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02/19/2019

Research Article

Multi-modality in gene regulatory networks with slow promoter kinetics

In contrast to previously reported numerical simulations, Ali Al-Radhawi et al. introduce in this paper a theoretical and computational approach to the characterization of the multi-attractor dynamic landscape of gene networks with slow promoter kinetics. They obtain precise formulas that are then illustrated through applications to several systems biology models including a trans-differentiation network and a communicating population of synthetic toggle switches.

Image credit: Ali Al-Radhawi et al, pcbi.1006784.

Multi-modality in gene regulatory networks with slow promoter kinetics

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Current Issue January 2019

02/19/2019

Research Article

Allosteric mechanism of the circadian protein Vivid resolved through Markov state model and machine learning analysis

Quantitative analysis is needed to provide significant insight into protein allostery and lead to better prediction power of this ubiquitous phenomenon. Zhou et al. developed machine learning methods based on robust Markov state model to delineate allosteric mechanism of Vivid as an allosteric protein in the filamentous fungus Neurospora crassa, regulating circadian rhythm of this organism.

Image credit: Zhou et al, pcbi.1006801.

Allosteric mechanism of the circadian protein Vivid resolved through Markov state model and machine learning analysis

02/21/2019

Research Article

Independent working memory resources for egocentric and allocentric spatial information

While studies of visual working memory typically examine recall in displays consisting only of the stimuli to remember, natural scenes are filled with other objects that–although not required to be remembered–may nevertheless influence subsequent localization. Aagten-Murphy et al. demonstrate that memory for spatial location depends on independent stores for egocentric (relative to the observer) and allocentric (relative to other stimuli) information about object position.

Image credit: Aagten-Murphy et al, pcbi.1006563.

Independent working memory resources for egocentric and allocentric spatial information

02/19/2019

Research Article

Identifying individual risk rare variants using protein structure guided local tests (POINT)

Tzeng et al. use the observation that important variants tend to cluster together on functional domains to propose a new approach for prioritizing rare variants: the protein structure guided local test. POINT uses a gene’s 3-dimensional protein folding structure to guide aggregation of information from neighboring variants in the protein in a robust manner.

Identifying individual risk rare variants using protein structure guided local tests (POINT)

Image credit: Marceau West et al, pcbi.1006722.

02/15/2019

Research Article

Efficient neural decoding of self-location with a deep recurrent network

Tampuu et al. recorded the activity of a population of hippocampal neurons from freely moving rodents and carried out neural decoding to determine the animals’ locations. They found that using RNNs allowed them to predict the rodents’ positions more accurately than a standard Bayesian method with flat priors as well as a Bayesian approach with memory.

Efficient neural decoding of self-location with a deep recurrent network

Image credit: Tampuu et al, pcbi.1006822.

01/11/2019

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Complexity

This Channel brings together all aspects of complexity research and includes interdisciplinary topics from network theory to applications in neuroscience and the social sciences.

Complexity

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02/20/2019

Featured Collection

Machine Learning in Health and Biomedicine

In this Collection, PLOS ONE, PLOS Computational Biology, PLOS Medicine and our teams of Guest Editors feature research that applies machine learning methods to health and biomedicine.

Machine Learning in Health and Biomedicine

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