Multiscale Modelling

In diverse cellular pathways including clathrin-mediated endocytosis (CME) and viral bud formation, cytosolic proteins must self-assemble and induce membrane deformation. To understand the mechanisms whereby assembly is triggered and how perturbations can lead to dysfunction requires dynamics of not just assembly components, but their coupling to active, force-responsive, and ATP-consuming structures in cells. Current computational tools for studying self-assembly dynamics are not feasible for simulating cellular dynamics due to the slow time-scales and the dependence on energy-consuming events such as phosphorylation. We recently developed novel reaction-diffusion algorithms and software that enable detailed computer simulations of nonequilibrium self-assembly over long time-scales [1]. Our simulations of clathrin-coat assembly in CME reveal how the formation of structured lattices impacts the kinetics of assembly, and how localization to the membrane can stabilize large, dynamic assemblies not observed in solution. We developed a relatively simple equilibrium theory to quantify how localization of protein binding partners to the membrane can dramatically enhance binding through dimensionality reduction, providing a trigger for assembly [2]. Tuning the speed and success of vesicle formation can be sensitively controlled by the stoichiometry of assembly components, particularly those that control membrane localization through lipid binding [3]. Our results suggest that stoichiometric balance and membrane localization can act as potent regulators of self-assembly, and our reaction-diffusion software provides a powerful tool to characterize dynamics within the cell. Full Abstract

Clinical trials are expensive, and the risks posed to the participants are not fully known. Yet, participant numbers in clinical trials are often too small to conclude a statistically significant positive effect of the proposed intervention. A case in point are interventions targeting the reduction of hip fracture risk due to ageing in women. The socioeconomic relevance of this condition is well-known: for women over the age of 50, the remaining lifetime risk of suffering a hip fracture is equivalent to that of breast cancer; the cost of treating fragility fractures at the hip is over £2 billion annually in the UK. Yet, hip fracture incidence in the general population is very small (32 fractures per 10,000 person-years in British women over 50 [1]). This impedes reaching a statistically significant conclusion in a clinical trial with fracture as endpoint. In silico clinical trials (ISCTs) have been proposed as a computational tool to alleviate such challenges. Here, virtual patients are recruited in the trial increase the confidence in the study result. A virtual patient is a digitised data-set comprising biomedical information relevant to the disease/condition and treatment in question. An ISCT simulates a standard trial by subjecting virtual patients to untreated and treated conditions, where each condition is expressed by a mathematical model. Therefore, two ingredients are indispensable in any ISCT, irrespective of the intervention: a virtual patient definition and a mathematical model for the untreated condition. Full Abstract

Image-based subject-specific (SS) musculoskeletal models are currently considered the preferred solution in the estimation of biomechanical parameters due to their high accuracy and reliability compared to scaled generic models (GS) (Lenaerts et al., 2009). The high accuracy and reliability however come at the extra burden of obtaining medical images and time to build the models from these images (Valente et al 2014). The use of subject-specific models also becomes a challenge when analysis must be performed based on data for which imaging data is not available such as in a retrospective study and in most of standard clinical scenarios. Full Abstract

Despite significant progress in structure determination techniques such as X-ray crystallography/nuclear magnetic resonance (NMR), our ability to obtain atomistic insights into complex biological phenomena remains challenging. Understanding the relationship between conformational flexibility and function is an issue that experimental structural biology continues to struggle with and flexible regions in proteins are often absent from the structures obtained using X-ray crystallography and NMR techniques. Techniques such as neutron/Xray scattering and cryo-electron microscopy (cryo-EM) can potentially provide structural insight into flexible and heterogeneous biomolecular assemblies, but their inherent low resolution makes it difficult to elucidate atomistic/mechanistic details.
Although single particle cryo-EM is projected to displace traditional techniques for structure determination of large proteins and complexes (~150 kDa), flexible and heterogeneous biological assemblies present obvious challenges in approaching atomic resolution with this technique. On the other hand, small-angle scattering (SAS) with X-rays (SAXS) and neutrons (SANS) has been very informative for studying the structures of flexible systems such as multi-domain proteins and membrane proteins. When combined with computational modeling, it is possible to determine not only the ensemble of structures that reflect the conformational state of a protein but also the dynamical properties at relevant temporal scales of the system.Full Abstract

Increasingly many scientific questions, that need modelling solutions, target processes residing on multiple scale levels. This is especially true in the domain of biomedicine, where understanding a given disease, or the effects of a treatment might involve numerous components. For a single problem blood flow mechanics can be just as important as cellular trafficking or sub-cellular biochemical signalling. One such problem presents itself with the disease of brain aneurysms. These are focal dilatations on major brain arteries with a chance to burst. The outcome of a rupture event can be devastating for the patient. The treatment usually involves endovascular brain surgery and the placement of a blood flow diverter implant, with the intent to thrombose the dilatation and therefore to close it out from the active circulation. In the following, a multiscale, multicomponent model will be presented that aims to model aspects of the thrombus formation mechanism after the medical intervention. The sub-models are fully developed and operational, and the couplings are currently under development. The model structure is discussed from the viewpoint of inter-model communication and requirements for the execution environment for the model components. Full Abstract

Hypothermia is known to impact multiple physiological mechanisms that include neurologic and cardiovascular systems. Therapeutic hypothermia (TH), as a mild reduction of body core temperature, has become the standard cardioprotective treatment for several patient groups, including those affected by ischemia. Patients undergoing long term treatments such as dialysis experience global ischemia in addition to the presence of localized myocardial stunning [1], which together may promote persistent ventricular fibrillation. Fibrillation avoidance or reduction of initiation risk using non-pharmacological TH may be beneficial to critically ill patients.
Basic science experimental studies have shown that hypothermia prolongs cardiomyocyte action potential [2] and reduces cardiac conduction velocity. However, the clinical effectiveness of TH on arrythmia abrogation remain debated. In this study, a multi-scale computational cardiology approach was used to illuminate the effects of TH on cardiomyocytes and tissue. Full Abstract

Acute myocardial ischemia is a major cause of sudden cardiac death. Anti-arrhythmic treatments or side-effects associated with cancer therapies can produce cardiotoxic effects increasing the occurrence of adverse cardiac events especially in patients with coronary artery disease. In-vivo and in-vitro drug trials have associated complications regarding ethics and costs, whereas cardiotoxic evaluation in animal experiments is not necessarily translatable to humans. Full Abstract

Human sinoatrial node (SAN) structure-function relationships remain poorly understood, and may be drastically dierent from those in smaller mammals. Recent studies based on histology for structure and optical mapping for function (e.g. see [1]) suggest that the human SAN may be electrically insulated from atrial tissue by an insulating border, except at four discrete exit pathways (SEPs) that permit atrial excitation by the SAN. Experimental data suggests that the funny current density is three fold lower in the human SAN as compared to small animals. The lower density of this important pacemaking ion channel may lead to SAN electrical activity suppression by the physiological atrial load in the absence of substantial SAN electrical insulation. In addition to experimental evidence, a recent computer modelling study provided some insights into the human SAN electrical function [2]. However, previous studies used simplied Fenton-Karma ionic model to simulate SAN activity, while a biophysically and anatomically detailed modelling has yet to be used to investigate the role of SEPs and human SAN behavior. In this study, a multi-scale biophysically detailed model of the human SAN is presented. The model is being used to investigate the role of SEPs, as well as relevant clinical conditions that promote bradycardia and brady-tachycardia. Full Abstract

Cancer results from modifications to cellular decision-making processes. In normal cells, the protein-mediated signaling networks that control growth and movement are tightly regulated. However, mutations that disrupt or over-activate signaling proteins can drive uncontrolled cell growth resulting in cancer. RAS, a peripheral membrane signaling protein, is mutated in 30% of all cancers, especially those of the pancreas, colon and lung. These oncogenic mutations result in the loss of GTPase activity which in turn causes persistent engagement of effectors and enhanced or continuous growth signaling. Full Abstract