Modelling biological complexity: a physical scientist's perspective.
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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. (+info)
CONTRAfold: RNA secondary structure prediction without physics-based models.
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MOTIVATION: For several decades, free energy minimization methods have been the dominant strategy for single sequence RNA secondary structure prediction. More recently, stochastic context-free grammars (SCFGs) have emerged as an alternative probabilistic methodology for modeling RNA structure. Unlike physics-based methods, which rely on thousands of experimentally-measured thermodynamic parameters, SCFGs use fully-automated statistical learning algorithms to derive model parameters. Despite this advantage, however, probabilistic methods have not replaced free energy minimization methods as the tool of choice for secondary structure prediction, as the accuracies of the best current SCFGs have yet to match those of the best physics-based models. RESULTS: In this paper, we present CONTRAfold, a novel secondary structure prediction method based on conditional log-linear models (CLLMs), a flexible class of probabilistic models which generalize upon SCFGs by using discriminative training and feature-rich scoring. In a series of cross-validation experiments, we show that grammar-based secondary structure prediction methods formulated as CLLMs consistently outperform their SCFG analogs. Furthermore, CONTRAfold, a CLLM incorporating most of the features found in typical thermodynamic models, achieves the highest single sequence prediction accuracies to date, outperforming currently available probabilistic and physics-based techniques. Our result thus closes the gap between probabilistic and thermodynamic models, demonstrating that statistical learning procedures provide an effective alternative to empirical measurement of thermodynamic parameters for RNA secondary structure prediction. AVAILABILITY: Source code for CONTRAfold is available at http://contra.stanford.edu/contrafold/. (+info)
A novel method for apoptosis protein subcellular localization prediction combining encoding based on grouped weight and support vector machine.
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Apoptosis proteins have a central role in the development and homeostasis of an organism. These proteins are very important for understanding the mechanism of programmed cell death. Based on the idea of coarse-grained description and grouping in physics, a new feature extraction method with grouped weight for protein sequence is presented, and applied to apoptosis protein subcellular localization prediction associated with support vector machine. For the same training dataset and the same predictive algorithm, the overall prediction accuracy of our method in Jackknife test is 13.2% and 15.3% higher than the accuracy based on the amino acid composition and instability index. Especially for the else class apoptosis proteins, the increment of prediction accuracy is 41.7 and 33.3 percentile, respectively. The experiment results show that the new feature extraction method is efficient to extract the structure information implicated in protein sequence and the method has reached a satisfied performance despite its simplicity. The overall prediction accuracy of EBGW_SVM model on dataset ZD98 reach 92.9% in Jackknife test, which is 8.2-20.4 percentile higher than other existing models. For a new dataset ZW225, the overall prediction accuracy of EBGW_SVM achieves 83.1%. Those implied that EBGW_SVM model is a simple but efficient prediction model for apoptosis protein subcellular location prediction. (+info)
Physics and the origins of molecular biology.
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Bohr, Delbruck and Schrodinger were physicists who had important influences on biology in the second half of the twentieth century. They thought that future studies of the gene might reveal new principles or paradoxes, analogous to the wave/particle paradox of light propagation, or even new physical laws. This stimulated several physicists to enter the field of biology. Delbruck founded the bacteriophage group which provided one of the roots of molecular biology. Another was X-ray crystallography which led to the discovery of DNA structure. The strength and success of molecular biology came from the many interactions between geneticists, physicists, chemists and biochemists. It was also characterized by a powerful combination of theoretical and experimental approaches. (+info)
Photophysics and photochemistry of horseradish peroxidase A2 upon ultraviolet illumination.
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Detailed analysis of the effects of ultraviolet (UV) and blue light illumination of horseradish peroxidase A2, a heme-containing enzyme that reduces H(2)O(2) to oxidize organic and inorganic compounds, is presented. The effects of increasing illumination time on the protein's enzymatic activity, Reinheitzahl value, fluorescence emission, fluorescence lifetime distribution, fluorescence mean lifetime, and heme absorption are reported. UV illumination leads to an exponential decay of the enzyme activity followed by changes in heme group absorption. Longer UV illumination time leads to lower T(m) values as well as helical content loss. Prolonged UV illumination and heme irradiation at 403 nm has a pronounced effect on the fluorescence quantum yield correlated with changes in the prosthetic group pocket, leading to a pronounced decrease in the heme's Soret absorbance band. Analysis of the picosecond-resolved fluorescence emission of horseradish peroxidase A2 with streak camera shows that UV illumination induces an exponential change in the preexponential factors distribution associated to the protein's fluorescence lifetimes, leading to an exponential increase of the mean fluorescence lifetime. Illumination of aromatic residues and of the heme group leads to changes indicative of heme leaving the molecule and/or that photoinduced chemical changes occur in the heme moiety. Our studies bring new insight into light-induced reactions in proteins. We show how streak camera technology can be of outstanding value to follow such ultrafast processes and how streak camera data can be correlated with protein structural changes. (+info)
Ventilation equations for improved exothermic process control.
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Exothermic or heated processes create potentially unsafe work environments for an estimated 5-10 million American workers each year. Excessive heat and process contaminants have the potential to cause acute health effects such as heat stroke, and chronic effects such as manganism in welders. Although millions of workers are exposed to exothermic processes, insufficient attention has been given to continuously improving engineering technologies for these processes to provide effective and efficient control. Currently there is no specific occupational standard established by OSHA regarding exposure to heat from exothermic processes, therefore it is important to investigate techniques that can mitigate known and potential adverse occupational health effects. The current understanding of engineering controls for exothermic processes is primarily based on a book chapter written by W. C. L. Hemeon in 1955. Improvements in heat transfer and meteorological theory necessary to design improved process controls have occurred since this time. The research presented involved a review of the physical properties, heat transfer and meteorological theories governing buoyant air flow created by exothermic processes. These properties and theories were used to identify parameters and develop equations required for the determination of buoyant volumetric flow to assist in improving ventilation controls. Goals of this research were to develop and describe a new (i.e. proposed) flow equation, and compare it to currently accepted ones by Hemeon and the American Conference of Governmental Industrial Hygienists (ACGIH). Numerical assessments were conducted to compare solutions from the proposed equations for plume area, mean velocity and flow to those from the ACGIH and Hemeon. Parameters were varied for the dependent variables and solutions from the proposed, ACGIH, and Hemeon equations for plume area, mean velocity and flow were analyzed using a randomized complete block statistical design (ANOVA). Results indicate that the proposed plume mean velocity equation provides significantly greater means than either the ACGIH or Hemeon equations throughout the range of parameters investigated. The proposed equations for plume area and flow also provide significantly greater means than either the ACGIH or Hemeon equations at distances >1 m above exothermic processes. With an accurate solution for the total volumetric flow, ventilation engineers and practicing industrial hygienists are equipped with the necessary information to design and size hoods, as well as place them at an optimal distance from the source to provide adequate control of the rising plume. The equations developed will allow researchers and practitioners to determine the critical control parameters for exothermic processes, such as the exhaust flow necessary to improve efficacy and efficiency, while ensuring adequate worker protection. (+info)
Thermodynamic constraints on stochastic acceleration in compressional turbulence.
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Recent observations in the solar wind have revealed an important phenomenon. In circumstances where stochastic acceleration is expected, a suprathermal tail on the distribution function is formed with a common spectral shape: the spectrum is a power law in particle speed with a spectral index of -5. This common spectrum occurs in the quiet solar wind; in disturbed conditions downstream from shocks; and, in particular, throughout the heliosheath downstream from the termination shock of the solar wind currently being explored by Voyager 1. In this article, simple thermodynamic principles are applied to stochastic acceleration in compressional turbulence. The unique spectral index results when the entropy of the suprathermal tail has increased to the maximum allowable value. Relationships for the pressure in the suprathermal tail are also derived and found to be in agreement with observations. The results are shown to be consistent with the suprathermal tail arising from a cascade in energy, analogous to a turbulent cascade. The results may be applied broadly, because stochastic acceleration in compressional turbulence should be common in many astrophysical settings. (+info)
Can visco-elastic phase separation, macromolecular crowding and colloidal physics explain nuclear organisation?
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BACKGROUND: The cell nucleus is highly compartmentalized with well-defined domains, it is not well understood how this nuclear order is maintained. Many scientists are fascinated by the different set of structures observed in the nucleus to attribute functions to them. In order to distinguish functional compartments from non-functional aggregates, I believe is important to investigate the biophysical nature of nuclear organisation. RESULTS: The various nuclear compartments can be divided broadly as chromatin or protein and/or RNA based, and they have very different dynamic properties. The chromatin compartment displays a slow, constrained diffusional motion. On the other hand, the protein/RNA compartment is very dynamic. Physical systems with dynamical asymmetry go to viscoelastic phase separation. This phase separation phenomenon leads to the formation of a long-lived interaction network of slow components (chromatin) scattered within domains rich in fast components (protein/RNA). Moreover, the nucleus is packed with macromolecules in the order of 300 mg/ml. This high concentration of macromolecules produces volume exclusion effects that enhance attractive interactions between macromolecules, known as macromolecular crowding, which favours the formation of compartments. In this paper I hypothesise that nuclear compartmentalization can be explained by viscoelastic phase separation of the dynamically different nuclear components, in combination with macromolecular crowding and the properties of colloidal particles. CONCLUSION: I demonstrate that nuclear structure can satisfy the predictions of this hypothesis. I discuss the functional implications of this phenomenon. (+info)