In a groundbreaking fusion of computational engineering and immunotherapy, Dr. Natasa Miskov-Zivanov, an assistant professor of electrical and computer engineering at the University of Pittsburgh, has been awarded the highly coveted Faculty Early Career Development (CAREER) Award from the National Science Foundation (NSF). Her project, titled “Artificial Intelligence-Driven Framework for Efficient and Explainable Immunotherapy Design,” embarks on a transformative journey to revolutionize the engineering of immune cells, specifically lymphocytes, to devise next-generation therapies against cancer. Armed with a $581,503 grant, Miskov-Zivanov’s research employs advanced artificial intelligence (AI) techniques intertwined with knowledge graphs to automate and optimize the discovery and design of immunotherapeutic agents.
Immunotherapy, particularly Chimeric Antigen Receptor (CAR) T cell therapy, has already redefined the landscape of hematologic cancers such as leukemia and lymphoma by harnessing the patient’s own immune cells to eradicate malignant cells. The process involves extraction of T cells, their genetic reprogramming with a synthetic receptor, and reinfusion into the patient’s bloodstream. Despite its seminal success against blood cancers, this modality faces formidable hurdles when applied to solid tumors. The tumor microenvironment’s complexity and the difficulty of CAR T cells to adequately recognize and penetrate solid masses call for novel receptor configurations and sophisticated cell engineering approaches.
The combinatorial explosion of possible CAR T cell designs, coupled with the growing wealth of accumulated experimental data and literature, presents a daunting analytical challenge. To tackle this, Miskov-Zivanov aims to build an AI-powered system capable of sifting through vast bodies of scientific literature and heterogeneous data repositories to integrate expert knowledge and raw experimental insights. This system will intelligently recommend superior therapeutic lymphocyte designs, including both CAR T cells and tumor-infiltrating lymphocytes (TILs), by synthesizing disparate sources of information into actionable engineering guidance.
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Drawing on her unique background as a computer engineer with extensive postdoctoral experience in computational and systems biology, Miskov-Zivanov emphasizes automation in a field traditionally dominated by labor-intensive manual processes. She envisions her computational framework as a catalyst that automates the complex tasks typically performed by biologists, thereby accelerating and refining the design cycle for immunotherapeutic cells. This aspiration springs from her conviction that the convergence of computation and biology can unveil novel pathways that manual curation might never reveal.
Building on her earlier NSF-funded EAGER award, which developed a prototype tool utilizing Natural Language Processing (NLP) to extract pertinent data from scientific texts, she now evolves the approach to incorporate state-of-the-art large language models (LLMs) and neural networks. This hybrid system will not only parse and analyze scientific papers but also interpret experimental datasets to conduct comprehensive in silico experiments. By simulating thousands of potential cell designs computationally, this framework will perform hypothesis-driven screening prior to laboratory validation.
A critical innovation in Miskov-Zivanov’s project lies in developing improved prompting techniques for AI models, enabling more precise and relevant extraction of meaningful data from the overwhelming corpus of biomedical literature. Instead of forcing researchers to navigate tens of thousands of papers, many irrelevant to their queries, the system will pinpoint high-impact insights and knowledge, distilling the essence of complex biological narratives. This capability could dramatically reduce time and resources consumed in immunotherapy research and design.
To represent and utilize the extracted knowledge efficiently, Miskov-Zivanov converts science-derived data into knowledge graphs (KGs)—structured semantic networks encoding relationships among biological entities like proteins, signaling pathways, and cellular behaviors. These KGs serve as a scaffolding layer upon which graph neural networks (GNNs) operate. GNNs, leveraging their prowess in modeling graph-structured data, analyze interconnections within the KGs to predict the efficacy of various immunotherapeutic cell configurations. This synergistic blend amplifies predictive accuracy beyond what isolated datasets or traditional statistical models can achieve.
Understanding the imperative for educating emerging engineers in these frontier methodologies, Miskov-Zivanov has introduced a novel graduate-level course focused on knowledge graphs and their construction, interpretation, and application. She believes that equipping the next generation of researchers with computational tools capable of integrating structured knowledge and data-driven learning models is vital for addressing increasingly complex biomedical challenges. By nurturing interdisciplinary expertise, this educational initiative seeds future innovation in synthetic biology and therapeutic design.
Underlying this ambitious technological endeavor is the goal to establish a reliable methodology for engineering and systematically testing thousands of immunotherapeutic cell designs with diverse receptor systems. Success could catalyze breakthroughs in developing cellular therapies that effectively infiltrate and neutralize solid tumors—an enduring challenge in oncology. Moreover, the project aspires to contribute novel algorithmic innovations to identify, present, and validate trustworthy predictive data in biomedical research.
Reflecting on her motivation, Miskov-Zivanov shares a poignant narrative of how a childhood news story about a young leukemia patient cured by immunotherapy ignited her passion. Her dual lens as a computer engineer and a scientifically curious individual fuels her drive to forge impactful applications of computing technologies in life-saving medical research. Her work epitomizes the compelling convergence of artificial intelligence and biotechnology, promising to reshape cancer treatment paradigms.
Her department chair, Alan George, lauds her as a rising star and innovator whose research lab, the MeLoDy (Mechanisms and Logic of Dynamics) Laboratory, bridges digital circuits, synthetic biology, AI, and dynamic systems. The award spotlights Miskov-Zivanov’s pioneering approach to designing immunotherapies and teaching complex computational methods, setting the stage for profound future contributions in science and engineering.
Dr. Miskov-Zivanov’s project embodies the forefront of biomedical innovation, where AI-powered automation intersects with molecular engineering to tackle the enduring challenge of cancer therapy. By weaving together computational linguistics, graph theory, machine learning, and synthetic biology, she charts a new course toward more efficient, interpretable, and impactful immunotherapy design. The convergence of these fields promises to accelerate discovery and ultimately transform patient outcomes in oncology.
Subject of Research: Artificial Intelligence-driven design of immunotherapy cells, focusing on CAR T cells and tumor-infiltrating lymphocytes.
Article Title: Artificial Intelligence-Driven Framework Poised to Revolutionize Immunotherapy Design
News Publication Date: Not specified in the provided content.
Web References:
Natasa Miskov-Zivanov Faculty Page
NSF Award Detail
Pitt News on NSF EAGER Award
MeLoDy Laboratory
Keywords: Cancer immunotherapy, Generative AI, Computer science, Artificial intelligence, Deep learning, Systems neuroscience, T lymphocytes, Immune system
Tags: advanced receptor configurationsArtificial Intelligence in Medicineautomated immunotherapy optimizationcancer treatment breakthroughsCAR T cell therapy advancementscomputational biology applicationsimmunotherapeutic agent discoveryimmunotherapy designlymphocyte engineering innovationsNational Science Foundation CAREER awardsolid tumor challengestransformative medical research
