Cells are packed with thousands of interacting molecular networks that control growth, metabolism, and communication, but scientists lack tools to watch these networks in action. This project builds a "Molecular Nose"—a platform that uses up to 1,000 artificial sensors to simultaneously track how hundreds of cellular components respond to stimuli, much like an electronic nose identifies complex odours. Existing methods can only measure a handful of components at once, leaving the dynamic behaviour of entire networks invisible. Without this, researchers cannot systematically predict how drugs affect cells, why stem cells choose one fate over another, or how synthetic pathways produce desired molecules. The Molecular Nose fills that gap by generating libraries of response patterns and using algorithms to deconvolute complex inputs. The team will create three versions: a high-capacity sensor array requiring cell lysis for training, a nanoparticle-based system for real-time monitoring in living cells, and a transgenic sensor mouse for studying networks in whole organisms. If successful, this generic platform could transform drug development by revealing side effects early, guide stem cell differentiation for tissue engineering, and optimise synthetic biology for manufacturing proteins and metabolites. The work is fundamental science—building a tool to see what has been invisible—but it could eventually reshape how we design therapies and engineer cells.
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THE MOLECULAR NOSEMammalian cells use more genes to regulate biological processes than to carry them out. These include all fundamental processes such as cell growth, differentiation, survival, metabolism and the ability to sense and produce the correct complement of biomolecules to communicate with the environment. Complexity is further enhanced by the organization of biological processes as networks. Understanding the behaviour and responses of such complex networks will be crucial to solve eminent questions in biology. To address this need we will build a multiplexed sensor platform that can assess and quantify dynamic changes in the functional state of biochemical networks in mammalian cells, and use these data to reconstruct cell network interactions and their dynamic behaviour on a systems wide level. The concept underpinning this platform is fundamentally different from existing methods used in the biological sciences to assess cell function, and similar to the Electronic Nose , where an array of sensors is first trained with individual stimuli to establish a library of response patterns which subsequently are used to deconvolute complex inputs. The Molecular Nose will monitor the outputs of several hundred network components simultaneously in cell populations or single cells using artificial transcriptional reporters, and design a software framework and algorithms for their functional analysis. The Molecular Nose will be built in three versions. One will be constructed using molecular biology tools, and will permit to use a large array (up to 1000) of sensors. However, it requires the cell being lysed for the measurement as the detector is outside of the cell. This version will be particularly useful for training the system and establishing a large library of response patterns. The second version will be built by integrating both individual sensors and their corresponding detectors onto bar-coded nanoparticles which will be introduced into cells and read using surface enhanced resonance Raman scattering spectroscopy. This setup will use a smaller number of sensors (up to 30), but can be used to monitor responses in living cells in real time. In parallel we will develop methods for the controlled introduction of these particle libraries into cells. The third version is the stable integration of a plasmid based sensor library into the embryonic mouse stem cells with the aim to generate a transgenic sensor mouse. The stem cells also can be used for organotypic cultures and in vitro differentiation systems.The Molecular Nose will enable the systematic testing and rational interpretation of the behaviour of cellular networks. The technique is generic with a wide range of applications in both single cells and cell populations, including eminent biological problems such as the analysis of drug effects and prediction of side effects; stem cell differentiation with a view to eventually control differentiation; cell fate specification in order to support tissue engineering; genetic and biochemical networks for the production of desired proteins and metabolites by synthetically engineered pathways; and the investigation of adaptive network responses and evolution. Currently, we are lacking efficient experimental tools to analyse these complex interactions.
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