scholarly journals Modelling biological complexity: a physical scientist's perspective

2005 ◽  
Vol 2 (4) ◽  
pp. 267-280 ◽  
Author(s):  
Peter V Coveney ◽  
Philip W Fowler
Keyword(s):  
Systems Biology ◽  
Complex Systems ◽  
Time Scales ◽  
Rna World ◽  
Molecular Model ◽  
World View ◽  
Cellular Level ◽  
Coarse Grained ◽  
Coupled Models ◽  
Systems Research

We discuss the modern approaches of complexity and self-organization to understanding dynamical systems and how these concepts can inform current interest in systems biology. From the perspective of a physical scientist, it is especially interesting to examine how the differing weights given to philosophies of science in the physical and biological sciences impact the application of the study of complexity. We briefly describe how the dynamics of the heart and circadian rhythms, canonical examples of systems biology, are modelled by sets of nonlinear coupled differential equations, which have to be solved numerically. A major difficulty with this approach is that all the parameters within these equations are not usually known. Coupled models that include biomolecular detail could help solve this problem. Coupling models across large ranges of length- and time-scales is central to describing complex systems and therefore to biology. Such coupling may be performed in at least two different ways, which we refer to as hierarchical and hybrid multiscale modelling. While limited progress has been made in the former case, the latter is only beginning to be addressed systematically. These modelling methods are expected to bring numerous benefits to biology, for example, the properties of a system could be studied over a wider range of length- and time-scales, a key aim of systems biology. Multiscale models couple behaviour at the molecular biological level to that at the cellular level, thereby providing a route for calculating many unknown parameters as well as investigating the effects at, for example, the cellular level, of small changes at the biomolecular level, such as a genetic mutation or the presence of a drug. The modelling and simulation of biomolecular systems is itself very computationally intensive; we describe a recently developed hybrid continuum-molecular model, HybridMD, and its associated molecular insertion algorithm, which point the way towards the integration of molecular and more coarse-grained representations of matter. The scope of such integrative approaches to complex systems research is circumscribed by the computational resources available. Computational grids should provide a step jump in the scale of these resources; we describe the tools that RealityGrid, a major UK e-Science project, has developed together with our experience of deploying complex models on nascent grids. We also discuss the prospects for mathematical approaches to reducing the dimensionality of complex networks in the search for universal systems-level properties, illustrating our approach with a description of the origin of life according to the RNA world view.

scholarly journals Are We Doing ‘Systems’ Research? An Assessment of Methods for Climate Change Adaptation to Hydrohazards in a Complex World

2019 ◽  
Vol 11 (4) ◽  
pp. 1163 ◽  
Author(s):  
Melissa Bedinger ◽  
Lindsay Beevers ◽  
Lila Collet ◽  
Annie Visser
Keyword(s):  
Climate Change ◽  
Human Nature ◽  
Climate Change Adaptation ◽  
Time Scales ◽  
Spatial Scales ◽  
Local Context ◽  
Multiple Time Scales ◽  
Future Research ◽  
Multiple Time ◽  
Systems Research

Climate change is a product of the Anthropocene, and the human–nature system in which we live. Effective climate change adaptation requires that we acknowledge this complexity. Theoretical literature on sustainability transitions has highlighted this and called for deeper acknowledgment of systems complexity in our research practices. Are we heeding these calls for ‘systems’ research? We used hydrohazards (floods and droughts) as an example research area to explore this question. We first distilled existing challenges for complex human–nature systems into six central concepts: Uncertainty, multiple spatial scales, multiple time scales, multimethod approaches, human–nature dimensions, and interactions. We then performed a systematic assessment of 737 articles to examine patterns in what methods are used and how these cover the complexity concepts. In general, results showed that many papers do not reference any of the complexity concepts, and no existing approach addresses all six. We used the detailed results to guide advancement from theoretical calls for action to specific next steps. Future research priorities include the development of methods for consideration of multiple hazards; for the study of interactions, particularly in linking the short- to medium-term time scales; to reduce data-intensivity; and to better integrate bottom–up and top–down approaches in a way that connects local context with higher-level decision-making. Overall this paper serves to build a shared conceptualisation of human–nature system complexity, map current practice, and navigate a complexity-smart trajectory for future research.

scholarly journals FOLDING ELASTIC TRANSMEMBRANE HELICES TO FIT IN A LOW-RESOLUTION IMAGE BY ELECTRON MICROSCOPY

2011 ◽  
Vol 09 (supp01) ◽  
pp. 37-50 ◽  
Author(s):  
YUTAKA UENO ◽  
KAZUNORI KAWASAKI ◽  
OSAMU SAITO ◽  
MASAFUMI ARAI ◽  
MAKIKO SUWA
Keyword(s):  
Membrane Proteins ◽  
Structure Prediction ◽  
Molecular Model ◽  
Imaging Techniques ◽  
Transmembrane Helix ◽  
Prediction Method ◽  
Molecular Shape ◽  
Coarse Grained ◽  
Transmembrane Helices ◽  
Low Resolution

Structure prediction of membrane proteins could be constrained and thereby improved by introducing data of the observed molecular shape. We studied a coarse-grained molecular model that relied on residue-based dummy atoms to fold the transmembrane helices of a protein in the observed molecular shape. Based on the inter-residue potential, the α-helices were folded to contact each other in a simulated annealing protocol to search optimized conformation. Fitting the model into a three-dimensional volume was tested for proteins with known structures and resulted in a fairly reasonable arrangement of helices. In addition, the constraint to the packing transmembrane helix with the two-dimensional region was tested and found to work as a very similar folding guide. The obtained models nicely represented α-helices with the desired slight bend. Our structure prediction method for membrane proteins well demonstrated reasonable folding results using a low-resolution structural constraint introduced from recent cell-surface imaging techniques.

scholarly journals PNA Predictability at Various Time Scales

2013 ◽  
Vol 26 (22) ◽  
pp. 9090-9114 ◽  
Author(s):  
Waqar Younas ◽  
Youmin Tang
Keyword(s):  
Time Scales ◽  
Initial Conditions ◽  
Prediction Skill ◽  
Multiple Model ◽  
Coupled Models ◽  
Potential Predictability ◽  
Effective Measure ◽  
Dispersion Component ◽  
Significant Difference ◽  
Better Than

Abstract In this study, the predictability of the Pacific–North American (PNA) pattern is evaluated on time scales from days to months using state-of-the-art dynamical multiple-model ensembles including the Canadian Historical Forecast Project (HFP2) ensemble, the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) ensemble, and the Ensemble-Based Predictions of Climate Changes and their Impacts (ENSEMBLES). Some interesting findings in this study include (i) multiple-model ensemble (MME) skill was better than most of the individual models; (ii) both actual prediction skill and potential predictability increased as the averaging time scale increased from days to months; (iii) there is no significant difference in actual skill between coupled and uncoupled models, in contrast with the potential predictability where coupled models performed better than uncoupled models; (iv) relative entropy (REA) is an effective measure in characterizing the potential predictability of individual prediction, whereas the mutual information (MI) is a reliable indicator of overall prediction skill; and (v) compared with conventional potential predictability measures of the signal-to-noise ratio, the MI-based measures characterized more potential predictability when the ensemble spread varied over initial conditions. Further analysis found that the signal component dominated the dispersion component in REA for PNA potential predictability from days to seasons. Also, the PNA predictability is highly related to the signal of the tropical sea surface temperature (SST), and SST–PNA correlation patterns resemble the typical ENSO structure, suggesting that ENSO is the main source of PNA seasonal predictability. The predictable component analysis (PrCA) of atmospheric variability further confirmed the above conclusion; that is, PNA is one of the most predictable patterns in the climate variability over the Northern Hemisphere, which originates mainly from the ENSO forcing.

scholarly journals An individual-based mechanical model of cell movement in heterogeneous tissues and its coarse-grained approximation

2018 ◽  
Author(s):  
R. J. Murphy ◽  
P. R. Buenzli ◽  
R. E. Baker ◽  
M. J. Simpson
Keyword(s):  
Mechanical Model ◽  
Numerical Solutions ◽  
Cell Movement ◽  
Biological Tissues ◽  
Tissue Level ◽  
Cellular Level ◽  
Coarse Grained ◽  
Mechanical Heterogeneity ◽  
Single Scale ◽  
The Individual

AbstractMechanical heterogeneity in biological tissues, in particular stiffness, can be used to distinguish between healthy and diseased states. However, it is often difficult to explore relationships between cellular-level properties and tissue-level outcomes when biological experiments are performed at a single scale only. To overcome this difficulty we develop a multi-scale mathematical model which provides a clear framework to explore these connections across biological scales. Starting with an individual-based mechanical model of cell movement, we subsequently derive a novel coarse-grained system of partial differential equations governing the evolution of the cell density due to heterogeneous cellular properties. We demonstrate that solutions of the individual-based model converge to numerical solutions of the coarse-grained model, for both slowly-varying-in-space and rapidly-varying-in-space cellular properties. Applications of the model are discussed, including determining relative cellular-level properties and an interpretation of data from a breast cancer detection experiment.

scholarly journals Controlling the length of porphyrin supramolecular polymers via coupled equilibria and dilution-induced supramolecular polymerization

2022 ◽  
Vol 13 (1) ◽  
Author(s):  
Elisabeth Weyandt ◽  
Luigi Leanza ◽  
Riccardo Capelli ◽  
Giovanni M. Pavan ◽  
Ghislaine Vantomme ◽  
...  
Keyword(s):  
Complex Systems ◽  
Self Assembly ◽  
Chiral Discrimination ◽  
Coarse Grained ◽  
Supramolecular Polymers ◽  
Supramolecular System ◽  
Precise Control ◽  
Aggregate Morphology ◽  
The Individual ◽  
Supramolecular Polymerization

AbstractMulti-component systems often display convoluted behavior, pathway complexity and coupled equilibria. In recent years, several ways to control complex systems by manipulating the subtle balances of interaction energies between the individual components have been explored and thereby shifting the equilibrium between different aggregate states. Here we show the enantioselective chain-capping and dilution-induced supramolecular polymerization with a Zn2+-porphyrin-based supramolecular system when going from long, highly cooperative supramolecular polymers to short, disordered aggregates by adding a monotopic Mn3+-porphyrin monomer. When mixing the zinc and manganese centered monomers, the Mn3+-porphyrins act as chain-cappers for Zn2+-porphyrin supramolecular polymers, effectively hindering growth of the copolymer and reducing the length. Upon dilution, the interaction between chain-capper and monomers weakens as the equilibria shift and long supramolecular polymers form again. This dynamic modulation of aggregate morphology and length is achieved through enantioselectivity in the aggregation pathways and concentration-sensitive equilibria. All-atom and coarse-grained molecular simulations provide further insights into the mixing of the species and their exchange dynamics. Our combined experimental and theoretical approach allows for precise control of molecular self-assembly and chiral discrimination in complex systems.

Integrative Nursing in Sweden

2014 ◽  
pp. 515-525 ◽  
Author(s):  
Mats Jong ◽  
Miek C. Jong ◽  
Torkel Falkenberg
Keyword(s):  
Health Care ◽  
Complex Systems ◽  
Integrative Medicine ◽  
Real World ◽  
Complex Interventions ◽  
Holistic Care ◽  
Nursing Curricula ◽  
Specific Effects ◽  
Systems Research ◽  
Research Complex

In Sweden concepts of holistic care are well integrated in nursing curricula and health care legislation, but terms such as integrative nursing and integrative medicine is unfamiliar. A major challenge in Sweden is to inform and reform stakeholders in healthcare to acknowledge the benefits and value of evidence generated in (pragmatic-real world research) complex systems research since often integrative nursing methods are complex interventions where it is hard to rely on evidence of specific effects from individual elements of interventions. Experience based programs (on evidence informed integrative nursing practices) may be a key to create awareness among university staff, students, and future healthcare professionals of the qualities of integrative practices to promote and maintain health.

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