Post Doctoral Fellow in Deep Learning for Microbiome Spatial Omics – Gerber Lab

The Gerber Lab (http://gerber.bwh.harvard.edu) is a multidisciplinary group at Brigham and Women’s Hospital/Harvard Medical School that develops novel computational models and high-throughput experimental systems to understand the role of the microbiota in human diseases, and applies these findings to develop new diagnostic tests and therapies. A long-standing and continuing focus of the lab is on incorporating principled probabilistic models into machine learning methods. The director of the lab, Dr. Georg Gerber, MD, PhD, MPH, uses his unique expertise, combining deep learning method development, medical microbiology, and human pathology, to leverage cutting-edge technologies to tackle scientifically and clinically important problems.

We are looking for an exceptional researcher who will play a major role in new initiatives in the lab to develop novel deep learning (DL) approaches to further understanding of the spatial organization of the microbiome–the trillions of microbes living on and within us—and its interactions with mammalian cells. The successful candidate will be highly motivated and creative, taking a lead role in developing new deep learning-based methods, analyzing data, and interpreting results. Although experience analyzing data from biological systems is required, microbiome specific knowledge is not.

Qualifications:

  • PhD in Computer Science, Computational Biology, or other highly quantitative discipline.
  • Outstanding publication track record.
  • Strong mathematical background and skills.
  • Experience developing DL methods.
  • Experience analyzing data from biological systems, including sequencing data.
  • Solid programming skills in Python, including PyTorch.
  • Superior verbal and written communication skills, and ability to work on multidisciplinary teams.

Environment:  the Gerber Lab is located in the Brigham and Women’s Hospital Division of Computational Pathology (http://comp-path.bwh.harvard.edu) at Harvard Medical School (HMS). With a recent grant from the Massachusetts Life Science center, the Division has built the Lab for AI/Deep Learning for the Microbiome, which has a state-of-the-art GPU cluster for model development, training and deployment. BWH is part of the greater Longwood Medical Area in Boston, a rich, stimulating environment conducive to intellectual development and research collaborations, which includes HMS, Harvard School of Public Health and Boston Children’s Hospital.

To apply: email a single PDF including cover letter, CV, brief research statement and a list of at least three references to Dr. Georg Gerber (ggerber@bwh.harvard.edu).

We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, gender identity, sexual orientation, pregnancy and pregnancy-related conditions or any other characteristic protected by law.

Graduate Students – Gerber Lab

I am always excited to work with talented graduate students with interests relevant to my lab, which focuses on developing novel machine learning/computational biology/wet lab approaches to further understanding of the microbiome–the trillions of microbes living on and within us. This fascinating, complex and dynamic ecosystem is crucial for human health, and when disrupted may contribute to a variety of diseases including infections, arthritis, allergies, cancer, heart and bowel disorders.

In general, I can only be a primary advisor (and provide financial support) for students enrolled at Harvard or MIT. However, I am open to co-advising students at other institutions.

If you’re interested, email me at ggerber#bwh.harvard.edu. Please include your CV and a brief description of your research interests.

Students should have a high level of interest in:

  • Developing and applying new technologies to biomedical problems.
  • Advancing knowledge of the microbiome and its role in human health and disease.
  • Having your work make an impact on healthcare outcomes.
  • Working on an interdisciplinary team and collaborating with computational, wet lab and clinical scientists.

About the lab: the Gerber Lab develops novel statistical machine learning models and high-throughput experimental systems to understand the role of the microbiota in human diseases, and applies these findings to develop new diagnostic tests and therapies. A particular focus of the Gerber Lab is understanding dynamic behaviors of host-microbial ecosystems. Our work in this area includes Bayesian statistical machine learning methods for discovering temporal patterns in microbiome data, inferring dynamical systems models from microbiome time-series data, or predicting host status from microbiome time-series data with human interpretable rules. We have applied these methods to a number of clinically relevant questions including understanding dynamic effects of antibiotics, infections and dietary changes on the microbiome, and designing bacteriotherapies for C. difficile infection and food allergy. We also apply our methods to synthetic biology problems, to engineer consortia of bacteria for diagnostic and therapeutic purposes.

Environment:  the Gerber Lab is located in the Division of Computational Pathology, which Dr. Gerber heads, at Brigham and Women’s Hospital (BWH) at Harvard Medical School (HMS), and the Massachusetts Host-Microbiome Center, which Dr. Gerber co-directs. BWH, an HMS affiliated teaching hospital is adjacent to the HMS main quad and is the second largest non-university recipient of NIH research funding. The broad mandate of the BWH Division of Computational Pathology is to develop and apply advanced computational methods for furthering the understanding, diagnosis and treatment of human diseases. The Division is situated within the BWH Department of Pathology, which houses over 40+ established investigators, 50+ postdoctoral research fellows, and 100+ research support staff. In addition, BWH is part of the greater Longwood Medical Area in Boston, a rich, stimulating environment conducive to intellectual development and research collaborations, which includes HMS, Harvard School of Public Health, Boston Children’s Hospital and the Dana Farber Cancer Institute.

Gerber Lab awarded $3.1 Million Five Year NIH-NIGMS R35 Grant “Probabilistic deep learning models and integrated biological experiments for analyzing dynamic and heterogeneous microbiomes”

Gerber Lab awarded $3.1 Million Five Year NIH-NIGMS R35 Grant “Probabilistic deep learning models and integrated biological experiments for analyzing dynamic and heterogeneous microbiomes”

This work will leverage deep learning technologies to advance the microbiome field beyond finding associations in data, to accurately predicting the effects of perturbations on microbiota, elucidating mechanisms through which the microbiota affects the host, and improving bacteriotherapies to enable their success in the clinic. New deep learning models will be developed that address specific challenges for the microbiome, including noisy/small datasets, highly heterogenous human microbiomes, the need for direct interpretability of model outputs, complex multi-modal datasets, and constraints imposed by biological principles. Computational models and biological experiments will be directly coupled through reinforcing cycles of predicting, testing predictions with new experiments, and improving models. An important objective will also be to make computational tools widely available to the research community, through release of quality open-source software.

RePORTER Link

 

Gerber Lab at ICML Workshop on Computational Biology 2023

Gerber Lab at ICML Workshop on Computational Biology 2023

The ICML Workshop on Computational Biology (WCB) highlights how ML approaches can be tailored to making both translational and basic scientific discoveries with biological data, such as genetic sequences, cellular features or protein structures and imaging datasets, among others. It aims to bring together interdisciplinary ML researchers working in areas such as computational genomics; neuroscience; metabolomics; proteomics; bioinformatics; cheminformatics; pathology; radiology; evolutionary biology; population genomics; phenomics; ecology, cancer biology; causality; representation learning and disentanglement to present recent advances and open questions to the machine learning community.

The Gerber Lab had the following two papers accepted:

Gerber GK, Bhattarai SK, Du M, Glickman MS, Bucci V. Discovery of Host-Microbiome Interactions Using Multi-Modal, Sparse, Time-Aware, Bayesian Network-Structured Neural Topic Models. International Conference on Machine Learning Workshop on Computational Biology, 2023.

Uppal G, Urtecho G, Richardson M, Moody T, Wang HH, Gerber GK. MC-SPACE: Microbial communities from spatially associated counts engine. International Conference on Machine Learning Workshop on Computational Biology, 2023.

 

Gerber Lab’s “MDITRE: Scalable and Interpretable Machine Learning for Predicting Host Status from Temporal Microbiome Dynamics” is mSystems Editor’s Pick

Gerber Lab’s “MDITRE: Scalable and Interpretable Machine Learning for Predicting Host Status from Temporal Microbiome Dynamics” is mSystems Editor’s Pick

Longitudinal microbiome data sets are being generated with increasing regularity, and there is broad recognition that these studies are critical for unlocking the mechanisms through which the microbiome impacts human health and disease. However, there is a dearth of computational tools for analyzing microbiome time-series data. To address this gap, we developed an open-source software package, Microbiome Differentiable Interpretable Temporal Rule Engine (MDITRE), which implements a new highly efficient method leveraging deep-learning technologies to derive human-interpretable rules that predict host status from longitudinal microbiome data. Using semi-synthetic and a large compendium of publicly available 16S rRNA amplicon and metagenomics sequencing data sets, we demonstrate that in almost all cases, MDITRE performs on par with or better than popular uninterpretable machine learning methods, and orders-of-magnitude faster than the prior interpretable technique. MDITRE also provides a graphical user interface, which we show through case studies can be used to derive biologically meaningful interpretations linking patterns of microbiome changes over time with host phenotypes. 

The Massachusetts Lab for Artificial Intelligence/Deep Learning for the Microbiome

The Massachusetts Lab for Artificial Intelligence/Deep Learning for the Microbiome

Through a $3.3M grant from the Massachusetts Life Science Center and in-kind support from Brigham and Women’s Hospital and Mass General Brigham, the BWH Massachusetts Host-Microbiome Center (MHMC) and Division of Computational Pathology will establish a new lab to develop and apply advanced AI/deep learning technologies to microbiome research. Dr. Georg Gerber, Chief of BWH Computational Pathology and co-director of the MHMC will head the new lab.

The microbiome is inherently complex and dynamic. Multi-omic data characterizing microbes in culture systems, animal models, and human populations can provide unique and complementary insights into these rich host-microbial ecosystems. However, to fully realize the potential of these data, sophisticated computational approaches are needed.

Artificial Intelligence (AI), and in particular Deep Learning (DL), are revolutionizing many fields, such as speech and image recognition. These technologies are also increasingly impacting the biomedical sciences.

The Lab aims to unleash the power of AI and DL technologies for the microbiome field.

Anchored by a dedicated large GPU with Tesla A100 nodes and CPU compute clusters, the Lab will develop custom AI/DL applications for the microbiome, deploy existing software in a managed and easy-to-use environment, and provide outreach and education to the microbiome community. The Lab will be staffed by principal investigators in the Division of Computational Pathology, as well as an application scientist and network engineers.

A joint initiative between the Brigham and Women’s Hospital (BWH) Division of Computational Pathology and the Massachusetts Host-Microbiome Center (MHMC), the Lab is funded by the Massachusetts Life Sciences Center and Brigham and Women’s Hospital/Mass General Brigham. Industry and academic users will be able to access the Lab through the MHMC’s existing core services model and through collaborations.

Gerber lab study showing gut metabolites predict C. diff recurrence

Gerber lab study showing gut metabolites predict C. diff recurrence

Clostridioides difficile infection (CDI) is the most common hospital acquired infection in the USA, with recurrence rates > 15%. Although primary CDI has been extensively linked to gut microbial dysbiosis, less is known about the factors that promote or mitigate recurrence. Using broad metabolomics data and statistics and machine learning models, Jen Dawkins, a HST PhD student and member of the Gerber lab, showed the metabolites in the gut can accurately predict C. difficile recurrence. These findings have implications for development of diagnostic tests and treatments that could ultimately short-circuit the cycle of CDI recurrence, by providing candidate metabolic biomarkers for diagnostics development, as well as offering insights into the complex microbial and metabolic alterations that are protective or permissive for recurrence.

Dawkins JJ, Allegretti JR, Gibson TE, McClure E, Delaney M, Bry L, Gerber GK. Gut metabolites predict Clostridioides difficile recurrence. Microbiome. 2022 Jun 9;10(1):87. doi: 10.1186/s40168-022-01284-1. PMID: 35681218; PMCID: PMC9178838.

$2.9M grant from the National Science Foundation  “The rules of microbiota colonization of the mammalian gut”

$2.9M grant from the National Science Foundation “The rules of microbiota colonization of the mammalian gut”

The Gerber lab in collaboration with the Wang lab at Columbia and the Gibson Lab at BWH have received a $2.9M grant from the National Science Foundation to develop and apply novel computational and experimental methods to elucidate fundamental rules governing the formation and maintenance of complex microbial ecosystems in the mammalian gut.

Abstract: Microbiomes, or the collections of trillions of bacteria and other micro-organisms living on, within and around us, have enormous impact on human life. For example, they help people digest food, promote the growth of farm animals and crops, and degrade pollutants in the environment. Despite the importance of microbiomes, the processes governing their formation and maintenance remain poorly understood. The mammalian gut is a particularly intriguing system for microbiome studies, since a diverse collection of microbes has evolved that specifically colonizes and functions in that environment. The goal of the project is to derive fundamental rules that describe and predict the dynamic process of microbial colonization of the mammalian gut. To achieve this goal, the team of investigators will develop new computer-based methods to automatically extract predictive and explanatory rules from large microbiome data sets. The team will also develop new experimental tools and generate data sets in mouse measuring how microbiomes change over time and across space in the mammalian gut. Overall, the project will further the understanding of the formation of microbiomes in mammals and can provide broader insights into the emergence of other microbial ecosystems, such as those in soil and marine environments. These insights could ultimately help scientists to rationally alter or maintain microbiomes in different environments to benefit human activities. The project will also generate practical resources for the scientific community (computer-based tools and datasets) and provide education on the microbiome to college and elementary school students through courses and hands-on labs.

A wealth of genomic data provides information as to which microbes are present in environments, but little insight into underlying factors that explain or predict complex assemblages of microbial consortia. This project aims to elucidate mechanistic factors that drive the dynamic process of microbial colonization of the mammalian gut. These determinants will be investigated at multiple systems scales, from the level of microbial communities down to the level of individual genes. The project will leverage high-throughput experimental methods developed by the investigators, to generate data characterizing functional genetic selection and spatial organization of microbiota in the mammalian gut. From the Computer Science perspective, the project will develop new computational methods to infer human-interpretable rules and other structured outputs from complex and noisy high-throughput microbiome datasets, using Bayesian and neural-style approaches that incorporate prior biological knowledge while scaling to massive datasets. This project has three main thrusts: 1) Learn microbial community-level rules that quantitatively predict population dynamics of mouse gut colonization and assess these rules across differing ranges of microbial diversity and composition, 2) Elucidate microbial gene-level mechanisms that predict mouse gut colonization dynamics, and 3) Profile microbial spatiotemporal organization and dynamics during gut colonization at the species and gene level to predict microbial community dynamics. The project is expected to establish a set of new computational and experimental tools and principles for understanding the rules of microbial colonization of the gut, with potential applications to other ecosystems including gut microbiota of non-mammalian species as well as complex environmental microbiota.