Simulated Time Courses Research Articles

Integrated PBPK-EO modeling of osimertinib to predict plasma concentrations and intracranial EGFR engagement in patients with brain metastases

The purpose of this study was to develop and validate a physiologically based pharmacokinetic (PBPK) model combined with an EGFR occupancy (EO) model for osimertinib (OSI) to predict plasma trough concentration (Ctrough) and the intracranial time-course of EGFR (T790M and L858R mutants) engagement in patient populations. The PBPK model was also used to investigate the key factors affecting OSI pharmacokinetics (PK) and intracranial EGFR engagement, analyze resistance to the target mutation C797S, and determine optimal dosing regimens when used alone and in drug-drug interactions (DDIs). A population PBPK-EO model of OSI was developed using physicochemical, biochemical, binding kinetic, and physiological properties, and then validated using nine clinical PK studies, observed EO study, and two clinical DDI studies. The PBPK-EO model demonstrated good consistency with observed data, with most prediction-to-observation ratios falling within the range of 0.7 to 1.3 for plasma AUC, Cmax, Ctrough and intracranial free concentration. The simulated time-course of C797S occupancy by the PBPK model was much lower than T790M and L858R occupancy, providing an explanation for OSI on-target resistance to the C797S mutation. The PBPK model identified ABCB1 CLint,u, albumin level, and EGFR expression as key factors affecting plasma Ctrough and intracranial EO for OSI. Additionally, PBPK-EO simulations indicated that the optimal dosing regimen for OSI in patients with brain metastases is either 80 mg once daily (OD) or 160 mg OD, or 40 mg or 80 mg twice daily (BID). When used concomitantly with CYP enzyme perpetrators, the PBPK-EO model suggested appropriate dosing regimens of 80 mg OD with fluvoxamine (FLUV) itraconazole (ITR) or fluvoxamine (FLUC) for co-administration and an increase to 160 mg OD with rifampicin (RIF) or efavirenz (EFA). In conclusion, the PBPK-EO model has been shown to be capable of simulating the pharmacokinetic concentration–time profiles and the time-course of EGFR engagement for OSI, as well as determining the optimum dosing in various clinical situations.

Abstract 7180: Tumor growth modeling to support dose justification of Dato-DXd in hormone receptor positive, HER2 negative breast cancer (HR+HER2-BC) subjects

Abstract BACKGROUND: Dato-DXd, a TROP2-directed antibody-drug conjugate, has demonstrated efficacy and a manageable safety profile in multiple tumor types. A dosing regimen of 6 mg/kg every three weeks (Q3W) has been proposed as the optimal schedule based on phase I data in patients with non-small cell lung cancer (NSCLC)1. Dato-DXd demonstrated statistically significant and clinically meaningful improvement in PFS compared with ICC in patients with HR+HER2-BC in TROPION-Breast012. A tumor growth inhibition (TGI) model was developed to support dose justification at 6 mg/kg in HR+HER2-BC subjects. METHODS: A tumor growth inhibition (TGI) model was developed based on tumor size data from HR+HER2-BC patients received 6 mg/kg Dato-DXd Q3W in a Phase I study (N = 39, Data cutoff July 2022). The TGI model described the tumor size as the result of an exponential tumor growth, a tumor shrinkage that was driven by Dato-DXd cycle-specific AUC, and a resistance term suggesting that the shrinkage effect by Dato-DXd decreases over time. Cycle-specific Dato-DXd AUC was derived based on the AUC and time at each cycle for each individual. Simulations were conducted using the TGI model to compare tumor size change from baseline at 4, 6, and 8 mg/kg in HR+HER2-BC patients, with consideration of dropouts. RESULTS: The TGI model well-described tumor size data with reasonable parameter estimations. Simulated time-courses of tumor sizes at Dato-DXd 4, 6, and 8 mg/kg suggested that Dato-DXd at 6 mg/kg may lead to greater tumor shrinkage effect than that at 4 mg/kg in HR+/HER2-BC patients. At approximately 180 days post first treatment, tumor shrinkage at 6 mg/kg was almost 2-times greater than at 4 mg/kg (-51% vs. -25%, respectively). CONCLUSION: Simulations based on the TGI model suggested greater tumor size reduction at Dato-DXd 6 mg/kg compared to 4 mg/kg in HR+HER2-BC patient population. Reference: 1 ACoP13 2022 2 ESMO 2023. Citation Format: Zoey Tang, Yu Jiang, Haitao Yang, David Dai, Chang Gong, Linda Irons, Yuzhuo Pan, Ying Hong, Shuang Liang, Song Ren, Alex Phipps, Diansong Zhou. Tumor growth modeling to support dose justification of Dato-DXd in hormone receptor positive, HER2 negative breast cancer (HR+HER2-BC) subjects [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7180.

DrForna: visualization of cotranscriptional folding.

Understanding RNA folding at the level of secondary structures can give important insights concerning the function of a molecule. We are interested to learn how secondary structures change dynamically during transcription, as well as whether particular secondary structures form already during or only after transcription. While different approaches exist to simulate cotranscriptional folding, the current strategies for visualization are lagging behind. New, more suitable approaches are necessary to help with exploring the generated data from cotranscriptional folding simulations. We present DrForna, an interactive visualization app for viewing the time course of a cotranscriptional RNA folding simulation. Specifically, users can scroll along the time axis and see the population of structures that are present at any particular time point. DrForna is a JavaScript project available on Github at https://github.com/ViennaRNA/drforna and deployed at https://viennarna.github.io/drforna.

Development of a quantitative semi-mechanistic model of Alzheimer's disease based on the amyloid/tau/neurodegeneration framework (the Q-ATN model).

A quantitative model of Alzheimer's disease (AD) based on the amyloid/tau/neurodegeneration biomarker framework (Q-ATN model) was developed to sequentially link amyloid positron emission tomography (PET), tau PET, medial temporal cortical thickness, and clinical outcome (Clinical Dementia Rating - Sum of Boxes; CDR-SB). Published data and biologically plausible mechanisms were used to construct, calibrate, and validate the model. Clinical trial simulations were performed for different anti-amyloid antibodies, including a 5-year simulation of subcutaneous gantenerumab treatment. The simulated time-course of biomarkers and CDR-SB was consistent with natural history studies and described the effects of several anti-amyloid antibodies observed in trials with positive and negative (or non-significant) outcomes. The 5-year simulation predicts that the beneficial effects of continued anti-amyloid treatment should increase markedly over time. The Q-ATN model offers a novel approach for linking amyloid PET to CDR-SB, and provides theoretical support for the potential clinical benefit of anti-amyloid therapy. A semi-mechanistic model was developed to link amyloid/tau/neurodegeneration biomarkers to clinical outcome (Q-ATN model). The Q-ATN model describes the disease progression seen in natural history studies. Model simulations agree well with mean data from the aducanumab EMERGE study. A 5-year simulation of gantenerumab predicts greater benefit with longer treatment.

Performance of the SSM-iCrop model for predicting growth and nitrogen dynamics in winter wheat

Process-based crop models are essential tools for representing the fundamental interactions between the cropping environment (weather, soil, and management) and plant development, growth, resource use, and yield formation. Due to these capabilities, crop models are considered as an integral component of smart farming tools for evaluating and improving crop management at field, farm, and regional scales. However, prior to application of a crop model in geospatial decision support tools, its robustness should be established by comparing model predictions with observations from the target cropping environment. The objective of this study was to assess the performance of the Simple Simulation Model (SSM-iCrop) for predicting growth and nitrogen (N) dynamics of winter wheat (Triticum aestivum) cultivars in a temperate environment. Detailed plant and soil data were collected from three field experiments conducted with four widely-grown cultivars under four N application rates in Austria. Variation in N fertilisation and differences in soil properties and weather conditions in the three field experiments generated a wide range of observed crop total dry mass (585–2034 g m−2), N uptake (5–32 g N m−2), and grain yield (211–898 g m−2). The SSM-iCrop model required parameterisation of a relatively small number of plant input parameters. As these parameters could be directly calculated from the experimental data, except for two phenology-related coefficients, there was no need for calibrating the model. In initial simulations, SSM-iCrop was not able to predict the response of leaf area index (LAI) to decreasing N supply. Introducing an additional parameter defining the minimum stem N concentration from emergence to begin grain growth improved the model performance substantially. The simulated time-course of crop attributes through the growing season showed good overall correspondence with observed data. Across the three field experiments, the model performed well in simulating above-ground dry mass (CV=5.9, RMSE=115.6 g N m−2), grain yield (CV=1.9, RMSE=60.5 g N m−2), total crop N uptake (CV=4.5, RMSE=1.9 g N m−2), and grain N content (CV=1.1; RMSE=2.2 g N m−2). Overall, The results of this study confirmed the robustness of SSM-iCrop for predicting wheat development, growth, N dynamics, and yield in the target cropping environments. The relatively simple structure and high degree of transparency make the SSM-iCrop suitable for integration in smart farming tools for improving tactical decision making in crop production. This study also highlights the essential role of high-quality detailed experimental data for adequate parameterisation and evaluation of crop models..

Data and model considerations for estimating time-varying functional connectivity in fMRI

Functional connectivity (FC) in the brain has been shown to exhibit subtle but reliable modulations within a session. One way of estimating time-varying FC is by using state-based models that describe fMRI time series as temporal sequences of states, each with an associated, characteristic pattern of FC. However, the estimation of these models from data sometimes fails to capture changes in a meaningful way, such that the model estimation assigns entire sessions (or the largest part of them) to a single state, therefore failing to capture within-session state modulations effectively; we refer to this phenomenon as the model becoming static, or model stasis. Here, we aim to quantify how the nature of the data and the choice of model parameters affect the model's ability to detect temporal changes in FC using both simulated fMRI time courses and resting state fMRI data. We show that large between-subject FC differences can overwhelm subtler within-session modulations, causing the model to become static. Further, the choice of parcellation can also affect the model's ability to detect temporal changes. We finally show that the model often becomes static when the number of free parameters per state that need to be estimated is high and the number of observations available for this estimation is low in comparison. Based on these findings, we derive a set of practical recommendations for time-varying FC studies, in terms of preprocessing, parcellation and complexity of the model.

Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments.

The negative binomial distribution has been shown to be a good model for counts data from both bulk and single-cell RNA-sequencing (RNA-seq). Gaussian process (GP) regression provides a useful non-parametric approach for modelling temporal or spatial changes in gene expression. However, currently available GP regression methods that implement negative binomial likelihood models do not scale to the increasingly large datasets being produced by single-cell and spatial transcriptomics. The GPcounts package implements GP regression methods for modelling counts data using a negative binomial likelihood function. Computational efficiency is achieved through the use of variational Bayesian inference. The GP function models changes in the mean of the negative binomial likelihood through a logarithmic link function and the dispersion parameter is fitted by maximum likelihood. We validate the method on simulated time course data, showing better performance to identify changes in over-dispersed counts data than methods based on Gaussian or Poisson likelihoods. To demonstrate temporal inference, we apply GPcounts to single-cell RNA-seq datasets after pseudotime and branching inference. To demonstrate spatial inference, we apply GPcounts to data from the mouse olfactory bulb to identify spatially variable genes and compare to two published GP methods. We also provide the option of modelling additional dropout using a zero-inflated negative binomial. Our results show that GPcounts can be used to model temporal and spatial counts data in cases where simpler Gaussian and Poisson likelihoods are unrealistic. GPcounts is implemented using the GPflow library in Python and is available at https://github.com/ManchesterBioinference/GPcounts along with the data, code and notebooks required to reproduce the results presented here. The version used for this paper is archived at https://doi.org/10.5281/zenodo.5027066. Supplementary data are available at Bioinformatics online.

The risks of using the chi-square periodogram to estimate the period of biological rhythms.

The chi-square periodogram (CSP), developed over 40 years ago, continues to be one of the most popular methods to estimate the period of circadian (circa 24-h) rhythms. Previous work has indicated the CSP is sometimes less accurate than other methods, but understanding of why and under what conditions remains incomplete. Using simulated rhythmic time-courses, we found that the CSP is prone to underestimating the period in a manner that depends on the true period and the length of the time-course. This underestimation bias is most severe in short time-courses (e.g., 3 days), but is also visible in longer simulated time-courses (e.g., 12 days) and in experimental time-courses of mouse wheel-running and ex vivo bioluminescence. We traced the source of the bias to discontinuities in the periodogram that are related to the number of time-points the CSP uses to calculate the observed variance for a given test period. By revising the calculation to avoid discontinuities, we developed a new version, the greedy CSP, that shows reduced bias and improved accuracy. Nonetheless, even the greedy CSP tended to be less accurate on our simulated time-courses than an alternative method, namely the Lomb-Scargle periodogram. Thus, although our study describes a major improvement to a classic method, it also suggests that users should generally avoid the CSP when estimating the period of biological rhythms.

Identification of a rate-limiting step in a metabolic pathway using the kinetic model and in vitro experiment

Identification of the rate-limiting step in a metabolic pathway is an important challenge in metabolic engineering for enhancing pathway flow. Although specific enzyme activities (Vmax) provide valuable clues for the identification, it is time-consuming and difficult to measure multiple enzymes in the pathway because different assay protocols are required for each enzyme. In the present study, we propose a method to simultaneously determine the Vmax values of multiple enzymes using a kinetic model with a time course of the intermediate concentrations through an invitro experiment. To demonstrate this method, nine glycolysis reactions for converting glucose-6-phosphate (G6P) to pyruvate in Escherichia coli were considered. In a reaction mixture containing G6P and cofactors, glycolysis was initiated by adding a crude cell extract obtained from stationary phase cells. The Vmax values were optimized to minimize the difference between the measured and simulated time-courses using a kinetic model. Metabolic control analysis using the kinetic model with the estimated Vmax values revealed that fructose bisphosphate aldolase (FBA) was the rate-limiting step in the upper part of glycolysis. The addition of FBA in the reaction mixture successfully increased the glycolytic flux invitro. Furthermore, invivo, the specific glucose consumption rate of an FBA overexpression strain was 1.4 times higher than that of the control strain during the stationary phase. These results confirmed that FBA was the rate-limiting step in glycolysis under the stationary phase. This approach provides Vmax values of multiple enzymes in a pathway for metabolic control analysis with a kinetic model.

Analysis of time course and dose effect of tacrolimus on proteinuria in lupus nephritis patients.

Tacrolimus is used to treat patients with lupus nephritis; however, its time course and dose effect on proteinuria in lupus nephritis patients remain unknown. The purpose of this study was to determine the time course and dose effect of tacrolimus on proteinuria in lupus nephritis patients via model-based meta-analysis (MBMA). PubMed, Web of Science, Cochrane Library and ClinicalTrials.gov databases were systematically searched for information on the efficacy of tacrolimus against proteinuria in lupus nephritis patients. Useful data were extracted to build a model for the population studied using a non-linear mixed-effect model (NONMEM). This model was applied to simulate time course of tacrolimus on proteinuria using Monte Carlo simulations. Ten clinical studies that recruited 222 patients with lupus nephritis were included. Based on various diagnostic plots, we found that the established model described the observed data reasonably well. In addition, the typical Emax and ET50 of tacrolimus for 24-hour proteinuria in lupus nephritis patients were -5.88g and 0.37months, respectively. The baseline value of 24-hour proteinuria affected Emax . No significant dose-response relationship was observed in the range of tacrolimus concentration used in the present study (3-10ng/mL), indicating that the effect of tacrolimus on proteinuria depends on effective concentration range and not the dose. However, the time course relationship was obvious; the efficacy of tacrolimus increased over time, reaching a plateau (80% Emax ) at approximately 1.48months from the beginning of treatment. When the concentration range of tacrolimus is maintained at 3-10ng/mL, at least 1.48months of treatment is required to achieve a better outcome with regard to proteinuria in lupus nephritis patients.

Exceeding a "critical" muscle Pi: implications for [Formula: see text] and metabolite slow components, muscle fatigue and the power-duration relationship.

The consequences of the assumption that the additional ATP usage, underlying the slow component of oxygen consumption ([Formula: see text]) and metabolite on-kinetics, starts when cytosolic inorganic phosphate (Pi) exceeds a certain "critical" Pi concentration, and muscle work terminates because of fatigue when Pi exceeds a certain, higher, "peak" Pi concentration are investigated. A previously developed computer model of the myocyte bioenergetic system is used. Simulated time courses of muscle [Formula: see text], cytosolic ADP, pH, PCr and Pi at various ATP usage activities agreed well with experimental data. Computer simulations resulted in a hyperbolic power-duration relationship, with critical power (CP) as an asymptote. CP was increased, and phase II [Formula: see text] on-kinetics was accelerated, by progressive increase in oxygen tension (hyperoxia). Pi is a major factor responsible for the slow component of the [Formula: see text] and metabolite on-kinetics, fatigue-related muscle work termination and hyperbolic power-duration relationship. The successful generation of experimental system properties suggests that the additional ATP usage, underlying the slow component, indeed starts when cytosolic Pi exceeds a "critical" Pi concentration, and muscle work terminates when Pi exceeds a "peak" Pi concentration. The contribution of other factors, such as cytosolic acidification, or glycogen depletion and central fatigue should not be excluded. Thus, a detailed quantitative unifying mechanism underlying various phenomena related to skeletal muscle fatigue and exercise tolerance is offered that was absent in the literature. This mechanism is driven by reciprocal stimulation of Pi increase and additional ATP usage when "critical" Pi is exceeded.

Population pharmacokinetic analysis of weekly and biweekly IgPro20 (Hizentra®) dosing in patients with primary immunodeficiency

BackgroundIgPro20 (Hizentra®), a 20% subcutaneous immunoglobulin G (IgG), is an effective treatment for patients with primary immunodeficiencies with impaired IgG production. Flexible dosing regimens of IgPro20 have been supported by pharmacokinetic (PK) modeling and simulation. This study further describes the PK characteristics of serum IgG concentrations after weekly and biweekly administration of IgPro20 and compares predicted and actual serum IgG data using a previously-developed population PK (popPK) model. MethodsA popPK model was developed by combining data from a previously-published model with data from a Phase 4 study (IgPro20_4005). An external validation of the original model using dosing, demographics, and historic endogenous serum IgG concentrations from patients enrolled in study IgPro20_4005 was performed. This dataset was then simulated 300 times and predicted serum IgG PK characteristics compared with the observed data. ResultsA total of 173 patients (156 unique patients from original model and 17 patients from study IgPro20_4005) provided 4078 observations of serum IgG concentrations. The popPK estimates obtained demonstrated a clearance (% inter-individual variability) of 0.138 L/day (35%), volume of central compartment of 3.95 L (78.6%), inter-compartmental clearance of 0.260 L/day (56%), and volume of peripheral compartment of 4.44 L. Validation results indicated that observed serum IgG concentration vs time data fell within the 90% prediction intervals for median, 25th, and 75th percentiles of the simulated IgG concentration time courses. ConclusionsThe present analysis validated the ability of the previously published popPK model to predict serum IgG concentration time profiles after biweekly subcutaneous IgPro20 administration.

Linking internal dosimetries of the propyl metabolic series in rats and humans using physiologically based pharmacokinetic (PBPK) modeling

The metabolic series approach has successfully linked internal dosimetries of metabolically related compounds reducing cost and time for chemical risk assessments. Here, we developed a physiologically based pharmacokinetic (PBPK) model in rats and humans for the propyl metabolic series including propyl acetate, 1-propanol, propionaldehyde, and propionic acid. Manufacturers use these compounds as organic solvents and intermediates during chemical synthesis. Public exposures can occur through using consumer products containing propyl compounds like cosmetics, aerosol sprays, or foods, and occupational exposures can occur at manufacturing facilities. To develop the PBPK model, we measured in vitro metabolism of propyl acetate in blood and liver S9 fractions. We measured concentrations of propyl compounds in blood following intravenous (iv) infusion of 13C-propanol or 13C-propionic acid and closed chamber inhalation exposures to propyl acetate or propanol in rats. Using these studies and other published data, we modified an existing PBPK model for the butyl metabolic series to simulate time course concentrations of propyl compounds in rats and humans. Consistent with measured in vitro and in vivo data, the optimized propyl series model predicts rapid clearance of propyl acetate, higher concentrations of propanol in blood from propyl acetate inhalation compared to propanol inhalation in rats but not in humans, and low concentrations of propionic acid in blood from exposures to propyl acetate or propanol. Regulators can use this model as a tool for propyl compound risk assessment by linking internal dosimetries under various exposure scenarios.

Comparative in silico study of congocidine congeners as potential inhibitors of African swine fever virus.

African swine fever virus (ASFV) infection is fatal in domesticated pigs, with a mortality rate approaching 100%. This may result in economic losses and threats to food security. Currently, there are no approved vaccines or antiviral therapies for ASFV. Therefore, in this study, we evaluated congocidine congeners and a tris-benzimidazole as potential inhibitors of ASFV transcription using an in silico approach. We applied redocking of congocidine and docking of its congeners and a tris-benzimidazole to a receptor containing B-DNA with AT-motifs as a target to mimic conserved ASFV late gene promoters. Subsequently, the binding scores of DNA-ligand docked complexes were evaluated and their binding affinity was estimated. Molecular dynamics (MD) simulation was then used to assess ligand behavior within the minor groove. From our results, it is evident the less toxic congocidine congeners and tris-benzimidazole could dock to AT-rich regions significantly. Additionally, the predicted binding affinities had suitable values comparable to other experimentally determined minor groove binders, MD simulation of the docked DNA-ligand complexes and subsequent molecular trajectory visualization further showed that the ligands remained embedded in the minor groove during the time course of simulation, indicating that these ligands may have potential applications in abrogating ASFV transcription.

Non-invasive Imaging and Modeling of Liver Regeneration After Partial Hepatectomy.

The liver has a unique regenerative capability upon injury or partial resection. The regeneration process comprises a complex interplay between parenchymal and non-parenchymal cells and is tightly regulated at different scales. Thus, we investigated liver regeneration using multi-scale methods by combining non-invasive imaging with immunohistochemical analyses. In this context, non-invasive imaging can provide quantitative data of processes involved in liver regeneration at organ and body scale. We quantitatively measured liver volume recovery after 70% partial hepatectomy (PHx) by micro computed tomography (μCT) and investigated changes in the density of CD68+ macrophages by fluorescence-mediated tomography (FMT) combined with μCT using a newly developed near-infrared fluorescent probe. In addition, angiogenesis and tissue-resident macrophages were analyzed by immunohistochemistry. Based on the results, a model describing liver regeneration and the interactions between different cell types was established. In vivo analysis of liver volume regeneration over 21 days after PHx by μCT imaging demonstrated that the liver volume rapidly increased after PHx reaching a maximum at day 14 and normalizing until day 21. An increase in CD68+ macrophage density in the liver was detected from day 4 to day 8 by combined FMT-μCT imaging, followed by a decline towards control levels between day 14 and day 21. Immunohistochemistry revealed the highest angiogenic activity at day 4 after PHx that continuously declined thereafter, whereas the density of tissue-resident CD169+ macrophages was not altered. The simulated time courses for volume recovery, angiogenesis and macrophage density reflect the experimental data describing liver regeneration after PHx at organ and tissue scale. In this context, our study highlights the importance of non-invasive imaging for acquiring quantitative organ scale data that enable modeling of liver regeneration.

Prediction of Human Pharmacokinetics of Fomepizole from Preclinical Species Pharmacokinetics Based on Normalizing Time Course Profiles.

Fomepizole is used as an antidote to treat methanol poisoning due to its selectivity towards alcohol dehydrogenase. In the present study, the goal is to develop a method to predict the fomepizole human plasma concentration versus time profile based on the preclinical pharmacokinetics using the assumption of superimposability on simulated time course profiles of animals and humans. Standard allometric equations with/without correction factors were also assimilated in the prediction. The volume of distribution at steady state (Vss) predicted by simple allometry (57.55L) was very close to the reported value (42.17L). However, clearance (CL) prediction by simple allometry was at least 3-fold higher to the reported value (33.86mL/min); hence, multiple correction factors were used to predict the clearance. Both brain weight and maximum life span potential could predict the CL with 1.22- and 1.01-fold difference. Specifically, the predicted Vss and CL values via interspecies scaling were used in the prediction of series of human intravenous pharmacokinetic parameters, while the simulation of human oral profile was done by the use of absorption rate constant (Ka) from dog following the applicability of human bioavailability value scaled from dog data. In summary, the findings indicate that the utility of diverse allometry approaches to derive the human pharmacokinetics of fomepizole after intravenous/oral dosing.

CancerInSilico: An R/Bioconductor package for combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer.

Bioinformatics techniques to analyze time course bulk and single cell omics data are advancing. The absence of a known ground truth of the dynamics of molecular changes challenges benchmarking their performance on real data. Realistic simulated time-course datasets are essential to assess the performance of time course bioinformatics algorithms. We develop an R/Bioconductor package, CancerInSilico, to simulate bulk and single cell transcriptional data from a known ground truth obtained from mathematical models of cellular systems. This package contains a general R infrastructure for running cell-based models and simulating gene expression data based on the model states. We show how to use this package to simulate a gene expression data set and consequently benchmark analysis methods on this data set with a known ground truth. The package is freely available via Bioconductor: http://bioconductor.org/packages/CancerInSilico/

Molecular motor translocation kinetics: Application of Monte Carlo computer simulations to determine microscopic kinetic parameters

Methods for studying the translocation of motor proteins along a filament (e.g., nucleic acid and polypeptide) typically monitor the total production of ADP, the arrival/departure of the motor protein at/from a particular location (often one end of the filament), or the dissociation of the motor protein from the filament. The associated kinetic time courses are often analyzed using a simple sequential uniform n-step mechanism to estimate the macroscopic kinetic parameters (e.g., translocation rate and processivity) and the microscopic kinetic parameters (e.g., kinetic step-size and the rate constant for the rate-limiting step). These sequential uniform n-step mechanisms assume repetition of uniform and irreversible rate-limiting steps of forward motion along the filament. In order to determine how the presence of non-uniform motion (e.g., backward motion, random pauses, or jumping) affects the estimates of parameters obtained from such analyses, we evaluated computer simulated translocation time courses containing non-uniform motion using a simple sequential uniform n-step model. By comparing the kinetic parameters estimated from the analysis of the data generated by these simulations with the input parameters of the simulations, we were able to determine which of the kinetic parameters were likely to be over/under estimated due to non-uniform motion of the motor protein.

A mathematical model-based analysis of the time-kill kinetics of ceftazidime/avibactam against Pseudomonas aeruginosa.

To characterize quantitatively the effect of avibactam in potentiating ceftazidime against MDR Pseudomonas aeruginosa by developing a mathematical model to describe the bacterial response to constant concentration time-kill information and validating it using both constant and time-varying concentration-effect data from in vitro and in vivo infection systems. The time course of the bacterial population dynamics in the presence of static concentrations of ceftazidime and avibactam was modelled using a two-state pharmacokinetic/pharmacodynamic (PK/PD) model, consisting of active and resting states, to account for bactericidal activities, bacteria-mediated ceftazidime degradation and inhibition of degradation by avibactam. Ceftazidime's effect on the bacterial population was described as an enhancement of the death rate of the active population, with the effect of avibactam being to increase ceftazidime potency. Model validation was performed by comparing simulated time courses of bacterial responses with those from in vitro and in vivo experimental exposures of ceftazidime and avibactam that represented those predicted in an average patient dosed with 2 g/0.5 g ceftazidime/avibactam administered every 8 h as 2 h infusions. The two-state model successfully described the bacterial population dynamics, ceftazidime degradation and its inhibition by avibactam. For external validation, the model correctly predicted the bacterial response of P. aeruginosa isolates evaluated in in vitro hollow-fibre and in vivo neutropenic mouse thigh and lung infection models. The PK/PD model and modelled strains successfully replicated the spread in activity when compared with a large selection of P. aeruginosa strains reported in the literature.

Stochastic Simulation of Biomolecular Networks in Dynamic Environments.

Simulation of biomolecular networks is now indispensable for studying biological systems, from small reaction networks to large ensembles of cells. Here we present a novel approach for stochastic simulation of networks embedded in the dynamic environment of the cell and its surroundings. We thus sample trajectories of the stochastic process described by the chemical master equation with time-varying propensities. A comparative analysis shows that existing approaches can either fail dramatically, or else can impose impractical computational burdens due to numerical integration of reaction propensities, especially when cell ensembles are studied. Here we introduce the Extrande method which, given a simulated time course of dynamic network inputs, provides a conditionally exact and several orders-of-magnitude faster simulation solution. The new approach makes it feasible to demonstrate—using decision-making by a large population of quorum sensing bacteria—that robustness to fluctuations from upstream signaling places strong constraints on the design of networks determining cell fate. Our approach has the potential to significantly advance both understanding of molecular systems biology and design of synthetic circuits.