Tong Wang

I am currently a postdoctoral research fellow at Yang-Yu Liu’s lab at Brigham and Women’s Hospital and Harvard Medical School. I am interested in developing mechanistic models and machine learning methods for solving problems in microbial communities.

Bio

I am a physicist by training and received my Ph.D. in Physics from the University of Illinois Urbana-Champaign in 2021, with my thesis supervised by Prof. Sergei Maslov. During my Ph.D. period, I focus on modeling microbial communities with cross-feeding and predator-prey interactions. The primary goal of my current research is to combine ecological models and omics data to reveal the assembly rules of microbial communities, especially the human gut microbiomes. Besides the mathematical modeling, I am intensively working on computational projects related to precision nutrition such as predicting metabolomic profiles based on microbial compositions and dietary information using ecology-based models and machine learning models. More information about me can be found in my CV.

Research

Microbial communities are complex due to the multitude of species and diverse types of interactions between them. I am broadly interested in modeling microbial communities with cross-feeding interactions and predator-prey interactions. More specifically, I develop mechanistic models and machine learning methods for solving problems in microbial communities. Motivated by models in statistical physics, math, ecology, epidemiology, and machine learning, I have built computational methods for

Prediction of gut fecal metabolite levels from microbial abundance using the ecological model with trophic levels and inference of cross-feeding interactions based on the trophic model

A mechanistic model for the gut microbiome to understand fecal metabolomic profiles?

Following the idea of trophic level in macroecology, we designed a trophic model that considers the sequential nutrient consumption and byproduct generation upon consumption. Using a manually-curated database of metabolite-microbe interactions (i.e. consumption or production), our model with four trophic levels generates fecal metabolomic profiles in the best agreement with the real data. Then we wonder if we can improve the prediction performance by adding new interactions or removing existing interactions in mechanistic models. To demonstrate this, we developed the algorithm GutCP (Gut Cross-feeding Predictor) that leverages the Monte Carlo algorithm to probabilistically search for interactions to add or remove and demonstrated on the trophic model.

Personalized prediction of metabolomic profiles of human gut microbiomes through deep learning

Personalized prediction of fecal and blood metabolomic profiles based on deep learning methods?

Many machine learning methods have been developed to predict fecal and blood metabolomic profiles based on microbiome compositions. However, the current state-of-the-art deep learning methods have not been leveraged. In a new study, we proposed a new method — mNODE (Metabolomic profile predictor using Neural Ordinary Differential Equations), based on the state-of-the-art deep neural network models “Neural Ordinary Differential Equations”. Our mNODE outperforms existing methods in predicting the metabolomic profiles on both synthetic data and real data such as human gut microbiomes and other natural microbiomes. Further, in the case of human gut microbiomes, mNODE can naturally incorporate dietary information to further enhance the prediction of metabolomic profiles. Finally, we revealed that mNODE can reveal microbe-metabolite interactions.

Later, we took a deeper investigation into how dietary intervention influences metabolomic profiles via the modulation of gut microbiota. Due to highly personalized biological and lifestyle characteristics, different individuals may have different metabolic responses to specific foods and nutrients. We developed a new method McMLP (Metabolic response predictor using coupled Multilayer Perceptrons) to accurately predict the metabolic responses after dietary interventions of avocado, walnut, almond, broccoli, etc. Beyond the superior performance of McMLP, we performed a sensitivity analysis to generate the tripartite food-microbe-metabolite interactions, which may inform us of their relationships in a data-driven way.

Infection dynamics of viruses on the chemotactic bacteria

How do phages infect moving bacteria?

In the past, studies of phage infection in space focused on what happens in bacterial lawns or biofilms. Those studies shed light on how phages attack non-motile bacteria. How do phages infect chemotactic bacteria? To study this question, Derek Ping, an undergraduate student from the lab of Prof. Seppe Kuehn, performed experiments by inoculating the chemotactic E. coli cells together with their phage P1vir at the center of an agar plate with a rich medium (see the YouTube video). In the YouTube video, the outermost bright rings are dense bacterial populations that are migrating at about half a centimeter per hour. However, at the center of the colony, there is a darkened area, about 6cm in diameter, which he showed resulted from the collapse of the bacterial population due to phage lysis. Further, at the center of the colony, we observed a dense region due to the rise of resistant bacteria.

We sought to understand how the phage could create the large central region of the colony where the bacterial population had collapsed. Existing theories based on studies with non-motile bacteria showed that phage could not move over such large distances (centimeters) in such short periods of time (hours) without being actively transported. Therefore, we speculated that the phages travel along with migrating bacteria either during the latent period of infection or while attached to the cell prior to injection. To test this hypothesis, I built a mathematical model that included the ability of phages to “hitchhike” with migrating bacteria. The model confirmed our hypothesis. This study can be found here.

Other projects

Besides, I studied the ecological and evolutionary dynamics influenced by interactions within microbial communities:

  • Functional redundancy in metagenome and metaproteome and how the redundancy difference between two types of data reveals ecological niches and metabolic essentiality.
  • Ecological models of microbial exchange of essential nutrients.
  • Models of microbial cross-feeding at intermediate scale mediated by carbon sources like acetate and amino acids.
  • CRISPR-induced arms-race co-evolution between bacteria and viruses: network structure, prediction of regime shift, and influence of phage migration.

I also worked on COVID-related projects:

  • Agent-based model for the University of Illinois at Urbana-Champaign.
  • Data-analysis of internal COVID case data for operational purposes.

Preprint - Microbiome-based correction of nutrient profiles derived from self-reported dietary assessments

Tong Wang, Yuanqing Fu, Menglei Shuai, Ju-Sheng Zheng, Lu Zhu, Qi Sun, Frank B. Hu, Scott T. Weiss, Yang-Yu Liu, bioRxiv, 2023

    November 2023

    Article - Removal of false positives in metagenomics-based taxonomy profiling via targeting Type IIB restriction sites

    Zheng Sun, Jiang Liu, Meng Zhang, Tong Wang, Shi Huang, Scott T. Weiss, Yang-Yu Liu, Nature Communications, 2023

      September 2023

      Article - Feasibility in MacArthur’s consumer-resource model

      Andrea Aparicio, Tong Wang, Serguei Saavedra, Yang-Yu Liu, Scott T. Weiss, Theoretical Ecology, 2023

        July 2023

        Article - Functional universality in slow-growing microbial communities arises from thermodynamic constraints

        Ashish George, Tong Wang, Sergei Maslov, ISME Journal, 2023

          June 2023

          Article - Revealing Protein-Level Functional Redundancy in the Human Gut Microbiome using Ultra-deep Metaproteomics

          Leyuan Li*, Tong Wang*, Zhibin Ning, Xu Zhang, James Butcher, Caitlin Simopoulos, Janice Mayne, Alain Stintzi, David R. Mack, Yang-Yu Liu, Daniel Figeys, Nature Communications, 2023

            June 2023

            Preprint - Data-driven prediction of colonization outcomes for complex microbial communities

            Lu Wu, Xu-Wen Wang, Zining Tao, Tong Wang, Wenlong Zuo, Yu Zeng, Yang-Yu Liu, Lei Dai, In Revision at Nature Communications, 2023

              April 2023

              Preprint - Predicting metabolic response to dietary intervention using deep learning

              Tong Wang, Hannah D. Holscher, Sergei Maslov, Frank B. Hu, Scott T. Weiss, Yang-Yu Liu, In Revision at Nature Communications, 2023

                March 2023

                Article - Predicting metabolomic profiles from microbial composition through neural ordinary differential equations

                Tong Wang, Xu-Wen Wang, Augusto A. Litonjua, Kathleen Lee-Sarwar, Scott T. Weiss, Yizhou Sun, Sergei Maslov, Yang-Yu Liu, Nature Machine Intelligence, 2023

                  March 2023

                  Article - Benchmarking omics-based prediction of asthma development in children

                  Xu-Wen Wang, Tong Wang, Darius P. Schaub, Can Chen, Zheng Sun, Shanlin Ke, Julian Hecker, Anna Maaser-Hecker, Oana A. Zeleznik, Roman Zeleznik, Augusto A. Litonjua, Dawn L. DeMeo, Jessica Lasky-Su, Edwin K. Silverman, Yang-Yu Liu, Scott T. Weiss, Respiratory Research, 2023

                    February 2023

                    Preprint - Pairing Metagenomics and Metaproteomics to Pinpoint Ecological Niches and Metabolic Essentiality of Microbial Communities

                    Tong Wang*, Leyuan Li*, Daniel Figeys, Yang-Yu Liu, In Revision at ISME Journal, 2022

                      November 2022

                      Article - Mitigation of SARS-CoV-2 Transmission at a Large Public University

                      Diana Rose Ranoa, Robin Holland, Fadi Alnaji, Kelsie Green, Leyi Wang, Richard Fredrickson, Tong Wang, George Wong, Johnny Uelmen, Sergei Maslov, et al., Nature Communications, 2022

                        June 2022

                        Article - Complementary resource preferences spontaneously emerge in diauxic microbial communities

                        Zihan Wang, Akshit Goyal, Veronika Dubinkina, Ashish George, Tong Wang, Yulia Fridman, and Sergei Maslov, Nature Communications, 2021

                          November 2021

                          Article - Stochastic social behavior coupled to COVID-19 dynamics leads to waves, plateaus, and an endemic state

                          Alexei Tkachenko, Sergei Maslov, Tong Wang, Ahmed Elbanna, George Wong, and Nigel Goldenfeld, eLife, 2021

                            November 2021

                            Article - Ecology-guided prediction of cross-feeding interactions in the human gut microbiome

                            Akshit Goyal*, Tong Wang*, Veronika Dubinkina, and Sergei Maslov, Nature Communications, 2021

                              February 2021

                              Article - The network structure and eco-evolutionary dynamics of CRISPR-induced immune diversification

                              Shai Pilosof, Sergio A. Alcala-Corona, Tong Wang, Ted Kim, Sergei Maslov, Rachel Whitaker, and Mercedes Pascual, Nature Ecology and Evolution, 2020

                                October 2020

                                Article - Modeling microbial cross-feeding at intermediate scale portrays community dynamics and species coexistence

                                Chen Liao, Tong Wang, Sergei Maslov, and Joao Xavier, PLoS Computational Biology, 2020

                                  August 2020

                                  Article - Hitchhiking, collapse, and contingency in phage infections of migrating bacterial populations

                                  Derek Ping*, Tong Wang*, David T Fraebel, Sergei Maslov, Kim Sneppen, and Seppe Kuehn, ISME Journal, 2020

                                    May 2020

                                    Article - Evidence for a multi-level trophic organization of the human gut microbiome

                                    Tong Wang*, Akshit Goyal*, Veronika Dubinkina, and Sergei Maslov, PLoS Computational Biology, 2019

                                      December 2019

                                      Experience

                                      Postdoctoral Research Fellow

                                      Brigham and Women’s Hospital, Harvard Medical School

                                      Working in the lab of Prof. Yang-Yu Liu with interests in

                                      • predictive models suitable for precision nutrition
                                      • predicting metabolomic profiles through deep learning models
                                      • metaproteomes and functional redundancy
                                      • omics-based diagnostics

                                      June 2021 - Present

                                      Graduate Research Assistant

                                      University of Illinois Urbana-Champaign

                                      Working in the lab of Prof. Sergei Maslov with interests in

                                      • community assembly under microbial cooperative interactions
                                      • predator-prey interactions between microbes and phages
                                      • predicting metabolomic profiles via ecological models

                                      January 2017 - May 2021

                                      Graduate Teaching Assistant

                                      University of Illinois Urbana-Champaign

                                      August 2014 - December 2016

                                      Education

                                      University of Illinois Urbana-Champaign

                                      Doctor of Philosophy
                                      Physics (Advisor: Prof. Sergei Maslov)

                                      2014 - 2021
                                      2010 - 2014
                                      Nifty tech tag lists from Wouter Beeftink