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Diseases are believed to arise from dysregulation of biological systems (pathways) perturbed by environmental triggers. Biological systems as a whole are not just the sum of their components, rather ever-changing, complex and dynamic systems over time in response to internal and external perturbation. In the past, biologists have mainly focused on studying either functions of isolated genes or steady-states of small biological pathways. However, it is systems dynamics that play an essential role in giving rise to cellular function/dysfunction which cause diseases, such as growth, differentiation, division and apoptosis. Biological phenomena of the entire organism are not only determined by steady-state characteristics of the biological systems, but also by intrinsic dynamic properties of biological systems, including stability, transient-response, and controllability, which determine how the systems maintain their functions and performance under a broad range of random internal and external perturbations. As a proof of principle, we examine signal transduction pathways and genetic regulatory pathways as biological systems. We employ widely used state-space equations in systems science to model biological systems, and use expectation-maximization (EM) algorithms and Kalman filter to estimate the parameters in the models. We apply the developed state-space models to human fibroblasts obtained from the autoimmune fibrosing disease, scleroderma, and then perform dynamic analysis of partial TGF-β pathway in both normal and scleroderma fibroblasts stimulated by silica. We find that TGF-β pathway under perturbation of silica shows significant differences in dynamic properties between normal and scleroderma fibroblasts. Our findings may open a new avenue in exploring the functions of cells and mechanism operative in disease development.
Citation: Xiong M, Arnett FC, Guo X, Xiong H, Zhou X (2008) Differential Dynamic Properties of Scleroderma Fibroblasts in Response to Perturbation of Environmental Stimuli. PLoS ONE 3(2): e1693. https://doi.org/10.1371/journal.pone.0001693
Academic Editor: Raya Khanin, University of Glasgow, Germany
Received: May 2, 2007; Accepted: January 31, 2008; Published: February 27, 2008
This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
Funding: Supported by UTHSC-H grants from NIH/NIAMS Center of Research Translation (CORT) in Scleroderma (1 P50 AR054144) (F.C.A) and 5RO3AR050517-02 (X.Z.), NIH/NCRR Center for Clinical and Translational Sciences (Houston CTSA Program) (1UL1 RR 024148), NIH PHS NCRR GCRC grant M01RR002558, University of Texas Health Science Center at Houston CreFF (X.Z.), Department of the Army, Medical Research Acquisition Activity: PR064803 (X.Z) and the Scleroderma Foundation (X.Z.).
Competing interests: The authors have declared that no competing interests exist.
Identifying differentially expressed genes across distinct conditions and clustering co-expressed genes into different functional groups have been general approaches for unraveling molecular mechanisms involved in disease pathogenesis [1]. Although these approaches are valuable for looking at isolated events and their correlations, they do not explain the behavior of a bio-system.
Another approach to deciphering pathogenesis of complex diseases is system thinking. Human complex diseases are believed to arise from malfunction of a specific biological system, rather than from isolated events. It is increasingly recognized that biological systems as a whole are not just the sum of their components but, rather, ever-changing, complex, interacted and dynamic systems over time in response to internal events and environmental stimuli [2]. Cellular functions, such as growth, differentiation, division and apoptosis, and biological phenomena of the entire organisms are not only determined by steady-state characteristics of the biological systems, but also determined by inherent dynamic properties of biological systems. Dynamic properties include stability, transient-response, observability and controllability, which determine how the systems maintain their functions and performance under a broad range of random internal and external perturbations. Similar to differential expression of genes between normal and abnormal tissues, we can also observe the differential dynamic properties of the biological systems across different types of tissues and conditions. Dynamic properties are correlated with the health status of individuals and are of central importance for comprehensively understanding human biological systems and ultimately complex diseases.
The dynamic behavior of most complex biological systems emerges from the orchestrated activity of many components (e.g. genes and proteins) that interact with each other to form complicated biological networks involving gene regulation and signal transduction [3]. The nodes and links together are referred to as networks. This report is a study of gene regulatory network that focuses on dynamic properties of a biological system.
Investigation of dynamic properties of gene networks has three major tasks: development of mathematic models, estimation of the parameters in the models, and dynamic analysis. Mathematical modeling is to use mathematical language to describe the dynamic characteristics of a system [4]. In the past decade, various methods have been developed to model gene networks, including Boolean networks [5]–[7], differential equations and Bayesian networks [8]–[11], and vector autoregressive model [12]. A very powerful approach in modeling complex systems is the state-space approach [13], [14], which is a special subclass of dynamic Bayesian networks. It provides a general framework for the application of dynamic systems theory in the analysis of gene regulation. The state-space approach is the core of modern systems theory. Application of the state-space equations to modeling gene networks allows us to use a large body of methodologies and tools in dynamic systems theory for studying dynamics of gene networks. We use Kalman Filter and Expectation-Maximization (EM) to estimate the parameters in the model [15], [16]. After state-space model of the gene networks is established, the next task is to perform dynamic analysis for the model in response to perturbation of internal and external stimuli. Dynamic analysis attempts to extract inherent features of the systems that capture and describe the behaviors of the system over time under different operating conditions. The most important operating principle of a dynamic system is its stability (i. e., the ability to return to the original state or equilibrium state after perturbation). The concept of stability can be easily illustrated by the example of a marble sitting at the bowl. When the marble is in the bottom of the bowl it is stable. No matter where the marble is pushed, up the side of the bowl or from the bottom of the bowl, after it is released, the marble will finally settle to the bottom of the bowl at the original, stable equilibrium point. However, when the marble is on the top of an inverted bowl, it is unstable. The marble can remain on the top of the bowl only when the forces acting on the marble on the top of the bowl is completely balanced. Any slight perturbation in the marble's steady state will destroy the balance of the marble and cause it to move away from the top of the bowl. This indicates that when the system is in unstable state small perturbation can cause the system move away from the steady-state [17]. The biological systems are in constant change under the influences of genetic and environmental differences. The ability of the systems to maintain the stable states after perturbation and to resist diverse disturbance of the internal and external forces is critical to the viability of living organisms and plays a central role in biology [18], [19]. Consequently, studying stability of biological systems is of great importance for discovering mechanism of complex diseases. Although there has been long history to investigate the stability of biologic systems, to our knowledge, very few studies have been reported on stability of gene networks. Particularly, the relationship between stability of gene networks and status of diseases has not been explored. One of purpose of this paper is to use gene expression data to show that similar to the example of the marble in the bowl, the gene networks will also have stable and unstable states and that unstable gene networks may be associated with the diseases.
Another important property of the dynamic systems is the transient response to disturbance of internal noises and external environmental forces, which measures how fast the systems respond to the perturbation and characterizes damping and oscillation properties of the process in response to the perturbation [13]. Feedback close loops are the basis for maintaining normal function of cells and organisms in the face of internal and external perturbation [19], [20]. The essential feature of the transient response of a feedback closed-loop system largely depends on the location of the closed-loop poles. A simple and popular method for searching the poles of the closed-loop system is the root-locus analysis that plots a curve of the location of the poles of a transfer function of the feedback system over the range of the variable (usually loop gain) to determine whether the system will become unstable or oscillate [13]. The third important property of a dynamic system is controllability. Controllability is defined as the capacity of the system to move from undesired states to certain desired final states in finite time through accessible inputs [21]. Germline or somatic mutations lead to the subsequent transcriptional and translational alterations which will affect the phenotypes of the cells and cause diseases. Therapeutic interventions such as radiation, drug and gene therapy intend to alter gene expressions from an undesired state or abnormal state to a desired or normal state. Theoretic and practical analyses in modern control theory demonstrate that there exist systems which we are not able to change from undesired states to desired states. Now the question arises: are all genetic networks controllable? Can always therapeutic interventions change levels of gene expressions to desired states? Controllability provides answers to these questions. It provides a convenient and sufficient criterion for assessing whether we can change undesired gene expression levels to desired gene expression levels. Controllability describes the ability of biological systems to adapt to the changes of environments and deeply characterizes the internal structure of the system. The controllability of the biological networks may reflect the severity of the disease. Thus, the controllability is a fundamental design principle of biological system.
In summary, stability, transient response, feedback and controllability are basic dynamic properties of the biological systems and are essential to the function of the cells and organisms. As a proof of principle, in this report we investigate the differential dynamic properties of TGF-β pathway in response to perturbation of silica between normal and scleroderma fibroblasts. Scleroderma or systemic sclerosis (SSc) is a typical complex disease in which fibrosis occurs in multiple organs. Although etiopathogenesis is unknown, both genetic and environmental factors are believed to play critical roles. The major source of fibrosis in SSc is over production of collagens from fibroblasts. Fibroblasts obtained from SSc patients appeared to be genetically engineered to produce more collagens and cytokines [22]. Silica exposure is an important environmental risk factor in some cases, which has been found in association with the development and perturbation of SSc [23]. Subcutaneous injections of silica have been reported to induce sclerodermatous skin changes and activation of skin fibroblasts [23]. Therefore, interactions between fibroblasts and silica may represent a magnification of biological events occurring in SSc and/or SSc-like disorders. The biological system of fibroblasts reacting to silica exposure must involve complex regulations and coordination of molecules to maintain their desirable status. Although multiple experiments of the in vivo and the in vitro response to silica particles have revealed that fibroblasts are activated to produce more collagens and other extracellular matrix (ECM) components [24]–[26], there is a lack of a mathematical model to quantify interactions among the molecules, and to predict dynamic behaviors of this bio-system. The purpose of this report is to use gene expression responses of scleroderma and normal fibroblasts exposed to the stimulus of silica as an example to address the issue of differential dynamic properties of the biological systems in response to perturbation by environments across different conditions. To accomplish this, we first formulate a regulatory network involving TGFBRII, CTGF, SPARC, COL1A2, COL3A1 and TIMP3 as a biological system that is associated with TGF-β signaling, and then apply mathematical methods and computational algorithms from engineering and control theory [13] to perform dynamic analysis of this network for both normal and scleroderma fibroblasts in response to perturbation of environmental Stimuli. Based on the results of dynamic network analysis, we examine the differential dynamic properties of this network between normal and scleroderma fibroblasts and reveal the relationship between the dynamic properties of gene networks and the phenotypes of the cells.
State Space Model of a gene network responding to silica
Gene regulation involves a large number of biochemical events. Although kinetic models can be developed for gene regulation [12], [27], [28], they involve many kinetic parameters that are difficult to be estimated from gene expression data with small number of samples. An alternative model of gene expression is a state-space model. It can effectively deal with time invariant or time varying, linear or nonlinear complex systems with multiple inputs and outputs. A state-space model includes three types of variables: input variables, output variables and state variables. A key idea behind state-space model is the concept of the state. The state of a dynamic regulatory system is the smallest set of variables which are referred to as state variables such that the current knowledge of these variables together with the current and future knowledge of the input variables (environments or controls) will completely determine the behavior of the regulatory system. All state variables are hypothetical variables. State variables represent biological forces to regulate transcription of genes, which describe the behavior of gene transcription. Since the mechanisms of gene regulation in the network have still not been well understood, the state variables that determine the regulation may be unknown and hidden in the regulatory process, the concept of state variables is very suitable for description of the regulatory process. The expression levels of genes are output variables and can be observed. The expression levels of the genes are determined by the state variables, which describe states of regulation of the gene expressions.
Previously, we found that the SPARC (secreted protein, acidic, and rich in cysteine) gene is involved in the regulation of extracellular matrix genes such as COL1A2, COL3A1, CTGF and TIMP3, and this regulation is associated with activation of the TGF-β pathway [29], [30]. We used this partial TGF-β pathway as an example to illustrate how to perform dynamic analysis of biological networks. This regulatory network was modeled by linear state-space equations defined as:(1)where xk is the vector of state variables that describes the behavior of gene regulation, but are hidden; yk is the output vector whose elements denote the measured gene expression levels; uk is the input vector; w and v are noises assumed to be white Gaussian noise with zero means and variance Q and R respectively, and they are independent of each other. The inputs can be any external stimuli that influence gene regulation, things like environmental forces, drugs, proteins, RNAs, or the effects from the genes outside the model. Matrix A is called state transition matrix whose elements denote the regulatory strength of one gene on another, B is input to state matrix whose elements quantify the regulatory effects of the input variables on the genes of the network, C is state to output matrix whose elements quantify the dependence of the measured gene expression levels on the hidden regulatory states, and D is input to output matrix whose elements measures the strength of dependence of the observed gene expression levels on the inputs. Matrices A, B, C, D and variance matrices Q and R together make up the parameters of the dynamical system for gene regulatory networks.
We performed experiments on cultured human fibroblasts. We have 5 pairs each of normal and SSc patients' samples. For each sample, we have two replications were perturbed by Silicon. The transcript levels of six genes: SPARC, CTGF, COL1A2, COL3A1, TIMP3 and TGFBRII were measured daily from 1 to 5 days. Let x1, x2, x3, x4 and x5 be the expression levels of the SPARC, TIMP3, COL3A1, CTGF and COL1A2, respectively. Let u1 and u2 be the expression of the TGFBRII [31] and 10 µg silica. The estimated state-space model for the normal cell line and SSc are respectively, given byand
A graph will be used to represent a genetic network. The nodes in the graph will represent the variables that correspond to the expressions of the genes. The edge between two nodes denotes that two variables are dependent. The number next to edges is the elements of the parameter matrices A, B, C and D in the state-space model. The estimated state-space model is shown in Figure 1 where the numbers next to the edges are the coefficients in the above equation for the normal (black color) and SSc (red color) fibroblasts, respectively. We observe differential regulation of SPRAC on CTGF, COL3A1 and COL1A2, and CTGF on COL3A1 between the SSc and normal fibroblasts. Figure 1 and above equations also demonstrate that the effects of silica (environmental factor) on TIMP3, COL3A1 and COL1A2 between the SSc and normal fibroblasts are different. Their coefficients in the state-space equations for the normal fibroblasts are negative, but become positive for the SSc fibroblasts. This implies that perturbation of scleroderma fibroblasts by silica will increase the expressions of COL1A2 and COL3A1. This statement can be supported by early observation that excessive amounts of various collagens mainly type I and type III collagens were generated in the fibroblasts from affected scleroderma skin [32]–[34].
The numbers next to the edges are the coefficients in the state-space equations for the normal (black color) and SSc (red color) fibroblasts, respectively. The numbers in the boxes denote the mean expression values of the genes in normal (black color) and SSc (red color) fibroblasts.
The most important dynamic property of gene regulatory networks is concerned with stability. Stability is an organizing principle of gene regulatory networks [35]–[37]. When gene regulatory networks are perturbed, the expressions of the genes in the network will be changed in response to perturbation of environments. There are two possibilities. One possibility is that although the expressions of the genes will be changed after perturbation of environments, they will finally return to their original values or stay at other equilibrium values forever. In this case, regulatory networks will maintain their steady states under perturbation of environments and hence will function normally. Another possibility is that the expressions of the genes after perturbation of the environments will diverge from their original states and never stay at any steady states, which will finally lead to damage and malfunction of the gene regulatory network. Formally, a dynamic system is called stable if their state variables return to, or towards their original states or equilibrium states following internal and external perturbations [38]. The stability of the system is a property of the system itself. One of the methods for assessing the stability of the linear dynamic systems is to analyze eigenvalues of the state transition matrix A of the linear dynamic systems. For a discrete linear system, if the norm of all eigenvalues of the transition matrix A is less than 1 then the system is stable.
The eigenvalues of the transition matrix A of the state-space model for silica responding gene network for normal and SSc fibroblasts are given in Table 1. It indicates that all eigenvalues of the transition matrix A for the normal fibroblasts are less than 1, but for SSc fibroblasts, three eigenvalues whose absolute values are larger than 1. Therefore, the examined network for normal fibroblasts after perturbation of silica stimulation are relatively stable, but for SSc fibroblasts are unstable. Unstable gene regulatory or signal transduction networks will lead to erratic changes and malfunction of the whole biological system, which may be the case in the SSc fibroblasts that are associated with dramatic and irregular changes of COL1A2 and COL3A1.
Transient-Response Analysis of Genetic Networks
The dynamic behavior of a system is encoded in the temporal evolution of its states. Cell functions are essentially temporary processes and largely determined by the dynamic properties of the biological systems in the cells. Transient and steady state responses are two steps of the response of a gene network to perturbation of external environments. The transient response of the gene network to perturbation of environments is defined as rapid changes of the expressions of the genes in the network over time which go from their initial states to final states after perturbation of external input [13]. Steady-state response studies the system behavior at infinite time. The transient response of the dynamic systems is also a property of the system itself. The transient response of the gene network to environmental changes characterizes the dynamical process of the gene regulation networks in response to perturbation of environments. It can be used to study damped vibration behavior of the gene network and reveal how fast the gene networks respond to perturbation of environments and how accurately the networks can finally achieve the designed steady-state values. In the previous section we studied stability of the whole gene network, but we did not investigate the stability of the expression of the individual gene in the network. Since the transient response analysis of the gene network will study the dynamic process of the expression of the individual gene, it can be used to assess whether the expression of individual gene after perturbation of environment is stable or unstable. Although many transient response analyses is concerned with delay time, rise time, peak time, maximum overshoot and setting time, in this report, our transient response analysis mainly focuses on investigation of the stability, divergence or oscillation of individual gene expression.
Popular methods for investigation of the transient responses of the dynamic systems are to study the time domain characteristics of the system under perturbation of the external signals. The transient response of the dynamic system depends on the input signal. Different input signal will lead to the different transient response of the system. There are numerous types of signal in practice. For the convenience of analysis and comparison, we consider two types of signals: (i) unit-step signal and (ii) unit-impulse signal as shown in the Figure 2.
For discrete dynamic systems, the transient response of the system is obtained by using the inverse z transform method [13]. To investigate the transient response of the genes in the network responding to silica, the silica was taken as input signal. Figures 3A and 3B show transient response of genes to a unit step perturbation of silica for normal and SSc fibroblasts, respectively. Figures 3C and 3D show transient response of genes to an impulse perturbation of silica for normal and SSc fibroblasts, respectively. Figures 3A, 3B, 3C and 3D demonstrate that the transient response of SPARC, TIMP3, CTGF to the perturbation of silica between the normal and SSc fibroblasts are similar, but the transient response of COL1A2 and COL3A1 to the perturbation of the silica between the normal and the SSc fibroblasts were dramatically different. The expressions of COL1A2 and COL3A1 after perturbation of the silica in normal fibroblasts quickly reach the steady states. However, the expressions of COL1A2 and COL3A1 in the SSc fibroblasts after perturbation of silica were unstable and will never reach the steady-state values. This phenomenon suggests that dynamic responses of the expressions of COL1A2 and COL3A1 in the SSc fibroblasts to environmental stimuli are irregular.
Root-Locus Analysis
The performance of the gene networks under the design for stability, time response and reliability can be studied by analysis of their corresponding closed-loop system. The basic features of the stability and transient response of the closed-loop system are largely determined by the location of the closed-loop poles, which in turn is related to the value of the loop gain [13]. The roots of the characteristic equation of a system which is derived from the denominator of transfer function of the closed-loop system are the system's closed-loop poles. In general, the poles are complex variable and can be represented on the complex plane which is called s-plane. (Negative real) poles on the left hand side of the complex plane cause the response to decrease, while poles on the right hand side cause it to increase. Consequently, if the poles of the closed-loop system lie in the left half s plane, the system is stable. If any of these poles lies in the right-hand side of the s-plane, then the system is unstable. In this case, with increasing time, the transient response of the system will increase monotonically or oscillate with increasing magnitude [13]. As the loop gain changes the location of the closed-loop, poles will also changes. A root locus is defined as the locus of the poles of a transfer function of a closed-loop as a specific parameter (generally, loop gain) is varied from zero to infinity. The locus of the poles will be plotted on the complex plane (s-plane) as the system gain is varied on some interval. Since the location of the poles will change as the gain changes a system that is stable for gain K1 may become unstable for a different gain K2. We often observe that the root-locus will move from the left-hand of the s-plane to the right-hand of the s-plane, which implies that stable system becomes unstable as the system gain changes from one region to another region.
The root-locus plots the locations of the poles of the closed-loop single input and single output system (SISO) as the system gain varies. We use the symbol "x" to denote the poles of the closed-loop SISO and the symbol circle "o" to denote the zeros of the open-loop SISO. If the pole and zero coincide then the symbol ∶ will be used to represent this situation. To study the dynamic behavior of the five genes to respond to the perturbation of silica, we consider the SISO system which takes one of the five genes as the output and silica as the input.
Figures 4A, 4B, 4C, 4D and 4E, and Figures 5A, 5B, 5C, 5D and 5E show the root-locus plot of SPARC, TIMP3, COL3A1, CTGF, and COL1A2 with silica as the input in the normal and SSc fibroblasts, respectively. We noted that three remarkable features emerged from two panels of the Figures. First, all poles of the closed-loop SISO systems for five genes in the normal cell lines lie in the left hand side of the s-plane, but their corresponding poles in the SSc fibroblasts lie in the right hand side of the s-plane. This indicates that the expressions of all five genes to respond to the disturbance of the environmental silica in normal fibroblasts are stable, but become unstable in the SSc fibroblasts, which confirms the previous stability assessment. Second, although all poles of the closed-loop SISO for five genes in the normal fibroblasts are on the left hand side of the s-plane, the SPARC, COL3A1, CTGF and COL1A2, each has at least one branch of the root-locus plot which will enter the right-hand side of the s-plane. This indicates that the system becomes unstable as the increasing system gain reaches the some range. This may imply that the regulations of these four genes are sensitive to the changes of the system. Third, the poles and zeros of the SISO on the right hand sides of the s-plane for the SPARC and TIMP3 in the SSc fibroblasts have the same location, i.e., the poles and zeros are cancelled out. This shows that the expressions of CTGF, COL1A2 and COL3A1 in response to the perturbation of the silica are more unstable than that of SPARC and TIMP3. Differential regulations of CTGF, COL1A2 and COL3A1 in response to the perturbation of silica between the normal and SSc fibroblasts may imply that the interactions of these three genes with the silica may be involved in the pathogenesis of the SSc. Forth, these Figures also demonstrate that when normal fibroblasts were changed to SSc fibroblasts, the root-locus will be moved toward the right-half s-plane. Classical control theory indicates that moving of the root-locus toward the right-half s-plane will reduce stability and increase response time of the system.
Changes in expression levels of genes and proteins in the regulatory networks will lead to status transition of the cells from normal cells to abnormal cells. One way to correct molecular changes is to transform cells from an undesirable state to a desirable one by altering gene or protein expressions. Now the question is whether we can use potentially therapeutic interventions to change gene or protein expressions from undesired states to desired states. This important issue can be addressed by examining the controllability of gene regulatory networks.
The fundamental controllability in gene regulation is associated with two questions. The first question is whether an input (therapy) can be found such that the system states can be driven from the undesired initial value to the desired values in a given time interval. The second question is how difficult it may be to change the system from undesired states to the desired states if the system is controllable.
The system (regulatory network) is called controllable, if for any state of the system, there exists a finite time and an admissible control function such that the system can achieve the desired state transition. In other words, the state controllability indicates that we can find an input to change the states from any initial value to any final value within some finite time. The controllability provides a binary information about whether the system is controllable or not, but it does not provide a quantitative measure to quantify the amount of control effort needed to accomplish the control task. It has been recognized that to get into insides of controllability of the system it is indispensible to define a quantity to measure how the system is controllable. In other words, we need to develop a measure to evaluate the amount of control efforts required to change the system from the initial state to the desired state [39]. The test for controllability is that the controllability matrix (see methods) has full rank, i.e., the rank of the controllability matrix is equal to the number of the state variables of the system. To assess how difficult to achieve control goal, we calculated the conditional number of the controllability matrix which measures the degree of difficulty to change the state of the system (or gene expression in our problem) by the external forces such as treatments. The larger the conditional number of controllability matrix, the more control efforts required to accomplish control task.
The rank of controllability matrix of the system in the state-space model of this partial TGF-β pathway under perturbation of silica in both normal and SSc fibroblasts is equal to 5 which is the number of the state variables in the model. Thus, TGF-β pathway is controllable in both normal and SSc fibroblasts. However, the conditional numbers of the controllability matrix of the system for normal and SSc fibroblasts were 80 and 398, respectively, which showed that the conditional number of the controllability matrix for the SSc fibroblasts is much larger than that for normal fibroblasts. Therefore, much more control efforts are required to change gene expressions to desired levels for the SSc fibroblasts than that for the normal fibroblasts. This implies that the controllability of this network between the normal and SSc fibroblasts are differentiable.
In the past, large efforts have been devoted to studying the function of individual genes and static properties of biological pathways. However, the molecular concentrations and activities in living organisms are in constant change as a result of their interactions. The pathogenesis of disease involves evolution and temporal process. The functions of the cells, tissues and entire organisms are not only due to the steady states of the biological pathways, but also due to the dynamic interactions of biological pathways with the external environments. Although investigation of the function of individual genes, proteins and steady-states of the biological pathways is still valuable, it is time to devote more efforts and resources to study dynamic behaviors and properties of the biological pathways. It is dynamic properties that play a central role in giving rise to the function of cells and organisms [40].
To exemplify this principle, we studied the differential dynamic properties of a partial TGF-β signaling network under perturbation of silica between normal and SSc fibroblasts. We took this network as a dynamic system and performed dynamic system analysis. Investigation of differential dynamics of this network between the normal and SSc fibroblasts consisted of three steps. The first step was to use the EM algorithm and Kalman filter to estimate the parameters in the state-space model of this TGF-β signaling network. The second step was to study stability, the transient response and controllability of the system, and to perform root-locus analysis based on the identified state-space model of the gene network. The third step was to assess whether the dynamic properties of this network between the normal and SSc fibroblasts were different.
Our study in dynamic analysis of these gene regulations addressed several remarkable issues. First, the stability analysis may be used as a powerful tool for identifying biological pathways that are associated with the diseases. The stability is one of systems-level principles underlying complex biological pathways [41]. The stability of the system is the ability of the system to return to the equilibrium states after perturbation of the internal and external stimuli. The requirements for stable biological pathways are necessary conditions for the normal operations of the cells and organisms. The unstable biological pathway will inevitably lead to the malfunction of cells or even death of the living organism. Our results showed that a gene network in responding to perturbation of silica is relatively stable in the normal fibroblasts, but unstable in the SSc fibroblasts. This assessment of differential stability of biological pathway between normal and abnormal cells represents a novel approach in study associations of biological pathways with human diseases.
Second, root-locus analysis can provide valuable information for finding genes that show strong differential dynamics between normal and abnormal cells. Not all genes in the unstable pathway show unstable dynamics. Expressions of some genes in the unstable pathway may be stable themselves. Our task is to distinguish the genes that show stable expressions from those show unstable expressions in the unstable pathway. The state transition matrix of the state-space model of the studied gene network in the SSc fibroblasts has three poles that were in the right hand sides of the complex s-plane (Figures 3 and 4), which implies that this network in the SSc fibroblasts is unstable. The zeros of the genes of SPARC and TIMP3 in SISO system coincided with three poles. Therefore, although this gene network was unstable in the SSc fibroblasts, the expressions of the genes of SPARC and TIMP3 were stable. At least one branch of the root locus plots of other three genes (CTGF, COL1A2 and COL3A1) were on the right hand sides of the s-plane. This indicates that the responses of the genes of CTGF, COL1A2 and COL3A1 to the perturbation of silica in the SSc fibroblasts were unstable no matter how the system gains were changed. These findings can be confirmed by the transient response analysis of the genes. The poles and zeros of characteristic equations of the SISO systems of the genes in response to the perturbation of internal and external signals are intrinsic properties of the gene regulations and are largely not affected by the expressions of other genes. Unlike the concept of differential expressions of the genes where the differentially expressed genes may be just consequences of differential expressions of other genes lying up in the pathway, the differential stability of the response of the genes to the perturbation of the signal between normal and abnormal tissues may be involved in the pathogenesis of the diseases. Therefore, the genes showing differential stability are supposed to be associated with the diseases. The root-locus analysis and the transient response will provide new tools for identifying the genes that are associated with the diseases. The differential stability and the transient response of the gene in the response to perturbation of the environment between the normal and abnormal cells characterize the interaction between the genes and environments. Therefore, the root-locus analysis and the transient response analysis also provide a powerful tool for detection of the gene-environment interaction.
Third, the controllability of biological pathway is an important property of the system. Germline or somatic mutations lead to the subsequent transcriptional and translational alterations which will finally cause diseases. Therapeutic interventions such as radiation, drug and gene therapy are intended to alter gene expressions from an undesired state to a desired or normal state. Gene regulation is a complex biological system. Theoretic and practical analyses in modern control theory demonstrate that there exist systems which we are not able (or find difficult) to change from undesired states to desired states of gene regulation. Now the question arises: are all biological pathways controllable? Are degrees of controllability of the biological pathways different between normal and abnormal Cells? The controllability measures the ability to move a system around in its entire state space using certain admissible intervention. In this report, we developed a conditional number of controllability matrix, to measure the degree of controllability of the system. Our results show that although a gene expression network responding to silica in both normal and SSc fibroblasts is controllable, the degree of controllability of this regulatory network between the normal and SSc fibroblasts is different. This regulatory network in the SSc fibroblasts has a low degree of controllability. In other words, adjustment of regulation of genes in the network by external intervention in the SSc fibroblasts is more difficult than that in the normal fibroblasts. We suspect that the degree of controllability is correlated with the severity of the diseases. When the diseases are at the initial stages, the biological systems are easy to move from abnormal states to the normal states. The degree of controllability of the system will provide valuable information on the curability of the diseases. Although in the past a number of authors have studied dynamic properties of biological networks, their studies have mainly used kinetic data or artificial data and nonlinear models [28], [35]–[37]. Due to limitation of experiments, many kinetic parameters in the genetic regulation have not been available in practice. Large-scale kinetic analysis of biological networks is infeasible. Here we use gene expressions and linear models to study dynamic properties of genetic networks. The results of this report showed that the dynamic properties of genetic network between normal and abnormal cells were differential.
In summary, dynamic properties of the biological systems are intrinsic system properties. The gene expressions are the phenotype of the cells. Their changes are governed by the hidden dynamic properties of the gene regulatory systems. It is dynamic properties that determine the phenotypes of the cells. This report represents a paradigm shift from the studies of individual components and static properties of the system to the studies of dynamic properties of the system consisting of individual components.
Although the preliminary results are appealing, they suffer from several limitations. First, sample sizes were small. Small sample size will limit the accuracy of the state-space models for biological pathways, which in turn affect estimation of dynamic properties of the systems. No much attention in control theory has been paid to investigation of impact of uncertainty inherent in dynamic systems on dynamic properties of the system. We will treat biological networks as stochastic dynamic system and study dynamic properties of stochastic dynamic systems in the future. Second, the quantities to characterize the dynamic properties are essentially random variables. Their distributions are unknown. We have not developed statistical methods to test significant differences in the dynamic properties of the pathways between the normal and abnormal cells. Third, the relations between the dynamic properties of the genes and their genotypes have not been investigated. Fourth, we have not performed large-scale dynamic analysis of the biological pathways. More theoretical development and large-scale real data analysis for the dynamic properties of the biological pathways are urgently needed.
Dermal fibroblast cultures
Skin biopsies of clinically uninvolved skin (3 mm punch) were obtained from 5 patients with SSc and 5 normal controls after informed consent was granted. All five patients fulfilled American College of Rheumatology criteria for SSc [42]. All five had diffuse skin involvement as defined by LeRoy et al [43], and disease duration of less than five years. Skin biopsies from five normal controls with no history of autoimmune diseases undergoing dermatologic surgery were matched for age (+/− 5 years) and sex. The study was approved by the Committee for the Protection of Human Subjects at University of Texas Health Science Center at Houston.
The skin sample was transported in Dulbecco's Modified Essential Media (DMEM) with 10% fetal calf serum (FCS) (supplemented with an antibiotic and antimycotic) for processing the same day. The tissue sample was washed in 70% ethanol, PBS, and DMEM with 10% FCS. Cultured fibroblast cell strains were established by mincing tissues and placing them into 60 mm culture dishes secured by glass cover slips. The primary cultures were maintained in DMEM with 10% FCS and supplemented with antibiotic and antimycotic.
Silica stimulation on fibroblasts
The 5th passage of fibroblast strains were plated at a density of 2.5×105 cells in a 35 mm dish and grown until 80% confluence. Culture media then were replaced with FCS–free DMEM containing different doses (1, 5, 10, 25 and 50 µM) of silica particles obtained from Sigma-Aldrich, St Louis, MO. After 24-hour culture at this condition, the fibroblasts were harvested for extraction of RNA. The RNAs were examined with RT-PCR for gene expression of COL1A2, COL3A1, TGFBRII, CTGF, SPARC and TIMP3. The results from this dose-response assay provided an optimal dose (10 µM) in a time-dependent exposure for fibroblasts, in which 24-, 48-, 72-, 96- and 120-hour exposure of silica were assayed in cultured fibroblasts.
Quantitative RT-PCR
Quantitative real time RT-PCR was performed using an ABI 7900 sequence detector (Applied Biosystems, Foster City, CA). The specific primers and probes for each gene were purchased through Assays-on-Demand from Applied Biosystems. As described previously (19), total RNA from each sample was extracted from cultured fibroblasts described above using TRIzol reagent (Invitrogen Life Technology) and treated with Dnase I (Ambion, Austin, TX). cDNA was synthesized using Superscript II reverse transcriptase (Invitrogen Life Technology). Synthesized cDNAs were mixed with primers/probes in the 2× Taqman universal PCR buffer, and then assayed on an ABI 7900. The data obtained from assays were analyzed with SDS 2.1 software (Applied Biosystems). The amount of total RNA in each sample was normalized with 18 S rRNA transcript levels.
State-Space Model and Parameter Estimation
A biological pathway is taken as a dynamic system. The biological system is modeled by linear state-space equations defined as(2)where xk is a vector of state variables at the time k that determine the dynamics of the regulation and unobserved, uk is a vector of input variables at the time k such as drugs, environmental forces, and other state variables that lie outside the model, yk are observed variables at the time , for example, the gene expressions, A, B, C, and D are matrices called state matrix, input matrix, output matrix and direct transmission matrix, respectively, w and v are noises assumed to be white Gaussian noise with zero means and variance Q and R respectively, and they are independent of each other.
A fundamental and widely applicable parameter estimation method is Maximum Likelihood (ML) method that maximizes the likelihood of the observed data with respect to parameters. However, the state-space models involve unobserved state variables that are unavailable. It makes calculation of the likelihood in the setting of state-space models very difficult. To solve this problem, we use expectation-maximization (EM) Algorithm that is widely used iterative parameter estimation method [15]. Specifically, we first assume that the state variables are available and then calculate the likelihood of both the observed data and hidden state variables which will be maximized with respect to the parameters in the models. After the estimated parameters are in our hands we then specify new state space models using the estimated parameters.
For the convenience of presentation, equation (2) can be rewritten as(3)where and Then, the conditional density function of ζk, given Zk is given by N(Γzk, Π), where . Assume that the distribution of the initial state is given by x1∼N(μ1, P1). Let a sequence of input-output data samples and the state be denoted byThe E-M algorithm for estimation of the parameters in the state-space model of discrete dynamic systems consists of two iterated steps: E-step and M-step. They are summarized as follows [16].
Calculate the expectation of the augmented log-likelihood function of both the observed data and hidden state variables defined as follows:To calculate Q(θ, θ′), we first need to calculate the conditional likelihood function Pθ(X, Y|U). From the model (3), we have(4)where(5)Combining equations (4) and (5), we have(6)LetTaking expectation Eθ{.|YN, UN} on both sides of equation (6), we obtain(7)To calculate the matrices Φ and Ψ, we use the following quantities(8)and they can be calculated using Kalman smoother [44]:(9)The quantities x̂k|k, Pk|k, Pk|k−1 are calculated by the Kalman filter equations as follows:(10)
Maximizing the likelihood function defined in equation (7) with respect to parameters yields(11)Since the network has structure, which enforces certain parameters in the model to be zeros and leaves others to free to change, we develop constrained EM algorithms.
First we define a matrix product operation of two matrices called Hadamard product, denoted by ○, as element wise product, i.e.Then, we define a modification of the vector V, denoted by [V]mod, as the vector in which all elements corresponding to the zeros elements in the matrix Γ are deleted. We define a modification of the matrix as the matrix in which if intersection of the row and column corresponds to the zeros elements in the matrix Γ then such row and column are deleted. The equation for estimation of parameters (18) is reduced to
Transient-Response Analysis
Response of a biological pathway to perturbation of internal and external stimuli has two parts: the transient and the steady state response. The time varying process generated in going from the initial state to the final state in the response to the perturbation of the internal and external stimuli is called transient response. Steady-state response studies the system behavior at infinite time. Transient-response analysis of biological pathways can be used to quantify their dynamics. It can reveal how fast the biological pathways respond to perturbation of environments and how accurately the pathways can finally achieve the desired steady-state values. It can also be used to study damped vibration behavior and stability of the biological pathways.
The transient response of the dynamic systems depends on the input signals. Different signal will cause different response. There are numerous types of signal in practice. For the convenience of comparison, we consider two types of signals: (i) unit-step signal and (ii) unit-impulse signal as shown in Figure 2.
The transient response of dynamic systems can be studied by transfer function that is used to characterize the input-output relationships of a linear, time-invariant, differential equation system. The transfer function is defined as the ratio of the Laplace transform of the output to the Laplace transform of the input under the assumption of zero initial conditions. The transfer function of the response of the biological pathway to unit-step and unit-impulse input signals are given by and Y(s) = G(s) respectively, where G(s) is the transfer function of the biological pathway. The transient-response analysis of the biological pathway can be performed by inverse Laplace transformation. We performed the transient-response analysis with MATLAB [13].
Stability Analysis
The most important dynamic property of biological pathways is concerned with stability. Dynamic systems are called stable if their variables such as gene expressions return to, or towards, their original or equilibrium states following internal and external perturbations. For any practical purpose, the biological pathways must be stable. Unstable gene regulations will lead to the malfunction or even the death of the cells. A biological pathway will remain at steady state until occurrence of external perturbation. Depending on dynamic behavior of the system after perturbation of environments, the steady-states of the system are either stable (the system returns to the initial state or changes to other steady-states) or unstable (the system leaves the initial equilibrium state).
One of the methods for assessing the stability of the linear dynamic systems is to analyze eigenvalues of the state transition matrix A of the linear dynamic systems. For a continuous linear dynamic system, if real parts of all eigenvalues of the transition matrix A are strictly negative then the system is stable. For a discrete linear system, if the norm of all eigenvalues of the state transition matrix A is less than 1 then the system is stable.
Root-Locus Analysis
Open and close loop poles and zeros largely determine the stability and performance of the open and close systems. They provide valuable information on how to improve stability and transient response of the systems. Root-locus analysis, in which the roots of the characteristic equation of the closed-loop system are plotted for all values of a system parameter, is a powerful tool for study and design of dynamic pathway. The loop gain is often chosen to be the parameter. Varying the gain value will change the location of the closed-loop poles.
Consider a SISO system that consists of a gene regulator and an input to the gene regulator shown in Figure 6. The transfer function of the closed-loop system is given bywhich implies the following characteristic equation of this closed-loop system:(12)
In general, G(s) H(s) involves a gain parameter K. A plot of the points in the complex plane satisfying the characteristic equation (12) is the root locus. As the gain parameter changes the root locus will plot a curve in the complex s-plane. A simple method for plotting root-locus has been developed by W. R. Evans [29]. In this report, we use MATLAB to generate root-locus plots [13].
A dynamic system is called controllable, if there is an admissible control function, which can change the system from any given initial state to any finite state or to the origin of the state space in the finite time. Define the controllability matrix of the system as H = [B, AB, …, An−1B], where A and B are the state transition matrix and input to the state matrix in the linear dynamic system, respectively. If rank (H) = n, i.e., the rank of the controllability matrix is equal to the number of the state variables of the system, then the genetic network is completely controllable.
We use the condition number of the controllability matrix to measure the degree of the controllability of the system. The condition number of the controllability matrix is defined as [45]where H− is a generalized inverse of the matrix H and ||.|| denotes a matrix norm. This can be justified by the following arguments. The general solution of the discrete linear system is given by [13](13)By definition, if the system is controllable, then at some time tk, we have xk = 0, which implies that(14)
Equation (14) can be reduced toOr(15)where u is a control vector. Solving the equation (15) yields(16)The norm of the control vector represents the amount of control efforts required to change the states from initial value to the desired value and hence measures the degree of the controllability. From equation (16), we note that the norm of the control vector ||u|| is proportional to the condition number: κ(H) = ||H−||||H||. Therefore, we can use the condition number of the controllability matrix to measure the degree of controllability.
Author Contributions
Conceived and designed the experiments: XZ MX. Performed the experiments: XG. Analyzed the data: MX HX. Contributed reagents/materials/analysis tools: XZ. Wrote the paper: XZ MX FA.
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The Cambridges went on another royal tour (click here to see what we had to say about the Australia/New Zealand trip) and the clothes were more fab than drab –HIP HIP HOORAY! They did not bring the children –BOO! Since the tour was in India and Bhutan I went a little overboard on photos when things were pretty or fun or both.
April 6, 2016 – Pre Tour Palace Reception
Dress by Saloni London
I'm not sure how Melissa is going to feel about this dress but it fits a lot of our favorite dress criteria, its navy, long-sleeved, and has polka dots! I myself love it. I think I might even enjoy the ruffled neckline. Her hair situation really makes me want to invest in curlers.
I like it because it looks like what my mom wore in the 70s and 80s, though I preferred it when I thought it was a jumpsuit. WHAT HAVE I BECOME? -M
April 10, 2016 – Day 1
Dress by Alexander McQueen
Despite my undying hatred of those fucking shoes I realized we were in for a good week when look one turned out to be this bright patterned Alexander McQueen. I will probably mention this a million times in this post but I really hope she continues this trend of patterns once she gets back to the UK. The patterns distract me from your boring fucking shoes.
She will not continue the patterns or the colors that are not shades of blue or green in the UK because she is not as cool as Maxima, though McQueen IS one of her faves. I like the peplum–quelle surprise!–but I wish the skirt was in a stiffer fabric, and either a pencil or true a-line. I am picky when it comes to Kate. -M
Dress by Anita Dongre
I want this fabric as a duvet cover, kaftan, pajama bottoms, throw pillows, shower curtain, tunic top, curtains, and tapestry (I hope I'm not forgetting anything). Outfit number two might just be one of my favorite looks hence the insane amount of photos. I also want to run out to World Market and buy earrings like the ones she is wearing here.
I want everything Katie wants above, plus a nice midi skirt with a waistband. I wish she dressed with this many patterns IRL. -M
Dress by Jenny Packham
I like this dress but the real star here are those earrings (and that giant bouquet).
This is typical Cate and it's nice–Jenny Packham will design my Oscar gown the first time I go–but I am disappointed after all the wonderful things she wore prior to this. -M
April 11, 2016 – Day 2
Dress by Emilia Wickstead
This is not my fave BUT not my least favorite either. These are the same earrings she wore with the Alexander McQueen on day 1. I want to burn those shoes.
I would like this in literally any other color. I think it would be cool in khaki-green because I love when people look like they're going on a safari. -M
Dress by Temperley
Crop top and skirt! This is super pretty and very appropriate for a garden party in which one must cut a cake with a sword.
Pretty! For some reason she reminds me of Britney Spears in the face, and that is not because I've had two Moscow mules. -M
April 12, 2016 – Day 3
Dress by Glamorous
Kate wore flats but they are beige so lets instead focus on the fabric of this dress because it's so pretty. I feel like Kate desperately wants to just go full kaftan but no one will allow it.
I don't like the lace-up aspect of the top, but otherwise this is great. I would support her decision to go full kaftan and I would even buy her a belt to make it event-appropriate. -M
Dress by Temperley London
The color of this is so great that it distracts me from the kind of matronlyness of the dress. I don't hate and it's better than both Wickstead looks but I want everything to be patterned.
It's definitely matronly. The skirt is too long and has too much fabric! Also, her boobs look sad. -M
Dress by Anna Sui
And then she ends the day in Anna Fucking Sui! OH INDIA YOU ARE GREAT FOR KATE. This dress is so much fun. Also this party looks like a ton of fun.
This is my favorite! So many patterns, and even in her acceptable blue and green!! -M
April 13, 2016 – Day 4
Top by RM Williams and pants are Zara Mid Rise Biker Trousers
Safari and biker pants –GOOD TIMES! Also love her top.
See–safaris are fun! Those look like driving loafers, but I feel some sharp oxfords, a fancier loafer or these shoes I love from Everlane might be better here. -M
Dress by Topshop
WOOHOO TOPSHOP! Melissa bought a really pretty purse from Topshop when we went to NYC. This dress is great and looks really comfy for elephant painting and rhino feeding.
I love that purse, it is really well made! This dress is delightful and NOT blue or green! WTG, Kate! -M
April 14, 2016 – Day 5
Dress by Emilia Wickstead
She has worn this before and compared to literally everything else she has worn on this tour (even that white dress with the boob napkins) this is THE WORST! I do love her earrings which she wore with the Tory Burch dress down below (SPOILER ALERT, its AMAZING).
Skirt a semi-traditional Bhutanese look (half-kira) and cape by Paul and Joe
The fabric for her half-kira skirt was designed by Kelzang Wangmo and then fashioned into a skirt by Kate's personal tailor. Here is a video of the designer.
This was Mrs Wangmo's reaction when we told her the news: pic.twitter.com/mbgdAKemp1
— Kensington Palace (@KensingtonRoyal) April 14, 2016
The fabric designer in me is just in heaven with every look of this tour but this is just divine. Also I really want a blazer cape NOW.
I feel like a blazer cape would be very versatile until I tried to wear it to work and couldn't get the full range of motion from my arms and then if I pulled the sleeves back like Kate did to shoot the bow and arrow I'd be cold. Anyway, I love the colors of her skirt! -M
Dress by Tory Burch
This might just be the best thing this woman has ever worn AND I don't even hate her shoes in fact I like them.
AND an orange wrap? WHO DOES SHE THINK SHE IS? -M
April 15, 2016 – Day 6
Hike to Paro Taktsang
I'm not going to lie I found this so beautiful that I have included A LOT of photos of them on this massively long hike. As for the clothes I think they are excellent hiking attire and I really love her brown leather boots.
This first photo is after they got down from the mountain.
The next two I think are from before they went up the mountain. That is a ridiculously tall mountain.
This photo is from the halfway point.
And these are at a museum that is also halfway up. They did make it to the top but I think the press was tired so they stayed at the halfway point or I don't know were too dead if they did make it to take photos of the experience. Did you see that mountain?
This is the prettiest her hair has ever looked down. -M
Dress by Beulah
FUN FACT: poppies are the national flower of Bhutan! Kate and her team picked this red dress with poppies for her last night in Bhutan.
Lovely! This fits her really well. -M
April 16, 2016 – Day 7
Leaving Bhutan in Alexander McQueen
She has worn this before but I love this McQueen white eyelet suit and I like it. It's a good suit to leave Bhutan in.
I wish it was red. Or god forbid ORANGE. -M
Touring the Taj Mahal in Naeem Khan
FINALLY NAEEM KHAN! If I had been Kate I'd have worn Naeem Khan the entire trip. Anyhoo this dress looks fantastic next to the Taj Mahal. ALSO the Taj Mahal is beautiful so there are A LOT of photos.
Remember when everything was in patterns like this about 10 years ago, even stuff from LOFT? I miss those times. I still have my LOFT skirt with a pattern like this. -M
Queen Máxima and King Willem-Alexander (Jesus, his name is long I hope he has a nickname) of the Netherlands in BAVARIA!
April 13, 2016 – Day 1
Look 1
What Máxima lacks in pattern she makes up with je ne sais quoi. This woman is a human delight. Also her shoes are very rarely, if ever, beige.
I would like to see her brooch collection. Maybe we can do some swapping. -M
Look 2
She also laughs a lot. And look her shoes are gold toed.
Of course she laughs a lot, she's probably drunk. Oh wait, that's me. -M
April 14, 2016 – Day 2
Coat by Claes Iversen
I think we have seen this coat before but I still love it. I also think it looks great with that pillbox hat. ALSO HATS!
This is like those sweatshirts J Crew made two years ago except way classier. She looks so happy. -M
Look 2
You two are just so cute together. This dress is nice and goes well with all of her grey accessories and the jewels in her coat.
Two brooches! Can you believe the balls of this woman???? -M
Crown Princess Mary and Prince Frederick of Denmark visit from MEXICO!
April 13, 2016 – Day 1
Coat by Missoni
The gang from Denmark hosted a visit from Mexico and they were greeted by CP Mary in this lovely coat and another pillbox hat!
What a rad ombre coat! I think she should give it to me. -M
Dress by Jesper Høvring
Mary always does a good job matching her gown to all of the sashes she has to wear. This dress is nice. ALSO TIARA!
Mary suffers from a lack of brooches. Maybe she can get in on me and Maximas' swap. -M
April 14, 2016 – Day 2
Look 1
Mary looks like she is the female DA on Law & Order.
Would be more regal with a brooch. -M
Look 2
And we end the post with a PATTERN! She looks good here especially that fuchsia lippie.
I wonder if her hair is naturally wavy. If so, she should do her hair like this more often. I wish her top was more interesting but the skirt is rad. -M

subset: commoncrawl


text:
Nike sunglasses
Tailwind12 White
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The same innovation and technology that was used for Tailwind, has been updated to develop the sleeker, faster, lighter, Tailwind12. This super lightweight frame, with Flying lens technology, reduces fogging and provides precise visual information at all angles of view with Nike's patented Nike Max Optics®. With secure wrap temple arms and cam-action hinges that lock into place, it is the perfect choice for serious athletes who want to run faster and compete longer. The Tailwind12 features interchangeable lenses and accepts an optional Rx clip.
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- Description
DetailsAtelier Red Colour is a range recommended for red hair the mask will repair damage, restore strength, moisture and vibrancy, making hair softer and shinier. It contains a mixture of ingredients that act will extend the life of colour treatments. It is sulfate and paraben free. it is suitable for brazilian keratin treated hair. 250 gr
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Atelier Red Colour Shampoo£14.00


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Farm Follies
Each blog i write shares bits & pieces of our family farm lief. Even though many topics are serious we have lots of fun on the farm. Plus it never hurts to have a good laugh even if it is at yourself. So here are couple of recent happenings that just might make you giggle!
|some of our cattle
Watch your step! That is a common phrase around the farm especially since our farm includes livestock. A good cowgirl learns this at an early age but apparently in the hustle and bustle of things we soon forget that cow pies can be dangerous and don't have cute little signs warning the pasture, barn or elsewhere might be slick. Just a few weeks ago I was walking into the barn to halter a few show heifers when I reached out to catch one just as my foot landed into a cow pie...no biggie..but the calf moved quickly to the left and i attempted to do the same and down I went!! My entire leg landed in the cow pie, ankle to knee!!! By the way since this was suppose to be a quick trip to the barn I was wearing flip flops and shorts! I don't recommend this look.
|Wyatt & Aunt Rachel
My hubby William and his lil' sister Rachel plus my lil' cowboy Wyatt had just finished morning chores and decided to take the bush hog to a pasture that needed some TLC. (For those of you who don't know a bush hog is like a giant lawn mower that attaches to a tractor). Wyatt watched as William backed up the flatbed and Rach hooked up the bush hog for road travel. Once they were ready Wyatt walked over to Rach and gave her a big high five and said "Rachel you're a great HOOKER!"
What wonderful family bonding moments! There are 100's more stories similar to these that I could share but that's it for today! Perhaps I can make this a regular feature Friday Farm Follies! What do ya think? Would you like to hear more about the crazy happenings around the family farm??? Let me know!


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Slick-Util Library - Introduction
Loading Images for LWJGL - Part 1
Loading Sounds for LWJGL - Part 2
TrueType Fonts for LWJGL - Part 3
What is the Slick-Util Library?
Slick-Util is a small library to enable you to load various image, sound and font formats for use with LWJGL.
Brief Introduction
In order for OpenGL to display images or OpenAL to play sounds, the data needs to be provided to them in the right format. There are lots of different formats out there (png, jpg, xm, ogg, wav, etc). Manually writing a loader for a format is a time consuming task and requires learning how the format is stored and works, so it makes sense to use a library to do this task for you.
Previously LWJGL bundled the Devil and FMOD libraries to assist in loading image and sound formats. However as these were native libraries they required a java wrapper to use and this had to be maintained and updated. This also clashed with LWJGL's minimalistic philosophy. Slick-Util is a pure java alternative to these and is much lighter, requires less maintenance and easier to use with a java library like LWJGL. So those native libraries were dropped and Slick-Util is now the recommended replacement.
Why is it called Slick-Util?
Slick-Util is actually a subset of the Slick2D Library (A 2d games library which uses LWJGL). As Slick2D had features generally useful for all LWJGL games it was only logical to create a subset library which could be used with any LWJGL application (2d or 3d).
If you are looking for a library to facilitate image rendering, sprite handling, game loop setup, tiled map loading, etc. then you may prefer Slick2D rather than SlickUtil.
Slick-Util provides the following features for LWJGL:
- Load various image formats for use with OpenGL (png, gif, jpg, etc).
- Load various sound formats for use with OpenAL (.wav, .xm, .ogg, etc).
- Load Unicode Fonts for use with OpenGL.
Where to get it
You can get the Slick-Util library from here.
Setting up the Slick-Util Library
Just add the slick-util.jar as an external java library to your project like you do with the LWJGL jars.
Loading Images for LWJGL - Part 1
Loading Sounds for LWJGL - Part 2
TrueType Fonts for LWJGL - Part 3


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Typhoon Trami ravaged the southern coastline of Japan this weekend, only 26 days since Typhoon Jebi made landfall on September 4. Trami first swept through the southwest chain of Nansei Islands, and then skirted along the southern coasts of the islands of Kyushu and Shikoku, eventually making landfall over Wakayama Prefecture in the Kansai Region, during the evening of Sunday, September 30.
The southwest of Japan bore the brunt of Trami, and as of Tuesday, October 2, the Japan Fire and Disaster Management Agency (FDMA) reported a total of 2,494 buildings damaged across 32 prefectures, with 1,749 of the 2,494 damaged buildings reported as "partially damaged", and 108 buildings "destroyed" or "partially-destroyed". A further 637 buildings were flooded in the event. At least 195 people across 29 prefectures have been injured, with many hurt by windows shattered in high winds. 750,000 homes across Japan lost power, together with significant disruption to transport. These numbers are expected to rise as damage assessments conclude.
Four hundred miles southwest of the Japanese mainland on Okinawa Island, the largest of the Nansei Islands with a population of 1.3 million, wind gusts reached 126 miles per hour (202 kilometers per hour) at Itokazu locality in Nanjō City. Five or so miles up from Itokazu, winds topped 103 miles per hour (165 kilometers per hour) and gusts exceeded 132 miles per hour (212 kilometers per hour), at the Kadena Air Base.


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Welcome to our family-run Göbel's Hotel zum Löwen right in the centre of the Waldhessen community of Friedewald with its romantic moated castle ruin. Enjoy a relaxing holiday with us in a stylish atmosphere and treat yourself to a restful break.
We invite you to feel good, enjoy and relax.
Kind regards,
Markus & Andrea Göbel and family and the entire team at Göbel's Hotel zum Löwen
"Small but sophisticated – in line with this motto, many nice holiday moments await you at attractive prices."
Kathrin Krahl
At the heart of the nature paradise of Waldhessen lies the charming community of Friedewald with its romantic moated castle ruin. Old buildings such as numerous churches and ruins of watchtowers decorate the idyllic little town, which, along with its surrounding area, offers attractive undertakings.
In a central location, the family-run Göbel's Hotel zum Löwen receives its guests in a fitting atmosphere of wellbeing. As soon as they arrive, the conservatory foyer – flooded in daylight – invites the guests to linger there. From here, one can proceed to the comfortable rooms or the restaurant and event room. The wellness oasis "Crystal SPA", located nearby in the 5-star Göbel's Schlosshotel "Prinz von Hessen", is just right for a break from the everyday routine.
Leave all your cares behind and enjoy restful days of wellbeing at Göbel's Hotel zum Löwen. Discover our offers and experience many nice holiday moments.
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Professional Japanese Translation
Your Japanese translation will be handled in-country by Amerigo's Japanese Translators, all of whom are well educated, native speakers of Japanese with extensive experience in medical Japanese translation, legal Japanese translation, business Japanese translation, financial Japanese translation and many other fields. Before choosing Amerigo Global, or any other company, here are a few things that you should know:
Japanese Translation: Expectations
Since Japanese is written in completely different scripts than that of European languages such as English, translated documents with images may take on a slightly different format than the source document. When possible, our professional document translators and project managers will maintain images in their proper spots. When a formatting problem is foreseen, our project managers will work with you to find an acceptable format for your translated document.
Written Japanese
Japanese is written in a complex set of three separate scripts. Kanji, which is a modified version of Chinese script, is a symbolic script with no phonetic meaning. Hiragana and Katakana are syllabic scripts made of modified Chinese characters. Additionally, Romanji, which mirrors the sounds of the Latin alphabet, is used for logos, business names and data entry. Each of these scripts has its place in document translation. Don't worry, our expert project managers and professional Japanese translators will take care of the details for you.
Learn more! Read our FAQ
Japanese Facts:
- Language Family: Sino-Tibetan
- Native Speakers: 1.2 Billion
- Writing System: Kanji, Katakana, Hiragana, Romanji
- Spoken in: Japan


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Swat Police added 12 new photos.
Sib la de allah der omar ao hoshalai warki ijaz khan zendabad
Excelent work done by our w/Dpo sb, may ullah give long life & prosperity.
wonderful work ….Highly appreciated
Good job
Long live SIR you always
Leading from the front we feel #Proud on you and Highly appreciated your #Hardwork
Great efforts sir g long live. ………
Good sir gee
Well Don sir g
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Buttons Nappy Inserts/Boosters
Price from: £4.65 In stock
* Image for illustration only: inserts are sold individually
Buttons inserts are designed for use with Buttons nappy wraps/covers.
Daytime inserts: choose from hemp/cotton (4 layers of 55% hemp/45% organic cotton with a stay-dry layer) or bamboo/cotton* (5 layers of 70% bamboo/30% organic cotton)
- Small inserts fit approx 7-15lbs (inserts are 11" in length)
- Large inserts fit approx 15-25lbs (insert are 13" in length)
- XLarge inserts fit approx 25-40lbs (inserts are 15" in length)
Simply snap the inserts into your Buttons nappy wrap for a customisable nappy system!
* Please note these measurements are pre-washing and both inserts will shrink after washing. Bamboo inserts will shrink more than the hemp inserts, so you may wish to upsize
Night-time boosters:
Buttons night-time boosters are ideal for adding extra absorbency. Simply snap underneath the Buttons daytime inserts. Choose from: Hemp/cotton (5 layers of 55% hemp/45% organic cotton) or bamboo/cotton
Available in sizes:
- Small: fits approx 7-15lbs (inserts are 12" in length)
- Large: fits approx 15-25lbs (inserts are 14" in length)
- XLarge: fits approx 25-40lbs (inserts are 15" in length)
* Please note the night-time boosters are designed for using with the daytime insert on top. Only the daytime insert has a stay-dry layer.
Need to check which size of inserts/boosters you have? Sizes and type of insert are determined by the stitch colour:


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The Turnaround Management Association of Taiwan (台灣重建協會) said yesterday that it faces a bumpy road in assisting underperforming Yamay International Development Corp (月眉開發) -- which runs the Mala Bay (馬拉灣) theme park in Taichung County -- with development of a plan to revive the nation's biggest recreation facility.
"We have encountered some resistance from the facility's owner, who believes that other investors are interested in making investments in the park," said Sherry Lin (
In early January the association and Yamay inked an agreement wherein Yamay sought professional management expertise from the association. The association dispatched a five-member team to work out a turnaround plan for the facility.
The facility's major shareholders and creditors -- such as the state-run Taiwan Sugar Co (Taisugar, 台糖) and banks participating in syndicated loans -- had previously been reluctant to take part in any turnaround plan due to conflicts of interests, Lin said.
Lin said that since the association stepped in, her committee has convinced all parties to review the association's draft turnaround plan, which is slated to finalized by April 10 and which proposes recapitalization.
"Until the facility's size of capital is reduced, new investors will not be interested in injecting new capital," Lin said.
Lin also said that the association is confident of being able to propose a feasible plan to revive the park and to satisfy the park's owner and its creditors.
Yamay opened the Mala Bay theme park in July 2000.
The nation's economic downturn over the last three years has contributed to the park's financial difficulties.
Since January last year, Yamay has been in default on a NT$6.9 billion (US$208 million) syndicated loan that involves 21 banks, including the First Commercial Bank (第一銀行) and Chang Hwa Commercial Bank (彰化銀行).
Chang Hwa has threatened to foreclose on the loan because Yamay has failed to propose a satisfactory repayment plan.
Yamay also owes NT$290 million in royalties and rent to Taisugar.
Lin said that five or six other enterprises have approached the association for help, including Pacific Electric Wire & Cable Co (
The association's chairman, Lee Yung-san (


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Smith & Wesson Model 34-1 .22/.32 Kit gun round Butt 22LR. Cal. MIB 1978 MFG 4"BBl.
Guns International #: 100640180 Seller's Inventory #:
Category: Smith & Wesson Revolvers - Rimfire - Smith & Wesson Revolvers - Model 34
Seller's Information
When emailing or calling sellers direct, please mention that you saw their listing on GunsInternational.com
Seller: R&G LLC
Company: R&G Quality Firearms LLC
Member Since: 3/8/07
First Name: Richard
Last Name: Beck
State: Connecticut
Zip: 06060
Country: United States
Phone: (860) 653-2304
Number of Active Listings: 39
Seller: FFL Dealer
Return Policy: 3 day inspection and return policy on used guns.
3 days
Payment Types Accepted: Checks Money orders & free layaways
Just in I have for sale a Smith & Wesson Model 34-1 .22/.32 kit Gun with a Round Butt .22LR. caliber , MFG in april 1978 Mint in the Original box. 4"BBl. with a mirror bore this little Kit Gun appears to have been test fired only a few times. Tight lock up, 98-99% Original Blue with a set of Round Butt Checkered walnut stocks. The original owner states that the Box is missing end label, but it was the original when he purchased it new. SER#. M-205042 A Very nice little kit gun!
Ask about our free layaway's!
$25.00 Shipping & Insurance to your FFL.
Price: $799.00


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Amanda Ortman
Bethel-tate High Class of 2010
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A hop, skip and a jump on a quick train journey and I was out in the countryside for Jason and Sam's portrait shoot. They wanted to do it at a place that meant something to them, which was their village and taking in the places they walk their lovely dog Woody.
We had a blast and it was a super sun-shiny day!
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Copyright 2013 - All rights reserved Celine Chaplin Photography


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I am a stay-at-home mom since the birth of my now seven-month old son. Going full-time mom has its struggles, too, same with juggling office work and household management. And the first thing I did as I assume "office" at the small house was to let my voice be heard.
I am no drama mother and once I got to talk to my boys about responsibility, I let them learn by themselves. I am, most of the time though, available to shed some light to uncertain problems or even familiar questions. I am very much a mom to them.
And so, to keep them reminded of our not-so-much of a house rule, I got these items from Lazada Philippines:
Talking much might bring more damage than value, I guess. So one afternoon, while the boys were still at school, I installed the wall decals I bought at Lazada Philippines. I hope to surprise the children and let them read the writings on the stairs. I know for sure, time will come they will ask me about the stickers. I would want them to understand the words one by one, reflect on them, and act upon them. In no time, the small house will be a place where love, understanding, and forgiveness strive. A place where one could have second chances, where loud laughter is heard,and more hugs are felt.
As for the second wall decal, I asked husband to install it with the boys' help. I intend them to read each line and as they stick each word, they come to meditate and absorb its meaning.
Our small house is no home to spoiled kids. Nor a place for the troubled. It's an abode where a husband and his wife aim to grow their four boys as real gentlemen and who believe in the power of love.
As I have mentioned, I am a stay-at-home mom. Leaving the house is no easy-peasy. I always have to consider the interest of my baby. So to make my shopping easier, I do it online. And one of the easiest and friendliest e-commerce companies I have tried so far is Lazada Philippines.
After my first purchase were a number of successful transactions online. I plan to buy a few more items still and I even installed the Lazada mobile application so I could shop with ease. My top reasons for considering Lazada Philippines?
1. The fun of receiving notification of what is new in Lazada's online store. I could even get hold of special promotions or flash sales.
2. I could read reviews from satisfied or not so satisfied buyers. The product info is also a great help from the size to color and usage.
3. The more fun part of cruising around the pages of Lazada as I breastfeed my baby. I could easily switch categories and even shop according to brands.
4. I could go back again and again without any saleslady stalking or annoying me to make that purchase.
5. And the most fun part of keeping me company on times the baby does not want to sleep even on wee hours of the morning, I get to shop for necessities and even on small splurges. I happen to chance upon these wall decals during one sleepless night when my then one-month baby would demand for milk every one or two hours.
All these I can do even on my bed and in my pajamas. 🙂
So are you looking for something but does not have the time to go even to the nearest mall? Try Lazada Philippines!
Happy shopping, everyone!
This post is an entry for the ShopCoupons X Lazada PH Blogger contest. Being hosted by ShopCoupons. Lazada voucher sponsored by Lazada Philippines.


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An Illinois federal district court recently credited sampling data as evidence sufficient to establish the citizenship of putative class members for purposes of invoking the local controversy exception to CAFA jurisdiction.
Plaintiffs filed a putative class action lawsuit in Illinois state court alleging that their property is being contaminated by silt particles released from defendants' steel mill facility in Granite City, Illinois. They asserted common law claims for temporary nuisance, trespass, and negligence. After defendants removed to federal court asserting CAFA jurisdiction, plaintiffs moved to remand, arguing that CAFA's "local controversy exception" applied.
The court noted that the purpose of the local controversy exception is to balance competing interests in making a federal forum available to genuinely national litigation, and allowing state courts to retain cases when the controversy is strongly linked to the state in which the case was filed. It applies, and requires a district court to decline to exercise CAFA jurisdiction, when:
- Greater than two-thirds of the members of the putative class are citizens of the State in which the action was originally filed;
- At least one defendant is a citizen of the State in which the action was originally filed from whom significant relief is sought by members of the class and whose alleged conduct forms a significant basis for the class claims; and
- Principal injuries resulting from the alleged unlawful conduct or any related conduct were incurred in the State in which the action was originally filed.
In this case, only the first element was in dispute, that is, whether two-thirds of the putative class members were Illinois citizens. Citizenship means domicile—where a person resides and intends to remain. It was plaintiffs' burden to establish the applicability of the exception by a preponderance of the evidence.
In considering plaintiffs' evidence, the court cited Seventh Circuit precedent rejecting the least expensive and least reliable method for establishing citizenship—simply analyzing phone numbers and mailing addresses of the putative class members. The Seventh Circuit, however, also has held that it is not necessary to use the most expensive and reliable method—surveying each putative class member to determine citizenship. Rather, there are methods for establishing class member citizenship between these two extremes that the Seventh Circuit has endorsed.
One method is to simply tailor the class definition to include citizens of only the state in which the case is filed. Another is to rely on class member affidavits or survey responses and to apply statistical principles to reach a conclusion as to the likelihood that a given state's citizens comprise at least two-thirds of the entire class, with the assistance of a statistician if the results do not provide a clear answer. Under this approach, according to the Seventh Circuit, any degree of certainty greater than 50 percent would permit a court to conclude that plaintiffs had met their burden of establishing that the two-thirds citizenship element is satisfied by a preponderance of evidence.
In this case, plaintiffs limited the proposed class definition to current or recent residents of Illinois. In addition, they obtained 181 putative class member declarations attesting that the declarants currently reside and intend to remain in Illinois. Finally, plaintiffs provided multiple declarations of an expert statistician who, after studying the class area and surveying a random sample of class members, concluded with 95 percent certainty that more than two-thirds of the class members reside in Illinois and intend to remain there.
Over defendants' objections that the class member declarations represented only a tiny and potentially non-representative portion of the putative class members, and that the expert statistician's survey methodology was faulty, the district court concluded that plaintiffs offered sufficient evidence to establish that at least two-thirds of the putative class members were Illinois residents, and met their burden with respect to the local controversy exception. Accordingly, the court granted plaintiffs' motion to remand the case back to Illinois state court.
Keltner v. Suncoke Energy, Inc., No. 3:14-cv-01374-DRH-PMF (S. D. Ill. May 26, 2015).