
Based on: Goyal et. al., Ecology-guided prediction of cross-feeding interactions in the human gut microbiome, Nature Communications (2021). https://www.nature.com/articles/s41467-021-21586-6
More details to follow
Goldford et al. Emergent simplicity in microbial community assembly. Science. 2018 Aug 3;361(6401):469-474.
More details to follow
Additional resources for students:
Lectures on Bacteriophages: KITP Bacteriophage forum
Lectures on Microbial Metabolism: KITP forum on Microbial Metabolism