BackgroundCells process signals using complex and dynamic networks. Studying how this is performed in a context and cell type specific way is essential to understand signaling both in physiological and diseased situations. Context-specific medium/high throughput proteomic data measured upon perturbation is now relatively easy to obtain but formalisms that can take advantage of these features to build models of signaling are still comparatively scarce.ResultsHere we present CellNOptR, an open-source R software package for building predictive logic models of signaling networks by training networks derived from prior knowledge to signaling (typically phosphoproteomic) data. CellNOptR features different logic formalisms, from Boolean models to differential equations, in a common framework. These different logic model representations accommodate state and time values with increasing levels of detail. We provide in addition an interface via Cytoscape (CytoCopteR) to facilitate use and integration with Cytoscape network-based capabilities.ConclusionsModels generated with this pipeline have two key features. First, they are constrained by prior knowledge about the network but trained to data. They are therefore context and cell line specific, which results in enhanced predictive and mechanistic insights. Second, they can be built using different logic formalisms depending on the richness of the available data. Models built with CellNOptR are useful tools to understand how signals are processed by cells and how this is altered in disease. They can be used to predict the effect of perturbations (individual or in combinations), and potentially to engineer therapies that have differential effects/side effects depending on the cell type or context.

CD8+ T cells can exert both protective and harmful effects on the virus-infected host. However, there is no systematic method to identify the attributes of a protective CD8+ T cell response. Here, we combine theory and experiment to identify and quantify the contribution of all HLA class I alleles to host protection against infection with a given pathogen. In 432 HTLV-1-infected individuals we show that individuals with HLA class I alleles that strongly bind the HTLV-1 protein HBZ had a lower proviral load and were more likely to be asymptomatic. We also show that in general, across all HTLV-1 proteins, CD8+ T cell effectiveness is strongly determined by protein specificity and produce a ranked list of the proteins targeted by the most effective CD8+ T cell response through to the least effective CD8+ T cell response. We conclude that CD8+ T cells play an important role in the control of HTLV-1 and that CD8+ cells specific to HBZ, not the immunodominant protein Tax, are the most effective. We suggest that HBZ plays a central role in HTLV-1 persistence. This approach is applicable to all pathogens, even where data are sparse, to identify simultaneously the HLA Class I alleles and the epitopes responsible for a protective CD8+ T cell response.

BackgroundOptimization is the key to solving many problems in computational biology. Global optimization methods, which provide a robust methodology, and metaheuristics in particular have proven to be the most efficient methods for many applications. Despite their utility, there is a limited availability of metaheuristic tools.ResultsWe present MEIGO, an R and Matlab optimization toolbox (also available in Python via a wrapper of the R version), that implements metaheuristics capable of solving diverse problems arising in systems biology and bioinformatics. The toolbox includes the enhanced scatter search method (eSS) for continuous nonlinear programming (cNLP) and mixed-integer programming (MINLP) problems, and variable neighborhood search (VNS) for Integer Programming (IP) problems. Additionally, the R version includes BayesFit for parameter estimation by Bayesian inference. The eSS and VNS methods can be run on a single-thread or in parallel using a cooperative strategy. The code is supplied under GPLv3 and is available at http://www.iim.csic.es/~gingproc/meigo.html. Documentation and examples are included. The R package has been submitted to BioConductor. We evaluate MEIGO against optimization benchmarks, and illustrate its applicability to a series of case studies in bioinformatics and systems biology where it outperforms other state-of-the-art methods.ConclusionsMEIGO provides a free, open-source platform for optimization that can be applied to multiple domains of systems biology and bioinformatics. It includes efficient state of the art metaheuristics, and its open and modular structure allows the addition of further methods.

Chemical dimerizers are powerful tools for non-invasive manipulation of enzyme activities in intact cells. Here we introduce the first rapidly reversible small-molecule-based dimerization system and demonstrate a sufficiently fast switch-off to determine kinetics of lipid metabolizing enzymes in living cells. We applied this new method to induce and stop phosphatidylinositol 3-kinase (PI3K) activity, allowing us to quantitatively measure the turnover of phosphatidylinositol 3,4,5-trisphosphate (PIP3) and its downstream effectors by confocal fluorescence microscopy as well as standard biochemical methods.

HBZ protein is expressed in vivo in patients with HAM and in ACs. Our results are consistent with the idea that the T cell response to HBZ plays an important part in restricting HTLV-1 viral load.

Killer cell immunoglobulin-like receptors (KIRs) influence both innate and adaptive immunity. But while the role of KIRs in NK-mediated innate immunity is well-documented, the impact of KIRs on the T cell response in human disease is not known. Here we test the hypothesis that an individual's KIR genotype affects the efficiency of their HLA class I-mediated antiviral immune response and the outcome of viral infection. We show that, in two unrelated viral infections, hepatitis C virus and human T lymphotropic virus type 1, possession of the KIR2DL2 gene enhanced both protective and detrimental HLA class I-restricted anti-viral immunity. These results reveal a novel role for inhibitory KIRs. We conclude that inhibitory KIRs, in synergy with T cells, are a major determinant of the outcome of persistent viral infection.

Despite the current wealth of high-throughput data, our understanding of signal transduction is still incomplete. Mathematical modeling can be a tool to gain an insight into such processes. Detailed biochemical modeling provides deep understanding, but does not scale well above relatively a few proteins. In contrast, logic modeling can be used where the biochemical knowledge of the system is sparse and, because it is parameter free (or, at most, uses relatively a few parameters), it scales well to large networks that can be derived by manual curation or retrieved from public databases. Here, we present an overview of logic modeling formalisms in the context of training logic models to data, and specifically the different approaches to modeling qualitative to quantitative data (state) and dynamics (time) of signal transduction. We use a toy model of signal transduction to illustrate how different logic formalisms (Boolean, fuzzy logic and differential equations) treat state and time. Different formalisms allow for different features of the data to be captured, at the cost of extra requirements in terms of computational power and data quality and quantity. Through this demonstration, the assumptions behind each formalism are discussed, as well as their advantages and disadvantages and possible future developments.

In human T-lymphotropic virus type 1 (HTLV-1) infection, a high frequency of HTLV-1-specific CTLs can co-exist stably with a high proviral load and the proviral load is strongly correlated with the risk of HTLV-1-associated inflammatory diseases. These observations led to the hypothesis that HTLV-1 specific CTLs are ineffective in controlling HTLV-1 replication but contribute to the pathogenesis of the inflammatory diseases. But evidence from host and viral immunogenetics and gene expression microarrays suggests that a strong CTL response is associated with a low proviral load and a low risk of HAM/TSP. Here, we quantified the frequency, lytic activity and functional avidity of HTLV-1-specific CD8+ cells in fresh, unstimulated PBMCs from individuals with natural HTLV-1 infection. The lytic efficiency of the CD8+ T cell response—the fraction of autologous HTLV-1-expressing cells eliminated per CD8+ cell per day—was inversely correlated with both the proviral load and the rate of spontaneous proviral expression. The functional avidity of HTLV-1-specific CD8+ cells was strongly correlated with their lytic efficiency. We conclude that efficient control of HTLV-1 in vivo depends on the CTL lytic efficiency, which depends in turn on CTL avidity of Ag recognition. CTL quality determines the position of virus-host equilibrium in persistent HTLV-1 infection.