From Social Simulation to Integrative System Design

As the recent financial crisis showed, today there is a strong need to gain "ecological perspective" of all relevant interactions in socio-economic-techno-environmental systems. For this, we suggested to set-up a network of Centers for integrative systems design, which shall be able to run all potentially relevant scenarios, identify causality chains, explore feedback and cascading effects for a number of model variants, and determine the reliability of their implications (given the validity of the underlying models). They will be able to detect possible negative side effect of policy decisions, before they occur. The Centers belonging to this network of Integrative Systems Design Centers would be focused on a particular field, but they would be part of an attempt to eventually cover all relevant areas of society and economy and integrate them within a "Living Earth Simulator". The results of all research activities of such Centers would be turned into informative input for political Decision Arenas. For example, Crisis Observatories (for financial instabilities, shortages of resources, environmental change, conflict, spreading of diseases, etc.) would be connected with such Decision Arenas for the purpose of visualization, in order to make complex interdependencies understandable to scientists, decision-makers, and the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c

Computational analysis of a plant receptor interaction network

Trabajo fin de máster en Bioinformática y Biología ComputacionalIn all organisms, complex protein-protein interactions (PPI) networks control major biological functions yet studying their structural features presents a major analytical challenge. In plants, leucine-rich-repeat receptor kinases (LRR-RKs) are key in sensing and transmitting non-self as well as self-signals from the cell surface. As such, LRR-RKs have both developmental and immune functions that allow plants to make the most of their environments. In the model organism in plant molecular biology, Arabidopsis thaliana, most LRR-RKs are still represented by biochemically and genetically uncharacterized receptors. To fix this an LRR-based Cell Surface Interaction (CSI LRR ) network was obtained in 2018, a protein-protein interaction network of the extracellular domain of 170 LRR-RKs that contains 567 bidirectional interactions. Several network analyses have been performed with CSI LRR . However, these analyses have so far not considered the spatial and temporal expression of its proteins. Neither has it been characterized in detail the role of the extracellular domain (ECD) size in the network structure. Because of that, the objective of the present work is to continue with more in depth analyses with the CSI LRR network. This would provide important insights that will facilitate LRR-RKs function characterization. The first aim of this work is to test out the fit of the CSI LRR network to a scale-free topology. To accomplish that, the degree distribution of the CSI LRR network was compared with the degree distribution of the known network models of scale-free and random. Additionally, three network attack algorithms were implemented and applied to these two network models and the CSI LRR network to compare their behavior. However, since the CSI LRR interaction data comes from an in vitro screening, there is no direct evidence whether its protein-protein interactions occur inside the plant cells. To gain insight on how the network composition changes depending on the transcriptional regulation, the interaction data of the CSI LRR was integrated with 4 different RNA-Seq datasets related with the network biological functions. To automatize this task a Python script was written. Furthermore, it was evaluated the role of the LRR-RKs in the network structure depending on the size of their extracellular domain (large or small). For that, centrality parameters were measured, and size-targeted attacks performed. Finally, gene regulatory information was integrated into the CSI LRR to classify the different network proteins according to the function of the transcription factors that regulate its expression. The results were that CSI LRR fits a power law degree distribution and approximates a scale- free topology. Moreover, CSI LRR displays high resistance to random attacks and reduced resistance to hub/bottleneck-directed attacks, similarly to scale-free network model. Also, the integration of CSI LRR interaction data and RNA-Seq data suggests that the transcriptional regulation of the network is more relevant for developmental programs than for defense responses. Another result was that the LRR-RKs with a small ECD size have a major role in the maintenance of the CSI LRR integrity. Lastly, it was hypothesized that the integration of CSI LRR interaction data with predicted gene regulatory networks could shed light upon the functioning of growth-immunity signaling crosstalk

Dynamics of Natural Killer cell receptor revealed by quantitative analysis of photoswitchable protein

Natural Killer (NK) cell activation is dynamically regulated by numerous activating and inhibitory surface receptors that accumulate at the immune synapse. Quantitative analysis of receptor dynamics has been limited by methodologies which rely on indirect measurements such as fluorescence recovery after photobleaching. Here, we report a novel approach to study how proteins traffic to and from the immune synapse using NK cell receptors tagged with the photoswitchable fluorescent protein tdEosFP, which can be irreversibly photoswitched from a green to red fluorescent state by ultraviolet light. Thus, following a localized switching event, the movement of the photoswitched molecules can be temporally and spatially resolved by monitoring fluorescence in two regions of interest. By comparing images with mathematical models, we evaluated the diffusion coefficient of the receptor KIR2DL1 (0.23 +- 0.06 micron^2/s) and assessed how synapse formation affects receptor dynamics. Our data conclude that the inhibitory NK cell receptor KIR2DL1 is continually trafficked into the synapse and remains surprisingly stable there. Unexpectedly however, in NK cells forming synapses with multiple target cells simultaneously, KIR2DL1 at one synapse can relocate to another synapse. Thus, our results reveal a previously undetected inter-synaptic exchange of protein.Comment: 25 pages, 5 figure

SCALE method for single-cell ATAC-seq analysis via latent feature extraction.

Single-cell ATAC-seq (scATAC-seq) profiles the chromatin accessibility landscape at single cell level, thus revealing cell-to-cell variability in gene regulation. However, the high dimensionality and sparsity of scATAC-seq data often complicate the analysis. Here, we introduce a method for analyzing scATAC-seq data, called Single-Cell ATAC-seq analysis via Latent feature Extraction (SCALE). SCALE combines a deep generative framework and a probabilistic Gaussian Mixture Model to learn latent features that accurately characterize scATAC-seq data. We validate SCALE on datasets generated on different platforms with different protocols, and having different overall data qualities. SCALE substantially outperforms the other tools in all aspects of scATAC-seq data analysis, including visualization, clustering, and denoising and imputation. Importantly, SCALE also generates interpretable features that directly link to cell populations, and can potentially reveal batch effects in scATAC-seq experiments

Influenza research database: an integrated bioinformatics resource for influenza research and surveillance.

BackgroundThe recent emergence of the 2009 pandemic influenza A/H1N1 virus has highlighted the value of free and open access to influenza virus genome sequence data integrated with information about other important virus characteristics.DesignThe Influenza Research Database (IRD, http://www.fludb.org) is a free, open, publicly-accessible resource funded by the U.S. National Institute of Allergy and Infectious Diseases through the Bioinformatics Resource Centers program. IRD provides a comprehensive, integrated database and analysis resource for influenza sequence, surveillance, and research data, including user-friendly interfaces for data retrieval, visualization and comparative genomics analysis, together with personal log in-protected 'workbench' spaces for saving data sets and analysis results. IRD integrates genomic, proteomic, immune epitope, and surveillance data from a variety of sources, including public databases, computational algorithms, external research groups, and the scientific literature.ResultsTo demonstrate the utility of the data and analysis tools available in IRD, two scientific use cases are presented. A comparison of hemagglutinin sequence conservation and epitope coverage information revealed highly conserved protein regions that can be recognized by the human adaptive immune system as possible targets for inducing cross-protective immunity. Phylogenetic and geospatial analysis of sequences from wild bird surveillance samples revealed a possible evolutionary connection between influenza virus from Delaware Bay shorebirds and Alberta ducks.ConclusionsThe IRD provides a wealth of integrated data and information about influenza virus to support research of the genetic determinants dictating virus pathogenicity, host range restriction and transmission, and to facilitate development of vaccines, diagnostics, and therapeutics

Characterizing the immune microenvironment of malignant peripheral nerve sheath tumor by PD-L1 expression and presence of CD8+ tumor infiltrating lymphocytes.

BackgroundMalignant peripheral nerve sheath tumor (MPNST) is an aggressive sarcoma with few treatment options. Tumor immune state has not been characterized in MPNST, and is important in determining response to immune checkpoint blockade. Our aim was to evaluate the expression of programmed death-ligand 1 (PD-L1), programmed cell death protein 1 (PD-1), and presence of CD8+ tumor infiltrating lymphocytes (TILs) in MPNST, and correlate these findings with clinical behavior and outcome.ResultsPD-L1 staining of at least 1% was seen in 0/20 nerves, 2/68 benign lesions and 9/53 MPNST. Two of 68 benign lesions and 7/53 (13%) MPNST had at least 5% PD-L1 staining. CD8 staining of at least 5% was seen in 1/20 (5%) nerves, 45/68 (66%) benign lesions and 30/53 (57%) MPNST. PD-L1 was statistically more prevalent in MPNST than both nerves and benign lesions (p=0.049 and p=0.008, respectively). Expression of PD-1 was absent in all tissue specimens. There was no correlation of PD-L1 or CD8 expression with disease state (primary versus metastatic) or patient survival.MethodsA comprehensive PNST tissue microarray was created from 141 surgical specimens including primary, recurrent, and metastatic MPNST (n=53), neurofibromas (n=57), schwannoma (n=11), and normal nerve (n=20). Cores were stained in triplicate for PD-L1, PD-1, and CD8, and expression compared between tumor types. These data were then examined for survival correlates in 35 patients with primary MPNST.ConclusionsMPNST is characterized by low PD-L1 and absent PD-1 expression with significant CD8+ TIL presence. MPNST immune microenvironment does not correlate with patient outcome