Nanoparticle Metrology of Silicates Using Time-Resolved Multiplexed Dye Fluorescence Anisotropy, Small Angle X-ray Scattering, and Molecular Dynamics Simulations.

We investigate the nanometrology of sub-nanometre particle sizes in industrially manufactured sodium silicate liquors at high pH using time-resolved fluorescence anisotropy. Rather than the previous approach of using a single dye label, we investigate and quantify the advantages and limitations of multiplexing two fluorescent dye labels. Rotational times of the non-binding rhodamine B and adsorbing rhodamine 6G dyes are used to independently determine the medium microviscosity and the silicate particle radius, respectively. The anisotropy measurements were performed on the range of samples prepared by diluting the stock solution of silicate to concentrations ranging between 0.2 M and 2 M of NaOH and on the stock solution at different temperatures. Additionally, it was shown that the particle size can also be measured using a single excitation wavelength when both dyes are present in the sample. The recovered average particle size has an upper limit of 7.0 ± 1.2 Å. The obtained results were further verified using small-angle X-ray scattering, with the recovered particle size equal to 6.50 ± 0.08 Å. To disclose the impact of the dye label on the measured complex size, we further investigated the adsorption state of rhodamine 6G on silica nanoparticles using molecular dynamics simulations, which showed that the size contribution is strongly impacted by the size of the nanoparticle of interest. In the case of the higher radius of curvature (less curved) of larger particles, the size contribution of the dye label is below 10%, while in the case of smaller and more curved particles, the contribution increases significantly, which also suggests that the particles of interest might not be perfectly spherical.

Keratin intrinsic fluorescence as a mechanism for non-invasive monitoring of its glycation.

We have studied the evolution of keratin intrinsic fluorescence as an indicator of its glycation. Steady-state and time-resolved fluorescence of free keratin and keratin-glucose samples were detected in PBS solutionsin vitro. The changes in the fluorescence response demonstrate that the effect of glucose is manifest in the accelerated formation of fluorescent cross-links with an emission peak at 460 nm and formation of new cross-links with emission peaks at 525 nm and 575 nm. The fluorescence kinetics of these structures is studied and their potential application for the detection of long-term complications of diabetes discussed.

Impact of the Flavonoid Quercetin on ß-Amyloid Aggregation Revealed by Intrinsic Fluorescence.

We report the effects of quercetin, a flavonoid present in the human diet, on early stage beta-amyloid (Aß) aggregation, a seminal event in Alzheimer's disease. Molecular level changes in Aß arrangements are monitored by time-resolved emission spectral (TRES) measurements of the fluorescence of Aß's single tyrosine intrinsic fluorophore (Tyr). The results suggest that quercetin binds ß-amyloid oligomers at early stages of their aggregation, which leads to the formation of modified oligomers and hinders the creation of ß-sheet structures, potentially preventing the onset of Alzheimer's disease.

Collagen Glycation Detected by Its Intrinsic Fluorescence.

Collagen's long half-life (in skin approximately 10 years) makes this protein highly susceptible to glycation and formation of the advanced glycation end products (AGEs). Accumulation of cross-linking AGEs in the skin collagen has several detrimental effects; thus, the opportunity for non-invasive monitoring of skin glycation is essential, especially for diabetic patients. In this paper, we report using the time-resolved intrinsic fluorescence of collagen as a biomarker of its glycation. Contrary to the traditional fluorescence intensity decay measurement at the arbitrarily selected excitation and detection wavelengths, we conducted systematic wavelength- and time-resolved measurements to achieve time-resolved emission spectra. Changes in the intrinsic fluorescence kinetics, caused by both collagen aggregation and glycation, have been detected.

Detecting beta-amyloid glycation by intrinsic fluorescence - Understanding the link between diabetes and Alzheimer's disease.

We monitor early stages of beta-amyloid (Aß1-40) aggregation, one of the key processes leading to Alzheimer's disease (AD), in the presence of high glucose concentrations by measuring Aß1- 40 intrinsic fluorescence. The multiple peaks and their shifts observed in the time-resolved emission spectra (TRES) reveal the impact of glycation on Aß1- 40 oligomerisation. The results show that formation of the advanced glycation end products (AGEs) alters the aggregation pathway. These changes are highly relevant to our understanding of the pathophysiology of AD and the implication of AGE and diabetes in these pathways.

Cu2+ Effects on Beta-Amyloid Oligomerisation Monitored by the Fluorescence of Intrinsic Tyrosine.

A non-invasive intrinsic fluorescence sensing of the early stages of Alzheimer's beta amyloid peptide aggregation in the presence of copper ions is reported. By using time-resolved fluorescence techniques the formation of beta amyloid-copper complexes and the accelerated peptide aggregation are demonstrated. The shifts in the emission spectral peaks indicate that the peptides exhibit different aggregation pathways than in the absence of copper.

Tracking Insulin Glycation in Real Time by Time-Resolved Emission Spectroscopy.

The application of time-resolved fluorescence sensing to the study of heterogenic biomolecular systems remains challenging because of the complexity of the resulting photophysics. Measuring the time-resolved emission spectroscopy (TRES) spectra can provide a more informative alternative to the modeling of the fluorescence decay that is currently employed. Here, we demonstrate this approach by monitoring real-time changes in intrinsic insulin fluorescence by TRES as a straightforward probe to directly measure kinetics of insulin aggregation and glycation. Our findings hold promise for monitoring the storage of insulin and its application in the control of diabetes and may support the development of more effective therapeutics against amyloidosis.

Protein fibrillogenesis model tracked by its intrinsic time-resolved emission spectra.

The excited-state kinetics of the fluorescence of tyrosine in a de novo protein fibrillogenesis model was investigated as a potential tool for monitoring protein fibre formation and complexation with glucose (glycation). In stark contrast to insulin the time-resolved emission spectra (TRES) recorded over the period of 700 hours in buffered solutions of the model with and without glucose revealed no apparent changes in Tyr fluorescence responses. This indicates the stability of the model and provides a measurement-supported basis for its use as a reference material in fluorescence studies of protein aggregation.

Resolving environmental microheterogeneity and dielectric relaxation in fluorescence kinetics of protein.

The fluorescence intensity decay of protein is easily measurable and reports on the intrinsic fluorophore-local environment interactions on the sub-nm spatial and sub-ns temporal scales, which are consistent with protein activity in numerous biomedical and industrial processes. This makes time-resolved fluorescence a perfect tool for understanding, monitoring and controlling these processes at the molecular level, but the complexity of the decay, which has been traditionally fitted to multi-exponential functions, has hampered the development of this technique over the last few decades. Using the example of tryptophan in HSA we present the alternative to the conventional approach to modelling intrinsic florescence intensity decay in protein where the key factors determining fluorescence decay, i.e. the excited-state depopulation and the dielectric relaxation (Toptygin and Brand 2000 Chem. Phys. Lett. 322 496-502), are represented by the individual relaxation functions. This allows quantification of both effects separately by determining their parameters from the global analysis of a series of fluorescence intensity decays measured at different detection wavelengths. Moreover, certain pairs of the recovered parameters of tryptophan were found to be correlated, indicating the influence of the dielectric relaxation on the transient rate of the electronic transitions. In this context the potential for the dual excited state depopulation /dielectric relaxation fluorescence lifetime sensing is discussed.

Tyrosine Photophysics During the Early Stages of ß-Amyloid Aggregation Leading to Alzheimer's.

We have monitored the formation of toxic ß-amyloid oligomers leading to Alzheimer's disease by detecting changes in the fluorescence decay of intrinsic tyrosine. A new approach based on the non-Debye model of fluorescence kinetics resolves the complexity of the underlying photophysics. The gradual disappearance of nonmonotonic fluorescence decay rates, at the early stages of aggregation as larger, tighter-packed oligomers are formed, is interpreted in terms of tyrosine-peptide dielectric relaxation influencing the decay. The results demonstrate the potential for a new type of fluorescence lifetime sensing based on dual excited-state/dielectric relaxation, with application across a broad range of biological molecules. The results also reconcile previously conflicting models of protein intrinsic fluorescence decay based on rotamers or dielectric relaxation by illustrating conditions under which both are manifest.

Fluorescence kinetics of tryptophan in a heterogeneous environment.

The potentially highly informative, but complex fluorescence decay of amino acids in protein is not fully understood and presents a barrier to understanding. Here we have tested a new and general approach to describing experimentally measured the fluorescence decay in a heterogeneous macroscopic sample. The decay parameters carry information on the features of the kinetics induced by the environment's heterogeneity. Bayesian interference demonstrated that the model fits well to the fluorescence decay of tryptophan in human serum albumin (HSA). The approach has the potential to accelerate photophysical research of heterogeneous media and, specifically, to solve a critical outstanding problem in interpreting protein fluorescence, paving the way to further progress in biomedical research.

Computer-aided biophysical modeling: a quantitative approach to complex biological systems.

When dealing with the biophysics of tumors, analytical and numerical modeling tools have long been regarded as potentially useful but practically immature tools. Further developments could not just overturn this predicament, but lead to completely new perspectives in biology. Here, we give an account of our own computational tool and how we have put it to good use, and we discuss a paradigmatic example to outline a path to making cell biology more quantitative and predictive.

Interplay between distribution of live cells and growth dynamics of solid tumours.

Experiments show that simple diffusion of nutrients and waste molecules is not sufficient to explain the typical multilayered structure of solid tumours, where an outer rim of proliferating cells surrounds a layer of quiescent but viable cells and a central necrotic region. These experiments challenge models of tumour growth based exclusively on diffusion. Here we propose a model of tumour growth that incorporates the volume dynamics and the distribution of cells within the viable cell rim. The model is suggested by in silico experiments and is validated using in vitro data. The results correlate with in vivo data as well, and the model can be used to support experimental and clinical oncology.

Modular model of TNFalpha cytotoxicity.

MOTIVATION: Tumour Necrosis Factor alpha (TNF) initiates a complex series of biochemical events in the cell upon binding to its type R1 receptor (TNF-R1). Recent experimental work has unravelled the molecular regulation of the signalling complexes that lead either to cell survival or death. Survival signals are activated by direct binding of TNF to TNF-R1 at the cell membrane whereas apoptotic signals by endocytosed TNF/TNF-R1 complexes. Here we describe a reduced, effective model with few free parameters, where we group some intricate mechanisms into effective modules, that successfully describes this complex set of actions. We study the parameter space to show that the model is structurally stable and robust over a broad range of parameter values. RESULTS: We use state-of-the-art Bayesian methods (a Sequential Monte Carlo sampler) to perform inference of plausible values of the model parameters from experimental data. As a result, we obtain a robust model that can provide a solid basis for further modelling of TNF signalling. The model is also suitable for inclusion in multi-scale simulation programs that are presently under development to study the behaviour of large tumour cell populations. AVAILABILITY: We provide supplementary material that includes all mathematical details and all algorithms (Matlab code) and models (SBML descriptions). CONTACT: edoardo.milotti@ts.infn.it

The mammalian MAPK/ERK pathway exhibits properties of a negative feedback amplifier.

Three-tiered kinase modules, such as the Raf-MEK (mitogen-activated or extracellular signal-regulated protein kinase kinase)-ERK (extracellular signal-regulated kinase) mitogen-activated protein kinase pathway, are widespread in biology, suggesting that this structure conveys evolutionarily advantageous properties. We show that the three-tiered kinase amplifier module combined with negative feedback recapitulates the design principles of a negative feedback amplifier (NFA), which is used in electronic circuits to confer robustness, output stabilization, and linearization of nonlinear signal amplification. We used mathematical modeling and experimental validation to demonstrate that the ERK pathway has properties of an NFA that (i) converts intrinsic switch-like activation kinetics into graded linear responses, (ii) conveys robustness to changes in rates of reactions within the NFA module, and (iii) stabilizes outputs in response to drug-induced perturbations of the amplifier. These properties determine biological behavior, including activation kinetics and the response to drugs.

Inferring signaling pathway topologies from multiple perturbation measurements of specific biochemical species.

The specification of biological decisions by signaling pathways is encoded by the interplay between activation dynamics and network topologies. Although we can describe complex networks, we cannot easily determine which topology the cell actually uses to transduce a specific signal. Experimental testing of all plausible topologies is infeasible because of the combinatorially large number of experiments required to explore the complete hypothesis space. Here, we demonstrate that Bayesian inference-based modeling provides an approach to explore and constrain this hypothesis space,permitting the rational ranking of pathway models. Our approach can use measurements of a limited number of biochemical species when combined with multiple perturbations. As proof of concept, we examined the activation of the extracellular signal-regulated kinase (ERK) pathway by epidermal growth factor. The predicted and experimentally validated model shows that both Raf-1 and, unexpectedly,B-Raf are needed to fully activate ERK in two different cell lines. Thus, our formal methodology rationally infers evidentially supported pathway topologies even when a limited number of biochemical and kinetic measurements are available.

Inferring signaling pathway topologies from multiple perturbation measurements of specific biochemical species.

The specification of biological decisions by signaling pathways is encoded by the interplay between activation dynamics and network topologies. Although we can describe complex networks, we cannot easily determine which topology the cell actually uses to transduce a specific signal. Experimental testing of all plausible topologies is infeasible because of the combinatorially large number of experiments required to explore the complete hypothesis space. Here, we demonstrate that Bayesian inference-based modeling provides an approach to explore and constrain this hypothesis space, permitting the rational ranking of pathway models. Our approach can use measurements of a limited number of biochemical species when combined with multiple perturbations. As proof of concept, we examined the activation of the extracellular signal-regulated kinase (ERK) pathway by epidermal growth factor. The predicted and experimentally validated model shows that both Raf-1 and, unexpectedly, B-Raf are needed to fully activate ERK in two different cell lines. Thus, our formal methodology rationally infers evidentially supported pathway topologies even when a limited number of biochemical and kinetic measurements are available.

BioBayes: a software package for Bayesian inference in systems biology.

MOTIVATION: There are several levels of uncertainty involved in the mathematical modelling of biochemical systems. There often may be a degree of uncertainty about the values of kinetic parameters, about the general structure of the model and about the behaviour of biochemical species which cannot be observed directly. The methods of Bayesian inference provide a consistent framework for modelling and predicting in these uncertain conditions. We present a software package for applying the Bayesian inferential methodology to problems in systems biology. RESULTS: Described herein is a software package, BioBayes, which provides a framework for Bayesian parameter estimation and evidential model ranking over models of biochemical systems defined using ordinary differential equations. The package is extensible allowing additional modules to be included by developers. There are no other such packages available which provide this functionality.

Bayesian ranking of biochemical system models.

MOTIVATION: There often are many alternative models of a biochemical system. Distinguishing models and finding the most suitable ones is an important challenge in Systems Biology, as such model ranking, by experimental evidence, will help to judge the support of the working hypotheses forming each model. Bayes factors are employed as a measure of evidential preference for one model over another. Marginal likelihood is a key component of Bayes factors, however computing the marginal likelihood is a difficult problem, as it involves integration of nonlinear functions in multidimensional space. There are a number of methods available to compute the marginal likelihood approximately. A detailed investigation of such methods is required to find ones that perform appropriately for biochemical modelling. RESULTS: We assess four methods for estimation of the marginal likelihoods required for computing Bayes factors. The Prior Arithmetic Mean estimator, the Posterior Harmonic Mean estimator, the Annealed Importance Sampling and the Annealing-Melting Integration methods are investigated and compared on a typical case study in Systems Biology. This allows us to understand the stability of the analysis results and make reliable judgements in uncertain context. We investigate the variance of Bayes factor estimates, and highlight the stability of the Annealed Importance Sampling and the Annealing-Melting Integration methods for the purposes of comparing nonlinear models. AVAILABILITY: Models used in this study are available in SBML format as the supplementary material to this article.

Computational methodologies for modelling, analysis and simulation of signalling networks.

This article is a critical review of computational techniques used to model, analyse and simulate signalling networks. We propose a conceptual framework, and discuss the role of signalling networks in three major areas: signal transduction, cellular rhythms and cell-to-cell communication. In order to avoid an overly abstract and general discussion, we focus on three case studies in the areas of receptor signalling and kinase cascades, cell-cycle regulation and wound healing. We report on a variety of modelling techniques and associated tools, in addition to the traditional approach based on ordinary differential equations (ODEs), which provide a range of descriptive and analytical powers. As the field matures, we expect a wider uptake of these alternative approaches for several reasons, including the need to take into account low protein copy numbers and noise and the great complexity of cellular organisation. An advantage offered by many of these alternative techniques, which have their origins in computing science, is the ability to perform sophisticated model analysis which can better relate predicted behaviour and observations.