Black-Box Test Generation from Inferred Models

Automatically generating test inputs for components without source code (are ‘black-box’) and specification is challenging. One particularly interesting solution to this problem is to use Machine Learning algorithms to infer testable models from program executions in an iterative cycle. Although the idea has been around for over 30 years, there is little empirical information to inform the choice of suitable learning algorithms, or to show how good the resulting test sets are. This paper presents an openly available framework to facilitate experimentation in this area, and provides a proof-of-concept inference-driven testing framework, along with evidence of the efficacy of its test sets on three programs

Analysis of GFP (γ-globin) expression during development.

<p>(<b>A</b>) Histogram overlay of embryonic blood from transgenic and WT embryos in the GFP axis. The percentages of positive GFP cells and primitive cells for each developmental stage are included in the table underneath (SSC: side scatter). Differences in the MFI of WT cells amongst the three histograms shown are due to the developmental stage and the gated cells plotted (i.e. SSC^high in the third histogram). (<b>B</b>) Dot plot depicting embryonic blood of representative γGPA-GFP/βDsRed transgenic mice at 14.5<i>dpc</i> in which the GFP⁺ population is depicted as green dots against the Forward (FSC) and Side Scatter (SSC). GFP⁺ cells (8.62±2.68%) are SSC^high, <i>i.e.</i> primitive cells. (<b>C</b>) qPCR analysis of GFP expression in WT and transgenic fetal liver cells at 11.5<i>dpc</i> and 14.5<i>dpc</i>. RFE is relative fold enrichment. Average and standard deviation derived from three mice per group is depicted. T-test was performed to calculate the <i>p</i> values.</p

Characterization of the transgenic dual reporter fetal liver cell lines.

<p>(<b>A</b>) Flow cytometry analysis of transgenic fetal liver cell lines before and after differentiation. Histograms against forward scatter and erythroid surface markers CD117 (cKit) and CD71 (transferrin receptor) are depicted. (<b>B</b>) Flow cytometry analysis of transgenic fetal liver cell lines before and after differentiation. Histograms against DsRed and GFP are depicted. (<b>C</b>) Representative pictures taken during erythroid differentiation of transgenic fetal liver cell lines. Arrows indicate spontaneously differentiating cells expressing DsRed protein (left) and differentiated cells with much smaller size that are not as bright as the bigger ones, as a consequence of the continuous production of endogenous hemoglobin and subsequent quenching of cytoplasmic DsRed fluorescent signal (right).</p

<i>In vivo</i> treatment of transgenic mice with 5-Azacytidine.

<p>(<b>A</b>) Graph representing absolute numbers of GFP⁺ cells/10⁶ events measured gated in blood of γGPA-GFP/βDsRed mice treated (PHZ+AZA, red dots) or mock treated (PBS, white dots). (<b>B</b>) Graph representing absolute numbers of GFP⁺ cells/10⁵ events measured after 2 days of bone marrow hanging drop culture (BM HD D2) derived from γGPA-GFP/βDsRed mice treated (PHZ+AZA, orange dots) or mock treated (PBS, white dots). A logarithmic scale is used to better visualize the distribution of values found (each dot represents a mouse). The average per group is depicted as a black line. <i>P</i> values were calculated from the Log transformed data with a T-test.</p

Knockdown assays in transgenic dual reporter fetal liver cell lines.

<p>(<b>A</b>) Flow cytometry analysis of the knockdown of cMyb, Bcl11a, Hdac3 and Fop in the γGFP/βDsRed cell line. The same vector with a non-specific shRNA sequence was used as a control. Percentages of cells positive for GFP (upper panel) and DsRed (lower panel) are shown. Contour plots show gated live cells. (<b>B</b>) Western blots of the knockdown experiments in protein extracts of transduced cells. Equal numbers of cells are loaded on each lane. <b>φ</b>, empty vector control extracts; KD, knockdown extracts.</p

Analysis of DsRed (β-globin) expression during development.

<p>(<b>A</b>) Flow cytometry analysis of fetal liver of γGPA-GFP/βDsRed transgenic mice during development. Arrowhead at 11.5<i>dpc</i> and 12.5<i>dpc</i> indicates the DsRed positive population. Representative data are depicted. (<b>B</b>) qPCR analysis of DsRed expression in WT and transgenic fetal liver cells at 11.5<i>dpc</i> and 14.5<i>dpc</i>. RFE is relative fold enrichment. Average and standard deviation derived from three mice per group is depicted. T-test was performed to calculate the <i>p</i> values. (<b>C</b>) Histogram overlays of DsRed and GFP expression in 14.5<i>dpc</i> fetal liver cells cultured for 2 days in hanging drops. DsRed expression is detected in transgenic cells differentiated <i>in vitro</i> when compared to WT while GFP is not. Representative data are depicted.</p

Modification of the human β-globin locus and generation of transgenic dual reporter mouse lines.

<p>(<b>A</b>) The human β-globin locus (<i>SceI</i> flanked) PAC used for the modifications made in the γ- and β-globin genes. GFP and DsRed were introduced in the ATG (+1) position of the transcripts followed by a stop codon (*). (<b>B</b>) Schematic representation of the GPA-GFP construct. The grey numbered stretches of the cartoon (1–114, 834–1234 bp) represent the glycophorin-A cDNA and the green stretch represents the GFP cDNA (114–834). The bilayer represents the transmembrane part of the protein, thus the GFP is expressed in the extracellular part of the fusion GPA-GFP protein. (<b>C</b>) Representative picture of K562 cells transfected with the γGFP/βDsRed modified human β-globin locus to check expression of γ-globin and flow cytometry analysis of GFP expression in 12.5<i>dpc</i> embryonic blood of γGFP/βDsRed transgenic embryos (left). Representative picture of fetal liver cells transduced with the γGPA-GFP construct to check expression of GFP protein in the plasma membrane and flow cytometry analysis of GFP expression in 12.5<i>dpc</i> embryonic blood of γGPA-GFP/βDsRed transgenic embryos (right). Mean fluorescence intensity (MFI) ratio is indicated in both graphs. (<b>D</b>) Southern blot of both mouse transgenic lines (γGFP/βDsRed and γGPA-GFP/βDsRed). Tail genomic DNA was digested with <i>SacI</i> restriction enzyme and hybridized with cosLCRε (left) and cosγγδβ (right), as previously described <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051272#pone.0051272-Strouboulis1" target="_blank">[2]</a>. Lane 1: γGPA-GFP/βDsRed tail DNA, Lanes 2, 3: mouse line PAC8 carrying the human β-globin locus and Lane 4: γGFP/βDsRed tail DNA. Symbol ▹ indicates end fragments, ▸ HGG1 3.6 Kb <i>SacI</i> fragment, ▸▸ HGG1-GFP 4.3 Kb <i>SacI</i> fragment, ▸▸▸ HGG1-GPA-GFP 4.9 Kb <i>SacI</i> fragment, ⧫ β-DsRed modification (16.4 to 17 Kb fragment). (<b>E</b>) S1 nuclease protection analysis of mouse globin expression of WT and transgenic mice at different developmental stages as indicated.</p

A comprehensive proteomics study on platelet concentrates: Platelet proteome, storage time and Mirasol pathogen reduction technology

<p>Platelet concentrates (PCs) represent a blood transfusion product with a major concern for safety as their storage temperature (20–24°C) allows bacterial growth, and their maximum storage time period (less than a week) precludes complete microbiological testing. Pathogen inactivation technologies (PITs) provide an additional layer of safety to the blood transfusion products from known and unknown pathogens such as bacteria, viruses, and parasites. In this context, PITs, such as Mirasol Pathogen Reduction Technology (PRT), have been developed and are implemented in many countries. However, several studies have shown <i>in vitro</i> that Mirasol PRT induces a certain level of platelet shape change, hyperactivation, basal degranulation, and increased oxidative damage during storage. It has been suggested that Mirasol PRT might accelerate what has been described as the platelet storage lesion (PSL), but supportive molecular signatures have not been obtained. We aimed at dissecting the influence of both variables, that is, Mirasol PRT and storage time, at the proteome level. We present comprehensive proteomics data analysis of Control PCs and PCs treated with Mirasol PRT at storage days 1, 2, 6, and 8. Our workflow was set to perform proteomics analysis using a gel-free and label-free quantification (LFQ) approach. Semi-quantification was based on LFQ signal intensities of identified proteins using MaxQuant/Perseus software platform. Data are available via ProteomeXchange with identifier PXD008119. We identified marginal differences between Mirasol PRT and Control PCs during storage. However, those significant changes at the proteome level were specifically related to the functional aspects previously described to affect platelets upon Mirasol PRT. In addition, the effect of Mirasol PRT on the platelet proteome appeared not to be exclusively due to an accelerated or enhanced PSL. In summary, semi-quantitative proteomics allows to discern between proteome changes due to Mirasol PRT or PSL, and proves to be a methodology suitable to phenotype platelets in an unbiased manner, in various physiological contexts.</p

Gata1cKO^MK mice have a defect in the hematopoietic early precursor compartment

<p>(a) Flow cytometry analysis of the stem cell and committed progenitor compartment. LSK, Lin^-|Sca-1⁺|Kit⁺ cells; MP (Lin^-|Sca-1^-|Kit⁺), multipotent progenitors; CMP (MP gate—CD34⁺|CD16/CD32^mid), common myeloid progenitor; GMP (MP gate—CD34^-|CD16/CD32⁺), granulocyte-monocyte progenitor; MEP (MP gate—CD34^-|CD16/CD32^-), megakaryocyte-erythroid progenitor. (b) Percentage of the different hematopoietic progenitors. Absolute cell number of bone marrow megakaryocytes at consecutive stages of differentiation [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0154342#pone.0154342.ref031" target="_blank">31</a>]. The dot plot depicts the extra population, named II+ found exclusively in Gata1cKO^MK bone marrow. (c) Whisker/Box plot depicts plasma TPO levels from Gata1cKO^MK and WT^lox blood samples, as measured by ELISA. At least 5 mice were analyzed per genotype. (d) qPCR analysis of Pf4 mRNA expression levels in cultured bone marrow derived Gata1cKO^MK and WT^lox megakaryocytes.</p

Gata1cKO^MKmice show alterations in the erythroid compartment.

<p>(a) Gating strategy to identify erythrocytes at consecutive stages of differentiation in the bone marrow and the spleen based on surface marker expression KIT, CD71 and Ter119. (b) Percentage of reticulocytes at consecutive stages of differentiation of live cells. Left graph depicts the bone marrow compartment, right the splenic compartment. (c) Photograph of representative spleens from Gata1cKO^MK and control mice shows the splenomegaly that Gata1cKO^MKdevelop.</p