Abstract: How do complex biological systems make decisions, and how can we learn to model, engineer, and ultimately direct those decisions toward better clinical outcomes? In this talk, Professor Sonali Chaturvedi will discuss her lab’s efforts to uncover the organizing principles that govern decision-making in living systems. Using a systems and synthetic biology framework, the Chaturvedi Lab explores how cellular and molecular circuits are wired, how they adapt, and how they can be reprogrammed. She will highlight recent advances in building synthetic circuits to probe network logic in pathogenesis, developing single-cell technologies that capture decision-making in real time, and constructing predictive frameworks that integrate machine learning with emerging tools in quantum computing. Professor Chaturvedi will also discuss how these approaches are being translated into the clinic, from designing therapies with stronger resistance barriers to improving transplantation safety and outcomes in chronic infection. Together, these studies point toward a future where biology can be treated not merely as a collection of pathways, but as an information system that can be decoded and engineered for medicine.
Bio: The Chaturvedi Lab at UC Riverside investigates how complex biological systems make decisions, and how those decisions can be modeled, engineered, and ultimately directed toward better clinical outcomes. We take a systems and synthetic biology approach to questions at the interface of development, oncogenesis, and host–disease interactions, asking how cellular and molecular circuits are wired, how they adapt, and how they can be reprogrammed. Our group develops both experimental and computational platforms to make these questions tractable. We build synthetic circuits to probe network logic, engineer single-cell technologies that capture decision-making in real time, and create predictive frameworks that integrate machine learning with emerging tools in quantum computing. These methods allow us to study biology as a dynamic, information-rich system and to uncover rules that generalize across contexts. The translational potential of our research is equally central. We are applying these principles with our clinical partners to design therapies with stronger barriers to resistance, to develop strategies that make transplantation safer, and to improve clinical outcomes in populations living with chronic infection. In all cases, our aim is to treat biology not as a collection of pathways, but as a system of principles that can be revealed, modeled, and engineered to create new possibilities for medicine.
Abstract: How do complex biological systems make decisions, and how can we learn to model, engineer, and ultimately direct those decisions toward better clinical outcomes? In this talk, Professor Sonali Chaturvedi will discuss her lab’s efforts to uncover the organizing principles that govern decision-making in living systems. Using a systems and synthetic biology framework, the Chaturvedi Lab explores how cellular and molecular circuits are wired, how they adapt, and how they can be reprogrammed. She will highlight recent advances in building synthetic circuits to probe network logic in pathogenesis, developing single-cell technologies that capture decision-making in real time, and constructing predictive frameworks that integrate machine learning with emerging tools in quantum computing. Professor Chaturvedi will also discuss how these approaches are being translated into the clinic, from designing therapies with stronger resistance barriers to improving transplantation safety and outcomes in chronic infection. Together, these studies point toward a future where biology can be treated not merely as a collection of pathways, but as an information system that can be decoded and engineered for medicine.
Bio: The Chaturvedi Lab at UC Riverside investigates how complex biological systems make decisions, and how those decisions can be modeled, engineered, and ultimately directed toward better clinical outcomes. We take a systems and synthetic biology approach to questions at the interface of development, oncogenesis, and host–disease interactions, asking how cellular and molecular circuits are wired, how they adapt, and how they can be reprogrammed. Our group develops both experimental and computational platforms to make these questions tractable. We build synthetic circuits to probe network logic, engineer single-cell technologies that capture decision-making in real time, and create predictive frameworks that integrate machine learning with emerging tools in quantum computing. These methods allow us to study biology as a dynamic, information-rich system and to uncover rules that generalize across contexts. The translational potential of our research is equally central. We are applying these principles with our clinical partners to design therapies with stronger barriers to resistance, to develop strategies that make transplantation safer, and to improve clinical outcomes in populations living with chronic infection. In all cases, our aim is to treat biology not as a collection of pathways, but as a system of principles that can be revealed, modeled, and engineered to create new possibilities for medicine.