Translational Medicine
Transdisciplinary Approaches
Our research is dedicated to making personalized medicine a practical reality. Achieving this vision requires a multidisciplinary approach—bringing together life sciences, engineering, clinical sciences, and social sciences to create integrated, patient-centered solutions.
Ongoing Research
P4 Medicine
The 4Ps of P4 Medicine—Predictive, Preventive, Personalized, and Participatory—form a transformative framework first proposed by Dr. Leroy Hood of the Institute for Systems Biology in the early 2000s. At the time, emerging technologies like microfluidics were revolutionizing biology, enabling access to novel and complex biological data. The core goal of the P4 framework is to shift healthcare from a reactive model focused on treating disease to a proactive approach centered on maintaining wellness.
Over the years, Dr. Hood and other scientists have continued to refine the framework to incorporate advances in systems biology, data science, and personalized care. By analyzing comprehensive datasets and understanding the biological system as a whole, P4 Medicine encourages a "big picture" perspective—taking into account genetic, environmental, and lifestyle factors to understand how external perturbations influence health and disease. This systems-level view holds the promise of transforming both diagnostics and therapeutic strategies.
Participatory Medicine
The Participatory component of P4 Medicine emphasizes empowering patients to become active participants in their healthcare journey and clinical decision-making. This approach focuses on enhancing both the patient’s interaction with clinicians and their engagement with healthcare technologies. As smartphones, tablets, and health apps have become ubiquitous over the past few decades, they have fundamentally reshaped the technological landscape surrounding patient care. However, while digital tools have advanced rapidly, the user experience—especially as it relates to participatory medicine—has often been underexplored and poorly integrated into the broader clinical ecosystem. At KrebsNeumann, we are developing digital pipelines designed to bridge this gap, with the goal of improving how patients experience care. By aligning technology, user experience, and clinical objectives, we aim to transform patient engagement and drive better clinical outcomes.
Computational Biology
Computational Biology is an interdisciplinary field that combines computer science, programming, and data science to unravel complex biological questions. At its core, it applies mathematical and algorithmic approaches to identify potential biomarkers critical for diagnostics and therapeutics. With the advent of multi-omics—including genomics, proteomics, transcriptomics, and metabolomics—computational biology has become a cornerstone of modern life sciences. Among these, genomics serves as the foundational layer, providing the blueprint upon which systems biology is built. Omics-driven discoveries are essential to advancing personalized medicine, enabling precise, data-driven insights into disease mechanisms and therapeutic strategies. As biological datasets grow more complex and comprehensive, computational biology continues to play a pivotal role in translating raw data into clinical solutions.
Immunotherapy
Cancer is a heterogeneous disease, posing significant challenges in designing effective therapeutic strategies. The emergence of multi-omics technologies has propelled the development of personalized medicine, enabling more precise and targeted approaches. One such breakthrough is immunotherapy, which harnesses the patient’s own immune system to identify and destroy cancer cells. Genetic mutations in cancer cells often give rise to neoantigens—tumor-specific antigens that can be recognized by the immune system. With rapid advancements in sequencing technologies and bioinformatics, neoantigen-based targeted therapies have become a promising frontier in personalized cancer treatment. At KrebsNeumann, we are focused on enhancing the prediction accuracy of peptide sequences, which is crucial for the success of neoantigen-based immunotherapies.
Digital Medicine
The emergence of the Internet of Things (IoT) and the widespread adoption of smartphones over the past few decades have introduced powerful new modalities for data acquisition in healthcare. Devices such as smartwatches, continuous glucose monitors, and smartphones are now equipped with a variety of hardware sensors capable of tracking vital signs, mobility patterns, sleep cycles, and more. Paired with their companion apps, these devices continuously collect and upload data to the cloud, generating rich, real-time health insights. As algorithms and data analytics continue to improve, these dynamic, patient-generated datasets are becoming increasingly valuable in clinical contexts. They offer a more comprehensive view of individual health, enabling proactive interventions and personalized care. At KrebsNeumann, we are exploring how to integrate these insights into the broader clinical ecosystem to drive better outcomes and empower patients through data.
Appomics
The convergence of wearables and health apps has given rise to a novel biomarker strategy we refer to as Appomics—a form of digital biomarker that captures the real-time, day-to-day fluctuations in a patient’s behavior and physiology. Unlike traditional biomarkers, Appomics reflects the dynamic and continuous nature of patient health, providing granular insights that were previously inaccessible. The implications of Appomics are twofold: It holds the potential to predict adverse clinical events before they manifest. It can significantly enhance Quality of Life (QoL) by enabling personalized, responsive care when analyzed within a comprehensive digital biomarker framework. At KrebsNeumann, we are focused on decoding these behavioral and physiological gyrations, even from limited datasets, and developing the clinical context necessary to translate them into actionable medical insights.
Innovation
Translating an invention into a market-ready product is a complex, high-risk, high-reward endeavor. In the technology sector, anecdotal data suggests that nearly 90% of startups fail within the first two years. Only 5% secure funding from angel investors, about 3% advance to Series A (venture capital), and fewer than 2% reach Series B or C. Ultimately, less than 1% achieve unicorn status—valued at over a billion dollars. In the life sciences and healthcare sectors, the path is even more challenging. Beyond the usual hurdles of fundraising and market fit, startups must navigate stringent regulatory frameworks that introduce additional layers of complexity, time, and cost. Each phase—from preclinical studies to clinical trials and final regulatory approval—presents potential bottlenecks that can derail even the most promising innovations. At KrebsNeumann, we recognize these challenges and aim to equip aspiring entrepreneurs and researchers with the tools, mentorship, and strategic insight necessary to navigate this difficult landscape and bring meaningful solutions to market.
Bench-to-Bed
The journey of a drug from discovery to clinical use is one of the most time-intensive and capital-heavy processes in modern science. On average, it takes 15 years and over $1 billion to bring a single drug to market. The odds of success are staggering—only 1 in 20,000 to 30,000 drug candidates will successfully navigate the full pipeline of preclinical studies, clinical trials, and regulatory approvals. The financial risk is immense, and even for successful drugs, it can take decades to realize meaningful revenue. Unlike the tech sector, where time to market and scalability can be rapid, the drug development process demands deep integration of science, business strategy, and regulatory navigation—each with its own timeline, complexity, and risk profile. At KrebsNeumann, our focus is on understanding what drives success at every stage of this process, with particular emphasis on clinical trial design, execution, and data interpretation. By dissecting and de-risking the clinical phase, we aim to improve the efficiency and success rate of translational medicine.