Swain Laboratory
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Gathering and processing information is fundamental to all life. In cells, this ability is conferred by biochemical networks, collections of genes and proteins that interact with each other and the extracellular environment. Information is detected by proteins at the cell membrane, processed by biochemical networks in the cytosol and nucleus, and then used to decide an appropriate cellular response. Such cellular decision-making is at the core of systems biology and its failure causes disease: whether it is a hijacking of the signalling network by a viral invader, the uncontrolled growth of cancer, or mistimings in the contractions of individual heart cells. The Laboratory uses a combination of both experiment and theory: microfluidics to generate stochastically changing environments, fluorescence microscopy to quantitatively monitor the responses of individual cells, and techniques from stochastic processes, non-linear dynamics, information theory, statistical inference, and evolutionary biology to develop mathematical models of biochemical information-processing.
Items in this Collection
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Distributed and dynamic intracellular organization of extracellular information in yeast
Data from single-cell microscopy experiments for "Distributed and dynamic intracellular organization of extracellular information" by Granados, Pietsch, Cepeda-Humerez, Tkačik and Swain. The data come from a microfluidics-based ... -
Morphologically Constrained and Data Informed Cell Segmentation of Budding Yeast
This is all the data associated with the paper "Morphologically Constrained and Data Informed Cell Segmentation of Budding Yeast": a description of the cell segmentation software developed in the Swain Laboratory for ... -
Distributing tasks via multiple input pathways increases cellular survival in stress
Data from single-cell microscopy experiments for "Distributing tasks via multiple input pathways increases cellular survival in stress" by Granados, Crane, Montano-Gutierrez, Tanaka, Voliotis, & Swain. Improving in one ... -
Inferring time derivatives, including cell growth rates, using Gaussian processes
Often the time-derivative of a measured variable is of as much interest as the variable itself. For a growing population of biological cells, for example, the population’s growth rate is typically more important than its ...
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