Imani Awarded $590K NIH Trailblazer Grant for Mathematical Modeling of Microbial Communities
June 13, 2022
ECE Assistant Professor Mahdi Imani was awarded a $590K NIH Trailblazer R21 grant for New and Early Stage Investigators from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) for “Bayesian Dynamical Modeling of Microbial Communities”. The project seeks to develop highly scalable and efficient mathematical models and tools that will allow researchers to gain a deeper understanding of the fundamental biology of microbial communities and their system-level interactions.
Mahdi Imani, assistant professor, electrical and computer engineering (ECE), has been awarded a $590,000 National Institutes of Health (NIH) Trailblazer R21 Award for new and early-stage investigators from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) for “Bayesian Dynamical Modeling of Microbial Communities.”
The project seeks to develop highly scalable and efficient mathematical models and tools that will allow researchers to gain a deeper understanding of the fundamental biology of microbial communities and their system-level interactions.
These interactions within microbial communities and their hosts are a source of interest for many researchers in areas from protecting humans and plants against diseases to developing the next generation of biofuels.
“Our associations with microbes are essential for our health through digestion of our food, training of our immune systems, and protecting us from pathogens,” says Imani, who began research in this area in the Genomic Signal Processing Lab during his PhD at Texas A&M University. “By understanding factors that promote the stability of a microbial community or cause its collapse, we can design targeted treatments that prevent microbiome disruption or rebuild a disrupted microbiome.”
Thanks to advances in high throughput multi-omics techniques like metagenomics, metatranscriptomics, exometabolomics, and proteomics, researchers have plenty of data to work with; what they need are appropriate tools and annotation databases for analysis and interpretation, which is the gap Imani hopes to fill.
“The models will be capable of characterizing the time component of omics features—such as microbial species, microbial genes, proteins, and small molecules—and maximally extracting information through data acquired from different molecular profiling technologies associated to various diseases,” says Imani. “By examining this multimodal data on a variety of levels, we can provide a more comprehensive picture of a range of biological interactions within disease activities—an unmet need in many studies.”
As part of this grant, Imani will collaborate with two PhD students from Northeastern. They will use their backgrounds in mathematics, computer science, and electrical and computer engineering to assist with modeling, the development of statistical tools, and software development over the course of the next three years.
Microbial communities and their hosts play a key role in many applications, including protecting humans or plants against diseases or developing the next generation of biofuels and biological remediation systems needed for sustainable growth. Gaining a deep understanding of the fundamental biology of these systems is the key to har- nessing their potential. Advances in high-throughput multi-omics techniques like metagenomics, metatranscrip- tomics, exometabolomics, and proteomics, allow us to capture multiple snapshots of these complex biological processes at once. These snapshots create large-scale high-dimensional datasets of omics features (e.g., mi- crobial species, microbial genes, proteins, and small molecules). The reduced cost has also allowed researchers to collect more multi-omics time-series data. These temporally resolved multi-omics features can together provide a comprehensive picture of biological processes and their underlying activities. These well-designed multi-omics studies have not been analyzed to their fullest potential yet, primarily due to the lack of appropriate tools and annotation databases required for such analyses. For example, systematically investigating the time component of this longitudinal data to investigate the temporal dynamics of omics features in relationship with disease activities is an unmet need in many studies. Therefore, there is a critical need for statistical tools to greatly improve research infrastructure by integrating different data types and systematically investigating the time component of this longitudinal data. This project’s overarching goal is to develop efficient, interpretable, and scalable tools based on our previously developed signal model, called partially-observed Boolean dynamical systems (POBDS), to characterize the time component and capture the dynamical behavior of microbial communities through multi-omics data. The original contributions can be organized across the following research goals: (i) Developing novel methods in the POBDS context capable of modeling multi-omics data obtained through various molecular profiling technologies and various diseases/domains. (ii) Developing Bayesian optimization frameworks for the efficient and scalable reconstruction of the network topology of microbial communities (i.e., inferring the type of interactions between a large number of genes, bacteria, and microbes) through high dimensional multi-omics data. (iii) Developing Bayesian reinforcement learning perturbation policies to decrease the number of data required for the modeling/learning process (overcoming the non-identifiability issue) and acquire the most informative data in microbial communities. All the developed tools in this project will be presented in a user-friendly software/tool freely accessible to other researchers.
