Lahdesmaki, H. | bioRxiv Science

bioRxiv2018

Data-driven multiscale modeling reveals the role of metabolic coupling for the spatio-temporal growth dynamics of yeast colonies

MotivationMulticellular entities, such as mammalian tissues or microbial biofilms, typically exhibit complex spatial arrangements that are adapted to their specific functions or environments. These structures result from intercellular signaling as well as from the interaction with the environment that allow cells of the same genotype to differentiate into well-organized communities of diversified cells. Despite its importance, our understanding on how cell-cell and metabolic coupling produce functionally optimized structures is still limited.\n\nResultsHere, we present a data-driven spatial framework to computationally investigate the development of one multicellular structure, yeast colonies. Using experimental growth data from homogeneous liquid media conditions, we develop and parameterize a dynamic cell state and growth model. We then use the resulting model in a coarse-grained spatial model, which we calibrate using experimental time-course data of colony growth. Throughout the model development process, we use state-of-the-art statistical techniques to handle the uncertainty of model structure and parameterization. Further, we validate the model predictions against independent experimental data and illustrate how metabolic coupling plays a central role in colony formation.\n\nAvailabilityExperimental data and a computational implementation to reproduce the results are available at http://research.cs.aalto.fi/csb/software/multiscale/code.zip.\n\nContactjukka.intosalmi@aalto.fi, alexander.skupin@uni.lu

bioinformatics↗

bioRxiv2018

SCHiRM: Single Cell Hierarchical Regression Model to detect dependencies in read count data

MotivationModern single cell RNA sequencing (scRNA-seq) technologies have made it possible to measure the RNA content of individual cells. The scRNA-seq data provide us with detailed information about the cellular states but, despite several pioneering efforts, it remains an open research question how regulatory networks could be inferred from these noisy discrete read count data.\n\nResultsHere, we introduce a hierarchical regression model which is designed for detecting dependencies in scRNA-seq and other count data. We model count data using the Poisson-log normal distribution and, by means of our hierarchical formulation, detect the dependencies between genes using linear regression model for the latent, cell-specific gene expression rate parameters. The hierarchical formulation allows us to model count data without artificial data transformations and makes it possible to incorporate normalization information directly into the latent layer of the model. We test the proposed approach using both simulated and experimental data. Our results show that the proposed approach performs better than standard regression techniques in parameter inference task as well as in variable selection task.\n\nAvailabilityAn implementation of the method is available at https://github.com/jeintos/SCHiRM.\n\nContactjukka.intosalmi@aalto.fi, harri.lahdesmaki@aalto.fi

bioinformatics↗