Data_Sheet_1_Prediction of Mutations to Control Pathways Enabling Tumor Cell Invasion with the CoLoMoTo Interactive Notebook (Tutorial).ZIP

<p>Boolean and multi-valued logical formalisms are increasingly used to model complex cellular networks. To ease the development and analysis of logical models, a series of software tools have been proposed, often with specific assets. However, combining these tools typically implies a series of cumbersome software installation and model conversion steps. In this respect, the CoLoMoTo Interactive Notebook provides a joint distribution of several logical modeling software tools, along with an interactive web Python interface easing the chaining of complementary analyses. Our computational workflow combines (1) the importation of a GINsim model and its display, (2) its format conversion using the Java library BioLQM, (3) the formal prediction of mutations using the OCaml software Pint, (4) the model checking using the C++ software NuSMV, (5) quantitative stochastic simulations using the C++ software MaBoSS, and (6) the visualization of results using the Python library matplotlib. To illustrate our approach, we use a recent Boolean model of the signaling network controlling tumor cell invasion and migration. Our model analysis culminates with the prediction of sets of mutations presumably involved in a metastatic phenotype.</p

Data_Sheet_2_Prediction of Mutations to Control Pathways Enabling Tumor Cell Invasion with the CoLoMoTo Interactive Notebook (Tutorial).CSV

<p>Boolean and multi-valued logical formalisms are increasingly used to model complex cellular networks. To ease the development and analysis of logical models, a series of software tools have been proposed, often with specific assets. However, combining these tools typically implies a series of cumbersome software installation and model conversion steps. In this respect, the CoLoMoTo Interactive Notebook provides a joint distribution of several logical modeling software tools, along with an interactive web Python interface easing the chaining of complementary analyses. Our computational workflow combines (1) the importation of a GINsim model and its display, (2) its format conversion using the Java library BioLQM, (3) the formal prediction of mutations using the OCaml software Pint, (4) the model checking using the C++ software NuSMV, (5) quantitative stochastic simulations using the C++ software MaBoSS, and (6) the visualization of results using the Python library matplotlib. To illustrate our approach, we use a recent Boolean model of the signaling network controlling tumor cell invasion and migration. Our model analysis culminates with the prediction of sets of mutations presumably involved in a metastatic phenotype.</p

Physical interactions within the <i>Krox20</i> locus.

<p><b>(A)</b> Alignment of data in the <i>Krox20</i> and adjacent loci from Hi-C in ES cells [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006903#pgen.1006903.ref011" target="_blank">11</a>], 4C-seq in E9.5 whole mouse embryos, using the <i>Krox20</i> and <i>Nrbf2</i> promoters as viewpoints (this work, 2 biological replicates) and CTCF ChIP-seq in E14.5 mouse brain (ENCODE, [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006903#pgen.1006903.ref058" target="_blank">58</a>]). <b>(B)</b> Zoom in on the <i>Krox20</i> locus, showing 4C-seq data from the <i>Krox20</i> promoter, element A, element B and element C as viewpoints. CTCF ChIP-seq data in E14.5 mouse brain (ENCODE) are indicated below. Signals from simultaneously processed E9.5 whole embryo (dark blue) and E8.5 embryo head (light blue) samples are shown. On the right, normalized distributions of the 4C-seq signals in different genomic regions are indicated. TADs as defined in [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006903#pgen.1006903.ref007" target="_blank">7</a>] or by our additional analysis (<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006903#pgen.1006903.s002" target="_blank">S2 Fig</a>) are indicated above, with dashed lines in the graphs demarcating TAD boundaries. Genes (black/red), <i>cis</i>-regulatory elements (orange) and genomic coordinates are indicated below each set of data. Arrowheads above each 4C track pinpoint viewpoints.</p

A model for <i>Krox20</i> regulation and the dual function of element C.

<p>(<b>A</b>) Schematic representation of the regulation of <i>Krox20</i> in r3. Three situations are envisaged in wild type embryos. Left: silent locus. If both element C and the new enhancer (NE) are inactive, no expression occurs. Middle: early expression phase. At this stage, elements C and NE have been bound by their respective transcription factors and have initiated the expression of <i>Krox20</i> via their classical enhancer functions. Nevertheless, element C has not yet been unlocked (decompacted) element A and/or the concentration of the KROX20 protein has not reached high enough levels to allow the establishment of a stable feedback loop with a significant probability. Right: late expression phase. Via its potentiator function, element C has unlocked element A, which can bind the KROX20 protein, which has now accumulated at a high enough concentration. Activation of enhancer A establishes the autoregulatory loop. <b>(B)</b> Three mutations that disrupt the positive feedback loop are presented at late expression phase. Left: mutation of the KROX20 protein preventing binding to element A. Middle: mutation of element A, preventing the binding of the KROX20 protein. Right: mutation of element C, preventing unlocking of element A.</p

Genetic analysis of element C function.

<p><b>(A)</b> Strategy for the construction of conditional and null alleles of element C. The targeting vector was introduced into the locus in ES cells by homologous recombination and one of the ES clones subsequently allowed germ line transmission in the mouse. The floxed allele, <i>Krox20</i>^<i>Cflox</i>, was obtained by crossing the founder mouse line with a <i>Flp</i> (targeting FRT sites) deletor line. The null allele, <i>Krox20</i>^<i>ΔC</i>, was obtained by crossing the <i>Krox20</i>^<i>Cflox</i> line with a <i>Cre</i> (targeting loxP sites) deletor line, PGK-Cre. <b>(B)</b> In situ hybridization for <i>Krox20</i> mRNA performed on <i>Krox20</i>^<i>+/ΔC</i> and <i>Krox20</i>^{<i>ΔC/ΔC</i>} embryos at the indicated somite stages. Embryos were flat-mounted with anterior toward the top. Rhombomere positions are indicated on the left.</p

<i>Krox20</i> hindbrain regulation incorporates multiple modes of cooperation between <i>cis</i>-acting elements - Fig 2

<p><b>Cooperation in <i>cis</i> between elements A and C. (A)</b> In situ hybridization for <i>Krox20</i> mRNA was performed on wild type (WT), <i>Krox20</i>^<i>+/Cre</i>, <i>Krox20</i>^{<i>ΔA/ΔA</i>}, <i>Krox20</i>^{<i>ΔC/ΔC</i>} and composite heterozygous <i>Krox20</i>^{<i>ΔA/ΔC</i>} embryos at the indicated somite stages. <b>(B)</b> In situ hybridization for <i>Krox20</i> mRNA was performed on <i>Krox20</i>^<i>+/Cre</i> and <i>Krox20</i>^{<i>Cflox/Cre</i>} embryos at the indicated somite stages. In (A) and (B) embryos were flat-mounted with anterior toward the top.</p

Data_Sheet_2_The CoLoMoTo Interactive Notebook: Accessible and Reproducible Computational Analyses for Qualitative Biological Networks.ZIP

<p>Analysing models of biological networks typically relies on workflows in which different software tools with sensitive parameters are chained together, many times with additional manual steps. The accessibility and reproducibility of such workflows is challenging, as publications often overlook analysis details, and because some of these tools may be difficult to install, and/or have a steep learning curve. The CoLoMoTo Interactive Notebook provides a unified environment to edit, execute, share, and reproduce analyses of qualitative models of biological networks. This framework combines the power of different technologies to ensure repeatability and to reduce users' learning curve of these technologies. The framework is distributed as a Docker image with the tools ready to be run without any installation step besides Docker, and is available on Linux, macOS, and Microsoft Windows. The embedded computational workflows are edited with a Jupyter web interface, enabling the inclusion of textual annotations, along with the explicit code to execute, as well as the visualization of the results. The resulting notebook files can then be shared and re-executed in the same environment. To date, the CoLoMoTo Interactive Notebook provides access to the software tools GINsim, BioLQM, Pint, MaBoSS, and Cell Collective, for the modeling and analysis of Boolean and multi-valued networks. More tools will be included in the future. We developed a Python interface for each of these tools to offer a seamless integration in the Jupyter web interface and ease the chaining of complementary analyses.</p

Data_Sheet_1_The CoLoMoTo Interactive Notebook: Accessible and Reproducible Computational Analyses for Qualitative Biological Networks.ZIP

<p>Analysing models of biological networks typically relies on workflows in which different software tools with sensitive parameters are chained together, many times with additional manual steps. The accessibility and reproducibility of such workflows is challenging, as publications often overlook analysis details, and because some of these tools may be difficult to install, and/or have a steep learning curve. The CoLoMoTo Interactive Notebook provides a unified environment to edit, execute, share, and reproduce analyses of qualitative models of biological networks. This framework combines the power of different technologies to ensure repeatability and to reduce users' learning curve of these technologies. The framework is distributed as a Docker image with the tools ready to be run without any installation step besides Docker, and is available on Linux, macOS, and Microsoft Windows. The embedded computational workflows are edited with a Jupyter web interface, enabling the inclusion of textual annotations, along with the explicit code to execute, as well as the visualization of the results. The resulting notebook files can then be shared and re-executed in the same environment. To date, the CoLoMoTo Interactive Notebook provides access to the software tools GINsim, BioLQM, Pint, MaBoSS, and Cell Collective, for the modeling and analysis of Boolean and multi-valued networks. More tools will be included in the future. We developed a Python interface for each of these tools to offer a seamless integration in the Jupyter web interface and ease the chaining of complementary analyses.</p