I'm a scientist studying the data science of microbiology -- how large scale sequencing, computational biology, and microbes can be used jointly to solve problems facing the world. I spend my time in the labs of Chris Mason at Weill Cornell Medical College and George Church at Harvard, and I also hold a position at Seed Health on the Reseach and Development team. My work currently spans monitoring pathogen and microbial genomic evolution in space, building bioreactors for carbon capture in space and on Earth, developing algorithms for pathogen classification and monitoring in wastewater, building and testing probiotics to fight coral bleaching, and building methods for optimizing the cultivability of rare microbes.

Prior to all this, I did a PhD at Harvard Medical School with Chirag Patel and Alex Kostic. Before that, I majored in Biology and Economics at Duke University in Durham, North Carolina. During my time there, I worked with Philip Benfey on the development of the Arabidopsis root microbiome, Beth Shank on the soil microbiome, and AgBiome, a company focused on building microbial-based treatments for crop-based diseases.

Outside of science, I spent most of my time playing music, trail running, scuba diving and, at every available opportunity, eating at Waffle Houses.

Employment History

Postdoctoral Fellow,
Laboratories of Dr. Christopher Mason and Dr. George Church

Leveraging the data science of microbiology to solve problems facing us on Earth and beyond
05/2021 – present, New York, NY
Weill Cornell Medical College/Harvard Medical School
PhD Candidate,
Laboratories of Dr. Chirag J Patel and Dr. Aleksandar D Kostic

Developing statistical methods for microbiome analysis
03/2017 – 05/2021, Boston, MA
Harvard Medical School/Joslin Diabetes Center
Rotation Student, Laboratory of Dr. Chirag J Patel
Using electronic health records and census data to model disease prevalence
01/2017 – 03/2017, Boston, MA
Harvard Medical School
Rotation Student, Laboratory of Dr. Paula Watnick
Finding drivers of biofilm formation in Vibrio cholera
10/2016 – 12/2016, Boston, MA
Harvard Medical School/Boston Children’s Hospital
Rotation Student, Laboratory of Dr. George Church
Engineering bacterially derived biosensors
07/2016 – 10/2016, Boston, MA
Harvard Medical School
Lead Microscopist
Finding and validating microbial methods for crop disease control
03/2014 – 06/2016, Durham, NC
AgBiome, LLC
Research Assistant, Laboratory of Dr. Elizabeth Shank
Developed an algorithm for blind spectral image analysis
04/2015 – 06/2016, Chapel Hill, NC
University of North Carolina Chapel Hill
Pianist in a hotel bar
02/2015 – 06/2016, Durham, NC
Doubletree Hilton
Research Assistant, Laboratory of Dr. Philip Benfey
Identified/engineered Arabidopsis root growth promoting bacteria
01/2014 – 06/2016, Durham, NC
Duke University
Research Assistant, Laboratory Dr. Rafael Valdivia
Searching for drivers of biofilm formation in Exiguobacterium
01/2013 – 05/2014, Durham, NC
Duke University
Research Assistant, Laboratory of Dr. Gary Borisy
Method development for studying spatial structure of the oral microbiome
06/2010 – 09/2013, Woods Hole, MA
Marine Biological Laboratories
Research Assistant, Sediment Core Laboratory
Analyzing ocean sediment core records to gauge ocean warming
06/2009 – 09/2009, Woods Hole, MA
Woods Hole Oceanographic Institution


PhD in Biological and Biomedical Sciences
Focus: Computational Microbiology/Bioinformatics
07/2017 – present, Boston, MA
Harvard Medical School
Bachelor of Science in Biology
Bachelor of Science in Economics
08/2012 – 05/2016, Durham, NC
Duke University

Recent New and Other Updates

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*This section is out of date and will be updated on a new site in the coming weeks! For now, check out my Google Scholar!

A systematic machine learning and data type comparison yields robust metagenomic predictors of age and other host phenotypes

Alan Le Goallec, Braden T. Tierney, Jacob M Luber, Evan Cofer, Aleksandar D. Kostic, Chirag J Patel

The microbiome is a new frontier for building predictors of human phenotypes. However, machine learning in the microbiome has struggled with issues of reproducibility, weak associations and variation in the wide range of analytic models and microbiome data types available. We aimed to build robust metagenomic predictors of host phenotype by comparing prediction performances and biological interpretation across 8 machine learning methods and 5 different metagenomic data types. Using 1,570 samples from 300 infants, we fit 7,295 models for 6 host phenotypes. Here, we present the first-published evidence that microbiome gene content can be used to predict age and sex. We additionally identified biological features predictive of breastfeeding status, historical antibiotic usage, country of origin, and delivery type. Finally, we demonstrate the dependence of accuracy on model choice and feature definition in microbiome data, and propose a framework for building microbiome-derived indicators of host phenotype. Our complete results can be viewed at http://apps.chiragjpgroup.org/ubiome_predictions/. Read more.

The Landscape of Genetic Content in the Gut and Oral Human Microbiome

Braden T. Tierney, Zhen Yang, Jacob M Luber, Marc Beaudin, Marsha C. Wibowo, Christina Baek, Chirag J Patel, Aleksandar D. Kostic

Despite substantial interest in the species diversity of the human microbiome and its role in disease, the scale of its genetic diversity, which is fundamental to deciphering human-microbe interactions, has not been quantified. Here, we conducted a cross-study meta-analysis of metagenomes from two human body niches, the mouth and gut, covering 3,655 samples from 13 studies. We found staggering genetic heterogeneity in the dataset, identifying a total of 45,666,334 non-redundant genes (23,961,508 oral, 22,254,436 gut) at the 95% identity level. 50% of all genes were “singletons”, or unique to a single metagenomic sample. Singletons were enriched for different functions (compared to non-singletons) and arose from sub-population specific microbial strains. Overall, these results provide potential bases for the unexplained heterogeneity observed in microbiome-derived human phenotypes. Based on these data we built a resource, which can be accessed at https://microbial-genes.bio. Read more.

Meta-Omics Analysis of Elite Athletes Identifies a Performance-Enhancing Microbe That Functions via Lactate Metabolism

Jonathan Scheiman, Jacob M. Luber, Theodore A. Chavkin,Tara MacDonald, Angela Tung, Loc-Duyen Pham, Marsha C. Wibowo, Renee C. Wurth, Sukanya Punthambaker, Braden T. Tierney, Zhen Yang, Mohammad W. Hattab, Julian Avila-Pacheco, Clary B. Clish, Sarah Lessard, George M. Church & Aleksandar D. Kostic

The human gut microbiome is linked to many states of human health and disease1. The metabolic repertoire of the gut microbiome is vast, but the health implications of these bacterial pathways are poorly understood. In this study, we identify a link between members of the genus Veillonella and exercise performance. We observed an increase in Veillonella relative abundance in marathon runners postmarathon and isolated a strain of Veillonella atypica from stool samples. Inoculation of this strain into mice significantly increased exhaustive treadmill run time. Veillonella utilize lactate as their sole carbon source, which prompted us to perform a shotgun metagenomic analysis in a cohort of elite athletes, finding that every gene in a major pathway metabolizing lactate to propionate is at higher relative abundance postexercise. Using 13C3-labeled lactate in mice, we demonstrate that serum lactate crosses the epithelial barrier into the lumen of the gut. We also show that intrarectal instillation of propionate is sufficient to reproduce the increased treadmill run time performance observed with V. atypica gavage. Taken together, these studies reveal that V. atypica improves run time via its metabolic conversion of exercise-induced lactate into propionate, thereby identifying a natural, microbiome-encoded enzymatic process that enhances athletic performance.Read more.

Repurposing large health insurance claims data to estimate genetic and environmental contributions in 561 diseases

Chirag M Lakhani, Braden T Tierney, Arjun K Manrai, Jian Yang, Peter M. Visscher, Chirag J Patel

We analyzed a large health insurance dataset to assess the genetic and environmental contributions of 560 disease-related phenotypes in 56,396 twin pairs and 724,513 sibling pairs out of 44,859,462 individuals that live in the United States. We estimated the contribution of environmental risk factors (socioeconomic status, air pollution, and climate) in each phenotype. Mean heritability (h2 = 0.311) and shared environmental variance (c2 = 0.088) were higher than variance attributed to specific environmental factors such as zip code-level socioeconomic status (varSES = 0.002), daily air quality (varAQI = 0.0004), and average temperature (vartemp = 0.001) overall, as well as for individual phenotypes. We found significant heritability and shared environment for a number of comorbidities (h2 = 0.433, c2 = 0.241) and average monthly cost (h2 = 0.290, c2 = 0.302). All results are available using our “Claims Analysis of Twin Correlation and Heritability (CaTCH)” web application. Read more.

ExposomeDW: A unified data warehouse of geotemporal demographic, economic, and environmental information to enhance clinical research

Chirag M Lakhani, Shreyas Bhave, Braden T Tierney, Andrew Deonarine, Rolando Acosta Jr, Isaac S Kohane, Chirag J Patel

Removal of a membrane anchor reveals the opposing regulatory functions of Vibrio cholerae glucose-specific enzyme llA

Vidhya Vijayakumar, Audrey Vanhove, Bradley Pickering, Julie Liao, Braden T. Tierney, John Asara, Roderick Bronson, and Paula Watnick

The Vibrio cholerae phosphoenolpyruvate phosphotransferase system (PTS) is a well-conserved, multicomponent phosphotransfer cascade that coordinates the bacterial response to carbohydrate availability through direct interactions of its components with protein targets. One such component, glucose-specific enzyme IIA (EIIAGlc), is a master regulator that coordinates bacterial metabolism, nutrient uptake, and behavior by direct interactions with cytoplasmic and membrane-associated protein partners. Here, we show that an amphipathic helix (AH) at the N terminus of V. cholerae EIIAGlc serves as a membrane association domain that is dispensable for interactions with cytoplasmic partners but essential for regulation of integral membrane protein partners. By deleting this AH, we reveal previously unappreciated opposing regulatory functions for EIIAGlc at the membrane and in the cytoplasm and show that these opposing functions are active in the laboratory biofilm and the mammalian intestine. Phosphotransfer through the PTS proceeds in the absence of the EIIAGlc AH, while PTS-dependent sugar transport is blocked. This demonstrates that the AH couples phosphotransfer to sugar transport and refutes the paradigm of EIIAGlc as a simple phosphotransfer component in PTS-dependent transport. Our findings show that Vibrio cholerae EIIAGlc, a central regulator of pathogen metabolism, contributes to optimization of bacterial physiology by integrating metabolic cues arising from the cytoplasm with nutritional cues arising from the environment. Because pathogen carbon metabolism alters the intestinal environment, we propose that it may be manipulated to minimize the metabolic cost of intestinal infection. Read more.

Aether: Leveraging Linear Programming For Optimal Cloud Computing In Genomics

Jacob M. Luber, Braden T. Tierney, Evan M. Cofer, Chirag J. Patel, and Aleksandar D. Kostic. Bioinformatics, December 2017.

Across biology we are seeing rapid developments in scale of data production without a corresponding increase in data analysis capabilities. Here, we present Aether (http://aether.kosticlab.org), an intuitive, easy-to-use, cost-effective, and scalable framework that uses linear programming (LP) to optimally bid on and deploy combinations of underutilized cloud computing resources. Our approach simultaneously minimizes the cost of data analysis while maximizing its efficiency and speed. As a test, we used Aether to de novo assemble 1572 metagenomic samples, a task it completed in merely 13 hours with cost savings of approximately 80% relative to comparable methods. Read more.