We envision a future when omics data-informed, computational model-based medical care is available to all. There are many ways to accelerate the pace of making this vision into a reality. For example, our group focuses on creating novel algorithms and computational tools for advancing precision medicine, as well as for better understanding how the human microbiome triggers, prolongs, or protects against disease. We elect to focus on the following three broad aims:
I. Designing multi-omic digital biomarkers for precision medicine
Large-scale, data-driven approaches to healthcare have great potential to address some of the most critical clinical challenges, such as early disease diagnostics, assessment of therapeutic regimen efficacy, and real-time monitoring of health and disease. Additionally, comprehensive biomolecular and cellular profiling technologies are now recognized as important tools for personal data metrics and analytics. We are currently using an extensive, multi-omic profiling approach (e.g., transcriptomic, metagenomic, metabolomic, and immunophenotyping) to deconvolute the biocomplexity underlying human diseases. With newly identified disease signatures, we aim to design robust digital (i.e., algorithm-based, computational) biomarkers that can serve as clinically actionable information.
Selected publications:
Hur et al., “Integrative Multi-omic Profiling in Blood Reveals Distinct Immune and Metabolic Signatures between ACPA-negative and ACPA-positive Rheumatoid Arthritis”. Frontiers in Immunology (2025).
Chang et al., “Gut Microbiome Wellness Index 2 for Enhanced Health Status Prediction from Gut Microbiome Taxonomic Profiles”. Nature Communications (2024).
Gupta et al., “Gut Microbial Determinants of Clinically Important Improvement in Patients with Rheumatoid Arthritis”. Genome Medicine (2021).
Hur et al., “Plasma Metabolomic Profiling in Patients with Rheumatoid Arthritis Identifies Biochemical Features Predictive of Quantitative Disease Activity”. Arthritis Research & Therapy (2021).
II. Developing computational frameworks for precision neurosurgery
Modern stereotactic neurosurgery increasingly depends on precise spatial localization and computational modeling to guide surgical interventions. Our lab is advancing precision neurosurgery through the development of algorithms and hardware-software frameworks that enable accurate, radiation-free surgical planning and navigation.
Building on our expertise in computational modeling and translational data science, we are pioneering the use of 3D structured-light surface scanning (3DSS) and related technologies to improve stereotactic localization and reduce reliance on radiation-based imaging such as CT. Our research further extends to AI-assisted registration algorithms and software integration that connect surface scanning data with neuronavigation systems, supporting real-time planning for procedures such as deep brain stimulation, subdural drain placement, and cranial base surgery. Ultimately, our goal is to establish a computational neurosurgery pipeline that bridges engineering, informatics, and clinical practice—laying the groundwork for adaptive, data-driven neurosurgical decision support.
Publication:
Sharaf et al., “3D Surface Scanning for Registration in Stereotactic Neurosurgery: A Cadaveric Feasibility Study”. Journal of Neurosurgery (2025).
III. Characterizing how prebiotics, probiotic foods, and pharmaceutical drugs influence gut microbiome taxonomy and function
In this research direction in our lab, we pose the following questions: Do prebiotics and probiotics actually work, and what does “work” even mean? Do gut microbes influence drug efficacy? How can we dissect the complex interactions between the gut microbiome and exogenous compounds? With collaborators who are dieticians, food scientists, and metabolic engineers, we are evaluating the efficacy of prebiotic compounds and probiotic foods in improving gut health.
Relevant to the clinical realm, we are performing longitudinal studies that monitor the influence of pharmaceutical drugs on the gut microbiome over time. Such studies are crucial for capturing the dynamic nature of drug-microbiome interactions and understanding the temporal stability of these relationships. These works may, in the short term, help predict drug response based on pre-treatment (baseline) gut microbiome compositions. Ultimately, in the long run, we aim to leverage the intricate interplay between the human gut microbiome and pharmacological agents to personalize therapeutic strategies and enhance drug safety.
Selected publications:
Gupta et al., “Alterations in Gut Microbiome-Host Relationships Induced after Immune Perturbation in Patients with Multiple Sclerosis”. Neurology: Neuroimmunology & Neuroinflammation (2025).
Gupta et al., “Safety, Feasibility, and Impact on the Gut Microbiome of Kefir Administration in Critically Ill Adults”. BMC Medicine (2024).
Lee et al., “Evaluating the Prebiotic Effect of Oligosaccharides on Gut Microbiome Wellness Using in vitro Fecal Fermentation”. NPJ Science of Food (2023).
IV. Understanding how the chemical interactions and environment within the gut microbial community influence health and disease outcomes.
The association between chronic disease and gut microbes is intriguingly complex, with no single microbe or pathogen, or microbial function, appearing to be causal. Instead, pathologies have been repeatedly linked to the overall gut ecology.
Compared to its early days, gut microbiome analyses have now evolved beyond descriptive, profiling investigations towards more hypothesis-driven, mechanism-focused studies. However, true progress in this direction will be contingent upon the maturation of our understanding of the systems-level, inner workings of a microbial community. Especially, we need quantitative models of how biological communities in our gut self-organize and interact, and how these relationships are relevant to a clinical phenotype. Moreover, to realize the full potential of microbiome-based therapy via either targeted intervention or whole microbiome transplantation, a comprehensive computational framework is required for directing such manipulations and predicting treatment outcomes. Therefore, we aim to develop computational tools that elucidate community-level interactions within the gut microbiome; and design ecological models that integrate statistical associations with metabolic information.
Selected publications:
Gupta et al., “TaxiBGC: a Taxonomy-guided Approach for Profiling Experimentally Characterized Microbial Biosynthetic Gene Clusters in Metagenomes”. mSystems (2022).
Kim et al., “Resource-allocation Constraint Governs Structure and Function of Microbial Communities in Metabolic Modeling”. Metabolic Engineering (2022).
Sung et al., “Global Metabolic Interaction Network of the Human Gut Microbiota for Context-specific Community-scale Analysis”. Nature Communications (2017).