Development and Verification of a Japanese Pediatric Physiologically Based Pharmacokinetic Model with Emphasis on Drugs Eliminated by Cytochrome P450 or Renal Excretion.

Physiologically based pharmacokinetic (PBPK) models are useful in bridging drug exposure in different ethnic groups, and there is increasing regulatory application of this approach in adults. Reported pediatric PBPK models tend to focus on the North European population, with few examples in other ethnic groups. This study describes the development and verification of a Japanese pediatric PBPK population. The development of the model was based on the existing North European pediatric population. Japanese systems and clinical data were collated from public databases and the literature, and the underlying demographics and equations were optimized so that physiological outputs represented the Japanese pediatric population. The model was tested using 14 different small molecule drugs, eliminated by a variety of pathways, including cytochrome P450 3A4 (CYP3A4) metabolism and renal excretion. Given the limitations of the clinical data, the overall performance of the model was good, with 44/62 predictions for PK parameters (area under the plasma drug concentration-time curve, AUC; maximum serum concentration, Cmax ; clearance, CL) being within 0.8- to 1.25-fold, 56/62 within 0.67- to 1.5-fold, and 61/62 within 0.5- to 2.0-fold of the observed values. Specific results for the 5 CYP3A4 substrates showed 20/31 cases were predicted within 0.8- to 1.25-fold, 27/31 within 0.67- to 1.5-fold, and all were within 0.5- to 2.0-fold of the observed values. Given the increased regulatory use of pediatric PBPK in drug development, expanding these models to other ethnic groups are important. Considering qualifying these models based on the context of use, there is a need to expand on the current research to include a larger range of drugs with different elimination pathways. Collaboration among academic, industry, model providers, and regulators will facilitate further development.

Application of the Interagency and Modeling Analysis Group Model Verification Approach for Scientific Reproducibility in a Study of Biomineralization.

Model verification is a critical aspect of scientific accountability, transparency, and learning. Here, we demonstrate an application of a model verification approach for a molecular dynamics (MD) simulation, where the interactions between silica and silk protein were studied experimentally toward understanding biomineralization. Following the ten rules for credible modeling and simulation of biosciences as developed in Erdemir et al., the authors of the original paper collaborated with an external modeling group to verify the key findings of their original simulation model and to document this verification approach. The process resulted in successful replication of the key findings of the original model. Beyond verification, study of the model from a new perspective generated new insight into the basic assumptions. We discuss key learnings for how model validation processes can be improved more generally, specifically through improved documentation methods. We anticipate that this application of our protocol for model verification can be further replicated and improved to verify and validate other simulations.

Verification of simplified model of inverter-Motor stator system.

A simplified inverter-motor stator system model is presented in the paper. The model is based on elementary dynamic blocks described by differential equations. The paper presents the results of research aimed at verifying the model on different devices with different types of inverters and motors. Model accuracy coefficients have been developed for all examined cases, confirming the high conformity of the model to the measured speeds and torques of asynchronous motors. The model was also used to simulate the operation of a drive system with a synchronous motor, obtaining high compliance of speed and torque mapping. The presented cases of verification of a simplified model confirm the possibility of using it to model different devices fed by frequency inverters, controlled both manually and automatically. The presented model, using simple description, ensures high accuracy of speed and torque mapping. It should be emphasized that most parameters in the presented model are calculated for motors and inverters based on data sheets.

Testing for a Random Walk Structure in the Frequency Evolution of a Tone in Noise.

Inference and hypothesis testing are typically constructed on the basis that a specific model holds for the data. To determine the veracity of conclusions drawn from such data analyses, one must be able to identify the presence of the assumed structure within the data. In this paper, a model verification test is developed for the presence of a random walk-like structure in the variations in the frequency of complex-valued sinusoidal signals measured in additive Gaussian noise. This test evaluates the joint inference of the random walk hypothesis tests found in economics literature that seek random walk behaviours in time series data, with an additional test to account for how the random walk behaves in frequency space.

Experimental Carbonation Study for a Durability Assessment of Novel Cementitious Materials.

Durability predictions of concrete structures are derived from experience-based requirements and descriptive exposure classes. To support durability predictions, a numerical model related to the carbonation resistance of concrete was developed. The model couples the rate of carbonation with the drying rate. This paper presents the accelerated carbonation and moisture transport experiments performed to calibrate and verify the numerical model. They were conducted on mortars with a water-cement ratio of either 0.6 or 0.5, incorporating either a novel cement CEM II/C (S-LL) (EnM group) or commercially available CEM II/A-S cement (RefM group). The carbonation rate was determined by visual assessment and thermogravimetric analysis (TGA). Moisture transport experiments, consisting of drying and resaturation, utilized the gravimetric method. Higher carbonation rates expressed in mm/day-0.5 were found in the EnM group than in the RefM group. However, the TGA showed that the initial portlandite (CH) content was lower in the EnM than in the RefM, which could explain the difference in carbonation rates. The resaturation experiments indicate an increase in the suction porosity in the carbonated specimens compared to the non-carbonated specimens. The study concludes that low clinker content causes lower resistance to carbonation, since less CH is available in the surface layers; thus, the carbonation front progresses more rapidly towards the core.

Stochastic Hazard Analysis of Genetic Circuits in iBioSim and STAMINA.

In synthetic biology, combinational circuits are used to program cells for various new applications like biosensors, drug delivery systems, and biofuels. Similar to asynchronous electronic circuits, some combinational genetic circuits may show unwanted switching variations (glitches) caused by multiple input changes. Depending on the biological circuit, glitches can cause irreversible effects and jeopardize the circuit's functionality. This paper presents a stochastic analysis to predict glitch propensities for three implementations of a genetic circuit with known glitching behavior. The analysis uses STochastic Approximate Model-checker for INfinite-state Analysis (STAMINA), a tool for stochastic verification. The STAMINA results were validated by comparison to stochastic simulation in iBioSim resulting in further improvements of STAMINA. This paper demonstrates that stochastic verification can be utilized by genetic designers to evaluate design choices and input restrictions to achieve a desired reliability of operation.

Establishment and verification of an osteoporosis risk model in patients with rheumatoid arthritis: a valuable new model.

To establish a model for osteoporosis risk in patients with rheumatoid arthritis and validate the model. A newly generated predictive model has been suggested to have good differentiation, calibration, and clinical validity and may be a useful clinical model for predicting osteoporosis in patients with rheumatoid arthritis. PURPOSE: To establish a prediction model for osteoporosis risk in patients with rheumatoid arthritis and validate the model internally and externally. METHODS: A total of 270 patients with rheumatoid arthritis who underwent bone mineral density measurement at our hospital from June 2019 to June 2020 were enrolled in the study. The patients were divided into two groups according to their entry time: a training set containing the first 2/3 of the patients (n = 180) and a validation set containing the remaining 1/3 of the patients (n = 90). Binary logistic regression analysis was used to establish the regression models, and the concordance index (C-index), calibration plot, and decision curve analysis were used to evaluate the prediction model. RESULTS: Five variables, including age (X1), course of disease (X2), the disease activity score using 28 joint counts (DAS28) (X4), anti-cyclic citrullinated peptide antibody (CCP) (X7), and 7-joint ultrasonic bone erosion (X14), were selected to enter the model. The prediction model is Logit Y = - 12.647 + 0.133X1 + 0.011X2 + 0.754X4 + 0.001X7 + 0.605X14. The model had good differentiation; the C-index in the internal verification was 0.947 (95% CI is 0.932-0.977) and the C-index in the external verification was 0.946 (95% CI is 0.940-0.994). The calibration plot of the model showed excellent consistency between the prediction probability and actual probability. When > 0.483 was taken as the cutoff value for the diagnosis of osteoporosis, the sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and Jordan index of the model were 90.24%, 87.76%, 7.37, 0.11, and 78.00%, respectively. CONCLUSION: A newly generated predictive model has been suggested to have good differentiation, calibration, and clinical validity and may be a useful clinical model for predicting osteoporosis in patients with rheumatoid arthritis.

Computational Verification of Large Logical Models-Application to the Prediction of T Cell Response to Checkpoint Inhibitors.

At the crossroad between biology and mathematical modeling, computational systems biology can contribute to a mechanistic understanding of high-level biological phenomenon. But as knowledge accumulates, the size and complexity of mathematical models increase, calling for the development of efficient dynamical analysis methods. Here, we propose the use of two approaches for the development and analysis of complex cellular network models. A first approach, called "model verification" and inspired by unitary testing in software development, enables the formalization and automated verification of validation criteria for whole models or selected sub-parts. When combined with efficient analysis methods, this approach is suitable for continuous testing, thereby greatly facilitating model development. A second approach, called "value propagation," enables efficient analytical computation of the impact of specific environmental or genetic conditions on the dynamical behavior of some models. We apply these two approaches to the delineation and the analysis of a comprehensive model for T cell activation, taking into account CTLA4 and PD-1 checkpoint inhibitory pathways. While model verification greatly eases the delineation of logical rules complying with a set of dynamical specifications, propagation provides interesting insights into the different potential of CTLA4 and PD-1 immunotherapies. Both methods are implemented and made available in the all-inclusive CoLoMoTo Docker image, while the different steps of the model analysis are fully reported in two companion interactive jupyter notebooks, thereby ensuring the reproduction of our results.

Mathematical Model Describing the Influence of Geometrical Parameters of Multichannel Dies on the Limit Force of Dry Ice Extrusion Process.

The article presents a formulated mathematical model that enables the determination of the required compressive force in the extrusion process of dry ice employing multichannel dies. This is the main parameter in the piston-based dry ice extrusion process. The indicated model was developed for the purpose of further improvement of the energy efficiency of this extrusion process. It allows for the determination of the value of compressive force by accounting for 12 variables related to the geometrical parameters of the die and the physical characteristics of dry ice. Furthermore, the paper also provides descriptions of the empirical study methodologies together with the results. These were carried out in order to determine the difference between the results of mathematical modeling and actual measurement results. The final part of the article presents the results of the analysis of the mathematical model's sensitivity to the change of the physical characteristics of dry ice. The formulated tool may be employed to adapt the geometric parameters of the die in order to obtain the desired compressive force value and dry ice granulation with reduced energy consumption.

Evaluation of the automated reference toolset as a method to select reference plots for oil and gas reclamation on Colorado Plateau rangelands.

Rangelands are typically characterized by low precipitation and low biomass which makes them susceptible to disturbance and difficult to reclaim. These characteristics become a management issue when considering the widespread and significant impact of oil and gas development on rangelands. Reclamation from this land use involves the complexities of dealing with multiple state and federal agencies, private landowners, and their sometimes conflicting rules. Reference plots (e.g., nearby undisturbed sites) can help with these issues by providing an objective context for reclamation planning. They are selected to provide a comparison that is similar to a reclamation site in most aspects except for the disturbance activity. This allows for the relative condition of the reclamation site to be determined. Because selection of reference plots is normally expert-driven on a site-by-site basis, it can be time consuming and thus ineffective in helping to meet reclamation goals over large landscapes. The Automated Reference Tool (ART) was developed to improve the efficiency and efficacy of reference plot selection. The ART improves reference plot selection through remote sensing and indicators of land potential by selecting reference plots of similar land potential to the reclamation site based on soil texture, topography, and geology. We evaluated the ART in the context of well-pad reclamation to determine if ART-selected plots were appropriate to use as reference when compared to an existing reference plot network. We applied the ART to reclamation sites managed by the Bureau of Land Management's (BLM) White River Field Office, Colorado which had existing expert-selected reference plots. We found that the ART-selected reference plots and their matching expert-selected reference plot had similar large-scale vegetative cover characteristics (total foliar: R2 = 0.34, p-value = 0.0012) and dissimilar finer-scale cover characteristics (plant diversity: R2 = 0.079, p-value = 0.15). In addition, we detected similarities in their soil water content (R2 = 0.43, p-value<0.001), depth to restricting layer (RMSD = 21.90), and rock fragment (RMSD = 19.99). These results demonstrate that ART could be a useful tool for managers to help meet their reclamation goals over large landscapes, but it is not a complete automation of the reference selection process.

Experimental data for the characterization of heat transfer processes in a cement based thermal energy storage system with helical heat exchanger.

This document compiles the detailed experimental data and description of four different heat charging tests presented in Nordbeck et al. ([1]), which aimed at the basic performance characterization of a lab-scale prototype of a new scalable, cement based, sensible heat storage system. The data set contains transient distributed measurements of temperatures within the storage as well as measurements of the experimental boundary conditions (heat carrier fluid flow rates, charging and laboratory temperatures) at high temporal resolution. In addition, the geometrical configuration of the storage and its component parts as well as the associated thermal material parameters are specified. The presented data is useful to assess and compare storage characteristics (storage capacities, charging/discharging rates, energy efficiency, heat loss behaviour) of the new heat storage system. The data can also be used as a reference data set for the development and verification of numerical models of modular solid-liquid heat storages or other related geothermal systems such as ground source heat pumps or energy piles using helical heat exchangers.

Evaluation of the Hippocampal Normal Tissue Complication Model in a Prospective Cohort of Low Grade Glioma Patients-An Analysis Within the EORTC 22033 Clinical Trial.

Purpose: To evaluate the performance of the hippocampal normal tissue complication model that relates dose to the bilateral hippocampus to memory impairment at 18 months post-treatment in a population of low-grade glioma (LGG) patients. Methods: LGG patients treated within the radiotherapy-only arm of the EORTC 22033-26033 trial were analyzed. Hippocampal dose parameters were calculated from the original radiotherapy plans. Difference in Rey Verbal Auditory Learning test delayed recall (AVLT-DR) performance pre-and 18 (±4) months post-treatment was compared to reference data from the Maastricht Aging study. The NTCP model published by Gondi et al. was applied to the dosimetric data and model predictions were compared to actual neurocognitive outcome. Results: A total of 29 patients met inclusion criteria. Mean dose in EQD2 Gy to the bilateral hippocampus was 39.8 Gy (95% CI 34.3-44.4 Gy), the median dose to 40% of the bilateral hippocampus was 47.2 EQD2 Gy. The model predicted a risk of memory impairment exceeding 99% in 22 patients. However, only seven patients were found to have a significant decline in AVLT-dr score. Conclusions: In this dataset of only LGG patients treated with radiotherapy the hippocampus NTCP model did not perform as expected to predict cognitive decline based on dose to 40% of the bilateral hippocampus. Caution should be taken when extrapolating this model outside of the range of dose-volume parameters in which it was developed.

GPS-coupled contaminant monitors on free-ranging Chernobyl wolves challenge a fundamental assumption in exposure assessments.

Measurements of external contaminant exposures on individual wildlife are rare because of difficulties in using contaminant monitors on free-ranging animals. Most wildlife contaminant exposure data are therefore simulated with computer models. Rarely are empirical exposure data available to verify model simulations, or to test fundamental assumptions inherent in exposure assessments. We used GPS-coupled contaminant monitors to quantify external exposures to individual wolves (Canis lupus) living within the Belarus portion of Chernobyl's 30-km exclusion zone. The study provided data on animal location and contaminant exposure every 35 min for 6 months, resulting in ~6600 individual locations and 137Cs external exposure readings per wolf, representing the most robust external exposure data published to date on free ranging animals. The data provided information on variation in external exposure for each animal over time, as well as variation in external exposure among the eight wolves across the landscape of Chernobyl. The exposure data were then used to test a fundamental assumption in screening-level risk assessments, espoused in guidance documents of the U.S. Environmental Protection Agency and U.S. Department of Energy, - Mean contaminant concentrations conservatively estimate individual external exposures. We tested this assumption by comparing our empirical data to a series of simulations using the ERICA modeling tool. We found that modeled simulations of mean external exposure (10.5 mGy y-1), based on various measures of central tendency, under-predicted mean exposures measured on five of the eight wolves wearing GPS-contaminant monitors (i.e., 12.3, 26.3, 28.0, 28.8 and 35.7 mGy y-1). If under-prediction of exposure occurs for some animals, then arguably the use of averaged contaminant concentrations to predict external exposure is not as conservative as proposed by current risk assessment guidance. Thus, a risk assessor's interpretation of simulated exposures in a screening-level risk analysis might be misguided if contaminant concentrations are based on measures of central tendency. We offer three suggestions for risk assessors to consider in order to reduce the probability of underestimating exposure in a screening-level risk assessment.

Measurement validation of treatment planning for a MR-Linac.

PURPOSE: The magnetic field can cause a nonnegligible dosimetric effect in an MR-Linac system. This effect should be accurately accounted for by the beam models in treatment planning systems (TPS). The purpose of the study was to verify the beam model and the entire treatment planning and delivery process for a 1.5 T MR-Linac based on comprehensive dosimetric measurements and end-to-end tests. MATERIAL AND METHODS: Dosimetry measurements and end-to-end tests were performed on a preclinical MR-Linac (Elekta AB) using a multitude of detectors and were compared to the corresponding beam model calculations from the TPS for the MR-Linac. Measurement devices included ion chambers (IC), diamond detector, radiochromic film, and MR-compatible ion chamber array and diode array. The dose in inhomogeneous phantom was also verified. The end-to-end tests include the generation, delivery, and comparison of 3D and IMRT plan with measurement. RESULTS: For the depth dose measurements with Farmer IC, micro IC and diamond detector, the absolute difference between most measurement points and beam model calculation beyond the buildup region were <1%, at most 2% for a few measurement points. For the beam profile measurements, the absolute differences were no more than 1% outside the penumbra region and no more than 2.5% inside the penumbra region. Results of end-to-end tests demonstrated that three 3D static plans with single 5 × 10 cm2 fields (at gantry angle 0°, 90° and 270°) and two IMRT plans successfully passed gamma analysis with clinical criteria. The dose difference in the inhomogeneous phantom between the calculation and measurement was within 1.0%. CONCLUSIONS: Both relative and absolute dosimetry measurements agreed well with the TPS calculation, indicating that the beam model for MR-Linac properly accounts for the magnetic field effect. The end-to-end tests verified the entire treatment planning process.

Using cultural, historical, and epidemiological data to inform, calibrate, and verify model structures in agent-based simulations.

Agent-based simulation models are excellent tools for addressing questions about the spread of infectious diseases in human populations because realistic, complex behaviors as well as random factors can readily be incorporated. Agent-based models are flexible and allow for a wide variety of behaviors, time-related variables, and geographies, making the calibration process an extremely important step in model development. Such calibration procedures, including verification and validation, may be complicated, however, and usually require incorporation of substantial empirical data and theoretical knowledge of the populations and processes under study. This paper describes steps taken to build and calibrate an agent-based model of epidemic spread in an early 20th century fishing village in Newfoundland and Labrador, including a description of some of the detailed ethnographic and historical data available. We illustrate how these data were used to develop the structure of specific parts of the model. The resulting model, however, is designed to reflect a generic small community during the early 20th century and the spread of a directly transmitted disease within such a community, not the specific place that provided the data. Following the description of model development, we present the results of a replication study used to confirm the model behaves as intended. This study is also used to identify the number of simulations necessary for high confidence in average model output. We also present selected results from extensive sensitivity analyses to assess the effect that variation in parameter values has on model outcomes. After careful calibration and verification, the model can be used to address specific practical questions of interest. We provide an illustrative example of this process.

Toward inverse generative social science using multi-objective genetic programming.

Generative mechanism-based models of social systems, such as those represented by agent-based simulations, require that intra-agent equations (or rules) be specified. However there are often many different choices available for specifying these equations, which can still be interpreted as falling within a particular class of mechanisms. Whilst it is important for a generative model to reproduce historically observed dynamics, it is also important for the model to be theoretically enlightening. Genetic programs (our own included) often produce concatenations that are highly predictive but are complex and hard to interpret theoretically. Here, we develop a new method - based on multi-objective genetic programming - for automating the exploration of both objectives simultaneously. We demonstrate the method by evolving the equations for an existing agent-based simulation of alcohol use behaviors based on social norms theory, the initial model structure for which was developed by a team of human modelers. We discover a trade-off between empirical fit and theoretical interpretability that offers insight into the social norms processes that influence the change and stasis in alcohol use behaviors over time.

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

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

Modeling the Metabolism of Arabidopsis thaliana: Application of Network Decomposition and Network Reduction in the Context of Petri Nets.

Motivation:Arabidopsis thaliana is a well-established model system for the analysis of the basic physiological and metabolic pathways of plants. Nevertheless, the system is not yet fully understood, although many mechanisms are described, and information for many processes exists. However, the combination and interpretation of the large amount of biological data remain a big challenge, not only because data sets for metabolic paths are still incomplete. Moreover, they are often inconsistent, because they are coming from different experiments of various scales, regarding, for example, accuracy and/or significance. Here, theoretical modeling is powerful to formulate hypotheses for pathways and the dynamics of the metabolism, even if the biological data are incomplete. To develop reliable mathematical models they have to be proven for consistency. This is still a challenging task because many verification techniques fail already for middle-sized models. Consequently, new methods, like decomposition methods or reduction approaches, are developed to circumvent this problem. Methods: We present a new semi-quantitative mathematical model of the metabolism of Arabidopsis thaliana. We used the Petri net formalism to express the complex reaction system in a mathematically unique manner. To verify the model for correctness and consistency we applied concepts of network decomposition and network reduction such as transition invariants, common transition pairs, and invariant transition pairs. Results: We formulated the core metabolism of Arabidopsis thaliana based on recent knowledge from literature, including the Calvin cycle, glycolysis and citric acid cycle, glyoxylate cycle, urea cycle, sucrose synthesis, and the starch metabolism. By applying network decomposition and reduction techniques at steady-state conditions, we suggest a straightforward mathematical modeling process. We demonstrate that potential steady-state pathways exist, which provide the fixed carbon to nearly all parts of the network, especially to the citric acid cycle. There is a close cooperation of important metabolic pathways, e.g., the de novo synthesis of uridine-5-monophosphate, the γ-aminobutyric acid shunt, and the urea cycle. The presented approach extends the established methods for a feasible interpretation of biological network models, in particular of large and complex models.

Physiologically grounded metrics of model skill: a case study estimating heat stress in intertidal populations.

Models of ecological responses to climate change fundamentally assume that predictor variables, which are often measured at large scales, are to some degree diagnostic of the smaller-scale biological processes that ultimately drive patterns of abundance and distribution. Given that organisms respond physiologically to stressors, such as temperature, in highly non-linear ways, small modelling errors in predictor variables can potentially result in failures to predict mortality or severe stress, especially if an organism exists near its physiological limits. As a result, a central challenge facing ecologists, particularly those attempting to forecast future responses to environmental change, is how to develop metrics of forecast model skill (the ability of a model to predict defined events) that are biologically meaningful and reflective of underlying processes. We quantified the skill of four simple models of body temperature (a primary determinant of physiological stress) of an intertidal mussel, Mytilus californianus, using common metrics of model performance, such as root mean square error, as well as forecast verification skill scores developed by the meteorological community. We used a physiologically grounded framework to assess each model's ability to predict optimal, sub-optimal, sub-lethal and lethal physiological responses. Models diverged in their ability to predict different levels of physiological stress when evaluated using skill scores, even though common metrics, such as root mean square error, indicated similar accuracy overall. Results from this study emphasize the importance of grounding assessments of model skill in the context of an organism's physiology and, especially, of considering the implications of false-positive and false-negative errors when forecasting the ecological effects of environmental change.

Integrated multiscale biomaterials experiment and modelling: a perspective.

Advances in multiscale models and computational power have enabled a broad toolset to predict how molecules, cells, tissues and organs behave and develop. A key theme in biological systems is the emergence of macroscale behaviour from collective behaviours across a range of length and timescales, and a key element of these models is therefore hierarchical simulation. However, this predictive capacity has far outstripped our ability to validate predictions experimentally, particularly when multiple hierarchical levels are involved. The state of the art represents careful integration of multiscale experiment and modelling, and yields not only validation, but also insights into deformation and relaxation mechanisms across scales. We present here a sampling of key results that highlight both challenges and opportunities for integrated multiscale experiment and modelling in biological systems.