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.     

Computational modeling of the obstructive lung diseases asthma and COPD

Asthma and chronic obstructive pulmonary disease (COPD) are characterized by airway obstruction and airflow limitation and pose a huge burden to society. These obstructive lung diseases impact the lung physiology across multiple biological scales. Environmental stimuli are introduced via inhalation at the organ scale, and consequently impact upon the tissue, cellular and sub-cellular scale by triggering signaling pathways. These changes are propagated upwards to the organ level again and vice versa. In order to understand the pathophysiology behind these diseases we need to integrate and understand changes occurring across these scales and this is the driving force for multiscale computational modeling.There is an urgent need for improved diagnosis and assessment of obstructive lung diseases. Standard clinical measures are based on global function tests which ignore the highly heterogeneous regional changes that are characteristic of obstructive lung disease pathophysiology. Advances in scanning technology such as hyperpolarized gas MRI has led to new regional measurements of ventilation, perfusion and gas diffusion in the lungs, while new image processing techniques allow these measures to be combined with information from structural imaging such as Computed Tomography (CT). However, it is not yet known how to derive clinical measures for obstructive diseases from this wealth of new data. Computational modeling offers a powerful approach for investigating this relationship between imaging measurements and disease severity, and understanding the effects of different disease subtypes, which is key to developing improved diagnostic methods.Gaining an understanding of a system as complex as the respiratory system is difficult if not impossible via experimental methods alone. Computational models offer a complementary method to unravel the structure-function relationships occurring within a multiscale, multiphysics system such as this. Here we review the current state-of-the-art in techniques developed for pulmonary image analysis, development of structural models of the respiratory system and predictions of function within these models. We discuss application of modeling techniques to obstructive lung diseases, namely asthma and emphysema and the use of models to predict response to therapy. Finally we introduce a large European project, AirPROM that is developing multiscale models to investigate structure-function relationships in asthma and COPD.     

Computational physiology and the Physiome Project

Bioengineering analyses of physiological systems use the computational solution of physical conservation laws on anatomically detailed geometric models to understand the physiological function of intact organs in terms of the properties and behaviour of the cells and tissues within the organ. By linking behaviour in a quantitative, mathematically defined sense across multiple scales of biological organization--from proteins to cells, tissues, organs and organ systems--these methods have the potential to link patient-specific knowledge at the two ends of these spatial scales. A genetic profile linked to cardiac ion channel mutations, for example, can be interpreted in relation to body surface ECG measurements via a mathematical model of the heart and torso, which includes the spatial distribution of cardiac ion channels throughout the myocardium and the individual kinetics for each of the approximately 50 types of ion channel, exchanger or pump known to be present in the heart. Similarly, linking molecular defects such as mutations of chloride ion channels in lung epithelial cells to the integrated function of the intact lung requires models that include the detailed anatomy of the lungs, the physics of air flow, blood flow and gas exchange, together with the large deformation mechanics of breathing. Organizing this large body of knowledge into a coherent framework for modelling requires the development of ontologies, markup languages for encoding models, and web-accessible distributed databases. In this article we review the state of the field at all the relevant levels, and the tools that are being developed to tackle such complexity. Integrative physiology is central to the interpretation of genomic and proteomic data, and is becoming a highly quantitative, computer-intensive discipline.     

Merging systems biology with pharmacodynamics

The emerging discipline of systems pharmacology aims to combine analysis and computational modeling of cellular regulatory networks with quantitative pharmacology approaches to drive the drug discovery processes, predict rare adverse events, and catalyze the practice of personalized precision medicine. Here, we introduce the concept of enhanced pharmacodynamic (ePD) models, which synergistically combine the desirable features of systems biology and current PD models within the framework of ordinary or partial differential equations. ePD models that analyze regulatory networks involved in drug action can account for a drug's multiple targets and for the effects of genomic, epigenomic, and posttranslational changes on the drug efficacy. This new knowledge can drive drug discovery and shape precision medicine.     

The IUPS human Physiome Project

The Physiome Project of the International Union of Physiological Sciences (IUPS) is attempting to provide a comprehensive framework for modelling the human body using computational methods which can incorporate the biochemistry, biophysics and anatomy of cells, tissues and organs. A major goal of the project is to use computational modelling to analyse integrative biological function in terms of underlying structure and molecular mechanisms. To support that goal the project is establishing web-accessible physiological databases dealing with model-related data, including bibliographic information, at the cell, tissue, organ and organ system levels. Here we discuss the background and goals of the project, the problems of modelling across multiple spatial and temporal scales, and the development of model ontologies and markup languages at all levels of biological function.     

Connectionist modeling of higher-level cognitive processes

Connectionist modeling is one approach to understanding human intelligence using simulated networks of neuron-like processing units. In this article, we report on recent progress in connectionist models that simulate empirical data of higher-level cognitive processes, these being memory, learning, language, thinking, cognitive development, and social cognition. We also review and summarize the advantages and disadvantages of these connectionist models. The computational framework of connectionist modeling has the potential to integrate specialized psychological findings of different areas using the same architectures and local functions of units and connections, inspired from neuroscience. In particular, the problems of dealing with structured information in distributed form, and doing tasks that require variable binding in connectionist networks are discussed from several different perspectives. As one possible solution to treat systematic mental representations properly, the symbolic connectionist model, which is a hybrid approach using symbolic representations and connectionist architectures, is explained. We argue that connectionist computer simulation offers significant benefits for today's psychological researches, and that connectionist modeling is likely to have an important influence on future studies.     

Model-based approaches to biomarker discovery and evaluation: a multidisciplinary integrated review

The most common use of any mathematical or statistical model in physiology, pathophysiology, or therapy evaluation is to organize all relevant components characterizing the system behavior into a rigorously testable framework. This approach is widely applied both to the study of complex homeostatic paradigms involving endogenous substances and to the evaluation of the kinetics and dynamics of xenobiotics such as toxicants and drugs. In either case, one seeks a quantitative framework of the system that is consistent with known physiology and pharmacology and is "compatible" (in some meaningful sense) with all available data. The models are then evaluated and subjected to identifiability and validity tests and can then be used to estimate unknown parameters of interest, to make predictions about system behavior, to simulate previously unobserved behavior in response to a putative perturbation, and to aid in further experimental design. In a broader context, however, the focus and ultimate goal of this set of methodologies (whether this is explicitly stated or not) lies in understanding the mechanisms of physiology and pathophysiology and measuring the effect of therapeutic interventions through the accurate quantification of biomarkers of interest. In this review, we attempt to bring together under this comprehensive framework more than four decades of investigation on modeling and simulation in the life sciences (in particular, we will concentrate on the areas of pharmacology, physiology, and bioengineering). We demonstrate that such modeling approaches, when appropriately designed and evaluated, have significant potential and can be used to understand the multiple factors of disease progression and response to therapeutic interventions, the most likely causes of variability in population and individual responses to therapy, and the most appropriate timing of treatment administration. Lastly, they may allow estimation and prediction of significant outcomes in feasibility and clinical studies.     

How good is the evidence that cellular senescence causes skin ageing?

Skin is the largest organ of the body with important protective functions, which become compromised with time due to both intrinsic and extrinsic ageing processes. Cellular senescence is the primary ageing process at cell level, associated with loss of proliferative capacity, mitochondrial dysfunction and significantly altered patterns of expression and secretion of bioactive molecules. Intervention experiments have proven cell senescence as a relevant cause of ageing in many organs. In case of skin, accumulation of senescence in all major compartments with ageing is well documented and might be responsible for most, if not all, the molecular changes observed during ageing. Incorporation of senescent cells into in-vitro skin models (specifically 3D full thickness models) recapitulates changes typically associated with skin ageing. However, crucial evidence is still missing. A beneficial effect of senescent cell ablation on skin ageing has so far only been shown following rather unspecific interventions or in transgenic mouse models. We conclude that evidence for cellular senescence as a relevant cause of intrinsic skin ageing is highly suggestive but not yet completely conclusive.     

Renal oxygenation: From data to insight

Computational models have made a major contribution to the field of physiology. As the complexity of our understanding of biological systems expands, the need for computational methods only increases. But collaboration between experimental physiologists and computational modellers (ie theoretical physiologists) is not easy. One of the major challenges is to break down the barriers created by differences in vocabulary and approach between the two disciplines. In this review, we have two major aims. Firstly, we wish to contribute to the effort to break down these barriers and so encourage more interdisciplinary collaboration. So, we begin with a “primer” on the ways in which computational models can help us understand physiology and pathophysiology. Second, we aim to provide an update of recent efforts in one specific area of physiology, renal oxygenation. This work is shedding new light on the causes and consequences of renal hypoxia. But as importantly, computational modelling is providing direction for experimental physiologists working in the field of renal oxygenation by: (a) generating new hypotheses that can be tested in experimental studies, (b) allowing experiments that are technically unfeasible to be simulated in silico, or variables that cannot be measured experimentally to be estimated, and (c) providing a means by which the quality of experimental data can be assessed. Critically, based on our experience, we strongly believe that experimental and theoretical physiology should not be seen as separate exercises. Rather, they should be integrated to permit an iterative process between modelling and experimentation.     

Animal and human models to understand ageing

Human ageing is the gradual decline in organ and tissue function with increasing chronological time, leading eventually to loss of function and death. To study the processes involved over research-relevant timescales requires the use of accessible model systems that share significant similarities with humans. In this review, we assess the usefulness of various models, including unicellular yeasts, invertebrate worms and flies, mice and primates including humans, and highlight the benefits and possible drawbacks of each model system in its ability to illuminate human ageing mechanisms. We describe the strong evolutionary conservation of molecular pathways that govern cell responses to extracellular and intracellular signals and which are strongly implicated in ageing. Such pathways centre around insulin-like growth factor signalling and integration of stress and nutritional signals through mTOR kinase. The process of cellular senescence is evaluated as a possible underlying cause for many of the frailties and diseases of human ageing. Also considered is ageing arising from systemic changes that cannot be modelled in lower organisms and instead require studies either in small mammals or in primates. We also touch briefly on novel therapeutic options arising from a better understanding of the biology of ageing.