Visible Blue Light Therapy: Molecular Mechanisms and Therapeutic Opportunities

BACKGROUND

Visible light is absorbed by photoacceptors in pigmented and non-pigmented mammalian cells, activating signaling cascades and downstream mechanisms that lead to the modulation of cellular processes. Most studies have investigated the molecular mechanisms and therapeutic applications of UV and the red to near infrared regions of the visible spectrum. Considerably less effort has been dedicated to the blue, UV-free part of the spectrum.

OBJECTIVE

In this review, we discuss the current advances in the understanding of the molecular photoacceptors, signaling mechanisms, and corresponding therapeutic opportunities of blue light photoreception in non-visual mammalian cells in the context of inflammatory skin conditions.

METHODS

The literature was scanned for peer-reviewed articles focusing on the molecular mechanisms, cellular effects, and therapeutic applications of blue light.

RESULTS

At a molecular level, blue light is absorbed by flavins, porphyrins, nitrosated proteins, and opsins; inducing the generation of ROS, nitric oxide release, and the activation of G protein coupled signaling. Limited and contrasting results have been reported on the cellular effects of blue light induced signaling. Some investigations describe a regulation of proliferation and differentiation or a modulation of inflammatory parameters; others show growth inhibition and apoptosis. Regardless of the elusive underlying mechanism, clinical studies show that blue light is beneficial in the treatment of inflammatory skin conditions.

CONCLUSION

To strengthen the use of blue light for therapeutic purposes, further in depth studies are clearly needed with regard to its underlying molecular and cellular mechanisms, and their translation into clinical applications.

Parameter uncertainty in biochemical models described by ordinary differential equations

Improved mechanistic understanding of biochemical networks is one of the driving ambitions of Systems Biology. Computational modeling allows the integration of various sources of experimental data in order to put this conceptual understanding to the test in a quantitative manner. The aim of computational modeling is to obtain both predictive as well as explanatory models for complex phenomena, hereby providing useful approximations of reality with varying levels of detail. As the complexity required to describe different system increases, so does the need for determining how well such predictions can be made. Despite efforts to make tools for uncertainty analysis available to the field, these methods have not yet found widespread use in the field of Systems Biology. Additionally, the suitability of the different methods strongly depends on the problem and system under investigation. This review provides an introduction to some of the techniques available as well as gives an overview of the state-of-the-art methods for parameter uncertainty analysis.

Combined in vivo and in silico investigations of activation of glycolysis in contracting skeletal muscle

The hypothesis was tested that the variation of in vivo glycolytic flux with contraction frequency in skeletal muscle can be qualitatively and quantitatively explained by calcium-calmodulin activation of phosphofructokinase (PFK-1). Ischemic rat tibialis anterior muscle was electrically stimulated at frequencies between 0 and 80 Hz to covary the ATP turnover rate and calcium concentration in the tissue. Estimates of in vivo glycolytic rates and cellular free energetic states were derived from dynamic changes in intramuscular pH and phosphocreatine content, respectively, determined by phosphorus magnetic resonance spectroscopy ((31)P-MRS). Computational modeling was applied to relate these empirical observations to understanding of the biochemistry of muscle glycolysis. Hereto, the kinetic model of PFK activity in a previously reported mathematical model of the glycolytic pathway (Vinnakota KC, Rusk J, Palmer L, Shankland E, Kushmerick MJ. J Physiol 588: 1961-1983, 2010) was adapted to contain a calcium-calmodulin binding sensitivity. The two main results were introduction of regulation of PFK-1 activity by binding of a calcium-calmodulin complex in combination with activation by increased concentrations of AMP and ADP was essential to qualitatively and quantitatively explain the experimental observations. Secondly, the model predicted that shutdown of glycolytic ATP production flux in muscle postexercise may lag behind deactivation of PFK-1 (timescales: 5-10 s vs. 100-200 ms, respectively) as a result of accumulation of glycolytic intermediates downstream of PFK during contractions.

Computational modeling of mitochondrial energy transduction

Mitochondria are the power plant of the heart, burning fat and sugars to supply the muscle with the adenosine triphosphate (ATP) free energy that drives contraction and relaxation during each heart beat. This function was first captured in a mathematical model in 1967. Today, interest in such a model has been rekindled by ongoing in silico integrative physiology efforts such as the Cardiac Physiome project. Here, the status of the field of computational modeling of mitochondrial ATP synthetic function is reviewed.

A Bayesian approach to targeted experiment design

Motivation: Systems biology employs mathematical modelling to further our understanding of biochemical pathways. Since the amount of experimental data on which the models are parameterized is often limited, these models exhibit large uncertainty in both parameters and predictions. Statistical methods can be used to select experiments that will reduce such uncertainty in an optimal manner. However, existing methods for optimal experiment design (OED) rely on assumptions that are inappropriate when data are scarce considering model complexity.

Results: We have developed a novel method to perform OED for models that cope with large parameter uncertainty. We employ a Bayesian approach involving importance sampling of the posterior predictive distribution to predict the efficacy of a new measurement at reducing the uncertainty of a selected prediction. We demonstrate the method by applying it to a case where we show that specific combinations of experiments result in more precise predictions.

Availability and implementation: Source code is available at: http://bmi.bmt.tue.nl/sysbio/software/pua.html

Contact:j.vanlier@tue.nl; N.A.W.v.Riel@tue.nl

Supplementary information:Supplementary data are available at Bioinformatics online.

An integrated strategy for prediction uncertainty analysis

Motivation: To further our understanding of the mechanisms underlying biochemical pathways mathematical modelling is used. Since many parameter values are unknown they need to be estimated using experimental observations. The complexity of models necessary to describe biological pathways in combination with the limited amount of quantitative data results in large parameter uncertainty which propagates into model predictions. Therefore prediction uncertainty analysis is an important topic that needs to be addressed in Systems Biology modelling.

Results: We propose a strategy for model prediction uncertainty analysis by integrating profile likelihood analysis with Bayesian estimation. Our method is illustrated with an application to a model of the JAK-STAT signalling pathway. The analysis identified predictions on unobserved variables that could be made with a high level of confidence, despite that some parameters were non-identifiable.

Availability and implementation: Source code is available at: http://bmi.bmt.tue.nl/sysbio/software/pua.html.

Contact:j.vanlier@tue.nl

Supplementary information:Supplementary data are available at Bioinformatics online.

Computational modelling identifies the impact of subtle anatomical variations between amphibian and mammalian skeletal muscle on spatiotemporal calcium dynamics

The physical sites of calcium entry and exit in the skeletal muscle cell are distinct and highly organised in space. It was investigated whether the highly structured spatial organisation of sites of Ca(2+) release, uptake and action in skeletal muscle cells substantially impacts the dynamics of cytosolic Ca(2+) handling and thereby the physiology of the cell. Hereto, the spatiotemporal dynamics of the free calcium distribution in a fast-twitch (FT) muscle sarcomere was studied using a reaction-diffusion computational model for two genotypes with known anatomical differences. A computational model of a murine FT muscle sarcomere is developed, de novo including a closed calcium mass balance to simulate spatiotemporal high stimulation frequency calcium dynamics at 35 degrees C. Literature data on high-frequency calcium dye measurements were used as a first step towards model validation. The murine and amphibian sarcomere models were phenotypically distinct to capture known differences in positions of troponin C, actin-myosin overlap and calcium release within the sarcomere between frog and mouse. The models predicted large calcium gradients throughout the myoplasm as well as differences in calcium concentrations near the mitochondria of frog and mouse. Furthermore, the predicted Ca(2+) concentration was high at positions where Ca(2+) has a regulatory function, close to the mitochondria and troponin C.

Modeling glucose and water dynamics in human skin

BACKGROUND

Glucose is heterogeneously distributed in the different physiological compartments in the human skin. Therefore, for the development of a noninvasive measurement method, both a good quantification of the different compartments of human skin and an understanding of glucose transport processes are important.

METHODS

The composition of human skin was quantified by histology research. Based on this information a mathematical model was developed to simulate glucose dynamics in human skin.

RESULTS

The model predicts dynamically glucose concentrations in the different layers of the skin as a result of changes in blood glucose concentration. The model was validated with published time course data of blood and interstitial fluid glucose during a clamp study with three different set points for blood glucose, and model outcomes were compared to measurements for the lag time and gradient. According to the model, glucose in the interstitial fluid of the dermis best matches the amplitude and dynamics of blood glucose.

CONCLUSIONS

The new data obtained from quantitative histology appeared crucial for the model. The proposed model was successfully validated. This result was obtained without tuning or fitting of any parameter. It was shown how the model can be used to set standards for measurements and to define the best measurement depth for noninvasive glucose monitoring.

Identification of a switching model of calcium cycling in isolated rat hearts

So far, the processes involved in regulation of intracellular calcium (Ca/sub i//sup 2+/) in cardiomyocytes have been mainly studied through biochemical and isolated cell analysis. Here, we present a novel technique to model and identify cardiac Ca/sub i//sup 2+/-cycling under physiologically relevant conditions in the intact beating heart. Ca/sub i//sup 2+/ was measured using fluorescence techniques in ex vivo perfused rat hearts. For analysis, we developed a parametric mathematical model, switching between active and inactive calcium release. The kinetic parameters of the two submodes of the model were computed using a recently developed technique from hybrid system identification. Application of the method to control and isoproterenol-stimulated hearts resulted in parameter values within a physiologically reliable range.

Identifiability analysis of the standard pharmacokinetic models in DCE MR imaging of tumours

The usage of dynamic contrast-enhanced MRI (DCE-MRI) as a clinical tool is still widely assessed. Application of the standard pharmacokinetic models to obtain physiologically relevant parameter values using DCE-MRI in tumours is not trivial, when the temporal resolution is low. Mathematical analysis and analysis by simulation of the identifiability for the generalized and extended Kety models was executed. Parameter estimation was executed using synthetic data sets and maximum likelihood estimation (MLE). The influence of temporal resolution was examined. The generalized and extended Kety model showed a large bias in the parameter estimates (10-120%) for sampling times >4 s, although the estimated variance was relatively low (<1%). This was in accordance with the generated contour plots of the hyperplane of the MLE cost-function. The influence of measurement noise on the input and output turned out to be less significant than the temporal resolution.

Computational model of excitable cell indicates ATP free energy dynamics in response to calcium oscillations are undampened by cytosolic ATP buffers

Mitochondria in excitable cells are recurrently exposed to pulsatile calcium gradients that activate cell function. Rapid calcium uptake by the mitochondria has previously been shown to cause uncoupling of oxidative phosphorylation. To test (i) if periodic nerve firing may cause oscillation of the cytosolic thermodynamic potential of ATP hydrolysis and (ii) if cytosolic adenylate (AK) and creatine kinase (CK) ATP buffering reactions dampen such oscillations, a lumped kinetic model of an excitable cell capturing major aspects of the physiology has been developed. Activation of ATP metabolism by low-frequency calcium pulses caused large oscillation of the cytosolic, but not mitochondrial ATP/ADP, ratio. This outcome was independent of net ATP synthesis or hydrolysis during mitochondrial calcium uptake. The AK/CK ATP buffering reactions dampened the amplitude and rate of cytosolic ATP/ADP changes on a timescale of seconds, but not milliseconds. These model predictions suggest that alternative sources of capacitance in neurons and striated muscles should be considered to protect ATP-free energy-driven cell functions.

Parameter estimation in models combining signal transduction and metabolic pathways: the dependent input approach

Biological complexity and limited quantitative measurements pose severe challenges to standard engineering methodologies for modelling and simulation of genes and gene products integrated in a functional network. In particular, parameter quantification is a bottleneck, and therefore parameter estimation, identifiability, and optimal experiment design are important research topics in systems biology. An approach is presented in which unmodelled dynamics are replaced by fictitious 'dependent inputs'. The dependent input approach is particularly useful in validation experiments, because it allows one to fit model parameters to experimental data generated by a reference cell type ('wild-type') and then test this model on data generated by a variation ('mutant'), so long as the mutations only affect the unmodelled dynamics that produce the dependent inputs. Another novel feature of the approach is in the inclusion of a priori information in a multi-objective identification criterion, making it possible to obtain estimates of parameter values and their variances from a relatively limited experimental data set. The pathways that control the nitrogen uptake fluxes in baker's yeast (Saccharomyces cerevisiae) have been studied. Well-defined perturbation experiments were performed on cells growing in steady-state. Time-series data of extracellular and intracellular metabolites were obtained, as well as mRNA levels. A nonlinear model was proposed and was shown to be structurally identifiable given data of its inputs and outputs. The identified model is a reliable representation of the metabolic system, as it could correctly describe the responses of mutant cells and different perturbations.