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Abstracts

Reinhard Laubenbacher

Digital Twins for Precision Medicine

Some new directions in the theory and practice of medicine today are often labeled as “precision/personalized” medicine or “systems” medicine. The former refers to a move away from one-size-fits-all approaches to therapeutics, the latter refers to an approach that could also be called holistic, moving toward therapeutics that consider the interrelatedness of the many components of a patient’s biology and environment rather than a “one target — one drug” strategy. Among the many tools that are being used and developed to realize these approaches, the digital twin concept has emerged in recent years as a key technology. A digital twin can be viewed as a computational model that captures some aspects of a patient’s biology, evolves with the patient over time, and can be used for diagnostic, prognostic, and therapeutic purposes. This introductory talk will clarify this concept, provide some examples, and discuss some of the obstacles that need to be overcome on the way to a scalable, widely used technology. 

Jon Weidanz

The Expanding Role of MHC/Peptide Targets for Precision Therapies

T-cell receptor mimic (TCRm) and T-cell receptor (TCR)-based therapies represent a transformative approach in precision oncology by targeting peptide–major histocompatibility complex (pMHC) complexes on cancer cells. Unlike conventional antibody therapies that are limited to surface antigens, these modalities enable recognition of intracellular tumor-specific antigens presented via MHC molecules, vastly expanding the therapeutic target space. Their high specificity for unique pMHC complexes allows for selective targeting of malignant cells while sparing healthy tissues, thereby reducing off-target toxicity. Furthermore, the personalized nature of these therapies—tailored to individual tumor antigen profiles and human leukocyte antigen (HLA) types—aligns with the core principles of precision medicine. TCRm antibodies can be engineered into bispecific T-cell engagers or antibody-drug conjugates, enhancing immune-mediated cytotoxicity and targeted delivery. Collectively, TCRm and TCR-based therapies exemplify precision medicine by integrating molecular specificity, personalized targeting, and immune activation to address previously undruggable cancer antigens.

David Lary

BREATHE (Biometric Real-time Exposome Analysis for Timely Health Evaluations)

Human aging, along with our cognitive and physical performance, is deeply influenced by our environmental context. Imagine a future where our grasp of the human environmental exposome, the complex web of non-genetic factors from our surroundings, empowers us to redefine human health and vitality. Real-time environmental sensing is a key ingredient of transformative solutions for objectively promoting healthy aging. By offering both preemptive protection and performance optimization, it unveils the profound impact of environmental factors on sleep, human performance (cognitive and physical), health, vitality, and longevity. When integrated with smart buildings, it enables seamless, automated adjustments that ensure optimal living conditions. As we age, our bodies grow more sensitive to external elements such as air quality, temperature, and humidity, which can significantly affect cognitive function, respiratory health, and overall resilience. Continuous monitoring of these variables empowers individuals to anticipate and mitigate risks before they escalate, fostering an environment that not only protects but also enhances physical and mental performance. This forward-thinking, data-driven approach not only aids in preventing chronic conditions but also supports sustained energy, focus, and adaptability, making it an invaluable ally in the journey to thrive through every stage of life.

Baowei Fei

AI-enhanced Hyperspectral Imaging Tool and Device for Cancer Detection and Image-Guided Surgery

Hyperspectral imaging (HSI) is an emerging modality for medical applications such as cancer detection & diagnosis, image-guided surgery, and computational & digital pathology. In this talk, various hyperspectral imaging technologies and their medical and biological applications will be presented. The talk will focus on how deep learning methods are applied to HSI and how HSI techniques are developed for cancer detection and image-guided surgery. Promising results from both preclinical and clinical studies will be presented in this talk. Future directions and potential challenges of medical hyperspectral imaging will be discussed. The audience will have an overview of the HSI technology and its specific applications. 

Carla Kumbale

Development of a QSP model for the prediction of mRNA influenza vaccine immunogenicity

Objective. Globally, seasonal influenza is estimated to cause up to 650,000 respiratory deaths annually, with vaccination being the most effective preventative measure. Influenza vaccination is a crucial public health strategy. However, the vaccine effectiveness of currently licensed seasonal influenza vaccines have suboptimal and inconsistent effectiveness from season to season, in part due to antigenic differences between vaccine strains and circulating viruses. Consequently, there has been an increased focus on the application of mRNA-based technologies for influenza vaccines which have been shown to elicit a robust, multi-faceted immune response, inducing both B- and T-cell immunity. Preclinical characterization of vaccine candidates across a combination of different formulations, antigen constructs, and doses provides valuable qualitative insights. However, uncertainties remain in translating from preclinical assessments to clinical outcomes remain given the complexities of prior immunity and, host factors that impact immunity such as the impact of aging on immunogenicity in humans. We developed a novel quantitative systems pharmacology (QSP) platform model with an ultimate goal to predict clinical vaccine immunogenicity from preclinical in vivo immunogenicity data to inform candidate selection and de-risk clinical trial design decisions. As an initial step, we use the model to recapitulate influenza vaccine responses in preclinical animal species including mice and non-human primates.

Methods. We adapted a previously developed multiscale mathematical model for vaccine-induced immunogenicity prediction. This model comprises key biological immune mechanisms including antigen presentation, activation, proliferation, and differentiation of immune cells. The model is modified to represent the immunogenicity responses of a monovalent influenza mRNA vaccine (mIRV) encoding an H1 hemagglutinin (HA) in mice and non-human primates.

Results.  The QSP model was parameterized to match the immune response to mIRV vaccination in mice and non-human primates. For each preclinical species, the model adequately predicted the dynamics of the immune response, specifically hemagglutination inhibition (HAI) antibody titers, following two doses of mIRV administered 28 days apart. Specifically, the model described the peak HAI titer response for the two-dose regimen and the durability of vaccine induced HAI titers up to 5 months post-vaccination.

Conclusion.  We developed a QSP model of immunogenicity that captures key features of the immune response to mIRV vaccination in preclinical animal species. This QSP framework will be updated with emerging data from the corresponding clinical studies of this vaccine, to investigate key preclinical predictors of clinical immunogenicity. Furthermore, future efforts will focus on leveraging the model to predict clinical vaccine immunogenicity based on preclinical assessments and will account for pre-existing immunological memory observed clinically. In the future, this can help streamline candidate selection and de-risk vaccine development decisions.

Melissa L. Kemp

From People to Cells: Personalizing Cancer Systems Biology Models to Identify Metabolic Vulnerabilities

Despite the growing number of therapeutic options available for cancer patients, predicting which course of treatment will the best response for an individual is still a major challenge for precision medicine. In this talk, I will describe computational systems biology strategies that we have developed to investigate heterogeneity of drug metabolism among head and neck cancers. Our platforms range from genome-scale metabolic flux modeling to single cell kinetic models and agent-based simulations of tumor architecture. The common theme among these diverse approaches is exploring the role of redox systems in amplifying or suppressing cytotoxicity associated with oxidative damage induced by radiation or chemotherapeutics. We have developed scRNAseq transcriptome-to-phenotype models at the single cell level to determine reactive oxygen species formation associated with quinone cycling. Over 4000 individual simulations reflected significant systems-level differences between the redox states of healthy and cancer cells, demonstrating in some patient samples a targetable cancer cell population. In a subset of patients, statistically indistinguishable effects between non-malignant and malignant cells highlights the role of alternate antioxidant components in dictating drug-induced oxidative stress. Finally, spatial agent-based modeling has helped to identify organizational features of tumor required to fully realize the desired bystander effects of drug-induced reactive oxygen species formation that is observed in quinone-based therapeutics. 

Chen Cao

The Dynamics of Parental Allelic Imbalance at Single-Cell Resolution in a Hybrid Proto-Vertebrate

The inheritance of both maternal and paternal copies of the zygotic genome is required for normal animal development.  Yet, we are not simply the sum of our parents’ genes. Many genes exhibit allelic imbalance, referring to a phenomenon where the two alleles of a gene, one inherited from each parent, are not expressed at equal levels. The role of allelic imbalance in various biological processes, particularly during embryo development, remains largely unclear, as significant challenges persist in capturing its dynamic changes in each single cell and precisely quantifying the distinct contributions of maternal and paternal alleles. Recent advances in single cell RNA sequencing technology have opened new avenues for characterizing cell-state specific genetic effects and quantifying allelic outcomes throughout the entirety of embryogenesis. As the closest living relatives of vertebrates, the ascidians Ciona intestinalis and C. savignyi serve a critical role in understanding developmental and physiological processes that are comparable to—but far less complex than—those of vertebrates. Here we obtain the first high-resolution map of allelic imbalance for a complete animal embryo in development by profiling the transcriptomes of individual cells in sequentially staged hybrid Ciona embryos, from gastrulation at the 110-cell stage to the neurula and larval stages. The dynamic changes of allelic expression along transcriptome trajectories, regulatory cascades and provisional gene networks provide insights into the underlying biological mechanism for allele dominance in specific lineages, including temporal coordinated gene regulatory cassette in heart, and spatial coordination of body patterning. 

Eric Kildebeck

Personalized Medical Training in the Age of AI

AI and digital simulation create new opportunities for personalization and scaling of job training with a focus on specific skills needed in the modern workforce. Traditional systems for medical training require trainees to spend a large portion of their working years in training (often ~13 years starting at college, or ~28% of the time from age 18 to 65), to incur large amounts of debt, and to spend the majority of training in general areas before narrowing in on their actual specialty for the last few years. Here we discuss low-cost training in digital simulations and personalization through AI, where trainees will have the opportunity to learn skills rapidly, focus on patient populations where they plan to practice instead of only those in their training hospital, and display soft skills and executive function skills such as bedside manner, leadership, and teamwork. This form of training could be used to substantially increase the working years of doctors, reduce debt, and allow medical school and residency programs to prioritize skills interacting with patients in addition to knowledge. 

Erdal Toprak

Decoding Bacterial Survival Mechanisms Driving Antibiotic Failure: Experimental, Computational, and Translational Insights

Antibiotics are among the most important discoveries in modern human history, not only for treating bacterial infections but also for enabling advanced medical procedures such as organ transplantation, immunotherapies, and surgery. However, antibiotic resistance has become a global health crisis, causing more than one million deaths annually and projected to result in 2–10 million deaths per year by 2050. With few new antibiotics in development, optimizing evidence-based use of existing compounds is critical to prolong their efficacy. In this talk, I will summarize our efforts to uncover canonical and non-canonical bacterial survival mechanisms that contribute to antibiotic failure. I will also highlight our experimental and computational work to map genetic trajectories leading to resistance, and discuss how this knowledge can be leveraged to preemptively block common evolutionary paths and slow the emergence of drug resistance.

Tian Hong

Mathematical models for gene regulatory networks controlling cell fate

Structure and dynamics of gene regulatory networks (GRNs) are important for cell fate decisions that underpin human development and disease progression. The complexity of GRNs gives many challenges in understanding these processes. Accurate prediction of behaviors at the levels of cells and cell populations therefore requires rigorous description with mathematical models and dynamical systems theory. More recently, large-scale, single-cell data have provided new opportunities to deepen the understanding of cellular dynamics by combining classical models and data science. Our group used “bottom-up” approaches to describe GRNs with interconnected feedback loops that generate high-order multistability crucial for stepwise cell lineage transitions. In addition, we developed “top-down” methods that derive cell-type specific GRNs from single-cell data. This allows us to build multiscale network models that interrogate the roles of cell-cell communications in cell fate decisions. These findings elicit exciting potentials of using computational methods to make new discoveries in fundamental biology for improving human health.

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Speakers

Reinhard Laubenbacher joined the University of Florida (UF) in 2020 as a professor in the Department of Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, where he established the Laboratory for Systems Medicine. Prior to joining UF, he served as director of the Center for Quantitative Medicine and Professor in the Department of Cell Biology at the University of Connecticut School of Medicine, with a joint appointment at the Jackson Laboratory for Genomic Medicine. He is a fellow of AAAS, the Society for Mathematical Biology, and the American Mathematical Society. He is president of the Society for Mathematical Biology, having previously served as editor-in-chief of the Bulletin of Mathematical Biology, its flagship journal. Dr. Laubenbacher is a mathematician by training, and his broad research interests lie in computational and mathematical systems biology, with applications to human health. Most of his research is in collaboration with a broad spectrum of scientists and clinicians. An important focus of his work in recent years has been on the development and applications of medical digital twin technology.

Jon Weidanz is a nationally recognized expert in cancer immunotherapy and biomedical innovation. He serves as Senior Associate Vice President for Research & Innovation at UTA, where he also holds faculty appointments in Kinesiology and Bioengineering. He is the Founding Director of IMPRINT, a regional hub for precision medicine and biomanufacturing, and previously led the North Texas Genome Center.

A Fellow of the National Academy of Inventors, Dr. Weidanz pioneered T-cell receptor mimicking (TCRm) antibody technologies that target intracellular tumor antigens via MHC presentation—advancing precision oncology through bispecific T-cell engagers and antibody-drug conjugates. He has published over 60 papers, holds 19 patents (with 20 pending), and co-founded four biotech companies, including Abexxa Biologics, acquired by Boehringer-Ingelheim in 2021. His work has attracted over $45 million in funding.

Prior to UTA, he held leadership roles at TTUHSC, where he founded the Department of Immunotherapeutics and Biotechnology and earned multiple teaching and research awards. He continues to drive innovation at the intersection of translational research, immuno-engineering, and precision health.

David J. Lary is an interdisciplinary physicist, chemist and data scientist whose career unites advanced sensing, machine learning, and exposure science for human protection. He earned a double First‑Class Honors B.Sc. in Physics & Chemistry from King’s College London and a Ph.D. from the University of Cambridge, where he created the first global 3‑D model of stratospheric ozone depletion. His early contributions were recognized with a ten‑year Royal Society University Research Fellowship and the first NASA’s inaugural Goddard Distinguished Fellowship in Earth Science. Now a Professor of Physics at The University of Texas at Dallas and founding director of the MINTS‑AI center, Prof. Lary leads programs that fuse nine sentinel layers—from wearables, to robot teams, to live streaming sentinels across dense urban environments, to satellites—into real‑time, edge‑enabled decision tools. His portfolio spans >240 publications, >7,000 citations, and more than $40 M in competitively awarded funding, including projects for NASA, NIH, VA, the Army, and SOCOM. Prof. Lary currently supports the U.S. Special Operations Command Futures Directorate (J5, SOFWERX) and is a Research Scholar at the VA’s Complex Exposure Threats Center. He also holds adjunct appointments at the Uniformed Services University of the Health Sciences, the University of North Texas Health Science Center School of Public Health, and Southern Methodist University, and collaborates with Baylor University’s Center for Astrophysics and UTD’s Departments of Electrical Engineering, GIS, Bioengineering, and the Center for BrainHealth. Across three continents he has pioneered chemical data assimilation, city‑scale mesh sensor networks, and autonomic‑signature inference of inhaled toxins—capabilities that directly enable the BASTION “Bodies‑as‑Sensors” concept. 

Bao Fei is a Professor of Bioengineering and Cecil H. and Ida Green Chair in Systems Biology Science at the University of Texas at Dallas. He is also a Professor of Radiology at the University of Texas Southwestern Medical Center. He is Director of the Quantitative Bioimaging Laboratory (www.fei-lab.org) and Director of the Center for Imaging and Surgical Innovation. Dr. Fei’s research interests include medical hyperspectral imaging, image-guided surgery, artificial intelligence, and augmented reality for medical applications. He received his master’s and PhD degrees and postdoctoral training from Case Western Reserve University. He was recognized as a Distinguished Investigator by the Academy for Radiology & Biomedical Imaging Research and as a Distinguished Scholar by the Georgia Cancer Coalition and the Governor of Georgia. He serves as Conference Chair for the International Conference of SPIE Medical Imaging – Image-Guided Procedures, Robotics Interventions, and Modeling from 2017-2020. He served as the Chair for multiple study section panels at the National Institutes of Health (NIH). He served as an Associate Editor for Medical Physics, an Editorial Board Member for Journal of Biomedical Optics, Journal of Medical Imaging, and other five journals in the field of biomedical imaging. He published more than 200 referred research articles. Dr. Fei is a Fellow of the International Society for Optics and Photonics (SPIE) and a Fellow of the American Institute for Medical and Biological Engineering (AIMBE).

Carla Kumbale is a quantitative systems pharmacologist in the Translational Clinical Sciences department at Pfizer where she leverages quantitative systems pharmacology models to predict clinical immunogenicity of influenza mRNA vaccines. She completed her PhD in Bioengineering from the Georgia Institute of Technology where she used mathematical models to assess the physiological effects of dioxin. Prior to that, she completed a master’s in public health from Emory University during which she also worked at the Centers for Disease Control (CDC) on developing epidemiological models of influenza.

Melissa Kemp is the Carol Ann and David D. Flanagan Endowed Chair in Biomedical Engineering in the Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, where she has been a member of the faculty since 2006. Her research focuses on the development of computational systems biology methods to investigate two overarching themes in cancer, immunology, and regenerative medicine applications: i) the role of cellular redox metabolism in influencing information processing and cell fate decisions; and ii) modes of communication that drive self-organization in multicellular engineered living systems. Dr. Kemp is currently the research director of a multi-institutional NSF Engineering Research Center in Cell Manufacturing Technologies and is the former co-chair of the NCI Cancer Systems Biology Consortium. She is the co-author of the 2025 textbook “A First Course in Systems Biology: 3^rd Edition” by Voit & Kemp. Dr. Kemp’s career honors include Georgia Cancer Coalition Distinguished Scholar and NIH New Innovator.

Chen Cao is a bioengineer and developmental biology researcher. She earned her Ph.D. in Biophysics from Peking University and completed her postdoctoral training in developmental biology at the Lewis-Sigler Institute of Integrative Genomics in Princeton, where she contributed significant efforts to cell lineage reconstruction with single-cell sequencing technology and the novel cell type evolution in the Ciona nervous system. The Cao Lab utilizes and develops cutting-edge single cell omics, imaging and microfluidics tools to investigate on lineages commitment, cell type evolution and cell-cell interactions. Her research aims to elucidate the gene regulatory grammar in embryogenesis and cancer development.

Eric Kildebeck, MD, PhD co-directs the Center for Engineering Innovation (CEI) at UT Dallas. The mission of this Center is to use an engineering and entrepreneurial mindset to innovate and translate technologies that solve real-world problems. Dr. Kildebeck directs the CEI’s Polycraft World team, which builds active learning environments for learners, AI agents, and human-AI teams in video games such as Minecraft. Eric co-founded EdTech startup Pedegree Studios, Inc. in 2024, a CEI-spinout focused on delivering personalized learning, where he serves as SVP of Education and leads pedagogy and AI strategy.

Erdal Toprak is an Associate Professor and Southwestern Medical Foundation Scholar in Biomedical Research at the University of Texas Southwestern Medical Center. Trained as a physicist at Boğaziçi University and later in biophysics at the University of Illinois at Urbana-Champaign, he completed his postdoctoral training at Harvard Medical School, where he developed the “Morbidostat,” an innovative system to study the evolution of bacterial drug resistance, and uncovered genetic mechanisms that shape cross-resistance and hypersensitivity in microbes. His work spans single-molecule imaging, microbial evolution, antibiotic resistance, and liquid biopsy methods for the early detection of cancer. His current research integrates systems biology, biophysics, and computational approaches to better understand and combat antibiotic resistance.

Tian Hong is a computational biologist interested in using mathematical and computational approaches to understand complex biological systems such as cancer cell plasticity. He is currently an Associate Professor at the Department of Biological Sciences, The University of Texas at Dallas. Before moving to The University of Texas at Dallas, he was an Associate Professor at the University of Tennessee, Knoxville. He obtained his Ph.D. in Genetics, Bioinformatics and Computational Biology from Virginia Tech, and he completed his postdoctoral training in the Department of Mathematics at University of California, Irvine. His current research focuses on modeling and single-cell analysis for cell fate transitions, including epithelial-mesenchymal transition, a reversible process that is critical for development and cancer progression. By combining data-driven and theory guided approaches, he developed models for explaining complex cell dynamical features such as multiple intermediate states, partial reversibility and divergence. These interdisciplinary approaches will be useful for understanding cellular functions of gene regulatory networks in a wide range of physiological and pathological contexts. His research is funded by NIH and NSF.

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