MOESM1 of Profiling of metalloprotease activities in cerebrospinal fluids of patients with neoplastic meningitis

Additional file 1. Sketch of proteases and CSF infiltration, correlation analyses, and ELISA results

Phenotypic sensitivity analysis.

<p>(A,F) Phenotypic behavior of the oscillator (A) and throttle (F), when isolated from the full system. Roughly 2000 different sets of rate constants were tested, with all oscillator or throttle rate constants simultaneously varied. Module phenotypes were recorded for each set of rate constants. (B) Observed S/N values as a function of variance in the “duration high” of the oscillator. (C) Heat map of the S/N values against the phenotypes resulting from the random parameter sets. (G) Average ‘images’ for the phenotype <i>R7 T to St. St.</i>, observed from the random parameter sets yielding an S/N value of either 5, 15 or 25. Black represents regions where no switch occurs and no value for <i>R7 T to St. St.</i> is recorded. (D,H) The most significant RS-HDMR sensitivity indices, , for phenotypic variations of the oscillator and throttle, respectively (see also Supplementary <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002579#pcbi.1002579.s027" target="_blank">Table S8</a>). (E,I) For the oscillator and throttle, respectively, RS-HDMR cross-validation predication accuracy using rate constants, phenotypes, or both.</p

Overview of system design.

<p>(A) The general tissue homeostasis design. Proliferation of stem cells (blue) is regulated by their population size through negative feedback (dashed blue line). Sequential differentiation into endodermic, pancreatic, and finally -cells (red) occurs when the stem cell population has sufficient size, and is governed through negative feedback from differentiated cells (dashed red line). (B) Design workflow. Starting with a high-level objective, iterative design proceeds through a top-down decomposition into modules and then basic reactions of the system, followed by analysis and redesign (left). The table columns (right) show the four iterations of system designs presented in this work. Table rows describe the top-down decomposition for each system, and correspond to the workflow at left.</p

System 1.

<p>(A) Circuit diagram: two Population Control modules (in gray) sense the density of stem- and -cells. The AND gate integrates the output of the modules to induce differentiation. Circles represent intercellular signaling molecules. (B) Two examples of population evolution showing sustained oscillations (point 1 in <i>C</i>) and a stable steady state (point 2 in <i>C</i>), with other parameters fixed (SI Sec. 2 and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002579#pcbi.1002579.s002" target="_blank">Figure S2</a>). (C) A planar slice of the parameter space where population oscillations occur for System 1.</p

Systems 3 and 4.

<p>(A) Circuit diagram for System 3: in addition to System 2 modules, the AND gate integrates the output of the oscillator (red module) that allows commitment only when peaking. (B) Time trajectories for a simulation starting with a small stem cell population. The oscillator activator () is plotted for some representative stem cells (right axis, a.u.). (C) Individual rows track the single-cell UPC module output (, shown as a heat map) in uncommitted cells within a population. White signifies single-cell commitment, followed by black “null space” that is filled by newly divided uncommitted cells. Due to the oscillator, only a fraction of the cells commit when the concentration is high. (D) Overall system performance, S/N, as a function of the module time-scale for cell communication, . Several hundred different sets of time-scales were tested, with all time-scale parameters simultaneously varied. Each point represents an individual set of time-scales. Color and contour lines indicate point density. (E) Circuit diagram for System 4: a throttle mechanism (red module) activates during a cell's commitment and represses commitment in its neighbors. (F–G) Time trajectories for a simulation starting with a small stem cell population, where <i>B</i> shows the average throttle signaling component () in the external medium (right axis, a.u.) over time. (H) S/N as a function of the module time-scale for cell communication, .</p

Parametric optimization of the UPC module.

<p>(A) GA optimization progress for three representative generations, using an ODE model of the UPC module. The GA objective function is a three-component step-function, with zero UPC activity below a defined threshold, an ignored transition region, and high activity above the transition region. (B) Gillespie simulations of System 3, corresponding to optimization progress in <i>A</i>. (C) Average UPC module transfer curves when the reverse response is either excluded or included in the subnetwork GA optimization. (D) Full system behavior corresponding by row to the module optimization results in <i>C</i>. (E) Distribution of rate constants for the optimized parameter vectors determined by 75 independent GA runs of 1000 generations each, using both forward and reverse response objective functions. (F) Clustered sensitivity analysis of the UPC Module. Each column corresponds to a “parameter sensitivity signature” for each of the 75 local parameter neighborhoods that we sampled; rows correspond to the analyzed parameters of the UPC module. First-order sensitivity values shown in the heat map range from 0.0 (black) to 0.5 (red).</p

Parametric sensitivity analysis.

<p>(A,G) Circuit diagrams of the genetic components considered in (A) oscillator and (G) throttle optimization. (B,H) The most significant RS-HDMR sensitivity indices, , for parametric variations of the oscillator and throttle, respectively. (C,I) Observed S/N values as a function of randomly sampled rate constant values. Around 2000 different parameter sets were tested, with all oscillator or throttle parameters simultaneously varied. Each point represents an individual parameter set. Warmer colors and contour lines indicate higher point density. (D,J) Inferred first-order RS-HDMR functions describing S/N as a function of the parameters sampled in <i>C</i> and <i>I</i>. (E,K) Heat map of the S/N values against the parameters resulting from the 2000 parameter sets tested in <i>C</i> and <i>I</i>. (F,L) RS-HDMR second-order functions describing the cooperative effects between rate constants, corresponding to <i>E</i> and <i>K</i>. Second-order RS-HDMR functions capture remaining variance after the first-order functions (see <i>D</i> and <i>J</i>) have been subtracted from the data.</p

System 2.

<p>(A) Circuit diagram: two Population Control modules sense the density of stem and committed cells. The AND gate integrates the output of the modules to induce commitment through the switch state (red module). (B) Deterministic time trajectories for System 2 with two different initial conditions: both converge to the same equilibrium populations. (C) Phase space diagram: all trajectories converge to a unique equilibrium point. Black lines correspond to trajectories plotted in <i>B</i>. See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002579#pcbi.1002579.s029" target="_blank">Text S1</a>, Sec. 2 and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002579#pcbi.1002579.s003" target="_blank">Figure S3</a> for other phase space diagrams. (D) Stochastic trajectories for a simulation starting with a small stem cell population, showing the output of the Committed Population Control module () in representative uncommitted cells (right axis, a.u.). (E) Individual rows track the single-cell UPC module output (, shown as a heat map) in uncommitted cells within a population. White signifies single-cell commitment, followed by black “null space” that is filled by newly divided uncommitted cells. As soon as UPC output is high (yellow), stem cells commit <i>en masse</i>. (F) Overall system performance, S/N, as a function of the module time-scale for cell communication, . Several hundred different sets of time-scales were tested, with all time-scale parameters simultaneously varied. Each point represents an individual set of time-scales. Color and contour lines indicate point density.</p

Legislative Documents

Also, variously referred to as: House bills; House documents; House legislative documents; legislative documents; General Court documents

MOESM3 of Comparative transcriptomics of choroid plexus in Alzheimer’s disease, frontotemporal dementia and Huntington’s disease: implications for CSF homeostasis

Additional file 3: Table S3. Geneset Annotations: Worksheets contain pathways enriched among genes upregulated in AD vs. Ctrl (S3a); downregulated in AD vs. Ctrl (S3b); upregulated in AD but not in HuD + FTD vs. Ctrl (S3c); and upregulated in HuD + FTD but not AD vs. Ctrl (S3d). Hypergeometric p-values (p-value), Bonferroni-corrected p-values (E-value), overlaps and input and background set sizes are provided