Data-driven modelling of biological multi-scale processes

Biological processes involve a variety of spatial and temporal scales. A holistic understanding of many biological processes therefore requires multi-scale models which capture the relevant properties on all these scales. In this manuscript we review mathematical modelling approaches used to describe the individual spatial scales and how they are integrated into holistic models. We discuss the relation between spatial and temporal scales and the implication of that on multi-scale modelling. Based upon this overview over state-of-the-art modelling approaches, we formulate key challenges in mathematical and computational modelling of biological multi-scale and multi-physics processes. In particular, we considered the availability of analysis tools for multi-scale models and model-based multi-scale data integration. We provide a compact review of methods for model-based data integration and model-based hypothesis testing. Furthermore, novel approaches and recent trends are discussed, including computation time reduction using reduced order and surrogate models, which contribute to the solution of inference problems. We conclude the manuscript by providing a few ideas for the development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and Multiscale Dynamics (American Scientific Publishers

Simulation of networks of spiking neurons: A review of tools and strategies

We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks.Comment: 49 pages, 24 figures, 1 table; review article, Journal of Computational Neuroscience, in press (2007

n-vitro time-kill assays and semi-mechanistic pharmacokinetic-pharmacodynamic modeling of a beta-lactam antibiotic combination against enterococcus faecalis: Optimizing dosing regimens for the geriatric population

Pharmacokinetic-pharmacodynamic (PKPD) modeling and simulation have emerged as pivotal tools in drug development and usage. Such models characterize typical trends in data and quantify the variability in relationships among dose, concentration, and desired effects. For antibacterial applications, models characterizing bacterial growth and antibiotic-induced bacterial killing offer insight into interactions between antibiotics, bacteria, and the host. Simulations from these models predict outcomes for untested scenarios, refine study designs, and optimize dosing regimens. Enterococcus faecalis, a significant opportunistic bacterial pathogen with increasing clinical relevance, is commonly found in the gastrointestinal tract but can lead to severe infection, such as endocarditis. Treatments for E. faecalis endocarditis involves combination antibiotic therapy, such as beta-lactam antibiotics and aminoglycosides. However, due to the toxicity of aminoglycosides, the primary treatment is typically double beta-lactam therapy—ampicillin and ceftriaxone. Eradicating an E. faecalis infection typically requires a lengthy six-week course of antibiotic treatment. However, keeping patients in hospitals for such an extended duration is impractical. Therefore, the objective of this thesis project is to explore the extension of double beta-lactam therapy to outpatient antibiotic treatment (OPAT). This approach is gaining importance due to the rising risks of hospital-acquired infections and escalating healthcare expenses. Leveraging the stability of penicillin G, which can be stored at room temperature for extended periods, makes it a promising candidate for OPAT, offering potential benefits in terms of both efficacy and cost-effectiveness. Despite limited evidence for penicillin G plus ceftriaxone, this research successfully bridges the gap through in-vitro time-kill assays and the subsequent development of a semi-mechanistic model for this antibiotic combination against E. faecalis isolates. This dissertation research evaluated 21 clinical strains of E. faecalis isolated from infected patients\u27 blood, sourced from Mount Sinai Health System and a hospital in Detroit as part of Dr. Jaclyn Cusumano’s American Association of Pharmacists (AACP) new investigator award research project. The first aim was to conduct susceptibility testing on these isolates. This testing played a pivotal role in guiding antibiotic therapy by determining a drug\u27s minimum inhibitory concentration (MIC) for a specific bacterial strain, offering insight into its effectiveness. The project highlights the importance of knowing a patient\u27s strain susceptibility since it influences the dosing regimen or treatment strategy. After susceptibility testing using broth microdilution techniques, strains were categorized as highly susceptible (MIC ≤ 2 μg/ml) or less susceptible (MIC = 4 μg/ml) to penicillin G. The next phase of the project involved in-vitro time-kill assays—a gold standard method for testing antibiotic concentrations and synergy in combination therapies. All 21 patient isolates were tested with penicillin G monotherapy and in combination with ceftriaxone, along with testing ampicillin and ceftriaxone combination therapies for comparison. It was noted that both combinations showed efficacy for strains highly susceptible to penicillin G (MIC ≤ 2 μg/ml), exhibiting bactericidal and synergistic activity. However, both treatments demonstrated poor performance for the less susceptible strains (MIC = 4 μg/ml). This observation focuses on the importance of in-vitro pharmacodynamic studies in understanding antibiotic action dynamics, forming the basis for the semi-mechanistic model. These 24-hour time-kill assays strongly suggested further investigation into the penicillin G and ceftriaxone combination, while considering the differential effects of the combination on more and less susceptible strains. Semi-mechanistic models were created for two out of the twenty-one tested strains, one with high susceptibility and another with lower susceptibility, with the goal of understanding the bacterial growth and drug kill effect in greater detail along with testing different dosing regimens. Following the typical progression of constructing a semi-mechanistic PK-PD model, a bacterial sub-model was created by employing intensive sampling during time-kill assays. This approach enabled the comprehension of the complete bacterial growth dynamics for both strains. By employing non-linear least squares regression within RStudio, the predictive model was effectively fitted to the observed data, providing estimates of essential bacterial growth parameters. The utilization of the Gompertz growth model yielded a remarkably close match between predicted and observed data, giving confidence in the accuracy of the estimated growth parameters. Subsequently, the focus shifted to obtaining the most suitable pharmacodynamic (PD) parameters to accurately encapsulate the drug\u27s antibacterial effects. This necessitated the use of a mathematical model. A widely employed model for this purpose is the Sigmoidal Emax model—an empirical model that is widely published. This model emerged as a valuable tool for formalizing the interpretation of experimental data and understanding the influence of altering penicillin G concentrations, both individually and in conjunction with ceftriaxone. Leveraging the data analysis capacity of RStudio, nonlinear least squares regression analysis was used to intricately fit the sigmoidal Emax equation to the observed data. This led to obtaining vital parameters, including Emax (maximum effect), EC50 (half-maximal effective concentration), and the sigmoidicity factor. Subsequent evaluation of goodness of fit based visual predictive checks and low standard errors in estimated parameters confirmed the favorable alignment between the predicted model and observed data. Physiologically based pharmacokinetic (PBPK) modeling and simulation stands as a well-established approach that bridges insights from preclinical studies to clinical outcomes. By combining drug-specific information with a comprehensive understanding of physiological and biological processes at the organism level, PBPK models mechanistically depict the behavior of drugs within biological systems. This enables the a priori simulation of drug concentration-time profiles. What distinguishes PBPK modeling is its unique capability to account for physiological variations within specific populations, offering predictive insights into pharmacokinetics tailored to those groups. This thesis project ventured into two vital applications of PBPK models: extrapolating novel clinical scenarios and exploring pharmacokinetics in special populations, particularly the geriatric demographic. With the aim of comprehending the pharmacokinetics of penicillin G and ceftriaxone, the project leveraged the Simcyp® Simulator, a modeling and simulation tool that is widely used in drug development. This platform pools the anatomical, physiological, drug-related, and trial design parameters to generate plasma drug concentration profiles. The simulated concentrations were compared against published data, with the fold error—a ratio of simulated to observed values—serving as a benchmark for model accuracy. Typically, predictions within a fold error range of 0.5 to 2 are deemed acceptable. Upon verification within the healthy population, the models were extended to geriatric subjects utilizing the Simcyp® population library. The same fold error criteria were applied, and the models adeptly predicted concentrations across both young and elderly populations. Remarkable differences in pharmacokinetics were seen in the geriatric cohort compared to a young adult population. Notably, for penicillin G, the AUC increased by 46% in the elderly due to an almost 47% decline in total clearance, stemming from a 49% reduction in glomerular filtration rate (GFR). Further expanding the PBPK model for penicillin G, the inclusion of a pharmacodynamic (PD) component led to the final goal of this project. Lua scripting in Simcyp® was utilized to build the PD model. This model used an equation that combined the bacterial growth model with the drug\u27s inhibitory effect via the Emax model. The impacts of monotherapy and combination were explored through the modulation of PD parameters. Consequently, when co-administered with ceftriaxone, kill rates for penicillin G increased, and IC50 values decreased, indicative of ceftriaxone\u27s augmentative effect. The free (unbound) plasma concentration-time profile from the developed PBPK model was linked as input to the PD model, facilitating testing and simulation of diverse penicillin G dosing regimens. Notably, penicillin G, a time-dependent beta-lactam antibiotic, exhibited a strong correlation with the PK/PD index %fT\u3eMIC (% of the dosing interval with a free concentration above MIC). This was especially pertinent for high-susceptibility strains, wherein continuous infusion of penicillin G led to the most significant reduction in bacterial density, irrespective of combination therapy or monotherapy. However, for low-susceptibility strains, the scenario differed, revealing that reliance on a single PK/PD index is not all-encompassing. For the geriatric population, the PBPK-PD model aligned with literature-backed dosing modifications for penicillin G. For highly susceptible strains, increasing the dosing interval or reducing the dose resulted in comparable reductions in bacterial density. Conversely, in low7 susceptibility strains, even an increase in AUC within the geriatric demographic failed to eradicate the bacteria. In summary, this comprehensive thesis journey navigates through the in-vitro bacterial studies and pharmacokinetic-pharmacodynamic (PKPD) modeling and simulation. This project sheds light on the ability to integrate in-vitro data with PBPK models which not only predict untested scenarios but also help dosing strategies. Overall, by addressing the clinical challenge of E. faecalis infections, the project showcased the extension of double beta-lactam therapy to penicillin G and ceftriaxone combination through a stepwise development of semi-mechanistic PK/PD model

Hydrodynamic Assessment of a Porcine Small Intestinal Sub-Mucosa Bioscaffold Valve for Pediatric Mitral Valve Replacement

Valve replacement for critical heart valve diseases is in many cases not an option. Our clinical experience in pediatric compassionate care has shown robust function of porcine small intestinal submucosa (PSIS) valves. We assessed functional effectiveness of 4ply (~320µm) and 2ply (~166µm) PSIS mitral valves under pediatric-relevant hemodynamic pulsatile conditions. Key conclusions: (i)PSIS valves demonstrated statistically similar acute functionality in comparison to a commercially available valve. (ii)Energy losses were similar (p\u3e0.05) under pediatric conditions which was not the case under adult aortic conditions. (iii)2ply valves were observed to be superior to 4ply, based on the robust hydrodynamic data, the mechanical properties suitable for pediatric applications and de-novo tissue replacement potential with less demand on the body. Demonstrating somatic growth, valve tissue filling matching PSIS degradation and PSIS-valve fatigue assessment are critical endeavors that need to be carried out to ensure mid to long term function of these bioscaffold mitral valves

Enhanced pre-clinical assessment of total knee replacement using computational modelling with experimental corroboration & probabilistic applications

Demand for Total Knee Replacement (TKR) surgery is high and rising; not just in numbers of procedures, but in the diversity of patient demographics and increase of expectations. Accordingly, greater efforts are being invested into the pre-clinical analysis of TKR designs, to improve their performance in-vivo. A wide range of experimental and computational methods are used to analyse TKR performance pre-clinically. However, direct validation of these methods and models is invariably limited by the restrictions and challenges of clinical assessment, and confounded by the high variability of results seen in-vivo.Consequently, the need exists to achieve greater synergy between different pre-clinical analysis methods. By demonstrating robust corroboration between in-silico and in-vitro testing, and both identifying & quantifying the key sources of uncertainty, greater confidence can be placed in these assessment tools. This thesis charts the development of a new generation of fast computational models for TKR test platforms, with closer collaboration with in-vitro test experts (and consequently more rigorous corroboration with experimental methods) than previously.Beginning with basic tibiofemoral simulations, the complexity of the models was progressively increased, to include in-silico wear prediction, patellofemoral & full lower limb models, rig controller-emulation, and accurate system dynamics. At each stage, the models were compared extensively with data from the literature and experimental tests results generated specifically for corroboration purposes.It is demonstrated that when used in conjunction with, and complementary to, the corresponding experimental work, these higher-integrity in-silico platforms can greatly enrich the range and quality of pre-clinical data available for decision-making in the design process, as well as understanding of the experimental platform dynamics. Further, these models are employed within a probabilistic framework to provide a statistically-quantified assessment of the input factors most influential to variability in the mechanical outcomes of TKR testing. This gives designers a much richer holistic visibility of the true system behaviour than extant 'deterministic' simulation approaches (both computational and experimental).By demonstrating the value of better corroboration and the benefit of stochastic approaches, the methods used here lay the groundwork for future advances in pre-clinical assessment of TKR. These fast, inexpensive models can complement existing approaches, and augment the information available for making better design decisions prior to clinical trials, accelerating the design process, and ultimately leading to improved TKR delivery in-vivo to meet future demands

Physiologically attentive user interface for improved robot teleoperation

User interfaces (UI) are shifting from being attention-hungry to being attentive to users’ needs upon interaction. Interfaces developed for robot teleoperation can be particularly complex, often displaying large amounts of information, which can increase the cognitive overload that prejudices the performance of the operator. This paper presents the development of a Physiologically Attentive User Interface (PAUI) prototype preliminary evaluated with six participants. A case study on Urban Search and Rescue (USAR) operations that teleoperate a robot was used although the proposed approach aims to be generic. The robot considered provides an overly complex Graphical User Interface (GUI) which does not allow access to its source code. This represents a recurring and challenging scenario when robots are still in use, but technical updates are no longer offered that usually mean their abandon. A major contribution of the approach is the possibility of recycling old systems while improving the UI made available to end users and considering as input their physiological data. The proposed PAUI analyses physiological data, facial expressions, and eye movements to classify three mental states (rest, workload, and stress). An Attentive User Interface (AUI) is then assembled by recycling a pre-existing GUI, which is dynamically modified according to the predicted mental state to improve the user's focus during mentally demanding situations. In addition to the novelty of the proposed PAUIs that take advantage of pre-existing GUIs, this work also contributes with the design of a user experiment comprising mental state induction tasks that successfully trigger high and low cognitive overload states. Results from the preliminary user evaluation revealed a tendency for improvement in the usefulness and ease of usage of the PAUI, although without statistical significance, due to the reduced number of subjects.info:eu-repo/semantics/acceptedVersio

Development of an In-Vitro Passive and Active Motion Simulator for the Investigation of Shoulder Function and Kinematics

Injuries and degenerative diseases of the shoulder are common and may relate to the joint’s complex biomechanics, which rely primarily on soft tissues to achieve stability. Despite the prevalence of these disorders, there is little information about their effects on the biomechanics of the shoulder, and a lack of evidence with which to guide clinical practice. Insight into these disorders and their treatments can be gained through in-vitro biomechanical experiments where the achieved physiologic accuracy and repeatability directly influence their efficacy and impact. This work’s rationale was that developing a simulator with greater physiologic accuracy and testing capabilities would improve the quantification of biomechanical parameters. This dissertation describes the development and validation of a simulator capable of performing passive assessments, which use experimenter manipulation, and active assessments – produced through muscle loading. Respectively, these allow the assessment of functional parameters such as stability, and kinematic/kinetic parameters including joint loading. The passive functionality enables specimen motion to be precisely controlled through independent manipulation of each rotational degree of freedom (DOF). Compared to unassisted manipulation, the system improved accuracy and repeatability of positioning the specimen (by 205% & 163%, respectively), decreased variation in DOF that are to remain constant (by 6.8°), and improved achievement of predefined endpoints (by 21%). Additionally, implementing a scapular rotation mechanism improved the physiologic accuracy of simulation. This enabled the clarification of the effect of secondary musculature on shoulder function, and the comparison of two competing clinical reconstructive procedures for shoulder instability. This was the first shoulder system to use real time kinematic feedback and PID control to produce active motion, which achieved unmatched accuracy ( These developments can be a powerful tool for increasing our understanding of the shoulder and also to provide information which can assist surgeons and improve patient outcomes