Multi-scale modelling and the IUPS physiome project

IP3R activity increases propensity of RyR-mediated sparks by elevating dyadic [Ca2＋]

Calcium (Ca^2＋) plays a critical role in the excitation contraction coupling (ECC) process that mediates the contraction of cardiomyocytes during each heartbeat. While ryanodine receptors (RyRs) are the primary Ca^2＋ channels responsible for generating the cell-wide Ca^2＋ transients during ECC, Ca^2＋ release, via inositol 1,4,5-trisphosphate (IP₃) receptors (IP₃Rs) are also reported in cardiomyocytes to elicit ECC-modulating effects. Recent studies suggest that the localization of IP₃Rs at dyads grant their ability to modify the occurrence of Ca^2＋ sparks (elementary Ca^2＋ release events that constitute cell wide Ca^2＋ releases associated with ECC) which may underlie their modulatory influence on ECC. Here, we aim to uncover the mechanism by which dyad-localized IP₃Rs influence Ca^2＋ spark dynamics. To this end, we developed a mathematical model of the dyad that incorporates the behaviour of IP₃Rs, in addition to RyRs, to reveal the impact of their activity on local Ca^2＋ handling and consequent Ca^2＋ spark occurrence and its properties. Consistent with published experimental data, our model predicts that the propensity for Ca^2＋ spark formation increases in the presence of IP₃R activity. Our simulations support the hypothesis that IP₃Rs elevate Ca^2＋ in the dyad, sensitizing proximal RyRs towards activation and hence Ca^2＋ spark formation. The stochasticity of IP₃R gating is an important aspect of this mechanism. However, dyadic IP₃R activity lowers the Ca^2＋ available in the junctional sarcoplasmic reticulum (JSR) for release, thus resulting in Ca^2＋ sparks with similar durations but lower amplitudes.

Hierarchical semantic composition of biosimulation models using bond graphs

Simulating complex biological and physiological systems and predicting their behaviours under different conditions remains challenging. Breaking systems into smaller and more manageable modules can address this challenge, assisting both model development and simulation. Nevertheless, existing computational models in biology and physiology are often not modular and therefore difficult to assemble into larger models. Even when this is possible, the resulting model may not be useful due to inconsistencies either with the laws of physics or the physiological behaviour of the system. Here, we propose a general methodology for composing models, combining the energy-based bond graph approach with semantics-based annotations. This approach improves model composition and ensures that a composite model is physically plausible. As an example, we demonstrate this approach to automated model composition using a model of human arterial circulation. The major benefit is that modellers can spend more time on understanding the behaviour of complex biological and physiological systems and less time wrangling with model composition.

Constraint Based Modeling Going Multicellular

Constraint based modeling has seen applications in many microorganisms. For example, there are now established methods to determine potential genetic modifications and external interventions to increase the efficiency of microbial strains in chemical production pipelines. In addition, multiple models of multicellular organisms have been created including plants and humans. While initially the focus here was on modeling individual cell types of the multicellular organism, this focus recently started to switch. Models of microbial communities, as well as multi-tissue models of higher organisms have been constructed. These models thereby can include different parts of a plant, like root, stem, or different tissue types in the same organ. Such models can elucidate details of the interplay between symbiotic organisms, as well as the concerted efforts of multiple tissues and can be applied to analyse the effects of drugs or mutations on a more systemic level. In this review we give an overview of the recent development of multi-tissue models using constraint based techniques and the methods employed when investigating these models. We further highlight advances in combining constraint based models with dynamic and regulatory information and give an overview of these types of hybrid or multi-level approaches.

Applications of computational models to better understand microvascular remodelling: a focus on biomechanical integration across scales

Microvascular network remodelling is a common denominator for multiple pathologies and involves both angiogenesis, defined as the sprouting of new capillaries, and network patterning associated with the organization and connectivity of existing vessels. Much of what we know about microvascular remodelling at the network, cellular and molecular scales has been derived from reductionist biological experiments, yet what happens when the experiments provide incomplete (or only qualitative) information? This review will emphasize the value of applying computational approaches to advance our understanding of the underlying mechanisms and effects of microvascular remodelling. Examples of individual computational models applied to each of the scales will highlight the potential of answering specific questions that cannot be answered using typical biological experimentation alone. Looking into the future, we will also identify the needs and challenges associated with integrating computational models across scales.

Integrating knowledge representation and quantitative modelling in physiology

A wealth of potentially shareable resources, such as data and models, is being generated through the study of physiology by computational means. Although in principle the resources generated are reusable, in practice, few can currently be shared. A key reason for this disparity stems from the lack of consistent cataloguing and annotation of these resources in a standardised manner. Here, we outline our vision for applying community-based modelling standards in support of an automated integration of models across physiological systems and scales. Two key initiatives, the Physiome Project and the European contribution - the Virtual Phsysiological Human Project, have emerged to support this multiscale model integration, and we focus on the role played by two key components of these frameworks, model encoding and semantic metadata annotation. We present examples of biomedical modelling scenarios (the endocrine effect of atrial natriuretic peptide, and the implications of alcohol and glucose toxicity) to illustrate the role that encoding standards and knowledge representation approaches, such as ontologies, could play in the management, searching and visualisation of physiology models, and thus in providing a rational basis for healthcare decisions and contributing towards realising the goal of of personalized medicine.

Biophysically based modeling of the interstitial cells of cajal: current status and future perspectives

Gastrointestinal motility research is progressing rapidly, leading to significant advances in the last 15 years in understanding the cellular mechanisms underlying motility, following the discovery of the central role played by the interstitial cells of Cajal (ICC). As experimental knowledge of ICC physiology has expanded, biophysically based modeling has become a valuable tool for integrating experimental data, for testing hypotheses on ICC pacemaker mechanisms, and for applications in in silico studies including in multiscale models. This review is focused on the cellular electrophysiology of ICC. Recent evidence from both experimental and modeling domains have called aspects of the existing pacemaker theories into question. Therefore, current experimental knowledge of ICC pacemaker mechanisms is examined in depth, and current theories of ICC pacemaking are evaluated and further developed. Existing biophysically based ICC models and their physiological foundations are then critiqued in light of the recent advances in experimental knowledge, and opportunities to improve these models are identified. The review concludes by examining several potential clinical applications of biophysically based ICC modeling from the subcellular through to the organ level, including ion channelopathies and ICC network degradation.

Left ventricular wall stress compendium

Left ventricular (LV) wall stress has intrigued scientists and cardiologists since the time of Lame and Laplace in 1800s. The left ventricle is an intriguing organ structure, whose intrinsic design enables it to fill and contract. The development of wall stress is intriguing to cardiologists and biomedical engineers. The role of left ventricle wall stress in cardiac perfusion and pumping as well as in cardiac pathophysiology is a relatively unexplored phenomenon. But even for us to assess this role, we first need accurate determination of in vivo wall stress. However, at this point, 150 years after Lame estimated left ventricle wall stress using the elasticity theory, we are still in the exploratory stage of (i) developing left ventricle models that properly represent left ventricle anatomy and physiology and (ii) obtaining data on left ventricle dynamics. In this paper, we are responding to the need for a comprehensive survey of left ventricle wall stress models, their mechanics, stress computation and results. We have provided herein a compendium of major type of wall stress models: thin-wall models based on the Laplace law, thick-wall shell models, elasticity theory model, thick-wall large deformation models and finite element models. We have compared the mean stress values of these models as well as the variation of stress across the wall. All of the thin-wall and thick-wall shell models are based on idealised ellipsoidal and spherical geometries. However, the elasticity model's shape can vary through the cycle, to simulate the more ellipsoidal shape of the left ventricle in the systolic phase. The finite element models have more representative geometries, but are generally based on animal data, which limits their medical relevance. This paper can enable readers to obtain a comprehensive perspective of left ventricle wall stress models, of how to employ them to determine wall stresses, and be cognizant of the assumptions involved in the use of specific models.

Toward a systems biology framework for understanding aging and health span

It is argued that aging research is at a stage where it could benefit greatly from a more intense engagement with the perspectives emphasized by systems biology and complexity science. A more integrated, systematic approach is needed if we are ever to have a fully developed, fundamental understanding of aging, longevity, and their relationship to health. A broader, deeper, more quantitative, and predictive conceptual framework can lead to theoretical approaches and realistic models that can be quantitatively confronted with data and, perhaps more importantly, stimulate novel questions and novel experiments. Integral to this is the search for underlying causal multilevel mechanisms and principles that can be quantified and developed into a serious predictive theoretical framework, providing a point of departure for framing a more integrated research agenda.

Computational modeling for bedside application

Advances in computer power, novel diagnostic and therapeutic medical technologies, and an increasing knowledge of pathophysiology from gene to organ systems make it increasingly feasible to apply multiscale patient-specific modeling based on proven disease mechanisms. Such models may guide and predict the response to therapy in many areas of medicine. This is an exciting and relatively new approach, for which efficient methods and computational tools are of the utmost importance. Investigators have designed patient-specific models in almost all areas of human physiology. Not only will these models be useful in clinical settings to predict and optimize the outcome from surgery and non-interventional therapy, but they will also provide pathophysiologic insights from the cellular level to the organ system level. Models, therefore, will provide insight as to why specific interventions succeed or fail.

Engineered cardiac organoid chambers: toward a functional biological model ventricle

A growing area in the field of tissue engineering is the development of tissue equivalents as model systems for in vitro experimentation and high-throughput screening applications. Although a variety of strategies have been developed to enhance the structure and function of engineered cardiac tissues, an inherent limitation with traditional myocardial patches is that they do not permit evaluation of the fundamental relationships between pressure and volume that characterize global contractile function of the heart. Therefore, in the following study we introduce fully biological, living engineered cardiac organoids, or simplified heart chambers, that beat spontaneously, develop pressure, eject fluid, contain residual stress, exhibit a functional Frank-Starling mechanism, and generate positive stroke work. We also demonstrate regional variations in pump function following local cryoinjury, yielding a novel engineered tissue model of myocardial infarction. With the unique ability to directly evaluate relevant pressure-volume characteristics and regulate wall stress, this organoid chamber culture system provides a flexible platform for developing a controllable biomimetic cardiac niche environment that can be adapted for a variety of high-throughput and long-term investigations of cardiac pump function.

In Silico Modeling in Infectious Disease

Infectious disease has witnessed the emergence of mathematical modeling a tool of synthesizing data of growing complexity now available to clinicians and basic scientists alike. The purpose of this review is to introduce mathematical tools commonly used to model infectious disease. We will illustrate the use of equation-based, agent-based or statistical modeling approaches to a variety of examples pertaining to acute inflammation, bacterial dynamics, viral dynamics, and signaling pathways, focusing of host-pathogen interactions rather than population models. We will discuss the strengths and weaknesses of these approaches and offer future perspectives for this rapidly evolving field.

Trevor Trust – AstraZeneca R&D Boston, 35 Gatehouse Drive, Waltham, MA 02451, USA

Systems approaches to the networks of aging

The aging of an organism is the result of complex changes in structure and function of molecules, cells, tissues, and whole body systems. To increase our understanding of how aging works, we have to analyze and integrate quantitative evidence from multiple levels of biological organization. Here, we define a broader conceptual framework for a quantitative, computational systems biology approach to aging. Initially, we consider fractal supply networks that give rise to scaling laws relating body mass, metabolism and lifespan. This approach provides a top-down view of constrained cellular processes. Concomitantly, multi-omics data generation build such a framework from the bottom-up, using modeling strategies to identify key pathways and their physiological capacity. Multiscale spatio-temporal representations finally connect molecular processes with structural organization. As aging manifests on a systems level, it emerges as a highly networked process regulated through feedback loops between levels of biological organization.

A strategy for integrative computational physiology

Organ function (the heart beat for example) can only be understood through knowledge of molecular and cellular processes within the constraints of structure-function relations at the tissue level. A quantitative modeling framework that can deal with these multiscale issues is described here under the banner of the International Union of Physiological Sciences Physiome Project.