Background Recommendations regarding personalized lung cancer screening are being informed by natural-history modeling. Therefore, understanding how differences in model assumptions affect model-based personalized screening recommendations is essential. Design Five Cancer Intervention and Surveillance Modeling Network (CISNET) models were evaluated. Lung cancer incidence, mortality, and stage distributions were compared across 4 theoretical scenarios to assess model assumptions regarding 1) sojourn times, 2) stage-specific sensitivities, and 3) screening-induced lung cancer mortality reductions. Analyses were stratified by sex and smoking behavior. Results Most cancers had sojourn times <5 y (model range [MR]; lowest to highest value across models: 83.5%–98.7% of cancers). However, cancer aggressiveness still varied across models, as demonstrated by differences in proportions of cancers with sojourn times <2 y (MR: 42.5%–64.6%) and 2 to 4 y (MR: 28.8%–43.6%). Stage-specific sensitivity varied, particularly for stage I (MR: 31.3%–91.5%). Screening reduced stage IV incidence in most models for 1 y postscreening; increased sensitivity prolonged this period to 2 to 5 y. Screening-induced lung cancer mortality reductions among lung cancers detected at screening ranged widely (MR: 14.6%–48.9%), demonstrating variations in modeled treatment effectiveness of screen-detected cases. All models assumed longer sojourn times and greater screening-induced lung cancer mortality reductions for women. Models assuming differences in cancer epidemiology by smoking behaviors assumed shorter sojourn times and lower screening-induced lung cancer mortality reductions for heavy smokers. Conclusions Model-based personalized screening recommendations are primarily driven by assumptions regarding sojourn times (favoring longer intervals for groups more likely to develop less aggressive cancers), sensitivity (higher sensitivities favoring longer intervals), and screening-induced mortality reductions (greater reductions favoring shorter intervals). Implications Models suggest longer screening intervals may be feasible and benefits may be greater for women and light smokers. Highlights Natural-history models are increasingly used to inform lung cancer screening, but causes for variations between models are difficult to assess. This is the first evaluation of these causes and their impact on personalized screening recommendations through easily interpretable metrics. Models vary regarding sojourn times, stage-specific sensitivities, and screening-induced lung cancer mortality reductions. Model outcomes were similar in predicting greater screening benefits for women and potentially light smokers. Longer screening intervals may be feasible for women and light smokers.

Abstract Current US screening guidelines recommend low dose computed tomography (LDCT) scans for high-risk individuals of lung cancer, however, only 13% of the eligible population adhere to the recommendation. Using circulating tumor DNA (ctDNA) as a blood-based cancer biomarker for screening may increase the adherence to screening programs while simultaneously decreasing radiation exposure and invasive follow-up procedures due to false-positives of LDCT. To estimate the health benefits of annual lung cancer screening with ctDNA, we built and applied a multi scale model of the natural history of lung cancer that includes clinical stage progression and ctDNA shedding, and models ctDNA detection. Our multi-scale model integrates data on lung cancer including clinical data from the Surveillance, Epidemiology, and End Results (SEER) database and molecular data on tumor growth, ctDNA shedding and ctDNA detection. The ctDNA detection function is based on a sequencing panel enriched for recurrent point mutations associated with lung cancer and leverages The Cancer Genome Atlas (TCGA) data. Using the model, we generated individual patient trajectories in term of the natural history of lung cancer and aggregated the results at the population level to compare overall health outcomes in the presence vs absence of an annual ctDNA screening. These outcomes include the tumor size and stage at detection, 5-year survival, age-specific incidence-based mortality rate, and cure fraction, which is identified by patients who would have died from lung cancer but instead died from other causes due to screen detection. Our work shows that annual ctDNA screening detected 35% of lung cancer patients while asymptomatic with 99% specificity. Among screen-detected patients, 36% were curable due to early detection but would have been uncurable without screening. Compared to not screening, annual ctDNA screening reduced the median tumor size at detection (from 3.2 cm to 2.2 cm), increased the proportion of early-stage cases (from 36% to 56%), increased 5-year survival (from 20% to 33%), and reduced the average age-specific mortality rate by 21%. In conclusion, an annual ctDNA screening program, even with low sensitivity but high specificity, is expected to provide measurable health benefits and warrants further consideration

Late 2019 saw the outbreak of COVID-19, a respiratory disease caused by the new coronavirus SARS-CoV-2, which rapidly turned into a pandemic, killing more than 2.77 million people and infecting more than 126 million as of late March 2021. Daily collected data on infection cases and hospitalizations informed decision makers on the ongoing pandemic emergency, enabling the design of diversified countermeasures, from behavioral policies to full lockdowns, to curb the virus spread. In this context, mechanistic models could represent valuable tools to optimize the timing and stringency of interventions, and to reveal non-trivial properties of the pandemic dynamics that could improve the design of suitable guidelines for future epidemics. We performed a retrospective analysis of the Italian epidemic evolution up to mid-December 2020 to gain insight into the main characteristics of the original strain of SARS-CoV-2, prior to the emergence of new mutations and the vaccination campaign. We defined a time-varying optimization procedure to calibrate a refined version of the SIDARTHE (Susceptible, Infected, Diagnosed, Ailing, Recognized, Threatened, Healed, Extinct) model and hence accurately reconstruct the epidemic trajectory. We then derived additional features of the COVID-19 pandemic in Italy not directly retrievable from reported data, such as the estimate of the day zero of infection in late November 2019 and the estimate of the spread of undetected infection. The present analysis contributes to a better understanding of the past pandemic waves, confirming the importance of epidemiological modeling to support an informed policy design against epidemics to come.

Mathematical models have grown in size and complexity becoming often computationally intractable. In sensitivity analysis and optimization phases, critical for tuning, validation and qualification, these models may be run thousands of times. Scientific programming languages popular for prototyping, such as MATLAB and R, can be a bottleneck in terms of performance. Here we show a compiler-based approach, designed to be universal at handling engineering and life sciences modeling styles, that automatically translates models into fast C code. At first QSPcc is demonstrated to be crucial in enabling the research on otherwise intractable Quantitative Systems Pharmacology models, such as in rare Lysosomal Storage Disorders. To demonstrate the full value in seamlessly accelerating, or enabling, the R&D efforts in natural sciences, we then benchmark QSPcc against 8 solutions on 24 real-world projects from different scientific fields. With speed-ups of 22000x peak, and 1605x arithmetic mean, our results show consistent superior performances.

MOTIVATION:Precision medicine is a promising field that proposes, in contrast to a one-size-fits-all approach, the tailoring of medical decisions, treatments or products. In this context, it is crucial to introduce innovative methods to stratify a population of patients on the basis of an accurate system-level knowledge of the disease. This is particularly important in very challenging conditions, where the use of standard statistical methods can be prevented by poor data availability or by the need of oversimplifying the processes regulating a complex disease.RESULTS:We define an innovative method for phenotype classification that combines experimental data and a mathematical description of the disease biology. The methodology exploits the mathematical model for inferring additional subject features relevant for the classification. Finally, the algorithm identifies the optimal number of clusters and classifies the samples on the basis of a subset of the features estimated during the model fit. We tested the algorithm in two test cases: an in silico case in the context of dyslipidemia, a complex disease for which a large population of patients has been generated, and a clinical test case, in the context of a lysosomal rare disorder, for which the amount of available data was limited. In both the scenarios, our methodology proved to be accurate and robust, and allowed the inference of an additional phenotype division that the experimental data did not show.AVAILABILITY AND IMPLEMENTATION:The code to reproduce the in silico results has been implemented in MATLAB v.2017b and it is available in the Supplementary Material.SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

With the recent rising application of mathematical models in the field of computational systems biology, the interest in sensitivity analysis methods had increased. The stochastic approach, based on chemical master equations, and the deterministic approach, based on ordinary differential equations (ODEs), are the two main approaches for analyzing mathematical models of biochemical systems. In this work, the performance of these approaches to compute sensitivity coefficients is explored in situations where stochastic and deterministic simulation can potentially provide different results (systems with unstable steady states, oscillators with population extinction and bistable systems). We consider two methods in the deterministic approach, namely the direct differential method and the finite difference method, and five methods in the stochastic approach, namely the Girsanov transformation, the independent random number method, the common random number method, the coupled finite difference method and the rejection-based finite difference method. The reviewed methods are compared in terms of sensitivity values and computational time to identify differences in outcome that can highlight conditions in which one approach performs better than the other.

Gaucher’s disease type 1 (GD1) leads to significant morbidity and mortality through clinical manifestations, such as splenomegaly, hematological complications, and bone disease. Two types of therapies are currently approved for GD1: enzyme replacement therapy (ERT), and substrate reduction therapy (SRT). In this study, we have developed a quantitative systems pharmacology (QSP) model, which recapitulates the effects of eliglustat, the only first‐line SRT approved for GD1, on treatment‐naïve or patients with ERT‐stabilized adult GD1. This multiscale model represents the mechanism of action of eliglustat that leads toward reduction of spleen volume. Model capabilities were illustrated through the application of the model to predict ERT and eliglustat responses in virtual populations of adult patients with GD1, representing patients across a spectrum of disease severity as defined by genotype‐phenotype relationships. In summary, the QSP model provides a mechanistic computational platform for predicting treatment response via different modalities within the heterogeneous GD1 patient population.

ABSTRACT As of July 14 th , COVID-19 has caused in Italy 34.984 deaths and 243.344 infection cases. Strict lockdown policies were necessary to contain the first outbreak wave and prevent the Italian healthcare system from being overwhelmed by patients requiring intensive care. After the progressive reopening, predicting how the epidemic situation will evolve is urgent and fundamental to control any future outbreak and prevent a second wave. We defined a time-varying optimization procedure to repeatedly calibrate the SIDARTHE model 1 with data up to June 24 th . The computed parameter distributions allow us to robustly analyse how the epidemic situation evolved and outline possible future scenarios. Assuming a seasonal regime for COVID-19, we tested different lockdown policies. Our results suggest that an intermittent lockdown where six “open days” are allowed every other week may prevent a resurgent exponential outbreak and, at the same time, ease the societal burden of an extensive lockdown.

Nowadays, mathematical modeling is playing a key role in many different research fields. In the context of system biology, mathematical models and their associated computer simulations constitute essential tools of investigation. Among the others, they provide a way to systematically analyze systems perturbations, develop hypotheses to guide the design of new experimental tests, and ultimately assess the suitability of specific molecules as novel therapeutic targets. To these purposes, stochastic simulation algorithms (SSAs) have been introduced for numerically simulating the time evolution of a well‐stirred chemically reacting system by taking proper account of the randomness inherent in such a system. In this work, we review the main SSAs that have been introduced in the context of exact, approximate, and hybrid stochastic simulation. Specifically, we will introduce the direct method (DM), the first reaction method (FRM), the next reaction method (NRM) and the rejection‐based SSA (RSSA) in the area of exact stochastic simulation. We will then present the τ‐leaping method and the chemical Langevin method in the area of approximate stochastic simulation and an implementation of the hybrid RSSA (HRSSA) in the context of hybrid stochastic‐deterministic simulation. Finally, we will consider the model of the sphingolipid metabolism to provide an example of application of SSA to computational system biology by exemplifying how different simulation strategies may unveil different insights into the investigated biological phenomenon.

Background The pathophysiologic processes underlying the regulation of glucose homeostasis are considerably complex at both cellular and systemic level. A comprehensive and structured specification for the several layers of abstraction of glucose metabolism is often elusive, an issue currently solvable with the hierarchical description provided by multi-level models. In this study we propose a multi-level closed-loop model of whole-body glucose homeostasis, coupled with the molecular specifications of the insulin signaling cascade in adipocytes, under the experimental conditions of normal glucose regulation and type 2 diabetes. Methodology/Principal findings The ordinary differential equations of the model, describing the dynamics of glucose and key regulatory hormones and their reciprocal interactions among gut, liver, muscle and adipose tissue, were designed for being embedded in a modular, hierarchical structure. The closed-loop model structure allowed self-sustained simulations to represent an ideal in silico subject that adjusts its own metabolism to the fasting and feeding states, depending on the hormonal context and invariant to circadian fluctuations. The cellular level of the model provided a seamless dynamic description of the molecular mechanisms downstream the insulin receptor in the adipocytes by accounting for variations in the surrounding metabolic context. Conclusions/Significance The combination of a multi-level and closed-loop modeling approach provided a fair dynamic description of the core determinants of glucose homeostasis at both cellular and systemic scales. This model architecture is intrinsically open to incorporate supplementary layers of specifications describing further individual components influencing glucose metabolism.

The electrical and mechanical functions of cardiomyocytes differ in relation to the spatial locations of cells in the ventricular wall. This physiological heterogeneity may change under pathophysiological conditions, providing substrates for arrhythmia and contractile dysfunctions. Previous studies have reported distinctions in the electrophysiological and mechanical responses to ischemia of unloaded subendocardial (ENDO) and subepicardial (EPI) single cardiomyocytes. In this paper, we briefly recapitulated the available experimental data on the ischemia effects on the transmural cellular gradient in the heart ventricles and for the first time evaluated the preload-dependent changes in passive and active forces in ENDO and EPI cardiomyocytes isolated from mouse hearts subjected to simulated ischemia. Combining the results obtained in mechanically loaded contracting cardiomyocytes with data from previous studies, we showed that left ventricular ENDO and EPI cardiomyocytes are different in their mechanical responses to metabolic inhibition. Simulated ischemia showed opposite effects on the stiffness of ENDO and EPI cells and greatly prolonged the time course of contraction in EPI cells than in ENDO cells, thereby changing the normal transmural gradient in the cellular mechanics.