The Spatial Distribution and Dynamics of CXCL13 in Lymphoid Tissues

Morphogens are soluble signalling molecules that regulate a broad spectrum of biological processes. However, the distances and scales over which this regulation occurs are unclear. To date, many studies have highlighted source-sink mechanisms for morphogen gradient formation but fail to take the role of the tissue microenvironment into account. Using a systems-based approach we show that the chemokine CXCL13 is regulated by the B-cell microenvironment on distinct but interconnected levels of biological organization. CXCL13 is a key determinant of humoral immune responses, regulating the localisation of lymphocytes within lymphoid tissues. Due to a complex and dynamic interaction network occurring over broad spatiotemporal scales, mapping the spatial distribution of CXCL13 in situ is challenging. To address this we have mapped the 3-dimensional organisation of CXCL13+ stromal cells in situ using a fluorescent reporter system, identifying three distinct but interconnected stromal subsets that are unique in their network properties. We quantify CXCL13 dynamics using high-speed narrowfield microscopy in collagen matrix and lymph node tissue sections with results suggesting that diffusion is highly constrained by local tissue microanatomy. However, this data alone is insufficient to describe CXCL13 gradient formation. To consolidate this data we employ a quantitative modelling approach hybridising different techniques into a high fidelity in silico representation of the B-follicle, where immune cells can interact with stroma capable of creating and shaping complex physiological gradients. Simulation analyses and immunohistochemistry suggest that chemokine fields within the follicle are dynamic and non-uniform, with multiobjective optimization analysis suggesting that this spatial configuration is designed to promote scanning rates. Taken in concert, our data suggests that CXCL13 acts over short distances creating a complex landscape of expression. Importantly, this study provides a basis for understanding the spatial distribution of morphogens with complex binding behaviours

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp