DOI to the publisher's website. • The final author version and the galley proof are versions of t... more DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:
Question The characteristics of plasma lipoprotein profiles codetermine metabolic and cardiovascu... more Question The characteristics of plasma lipoprotein profiles codetermine metabolic and cardiovascular disease risks. One example concerns the distribution of particle sizes, which provides information to assess the risk of atherosclerotic plaque formation. The factors involved in the generation of pro-atherogenic lipoprotein particles are not fully understood. We developed a computational framework to investigate the molecular mechanisms that underlie the characteristics of plasma lipoprotein distributions in mice. Methods Multiple data sets of wild-type C57BL/6J mice were acquired1 and included in the computational analysis. These sets contained distributions of plasma triglyceride and cholesterol concentrations obtained via fast protein liquid chromatography (FPLC), as well as information about the production of very low density lipoprotein (VLDL) particles. A computational model consisting of ordinary differential equations and describing the production and remodeling as well as uptake of endogenous ApoB and ApoA containing lipoproteins was constructed. The different lipoprotein classes are described by defining grids containing particles of varying cholesterol and triglyceride content. A calibration function was determined to relate FPLC fraction to the lipoprotein concentrations in the computational framework. Results A computational model simultaneously describing VLDL and high density lipoprotein (HDL) metabolism was constructed, in which the included particle types can assume different compositions and sizes. Results from the computational analysis indicated that experimentally observed profiles of triglyceride and cholesterol in the VLDL and HDL fractions can be reproduced by the model. Furthermore, the model provided predictions of the compositional contributions of free cholesterol, cholesterylester, and phospholipids. The prediction of latter unmeasured components was accomplished by defining additional calibration functions based on compositional data of different lipoprotein types. Conclusion A computational framework was presented to investigate plasma lipoprotein metabolism in mice. The framework provides opportunities to investigate a variety of phenotypes in which lipoprotein metabolism is disturbed resulting in changes in particle composition and size. The time-dependent changes in plasma lipoprotein metabolism upon administration of T0901317, a potential pharmaceutical compound for anti-atherosclerotic therapies, are of particular interest. 1. Grefhorst A., M. H. Oosterveer, G. Brufau, M. Boesjes, F. Kuipers, A. K. Groen, Pharmacological LXR activation reduces presence of SR-B1 in liver membranes contributing to LXR-mediated induction of HDL-cholesterol, Atherosclerosis, Available online 3 March 201
Background The last few decades have seen the approval of many new treatment options for Relapsin... more Background The last few decades have seen the approval of many new treatment options for Relapsing-Remitting Multiple Sclerosis (RRMS), as well as advances in diagnostic methodology and criteria. These developments have greatly improved the available treatment options for today’s Relapsing-Remitting Multiple Sclerosis patients. This increased availability of disease modifying treatments, however, has implications for clinical trial design in this therapeutic area. The availability of better diagnostics and more treatment options have not only contributed to progressively decreasing relapse rates in clinical trial populations but have also resulted in the evolution of control arms, as it is often no longer sufficient to show improvement from placebo. As a result, not only have clinical trials become longer and more expensive but comparing the results to those of “historical” trials has also become more difficult. Methods In order to aid design of clinical trials in RRMS, we have deve...
<p>To investigate propagation of parameter uncertainty in predictions and analyses a collec... more <p>To investigate propagation of parameter uncertainty in predictions and analyses a collection of parameter sets was selected. Measurements from D<sub>CLAMP1</sub> (hyperinsulinemic, euglycemic clamp) <b>[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135665#pone.0135665.ref032" target="_blank">32</a>]</b> (A,B, red errorbars) and D<sub>CLAMP2</sub><b>[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135665#pone.0135665.ref033" target="_blank">33</a>]</b> (C,D, red errorbars) with superimposed model outputs as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135665#pone.0135665.g003" target="_blank">Fig 3</a>. Here, simulations representing the complete collection of selected parameter sets (S<sub>sel</sub>) are shown, depictured as dots shaded from dark green for poor fits of EGP (high values of V<sub>EGP</sub>) to light green for low V<sub>EGP</sub>. We note in C, that not all parameter sets from S<sub>sel</sub> describe the data, and that a bad correspondence of the simulations in A and C is shown with dark green color.</p
<p>The mathematical model of systemic glucose (left), insulin (middle) and NEFA (right) met... more <p>The mathematical model of systemic glucose (left), insulin (middle) and NEFA (right) metabolism consists of a total of 18 differential equations. Glucose concentrations are determined by glucose rate of appearance (<i>Ra</i>), endogenous glucose production (<i>EGP</i>), insulin dependent (<i>U</i><sub><i>id</i></sub>) and independent (<i>U</i><sub><i>ii</i></sub>) glucose uptake and–if applicable–renal excretion (E). Plasma NEFA dynamics are described in Eqs <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135665#pone.0135665.e003" target="_blank">3</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135665#pone.0135665.e007" target="_blank">7</a>. In the model, glucose enters the system via simulated ingestion in <i>Q</i><sub><i>sto1</i></sub>, and lipid appearance is simulated by using the measured plasma TG concentration to calculate fatty acid spillover. For full model equations, we refer to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135665#pone.0135665.s003" target="_blank">S2 File</a>. Matlab implementation and simulation files are provided as <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135665#pone.0135665.s004" target="_blank">S3 File</a>.</p
<p>Measurements from D<sub>CLAMP1</sub> (hyperinsulinemic, euglycemic clamp) &l... more <p>Measurements from D<sub>CLAMP1</sub> (hyperinsulinemic, euglycemic clamp) <b>[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135665#pone.0135665.ref032" target="_blank">32</a>]</b> (A,B, red errorbars) and D<sub>CLAMP2</sub><b>[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135665#pone.0135665.ref033" target="_blank">33</a>]</b> (C,D, red errorbars) with superimposed model outputs. The simulation represents the parameter set in S<sub>sel</sub> that corresponds to a minimal value for V<sub>EGP</sub>. A. Mean EGP as measured over the final half of a 360 minute clamp with low, medium and high NEFA concentration. B. Total glucose uptake (conditions and measurement time as in A). C. EGP measured during the final 60 minutes of the 120 minute clamp in experiments of group C that underwent an eu-insulinemic, hyperglycemic clamp with a saline infusion (C-) and with a combined intralipid and heparin infusion (C+). D. Total glucose uptake as in C, for experiments with a hyperinsulinemic euglycemic clamp (group A-, A+), hyperinsulinemic, hyperglycemic clamp (group B-, B+), and an eu-insulinemic, hyperglycemic clamp (group C- and C+). A short summary of the implementation in the model is provided in the Materials and Methods; full details of implementation can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135665#pone.0135665.s002" target="_blank">S1 File</a>.</p
The use of in silico trials is expected to play an increasingly important role in the development... more The use of in silico trials is expected to play an increasingly important role in the development and regulatory evaluation of new medical products. Among the advantages that in silico approaches offer, is that they permit testing of drug candidates and new medical devices using virtual patients or computational emulations of preclinical experiments, allowing to refine, reduce or even replace time-consuming and costly benchtop/in vitro/ex vivo experiments as well as the involvement of animals and humans in in vivo studies. To facilitate and widen the adoption of in silico trials, InSilicoTrials Technologies has developed a cloud-based platform, hosting healthcare simulation tools for different bench, preclinical and clinical evaluations, and for diverse disease areas. This paper discusses four use cases of in silico trials performed using the InSilicoTrials.com platform. The first application illustrates how in silico approaches can improve the early preclinical assessment of drug-i...
<p><b>A.</b> Cholesterol FPLC profile of untreated mice, experimental data and ... more <p><b>A.</b> Cholesterol FPLC profile of untreated mice, experimental data and simulated profile. FPLC profile (black) of pooled plasma of moderately fasted, untreated C57Bl/6J mice and simulated FPLC profile (blue) total cholesterol content. The <i>in silico</i> profile was calculated with an optimized parameter set following model parametrisation (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003579#pcbi.1003579.s004" target="_blank">Text S4</a>, parameter set X1). <b>B.</b> TG FPLC profile of untreated mice, experimental data and simulated profile. Experimental (black) and simulated (blue) TG profiles as in A. <b>C.</b> Computed profile of HDL. In addition to calculation of the measured profiles (A and B), calculation of profiles of all included model components is facilitated by the model. The parameters used to generate this profile are provided in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003579#pcbi.1003579.s004" target="_blank">Text S4</a>. <b>D.</b> Computed PL profile, computed with the parameters provided in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003579#pcbi.1003579.s004" target="_blank">Text S4</a>. For clarity, the highest measured fractions of the FPLC profile have not been pictured.</p
<p>Cholesterol (A) and triglyceride (B) profiles of the treated mouse at all time points. T... more <p>Cholesterol (A) and triglyceride (B) profiles of the treated mouse at all time points. The 3-dimensional plot shows the <i>in silico</i> and experimental profiles as measured and simulated at six time points following initiation of treatment. For clarity, the time axis is scaled logarithmically. At each time point, the experimental profile is shown in black and <i>in silico</i> profiles generated with all accepted parameters sets of E1 (red), E2 (blue) and E3(green) are shown in colour. The vertical axis represents the fraction lipid content in nmol. The number of acceptable fits differs per time point and/or model extension, as optimized fits were evaluated for acceptability. Profiles from acceptable parameter sets are in many cases quite similar, and may not in all cases be distinguishable from each other. For clarity, the FPLC profiles have been pictured as lines; we note that both experimental and <i>in silico</i> profiles are in fact composed of discrete fraction measurements.</p
<p><b>A.</b> Simulated cholesterol profile of the SR-B1 knock-out transgenic mo... more <p><b>A.</b> Simulated cholesterol profile of the SR-B1 knock-out transgenic mouse. To simulate the SR-B1 knock-out mouse, the selective uptake parameter was set to between 10<sup>−1</sup>% (black) and 5% (blue) of the original value. For comparison, the untreated C57Bl/6J mouse cholesterol profile is drawn in red. For comparison, we refer to the FPLC profile of SR-B1 deficient mice in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003579#pcbi.1003579-Rigotti1" target="_blank">[30]</a>. <b>B.</b> Change in plasma cholesterol concentration for the <i>in silico</i> transgenic mice as depicted in A, C and E. <b>C.</b> Simulated cholesterol profile of the PLTP knock-out transgenic mouse. To simulate the PLTP knock-out mouse, the parameter was diminished to values between 30% (black) and 50% (light blue) of its original value, increasing in steps of 5. The untreated C57Bl/6J mouse profile is again shown in red. Note that because the perturbed parameter in this case cannot be presumed to be solely dependent on PLTP activity, the parameter value was not reduced below 30%. For comparison, we refer to the FPLC profile of PLTP deficient mice in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003579#pcbi.1003579-Jiang1" target="_blank">[37]</a>. <b>D.</b> Change in plasma triglyceride concentration for the in silico transgenic mice as depicted in A, C and E. <b>E.</b> Simulated cholesterol profile of the LDLr knock-out mouse. To simulate the LDLr knock-out mouse, both VLDL sub-model whole-uptake parameters and were diminished to a factor between 40% and 92.5% of their wild-type value. For comparison, we refer to the FPLC profile of LDLr deficient mice in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003579#pcbi.1003579-Ishibashi3" target="_blank">[43]</a>. The visualized <i>in silico</i> profiles have all been generated with parameter set X1. For clarity, the highest measured fractions of the FPLC profile have not been pictured. Further quantitative analysis of the results as well as the corresponding in silico profiles generated with X2 are presented in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003579#pcbi.1003579.s005" target="_blank">Text S5</a>.</p
<p>Following the acquisition of sets of acceptable parameters for all treatment durations, ... more <p>Following the acquisition of sets of acceptable parameters for all treatment durations, the fluxes of the (extended) HDL sub-model are plotted, as a function of time in days, as a ratio to the HDL TC production flux. Red = E1; Blue = E2, Green = E3. Fluxes are shown as lipid fluxes, of either cholesterol or triglycerides in various processes. <b>A</b> Cholesterol flux due to cholesterol uptake by the HDL particle, <b>B</b> Cholesterol flux due to HDL catabolism.</p
DOI to the publisher's website. • The final author version and the galley proof are versions of t... more DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:
Question The characteristics of plasma lipoprotein profiles codetermine metabolic and cardiovascu... more Question The characteristics of plasma lipoprotein profiles codetermine metabolic and cardiovascular disease risks. One example concerns the distribution of particle sizes, which provides information to assess the risk of atherosclerotic plaque formation. The factors involved in the generation of pro-atherogenic lipoprotein particles are not fully understood. We developed a computational framework to investigate the molecular mechanisms that underlie the characteristics of plasma lipoprotein distributions in mice. Methods Multiple data sets of wild-type C57BL/6J mice were acquired1 and included in the computational analysis. These sets contained distributions of plasma triglyceride and cholesterol concentrations obtained via fast protein liquid chromatography (FPLC), as well as information about the production of very low density lipoprotein (VLDL) particles. A computational model consisting of ordinary differential equations and describing the production and remodeling as well as uptake of endogenous ApoB and ApoA containing lipoproteins was constructed. The different lipoprotein classes are described by defining grids containing particles of varying cholesterol and triglyceride content. A calibration function was determined to relate FPLC fraction to the lipoprotein concentrations in the computational framework. Results A computational model simultaneously describing VLDL and high density lipoprotein (HDL) metabolism was constructed, in which the included particle types can assume different compositions and sizes. Results from the computational analysis indicated that experimentally observed profiles of triglyceride and cholesterol in the VLDL and HDL fractions can be reproduced by the model. Furthermore, the model provided predictions of the compositional contributions of free cholesterol, cholesterylester, and phospholipids. The prediction of latter unmeasured components was accomplished by defining additional calibration functions based on compositional data of different lipoprotein types. Conclusion A computational framework was presented to investigate plasma lipoprotein metabolism in mice. The framework provides opportunities to investigate a variety of phenotypes in which lipoprotein metabolism is disturbed resulting in changes in particle composition and size. The time-dependent changes in plasma lipoprotein metabolism upon administration of T0901317, a potential pharmaceutical compound for anti-atherosclerotic therapies, are of particular interest. 1. Grefhorst A., M. H. Oosterveer, G. Brufau, M. Boesjes, F. Kuipers, A. K. Groen, Pharmacological LXR activation reduces presence of SR-B1 in liver membranes contributing to LXR-mediated induction of HDL-cholesterol, Atherosclerosis, Available online 3 March 201
Background The last few decades have seen the approval of many new treatment options for Relapsin... more Background The last few decades have seen the approval of many new treatment options for Relapsing-Remitting Multiple Sclerosis (RRMS), as well as advances in diagnostic methodology and criteria. These developments have greatly improved the available treatment options for today’s Relapsing-Remitting Multiple Sclerosis patients. This increased availability of disease modifying treatments, however, has implications for clinical trial design in this therapeutic area. The availability of better diagnostics and more treatment options have not only contributed to progressively decreasing relapse rates in clinical trial populations but have also resulted in the evolution of control arms, as it is often no longer sufficient to show improvement from placebo. As a result, not only have clinical trials become longer and more expensive but comparing the results to those of “historical” trials has also become more difficult. Methods In order to aid design of clinical trials in RRMS, we have deve...
<p>To investigate propagation of parameter uncertainty in predictions and analyses a collec... more <p>To investigate propagation of parameter uncertainty in predictions and analyses a collection of parameter sets was selected. Measurements from D<sub>CLAMP1</sub> (hyperinsulinemic, euglycemic clamp) <b>[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135665#pone.0135665.ref032" target="_blank">32</a>]</b> (A,B, red errorbars) and D<sub>CLAMP2</sub><b>[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135665#pone.0135665.ref033" target="_blank">33</a>]</b> (C,D, red errorbars) with superimposed model outputs as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135665#pone.0135665.g003" target="_blank">Fig 3</a>. Here, simulations representing the complete collection of selected parameter sets (S<sub>sel</sub>) are shown, depictured as dots shaded from dark green for poor fits of EGP (high values of V<sub>EGP</sub>) to light green for low V<sub>EGP</sub>. We note in C, that not all parameter sets from S<sub>sel</sub> describe the data, and that a bad correspondence of the simulations in A and C is shown with dark green color.</p
<p>The mathematical model of systemic glucose (left), insulin (middle) and NEFA (right) met... more <p>The mathematical model of systemic glucose (left), insulin (middle) and NEFA (right) metabolism consists of a total of 18 differential equations. Glucose concentrations are determined by glucose rate of appearance (<i>Ra</i>), endogenous glucose production (<i>EGP</i>), insulin dependent (<i>U</i><sub><i>id</i></sub>) and independent (<i>U</i><sub><i>ii</i></sub>) glucose uptake and–if applicable–renal excretion (E). Plasma NEFA dynamics are described in Eqs <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135665#pone.0135665.e003" target="_blank">3</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135665#pone.0135665.e007" target="_blank">7</a>. In the model, glucose enters the system via simulated ingestion in <i>Q</i><sub><i>sto1</i></sub>, and lipid appearance is simulated by using the measured plasma TG concentration to calculate fatty acid spillover. For full model equations, we refer to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135665#pone.0135665.s003" target="_blank">S2 File</a>. Matlab implementation and simulation files are provided as <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135665#pone.0135665.s004" target="_blank">S3 File</a>.</p
<p>Measurements from D<sub>CLAMP1</sub> (hyperinsulinemic, euglycemic clamp) &l... more <p>Measurements from D<sub>CLAMP1</sub> (hyperinsulinemic, euglycemic clamp) <b>[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135665#pone.0135665.ref032" target="_blank">32</a>]</b> (A,B, red errorbars) and D<sub>CLAMP2</sub><b>[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135665#pone.0135665.ref033" target="_blank">33</a>]</b> (C,D, red errorbars) with superimposed model outputs. The simulation represents the parameter set in S<sub>sel</sub> that corresponds to a minimal value for V<sub>EGP</sub>. A. Mean EGP as measured over the final half of a 360 minute clamp with low, medium and high NEFA concentration. B. Total glucose uptake (conditions and measurement time as in A). C. EGP measured during the final 60 minutes of the 120 minute clamp in experiments of group C that underwent an eu-insulinemic, hyperglycemic clamp with a saline infusion (C-) and with a combined intralipid and heparin infusion (C+). D. Total glucose uptake as in C, for experiments with a hyperinsulinemic euglycemic clamp (group A-, A+), hyperinsulinemic, hyperglycemic clamp (group B-, B+), and an eu-insulinemic, hyperglycemic clamp (group C- and C+). A short summary of the implementation in the model is provided in the Materials and Methods; full details of implementation can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135665#pone.0135665.s002" target="_blank">S1 File</a>.</p
The use of in silico trials is expected to play an increasingly important role in the development... more The use of in silico trials is expected to play an increasingly important role in the development and regulatory evaluation of new medical products. Among the advantages that in silico approaches offer, is that they permit testing of drug candidates and new medical devices using virtual patients or computational emulations of preclinical experiments, allowing to refine, reduce or even replace time-consuming and costly benchtop/in vitro/ex vivo experiments as well as the involvement of animals and humans in in vivo studies. To facilitate and widen the adoption of in silico trials, InSilicoTrials Technologies has developed a cloud-based platform, hosting healthcare simulation tools for different bench, preclinical and clinical evaluations, and for diverse disease areas. This paper discusses four use cases of in silico trials performed using the InSilicoTrials.com platform. The first application illustrates how in silico approaches can improve the early preclinical assessment of drug-i...
<p><b>A.</b> Cholesterol FPLC profile of untreated mice, experimental data and ... more <p><b>A.</b> Cholesterol FPLC profile of untreated mice, experimental data and simulated profile. FPLC profile (black) of pooled plasma of moderately fasted, untreated C57Bl/6J mice and simulated FPLC profile (blue) total cholesterol content. The <i>in silico</i> profile was calculated with an optimized parameter set following model parametrisation (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003579#pcbi.1003579.s004" target="_blank">Text S4</a>, parameter set X1). <b>B.</b> TG FPLC profile of untreated mice, experimental data and simulated profile. Experimental (black) and simulated (blue) TG profiles as in A. <b>C.</b> Computed profile of HDL. In addition to calculation of the measured profiles (A and B), calculation of profiles of all included model components is facilitated by the model. The parameters used to generate this profile are provided in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003579#pcbi.1003579.s004" target="_blank">Text S4</a>. <b>D.</b> Computed PL profile, computed with the parameters provided in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003579#pcbi.1003579.s004" target="_blank">Text S4</a>. For clarity, the highest measured fractions of the FPLC profile have not been pictured.</p
<p>Cholesterol (A) and triglyceride (B) profiles of the treated mouse at all time points. T... more <p>Cholesterol (A) and triglyceride (B) profiles of the treated mouse at all time points. The 3-dimensional plot shows the <i>in silico</i> and experimental profiles as measured and simulated at six time points following initiation of treatment. For clarity, the time axis is scaled logarithmically. At each time point, the experimental profile is shown in black and <i>in silico</i> profiles generated with all accepted parameters sets of E1 (red), E2 (blue) and E3(green) are shown in colour. The vertical axis represents the fraction lipid content in nmol. The number of acceptable fits differs per time point and/or model extension, as optimized fits were evaluated for acceptability. Profiles from acceptable parameter sets are in many cases quite similar, and may not in all cases be distinguishable from each other. For clarity, the FPLC profiles have been pictured as lines; we note that both experimental and <i>in silico</i> profiles are in fact composed of discrete fraction measurements.</p
<p><b>A.</b> Simulated cholesterol profile of the SR-B1 knock-out transgenic mo... more <p><b>A.</b> Simulated cholesterol profile of the SR-B1 knock-out transgenic mouse. To simulate the SR-B1 knock-out mouse, the selective uptake parameter was set to between 10<sup>−1</sup>% (black) and 5% (blue) of the original value. For comparison, the untreated C57Bl/6J mouse cholesterol profile is drawn in red. For comparison, we refer to the FPLC profile of SR-B1 deficient mice in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003579#pcbi.1003579-Rigotti1" target="_blank">[30]</a>. <b>B.</b> Change in plasma cholesterol concentration for the <i>in silico</i> transgenic mice as depicted in A, C and E. <b>C.</b> Simulated cholesterol profile of the PLTP knock-out transgenic mouse. To simulate the PLTP knock-out mouse, the parameter was diminished to values between 30% (black) and 50% (light blue) of its original value, increasing in steps of 5. The untreated C57Bl/6J mouse profile is again shown in red. Note that because the perturbed parameter in this case cannot be presumed to be solely dependent on PLTP activity, the parameter value was not reduced below 30%. For comparison, we refer to the FPLC profile of PLTP deficient mice in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003579#pcbi.1003579-Jiang1" target="_blank">[37]</a>. <b>D.</b> Change in plasma triglyceride concentration for the in silico transgenic mice as depicted in A, C and E. <b>E.</b> Simulated cholesterol profile of the LDLr knock-out mouse. To simulate the LDLr knock-out mouse, both VLDL sub-model whole-uptake parameters and were diminished to a factor between 40% and 92.5% of their wild-type value. For comparison, we refer to the FPLC profile of LDLr deficient mice in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003579#pcbi.1003579-Ishibashi3" target="_blank">[43]</a>. The visualized <i>in silico</i> profiles have all been generated with parameter set X1. For clarity, the highest measured fractions of the FPLC profile have not been pictured. Further quantitative analysis of the results as well as the corresponding in silico profiles generated with X2 are presented in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003579#pcbi.1003579.s005" target="_blank">Text S5</a>.</p
<p>Following the acquisition of sets of acceptable parameters for all treatment durations, ... more <p>Following the acquisition of sets of acceptable parameters for all treatment durations, the fluxes of the (extended) HDL sub-model are plotted, as a function of time in days, as a ratio to the HDL TC production flux. Red = E1; Blue = E2, Green = E3. Fluxes are shown as lipid fluxes, of either cholesterol or triglycerides in various processes. <b>A</b> Cholesterol flux due to cholesterol uptake by the HDL particle, <b>B</b> Cholesterol flux due to HDL catabolism.</p
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