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Plaid Half Circle Skirt
My newest creation :) The plaid half circle skirt! I have had this fabric for a loooong time - I bought it from Joann's awhile ago from the "Fall Suitings" section b/c I loved the colors: black/gray/plum - it went with half my wardrobe! After I bought it, though, I didn't know what to make with it - it was too thick/heavy for a dress and so it sat in the box. A few weeks ago, I saw this post on four square wall's blog and I knew... I had to make it into a half circle skirt!
Although there were all these tutorials on how to draft your own with math and things, I was feeling lazy so I just bought a pattern - Simplicity 2758 - a Threads pattern for two kinds of shirts and three skirts, including the half circle skirt - for $1. It was worth it. It took about two seconds to cut out the pattern and the skirt (only two pieces) and put them together. The trickiest part to the skirt for me was the invisible zipper, since I have never done one before and figured this was the perfect opportunity. It was going great... until I realized it was completely wrong, and in the process of trying to fix it, burned the zipper with the iron, rendering it useless and putting my project on hold until the next day when Joann's opened. Haha! Let's just say the second attempt at the invisible zipper installation went MUCH smoother, thanks to the Threads Sewing Guide which I got from the library, and will probably buy (seriously, it has had an answer to every single question/problem I have had). My process was a combination of the Threads method, a tutorial online, and just trial and error. I actually used a regular zipper foot, and it was super easy!
|Front View
|Back view
|Sassy Front View :)
|Side/Back view
|The skirt up closer - love the colors! don't love the weird hip lump caused by my duct tape mannequin... lol
|My first invisible zipper!!! Pretty invisible, eh? :)


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- Research article
- Open Access
Thermodynamically consistent Bayesian analysis of closed biochemical reaction systems
- Garrett Jenkinson1,
- Xiaogang Zhong2 and
- John Goutsias1Email author
© Jenkinson et al; licensee BioMed Central Ltd. 2010
- Received: 21 April 2010
- Accepted: 5 November 2010
- Published: 5 November 2010
Estimating the rate constants of a biochemical reaction system with known stoichiometry from noisy time series measurements of molecular concentrations is an important step for building predictive models of cellular function. Inference techniques currently available in the literature may produce rate constant values that defy necessary constraints imposed by the fundamental laws of thermodynamics. As a result, these techniques may lead to biochemical reaction systems whose concentration dynamics could not possibly occur in nature. Therefore, development of a thermodynamically consistent approach for estimating the rate constants of a biochemical reaction system is highly desirable.
We introduce a Bayesian analysis approach for computing thermodynamically consistent estimates of the rate constants of a closed biochemical reaction system with known stoichiometry given experimental data. Our method employs an appropriately designed prior probability density function that effectively integrates fundamental biophysical and thermodynamic knowledge into the inference problem. Moreover, it takes into account experimental strategies for collecting informative observations of molecular concentrations through perturbations. The proposed method employs a maximization-expectation-maximization algorithm that provides thermodynamically feasible estimates of the rate constant values and computes appropriate measures of estimation accuracy. We demonstrate various aspects of the proposed method on synthetic data obtained by simulating a subset of a well-known model of the EGF/ERK signaling pathway, and examine its robustness under conditions that violate key assumptions. Software, coded in MATLAB®, which implements all Bayesian analysis techniques discussed in this paper, is available free of charge at http://www.cis.jhu.edu/~goutsias/CSS%20lab/software.html.
Our approach provides an attractive statistical methodology for estimating thermodynamically feasible values for the rate constants of a biochemical reaction system from noisy time series observations of molecular concentrations obtained through perturbations. The proposed technique is theoretically sound and computationally feasible, but restricted to quantitative data obtained from closed biochemical reaction systems. This necessitates development of similar techniques for estimating the rate constants of open biochemical reaction systems, which are more realistic models of cellular function.
- Root Mean Square Error
- Posterior Density
- Prior Density
- Maximum Absolute Error
- Reaction Rate Constant
Biochemical reaction systems are popular models of cellular function. These models are extensively used to represent the inter-connectivity and functional relationships among molecular species in cells and, most often, they provide accurate description of cellular behavior. Inferring a biochemical reaction system from experimental data is an important step towards building mathematical and computational tools for the analysis of cellular systems. This step requires both structure (stoichiometry) identification as well as parameter (rate constant) estimation [1–4]. Due however to the large combinatorial complexity of determining the stoichiometry of a biochemical reaction system, solving this problem requires large amounts of high quality experimental data and substantial computational resources, which are not usually available in practice.
Recently, several approaches have been proposed in the literature for addressing a simpler problem, known as model calibration. The objective of model calibration is to adjust the kinetic parameters of a biochemical reaction system with given stoichiometry in order to obtain a sufficiently good match between simulated and observed dynamics; e.g. see [2, 5–11].
Among known model calibration techniques, the ones based on Bayesian analysis [7, 10, 11] are perhaps the most versatile. Bayesian analysis allows us to effectively incorporate biophysical knowledge into the problem at hand and naturally draw statistical conclusions about the unknown kinetic parameters. This is done by employing a probability density function that encapsulates prior information about the rate constants of a biochemical reaction system and by deriving a posterior probability density function over the kinetic parameters after experimental data have been collected. By taking into account the experimental data and the information contained in the prior, the posterior density summarizes all knowledge available about the unknown kinetic parameters and quantifies uncertainty about their true values [12, 13]. Moreover, the posterior allows us to quantify our confidence about estimation accuracy, compute probabilities over alternative calibrations, and design additional experiments to improve inference.
Most published model calibration techniques do not take into account constraints on the reaction rate constants imposed by the fundamental laws of thermodynamics. If these constraints, known as Wegscheider conditions [14, 15], are not explicitly considered by a model calibration technique, then the method will spend most time examining impossible kinetic parameter sets and will most probably produce a biochemical reaction system that is not physically realistic [16]. This issue has been recently recognized in the literature, and new modeling formalisms have been suggested in an effort to address it [17–20]. The proposed formalisms describe a biochemical reaction system by well-defined thermodynamic parameters whose values always guarantee that the reaction rate constants satisfy the Wegscheider conditions. For example, in [19, 20], a biochemical reaction system is parameterized in terms of molecular capacities and reaction resistances, by using a thermodynamic kinetic modeling (TKM) formalism that enjoys a number of advantages over the ones suggested in [17, 18].
We believe that parameterizing a biochemical reaction system in terms of capacities and resistances is unnecessary and, in certain instances, problematic. It has been pointed out in [19] that different choices for the TKM parameters can lead to the same concentration dynamics. As a consequence, the TKM parameters cannot be determined uniquely from concentration measurements. A way to address this problem is to take the capacities to be the equilibrium concentrations (which is always possible in closed biochemical reaction systems), in which case the capacities are constrained by conservation relationships imposed by the system stoichiometry. Then, parameter estimation in the TKM formalism may be possible by arbitrarily fixing a subset of capacity values and estimating the remaining capacities and resistances. However, this approach can be very cumbersome when dealing with molecular perturbations (as we do in this paper) or when merging estimated TKM models, since, in both cases, the capacities may not refer to compatible equilibrium concentrations. It has been suggested in [19] that a way to merge two models using the TKM formalism is to first convert the capacities and resistance to the rate parameters, merge the two models, and then convert back to the TKM formalism. However, this approach seems to be overly complicated, especially in view of the model calibration methodology presented here.
In this paper, we introduce a thermodynamically consistent Bayesian analysis approach to model calibration that does not require reparametrization. Our approach relies on statistically modeling the reaction rate constants of the forward reactions as well as the equilibrium constants of individual reactions. We restrict our attention to closed systems (or systems that can be approximately considered to be closed), since thermodynamic analysis of such systems is easier to handle than open systems. The proposed approach controls thermodynamic consistency of the reaction rate constants by employing well-defined relationships between the kinetic parameters of a biochemical reaction system, imposed by the Wegscheider conditions. By embedding these relationships within an iterative algorithm that finds the mode of the posterior density, we arrive at a thermodynamically consistent Bayesian estimate for the rate constants.
Bayesian analysis can be appreciably influenced by the choice of the prior probability density functions. This is particularly true in systems biology problems in which only a small number of observations is usually available. It is therefore important to focus our effort on constructing appropriate prior densities for the unknown rate constants of the forward reactions and the equilibrium constants of individual reactions. Although a number of choices may be possible, it is imperative to use fundamental biophysical and thermodynamic principles to derive informative prior densities that effectively encapsulate such principles.
By using the classical Arrhenius formula of chemical kinetics [21], we construct an appropriate prior density for the log-rate constants of the forward reactions. To do so, we assume that the prefactor and activation energy associated with the Arrhenius formula are both random variables following log-normal and exponential distributions, respectively. This approach takes into account unpredictable changes in biochemical conditions affecting the structure of the reactant molecules and the probability of reaction after collision. On the other hand, by exploiting the thermodynamic relationship between rate constants, equilibrium concentrations, and stoichiometric coefficients, we derive an analytical expression for the joint prior density of the logarithms of the equilibrium constants. This expression depends on steady-state concentration measurements and on the stoichiometry of the biochemical reaction system under consideration.
Another important issue associated with the inference problem considered in this paper is the need to collect an informative set of measurements that can lead to sufficiently accurate parameter estimation. It has been increasingly recognized in the literature that a powerful approach to accomplish this goal in problems of systems biology is to selectively perturb key molecular components and measure the effects of these perturbations on the underlying concentrations [22–24]. We follow this strategy here and assume that we can selectively perturb, one at a time, the initial concentrations of a selected number of molecular species in a biochemical reaction system, by increasing or decreasing their values without altering the underlying stoichiometry. This can be achieved by a variety of experimental techniques, such as RNA interference (RNAi), transfection, or molecular injection. Therefore, our approach combines Bayesian analysis with current experimental practices, thus bridging the gap between statistical inference approaches and experimental design.
The Bayesian analysis technique discussed in this paper requires numerical evaluation of a number of statistical summaries of the posterior density. Although several methods are available to deal with this problem (e.g., see [25, 26]), we employ here a maximization-expectation-maximization (MEM) strategy that calculates a thermodynamically consistent estimate of the reaction rate constants as well as Monte Carlo estimates of posterior summaries used to evaluate the quality of inference. This strategy is based on sequentially combining a powerful stochastic optimization technique, known as simultaneous perturbation stochastic approximation (SPSA) [27], with Markov chain Monte Carlo (MCMC) sampling [25]. Our experience with extensive synthetic experiments, based on data obtained by simulating a subset of a well-known model of the EGF/ERK signaling pathway, indicates that the proposed algorithm is robust, producing excellent estimation results even in cases of high measurement errors and limited time measurements.
This paper is structured as follows. In the "Methods" section, we provide a brief overview of biochemical reaction systems, discuss how to model perturbations, and present a standard statistical model for the measurements. We then outline our Bayesian analysis approach to model calibration and present our choices for the prior and posterior densities. By emphasizing the fact that the prior density must assign zero probability over the thermodynamically infeasible region of the parameter space, and by employing an encompassing prior approach to Bayesian analysis, we derive an appropriate posterior density that satisfies this condition. We finally outline our proposed methodology for computing thermodynamically consistent Bayesian estimates of the kinetic parameters and for assessing estimation accuracy.
In the "Results/Discussion" section, we provide simulation results, based on a subset of a well-established model of the EGF/ERK signal transduction pathway. These results illustrate key aspects of the proposed model calibration methodology and show its potential for producing sufficiently accurate thermodynamically consistent estimates of a biochemical reaction system from noisy time-series measurements of molecular concentrations.
Finally, in the "Conclusions" section, we discuss a key statistical advantage of the proposed model calibration methodology, viewed from a bias-variance tradeoff perspective. Moreover, we provide suggestions for further research to address a number of practical issues associated with model calibration, such as estimating the initial concentrations and their perturbations, dealing with partially observed or missing data, and extending the proposed technique to the case of open biochemical reaction systems.
Extensive mathematical and computational details are required to rigorously formulate, derive, and understand various aspects of the proposed approach. We provide these details in three Additional files accompanying this paper. In Additional file 1, we present a detailed exposition of the underlying theory, whereas, in Additional file 2, we carefully discuss computational implementation. Finally, in Additional file 3, we provide all necessary details pertaining the biochemical reaction system we use in our simulations. Well-documented software, coded in MATLAB®, which implements all Bayesian analysis techniques discussed in this paper, is available to interested readers free of charge at http://www.cis.jhu.edu/~goutsias/CSS%20lab/software.html.
Biochemical reaction systems
s nm is the net stoichiometry coefficient of the nth molecular species associated with the mth reaction, defined by , and := [0, tmax] is an observation time window of interest.
Equations (2)-(4) are based on the assumption that we can selectively perturb, one at a time, the concentrations of molecular species in a set , by increasing or decreasing their values at time t = 0 without altering the underlying stoichiometry. For notational convenience, we include 0 in and assign p = 0 to the original unperturbed system. In this case, is the concentration of the nth molecular species in the unperturbed system at time t, whereas, , for p ≠ 0, is the concentration of the nth molecular species at time t, obtained by perturbing the initial concentration of the pth species. In (3), π p ≥ -c p quantifies the perturbation applied on the initial concentration c p of the pth molecular species at time t = 0. When -c p ≤ π p < 0, the initial concentration of the pth molecular species is reduced, a situation that can be achieved by a variety of experimental techniques, such as RNA interference (RNAi). On the other hand, when π p > 0, the initial concentration of the pth molecular species is increased, a situation that can be achieved by transfection or molecular injection.
Due to the enormous complexity of biological reaction networks, (1) is used to model a limited number of molecular interactions embedded within a larger and more complex system. Mass flow between the biochemical reaction system given by (1) and its surroundings complicates modeling. As a matter of fact, some molecular concentrations in the system may be influenced by unknown reactions, not modeled by (1), or by partially known reactions with reactants regulated by unknown biochemical mechanisms. To address this problem, we will assume that there is no appreciable mass transfer between the biochemical reaction system and its surroundings during the observation time interval = [0, tmax]. As a consequence, we can assume that (1) characterizes a closed biochemical reaction system within . Moreover, we will assume that the system reaches quasi-equilibrium at some time t* ≤ tmax, after which its thermodynamic properties do not appreciably change for t* < t ≤ tmax. Note however that the quasi-equilibrium assumption does not necessarily imply that the biochemical reaction system will be at thermodynamic equilibrium after time tmax, since mass transfer may take place at some time t > tmax. Although we may be able to satisfy these assumptions by appropriately designed synthetic or in vitro biological experiments, the assumptions are certainly not satisfied in vivo. For this reason, we believe that future research must be focused on extending the approaches and techniques discussed in this paper to the case of open biochemical reaction systems.
for q = 1, 2, ..., Q + 1, where is a multiplicative random error factor and . The assumption of multiplicative errors is common in most data acquisition procedures, such as DNA microarray-based genomics and mass spectrometry-based proteomics [28–30], whereas, the logarithm is used to obtain a convenient additive error model for the measurements.
In the following, we will assume that the biochemical reaction system, and all its perturbed versions, is sufficiently close to steady-state at time point tQ+1. We can justify this assumption by taking t* ≤ tQ+1≤ tmax and by recalling our previous assumption that the biochemical reaction system is at thermodynamic quasi-equilibrium at times t* ≤ t ≤ tmax. Our Bayesian analysis approach is based on data y, whereas, we use the steady-state measurements to derive a joint probability density function for the logarithms {ln(k2m-1/k2m), m ∈ ℳ} of the equilibrium constants of the reactions needed for specifying the posterior density.
Finally, we will assume that the error components are statistically independent zero-mean Gaussian random variables. The Gaussian assumption is quite common in genomic problems and has been experimentally verified in some cases; e.g., see [31]. This assumption is usually justified by the central limit theorem and the premise that the errors are due to a large number of independent multiplicative error sources. We may attempt to justify the independence assumption between measurement errors by arguing that an error occurred in a particular measurement may only be due to the acquisition process used to obtain that measurement and, hence, it may not affect the error values of other measurements. In general, however, this is only a mathematically convenient assumption that may not be realistic. We experimentally demonstrate later that, at least for the example considered in this paper, the proposed estimation methodology is quite effective even in the case of non-Gaussian and correlated measurement errors. For simplicity, we finally assume equal error variances; i.e., we will assume that , for every n, p, and q. This assumption is not crucial to our approach and can be relaxed if necessary.
Bayesian model calibration
In this paper, we deal with the following problem: Given noisy concentration measurements y and , we want to calculate thermodynamically consistent estimates of the log-rate constants κ:= {κ2m-1:= ln k2m-1, κ2m:= ln k2m, m ∈ ℳ} of a closed biochemical reaction system, such that (2), initialized by (3), produce molecular concentrations that "best" match (in some well-defined sense) the available measurements.
We should note here that it is convenient to estimate the logarithms of the rate constants instead of the constants themselves. By focusing on the logarithms, we can reduce the dynamic range of rate constant values and make their estimation numerically easier. To simplify our developments, we will assume that the initial concentrations {c n , n ∈ } and perturbations {π p , p ∈ } are known or have been estimated by an appropriate experimental procedure. When this is not true, these quantities must be treated as unknown parameters and estimated from data, together with the rate constants, provided that a sufficient amount of data is available to allow reliable estimation.
Note that the prior density of the log-rate constants κ depends on z. For this reason, we view z as a set of random hyperparameters (in Bayesian analysis, parameters used to specify prior densities are known as hyperparameters), specified by means of the prior density p(z).
The posterior density p(κ| y) takes into account our prior belief about the rate constant values and the data formation process, summarized by the prior density p(z) of the log-equilibrium constants, the conditional prior density p(κ| z) of the log-rate constants given the log-equilibrium constants, the conditional probability density p(σ2 | κ) of the error variance given the log-rate constants, and the likelihood p(y| κ, σ2). However, the posterior density is hard to interpret, especially in high-dimensional problems that involve many parameters, such as the problem we are dealing with here. As a consequence, the main objective of Bayesian analysis is to produce numerical information that can be effectively used to summarize the posterior density and simplify the task of statistical inference to the extent possible. Typical summaries include measures of location and scale of the posterior, which are used to produce estimates for the parameter values and to evaluate the accuracy of such estimates, respectively.
It is clear from (6) that, to evaluate the posterior p(κ| y), we need to compute the "effective" prior density ∫ p(κ| z)p(z) d z as well as the "effective" likelihood ∫ p(y| κ, σ2)p(σ2 | κ)dσ2. To do so, we must specify the aforementioned densities p(σ2 | κ), p(z), p(κ| z), and p(y| κ , σ2). We discuss this problem next.
Prior density of error variance
for two parameters α, b > 0.
The independence assumption between σ2 and κ is reasonable, in view of the fact that the errors are mainly due to the experimental methodology used to obtain the measurements, whereas, the rate constants are due to biophysical principles underlying the biochemical reaction system. On the other hand, the choice given by (9) has been well-justified in Bayesian analysis. In fact, the inverse gamma distribution is the conjugate prior for the variance of additive Gaussian errors [13]. Conjugate priors are common in Bayesian analysis, since they often lead to attractive analytical and computational simplifications. Note that E[σ2] = b/(α - 1) and var[σ2] = {E[σ2]}2/(α - 2) = b2/[(α - 1)2(α - 2)], for α > 2. Therefore, the parameters α, b control the location and scale of the inverse gamma distribution given by (9). We illustrate this prior in Figure S-1.3 of Additional file 1. In the following, we treat α and b as hyperparameters with known values. For a practical method to determine these values, the reader is referred to Additional file 1.
Prior density of log-equilibrium constants
Before we consider the problem of specifying a prior density for the log-equilibrium constants z, we first investigate how much information about z can be extracted from measurements.
known as Wegscheider conditions [14, 15], where r m is the mth element of the M × 1 vector r, is the N × M stoichiometry matrix of the biochemical reaction system with elements s nm , and null() is the null space of . As a consequence, for a biochemical reaction system to be physically realizable, it is required that the reaction rates satisfy the thermodynamically imposed Wegscheider conditions.
where ℍ is an M × M matrix with elements , and α, b are the two hyperparameters associated with the prior density of the measurement variance, given by (9).
The previous result suggests that we may be able to use as an informative prior for the log-equilibrium constants z; i.e., we may be able to replace p(z) by in (6). At a first glance, this idea may not seem appropriate. However, it perfectly agrees with the fact that, in Bayesian analysis, hyperparameters are often estimated directly from data [13]. Since we have shown here that steady-state measurements can be effectively used to construct the entire posterior probability density function of z, it seems reasonable to use this posterior as a prior density for z. Note however that, by replacing p(z) with in (6), we must make sure that is independent of y(see Additional file 1). Otherwise, our choice for p(z) may not lead to a proper posterior density p(κ| y) (i.e., it may not lead to a density that is finite for all y). Note that the independence of and y is assured by the independence between the measurement errors and .
which we can always evaluate, since matrix is invertible.
Prior density of log-rate constants
where , , and erfc[·] is the complementary error function. We illustrate this prior in Figure S-1.1 of Additional file 1.
Equations (16) and (17) provide an analytical form for the prior density of the log-rate constants. To use this expression, we must determine appropriate values for , which can be treated as hyperparameters. Although we could treat ϕ as random, we will choose here known values for these parameters. This is motivated by the fact that ϕ determines the location and scale of the prior densities of the forward rate constants; see Figure S-1.1 in Additional file 1. In certain problems of interest, there might be enough information to determine possible ranges for the forward rate constant values. As a consequence, we can use this information, together with an appropriate procedure, to effectively determine values for ϕ. The reader is referred to Additional file 1 for details on how to do so.
Effective likelihood
Note that evaluating φ(κ,y) at given values of κ and y requires integration of the system of ordinary differential equations (2).
Posterior density
where θ mm' are the elements of matrix obtained from the SVD decomposition of , and is given by (13).
Note that the posterior density of the log-rate constants is a compromise between the prior and the likelihood. The prior terms ω(κ) and ψ(κ, y) penalize log-rate values that do not fit well with available a-priori information, whereas, the likelihood term φ(κ, y) penalizes log-rate values that produce concentration dynamics which deviate appreciably from measurements. As the number N(P + 1)Q of available measurements increases, this compromise is controlled to a greater extent by the data through the factor φ(κ, y).
A problem arises with the posterior density p(κ| y), given by (21) and (22), since nonzero probabilities may be assigned to thermodynamically infeasible log-rate constants. A Bayesian analyst might argue that we have correctly done our job by formulating the problem as we did and that it is the data which will rule out the possibility that our biochemical reaction system can be characterized by thermodynamically infeasible parameters. However, we choose to trust thermodynamics far more than we would trust noisy data and appropriately modify the posterior density based on our knowledge that the kinetic parameters must satisfy the Wegscheider conditions given by (11).
Clearly, the values of the marginal posterior p W (κ f | y) are proportional to the corresponding values of the original posterior density p(κ f , κ d | y) over the thermodynamically feasible region of the parameter space, given by the hyperplane . In the following, we will base our Bayesian analysis approach on p W (κ f | y).
Computing the posterior mode
In a Bayesian setting, we use the location of the posterior density over the parameter space to provide an estimate of the unknown parameter values. Typically, two measures of location are employed, namely the mode and the mean of the posterior. The posterior mean minimizes the mean-square error between the estimated and true parameters, whereas, the posterior mode is more likely to produce dynamics that closely resemble the true dynamics (see Additional file 1 for why this is true). We note here that the main objective of parameter estimation in biochemical reaction systems is not necessarily to determine parameter values that are "close" to the true values (e.g., in the mean square sense) but to obtain appropriate values for the rate constants so that the resulting molecular concentration dynamics closely reproduce the dynamics observed in the true system [33]. As a consequence, we choose the posterior mode as our parameter estimator.
The posterior log-density lnp W (κ f | y) is usually not concave, especially when a limited amount of highly noisy data y is available. As a consequence, there is no optimization algorithm that can find the posterior mode in a finite number of steps. A method to address this problem would be to randomly sample the parameter space at a predefined (and usually large) number of points and use these points to initialize an optimization algorithm, such as the simultaneous perturbation stochastic approximation (SPSA) algorithm discussed in the Additional file 2. We can then calculate the parameters and the associated values of lnp W (κ f | y) obtained by each initialization after a set number of optimization steps, and declare the parameters associated with the highest log-posterior value as being the desired mode estimates.
Unfortunately, SPSA (and as a matter of fact any other appropriate optimization algorithm) is computationally costly, especially in the case of large biochemical reaction systems. Therefore, using SPSA in the previous multi-seed strategy may result in a computationally prohibitive approach for finding the posterior mode. In order to reduce computations, we may choose only a small number of initial points that we believe are sufficiently proximal to the posterior mode. Two such points might be the prior and posterior means. As a matter of fact, as the data sample size tends to infinity, we expect that the posterior mean will coincide with the posterior mode, since, under suitable regularity conditions, the posterior density converges asymptotically to a Gaussian distribution [12, 34]. This simple idea leads to the sequential maximization-expectation-maximization (MEM) algorithm we discuss in the Additional file 2. According to this algorithm, we perform a relatively small number of SPSA iterations, initialized by the prior mode, to obtain a posterior mode estimate . We then use an MCMC algorithm, initialized by , to obtain an estimate of the posterior mean . Subsequently, we perform another set of SPSA iterations, initialized by , to obtain the posterior mode estimate . We finally set to be the log-rate constants that produce the maximum posterior value during all SPSA and MCMC iterations, and set the optimal estimate of the log-rate constants κ equal to .
Estimation accuracy
A small value of ϵRMSE provides us with confidence that the estimated value of that constant is accurate. On the other hand, the estimate may be perceived as inaccurate if ϵRMSE is exceedingly large.
Another useful metric for evaluating estimation accuracy is , where is the determinant of the posterior covariance matrix . Note that D is the average of the log-eigenvalues of and is related to the well-known D-optimal criterion used in experimental design [27]. We can use D to quantify the overall accuracy of a model calibration result, with smaller values of D indicating better overall accuracy.
Note that the RMSE's ϵRMSE can be computed from the diagonal elements of . It turns out the we can approximate ϵRMSE and D from an estimate of the posterior covariance matrix obtained during the second (MCMC) phase of the proposed MEM algorithm (see Additional file 2 for details).
where and are the true and estimated dynamics of the nth molecular species under the pth perturbation, produced by the biochemical reaction system with log-rate constants κtrue and , respectively. Clearly, ϵMED-AE and ϵMAX-AE provide measures of closeness between the estimated molecular responses and the true molecular responses , normalized by the corresponding true integrated responses . Normalization is required in order to make sure that no one species dominates the error values more than any other. Finally, note that half of the normalized absolute errors will be between 0 and ϵMED-AE, whereas, the remaining half will be between ϵMED-AE and ϵMAX-AE.
In specifying the model depicted in Figure 1, we must provide three sets of physically reasonable values: true rate constant values, initial concentrations, and experimentally feasible perturbations to the initial concentrations. Published values for the reaction rate constants associated with our example are given in Equation (S-3.1) of Additional file 3. However, these values do not correspond to a thermodynamically feasible biochemical reaction system, since they do not satisfy the Wegscheider conditions, given by (11). We should point out here that this is a common problem in systems biology. Reaction rate values are usually amalgamated from various independent sources in the literature, so it is highly unlikely that these values will correspond to a thermodynamically feasible biochemical reaction system. As a consequence, it is desirable to develop a method that uses published values for the reaction rate constants and calculates an appropriate set of thermodynamically feasible values that can be considered as the "true" parameter values. In Additional file 3, we calculate "true" values for the log-rate constants by using a linear least squares approach to project the published values onto the thermodynamically feasible hyperplane. The resulting "true" values are given in Equation (S-3.3) of Additional file 3.
Regarding the initial concentrations, we use the values specified in [35, 36], with two minor modifications. First, molecular species with zero initial concentrations are modified to have a small number of molecules present. We do this to accommodate the fact that, in a real cellular system, these molecular species are constitutively expressed. The second modification comes from the fact that we are no longer modeling the entire EGF/ERK signaling cascade and, therefore, we must account for the upstream EGF stimulus. To take this into account, we increase the initial concentration of the most upstream molecular species in our model, namely (EGF-EGFR*)2-GAP. The initial concentrations used are given by Equation (S-3.4) in Additional file 3.
To specify appropriate perturbations to the initial molecular concentrations, note that molecular complexes, such as dimers, trimers, etc., are far more difficult to perturb than simple monomeric molecular species. For this reason, we focus our perturbation efforts on Shc*, Grb2, and Sos. Since Shc* is commercially available in a purified and quantified form, we will assume that we can increase its initial concentration by a factor of 100 using molecular injection. We will also assume that we can perturb Grb2 and Sos by RNAi, resulting in a decrease in their initial concentrations by a factor of 100. Thus, we set π1 = 99c1, π2 = -.99c2, and π4 = -.99c4.
To avoid specifying different hyperparameter values for the prior densities of the forward log-rate constants, we assume here that all densities share the same known values {κ0, τ, λ}, where κ0 = -5.1010, τ = 1.8990, and λ = 0.7409, whereas, we set α = 3 and b = 1 for the hyperparameters of the prior density of the variance σ2 of the measurement errors. These choices correspond to the prior densities depicted in Figure S-1.2(a) and Figure S-1.3(a) in Additional file 1. We implement our Bayesian analysis approach using the MEM algorithm described in Additional file 2, with I = 5,000 SPSA iterations in each maximization step and a total of L = 50,000 MCMC iterations in the expectation step. Finally, we observe the biochemical reaction system within a time period of 1 min.
Estimated posterior RMSE values for the case of i.i.d. zero-mean Gaussian errors with standard deviation σ = 0.3. Logarithmic sampling is used with Q = 6.
κ 1
κ 3
κ 5
κ 7
κ 9
κ 11
κ 13
κ 15
κ 17
κ 2
κ 4
κ 6
κ 8
κ 10
κ 12
κ 14
κ 16
κ 18
The concentration dynamics produced by the estimated rate constant values match well the dynamics produced by the true values. As a matter of fact, the calculated median and maximum absolute error values imply that half of the relative integrated absolute error values between the estimated and true concentration dynamics (across all molecular species and all applied perturbations) are smaller than 3.03%, whereas, the remaining values are between 3.03% and 16.8%. On the other hand, the estimated posterior RMSE values summarized in Table 1 indicate a high probability that, given the concentration measurements, the log-rate values will lie within a relatively small region around the corresponding posterior mode values.
We expect that, in general, by selecting appropriate perturbations and by increasing the number of concentration data collected during an experiment, we can improve estimation accuracy. However, how can one know if the right perturbations have been applied on the biochemical reaction system and if enough data has been collected in a practical situation? Inspection of RMSE values can provide an answer to these important questions. If the estimated RMSE values of the log-rate constants of many reactions are large, it may be worth collecting additional data by increasing P and Q. Additional data can improve estimation accuracy by shrinking the RMSE values to a size that indicates an acceptable degree of uncertainty. However, if the biochemical reaction system is insensitive to a given kinetic parameter, then the RMSE associated with that reaction may remain large even as the quality of data improves. Therefore, additional data should only be collected when the RMSE values are large and sensitivity analysis indicates that the values of the rate constants associated with these RMSE values appreciably affect the system dynamics.
Estimated values of the D-optimal criterion for uniform and logarithmic sampling schemes.
% change
Estimated values of the D-optimal criterion for different replications and perturbations.
NO: 1 replication
NO: 2 replications
NO: 3 replications
YES: Shc*
YES: Grb2
YES: Sos
YES: Shc*, Grb2
YES: Shc*, Sos
YES: Grb2, Sos
YES: Shc*, Grb2, Sos
Median and maximum absolute error values under a variety of measurement error conditions.
mean = 0
i.i.d. Gaussian
i.i.d. Uniform
correlated Gaussian
σ = 0.1
3.98 × 10-3
8.64 × 10-3
1.48 × 10-2
5.56 × 10-2
4.81 × 10-2
7.32 × 10-2
σ = 0.2
1.01 × 10-2
1.78 × 10-2
3.09 × 10-2
8.29 × 10-2
1.30 × 10-1
1.89 × 10-1
σ = 0.3
3.03 × 10-2
1.78 × 10-2
3.05 × 10-2
1.68 × 10-1
1.30 × 10-1
2.46 × 10-1
σ = 0.4
2.19 × 10-2
2.56 × 10-2
1.04 × 10-1
2.27 × 10-1
1.41 × 10-1
3.67 × 10-1
σ = 0.5
2.67 × 10-2
3.86 × 10-2
6.43 × 10-2
2.48 × 10-1
3.32 × 10-1
3.10 × 10-1
In this paper, we have introduced a novel Bayesian analysis technique for estimating the kinetic parameters (rate constants) of a closed biochemical reaction system from time measurements of noisy concentration dynamics. The proposed procedure enjoys a clear advantage over other published estimation techniques: the estimated kinetic parameters satisfy the Wegscheider conditions imposed by the fundamental laws of thermodynamics. As a consequence, it always leads to physically plausible biochemical reaction systems.
From a statistical perspective, there are additional advantages for thermodynamically restricting the kinetic parameters of a biochemical reaction system to satisfy the Wegscheider conditions. This may be seen through the well-known bias-variance tradeoff in estimation [27]. The mean squared error of a given estimator can be decomposed into a bias term and a variance term. In general, imposing constraints on the estimator may increase its bias but decrease its variance (hence the tradeoff). However, if the true parameter values satisfy the constraints, then the variance may decrease without increasing the bias term [27]. Since the true values of the kinetic parameters must lie on the thermodynamically feasible manifold in the parameter space, confining the Bayesian estimator to this manifold (which is of lower dimension than the parameter space itself) may lead to lower mean squared error due to a smaller variance. Since the thermodynamically feasible manifold is of lower dimension than the parameter space, gains in variance (and hence improvements in the mean squared error) are expected to be large. This may be seen through the "curse of dimensionality," which refers to the exponential increase in the volume of the parameter space as its dimension grows, making estimation exponentially harder in higher dimensional spaces (in our example, the unconstrained parameter space has 12.5% more dimensions than the thermodynamically feasible subspace). The Wegscheider conditions reduce the dimensionality of the parameter space to a feasible region in which estimation may be easier. Thus, the proposed Bayesian analysis procedure improves on other estimation techniques by producing a statistically superior, physically meaningful and plausible estimate for the kinetic parameters of a closed biochemical reaction system.
The Bayesian analysis methodology discussed in this paper has been formulated by assuming that all initial concentrations and perturbations are precisely known and that concentration measurements can be obtained by directly sampling all system dynamics. However, current experimental practices in quantitative systems biology restrict the amount and type of data that can be collected from living cells. As a consequence, further research is needed to develop approaches that can accommodate this important issue and make a Bayesian analysis approach to parameter estimation better applicable to systems biology problems.
If the initial concentrations and the perturbations applied on these concentrations are not known, then we may try to estimate them together with the unknown kinetic parameters. Although formulation of this problem is similar to the one considered in this paper, the additional computational burden will be substantial. Moreover, while quantitative biochemical techniques are improving, the vast majority of data available in problems of systems biology are obtained by measuring ratios of molecular concentrations (e.g., by using techniques such as SILAC [37]). Estimation of the rate constants of a biochemical reaction system from concentration measurements available as ratios relative to a reference system requires special consideration and extensive modification of the proposed Bayesian analysis procedure. Finally, it is very important to address the problem of missing observations. This is a common problem in systems biology, since it is not possible to monitor and measure the concentrations of all molecular species present in the system. Although appropriate modifications to the proposed algorithm can lead to a Bayesian analysis approach that can handle missing data, we think that development of a practically effective way to address this problem is challenging. Our future plan is to expand and improve the Bayesian analysis procedure discussed in this paper in order to provide practical solutions to the previous problems.
It is worth noting here that the estimation procedure suggested in this paper applies only to closed biochemical reaction systems (or to approximations of closed systems embedded in a larger open system). However, a cell is an open system, since it effectively interacts with its environment. If we include the cell's environment into our system and monitor the combined system until steady-state (i.e., until cell death), then we would have the necessary closed system. Unfortunately, this is clearly an unrealistic scenario. As a consequence, there is also a need to develop a theoretical and computational approach for dealing with thermodynamically consistent parameter estimation in open biochemical reaction systems.
To conclude, it has been argued in a recent paper [33] that most models of computational systems biology are "sloppy," in the sense that many parameters of such models do not appreciably alter system behavior. A key conclusion of this paper is that collective fitting procedures (such as the Bayesian analysis technique presented in the present paper) are far more desirable than piecewise construction of a biochemical reaction system model from individual parameter estimates (which is how most models are constructed when investigators scour the literature for individual rate constant values). Moreover, it has been pointed out in [33] that using a method to obtain precise parameter values may be difficult, even with an unlimited amount of data, since the behavior of a sloppy model is insensitive to the values of most parameters. As a consequence, the authors suggest that, instead of focusing on the quality of parameter estimation, it will be more wise to focus on the quality of prediction achieved by an estimated model (as we have also argued in this paper).
To a certain extent, our Bayesian analysis approach addresses some of the issues raised in [33]. By imposing the Wegscheider conditions on the kinetic parameters of a biochemical reaction system, we can effectively constrain these parameters to a thermodynamically feasible manifold in the parameter space, thus reducing sloppiness. Moreover, we can effectively use the RMSE values and the D-optimal criterion to determine an appropriate experimental design and distinguish those estimated values that can be trusted from those that cannot. For example, if the RMSE value associated with a kinetic parameter is small, then we may trust these values. On the other hand, a large RMSE value may indicate high uncertainty in the estimated parameter values, which may be untrustworthy. As we mentioned before, if a sensitivity analysis approach, such as the one proposed in [38], indicates that the kinetic parameters associated with large RMSE values are influential parameters, then we must reduce these RMSE values to an acceptable level of uncertainty by adopting a new and more effective experimental design approach. On the other hand, if these parameters correspond to a non-influential reaction, then we can accept the estimated values with no further consideration, since high uncertainty in the exact values of these parameters will not affect the predicted concentration dynamics.
GJ has been supported by The National Defense Science and Engineering Graduate (NDSEG) Fellowship through the Department of Defense. JG acknowledges the National Science Foundation (NSF) for support of this research. The authors thank the anonymous reviewers for their insightful comments, which resulted in a great improvement of the paper. Special thanks to Ali Afshar for identifying several problems with the original version of this paper and for suggesting appropriate fixes.
Authors' Affiliations
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This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

subset: commoncrawl


text:
With the inclusion of the 4.2 patch into the mix, we all finally have somewhere new to play in.
Aren't you all excited?
By now, hopefully a lot of us have had a chance to poke your heads in there, take a look-see at the various types of trash that you'll run into, maybe make a run at a boss or two.
The first time we stepped into the place, we went in fairly blind without much in the way of advance preparation so the various types of trash that we ran into proved to be a bit of an…interesting experience.
As previously stated…new instances are fun aren't they?
But after a few more attempts, there are a few things that we can all keep in mind so that at least the trash in the instance isn't quite as strenuous.
The first thing to keep in mind when entering is that instead of having most the mobs as stationary packs, almost all of the trash in the first section of the raid patrols along circuitous paths that can make it a little hard to keep track of them. It's important when designating and executing pulls that you keep in mind where those mobs are or at the least, which direction that they'll come from when they come around the bend once more.
Knowing at least where they'll come from next will let you plan and position your current pull accordingly. Remember to keep your eyes open and to scan around the area as you fight so that even if something does happen to sneak up and catch you by surprise, you'll have enough advance warning to move your current pack of mobs and your own raid out of it's path until you're ready for it.
There are a few specific mobs that have an ability or skill that you'll need to watch out for:
When it comes to tanking the Molten Lords, keep track of the stacking debuff and trade off tanking it with your offtank until the debuff drops off so that you can take it back. This only really becomes a problem if you accidentally pull an extra pack or so, so just monitor the debuff and taunt back and forth accordingly.
The roving packs of Hell Hounds shouldn't be AoE'd down as the longer that they're alive, the harder it will become to keep tanks and raid members alive through their damage. Mark them up as needed, CC what you can, when you can and burn them down one at a time to deal with them.
The Giant scorpion and his litter aren't too bad for an AoE as long as you keep in mind the fact that they'll blow up when they die so be sure to either get out of range or to stagger the blasts so that they don't all occur at once.
The turtle packs are best avoided if you can possibly do so and if you can't, know that there will be an awful lot of knockbacks going on and position your raid and yourself accordingly whenever possible.
The final tricky batch are the Unbound elementals, a big one and a couple little baby ones at it's side. All that you need to really do is to kill the big one slowly and work on whichever add is targetted by the big one's buff.
Best of luck!


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Call the Midwife actress Helen George and The Wanted's Jay McGuiness are confirmed to compete in the 2016 Strictly Come Dancing UK tour, together with EastEnders actor Jake Wood who starred in the 2014 TV series.
This completes the line-up for the tour, which stops off at Sheffield Arena for two nights on Tuesday 26th & Wednesday 27th January 2016.
Jay, Helen and Jake join Frankie Bridge from The Saturdays, actress Georgia May Foote from Coronation Street, Celebrity chef Ainsley Harriott and TV presenter Anita Rani. They will be joining the 2016 tour host Mel Giedroyc, together with judges Len Goodman, Craig Revel Horwood (who also directs the tour) and Bruno Tonioli for the live extravaganza.
The professional dancers from this year's ratings winning BBC One TV series are also now announced and the pairings are:
Frankie Bridge and Kevin Clifton
Georgia May Foote and Giovanni Pernice
Helen George and Aljaz Skorjanec
Ainsley Harriott and Natalie Lowe
Jay McGuiness and Aliona Vilani
Anita Rani and Gleb Savchenko
Jake Wood and Janette Manrara
Karen Clifton will also join the tour for the professional dances and group numbers.
Helen George is best known for her role of Nurse Trixie Franklin in the multi award winning and hit BBC series Call the Midwife. She has also received critical acclaim for her varied stage credits in productions such as The Woman in White; High School Musical, and also appeared in Paul W S Anderson's feature film The Three Musketeers.
Helen says: "I had a brilliant time on Strictly, so I'm very excited to continue my journey and have the chance to show off some of my favourite dances to audiences on the UK Tour. I'm sure that it'll be amazing to dance in front of huge arena audiences with Aljaz! I'm very much looking forward to the audience helping me feel at home on the dance floor each night!"
Jay McGuiness is one fifth of the globally successful band, The Wanted. Since their 2010 debut album was released, they have enjoyed huge success all over the world, including two number one singles in the UK with All Time Low and Glad You Came.
Jay says: "I'm used to performing in front of massive crowds when touring with The Wanted, but performing with Aliona on the Strictly Live Tour will be a totally different experience. I'm loving my time on the TV series, but the idea of being back out in front of live arena audiences is something I'm very much looking forward to."
Jake Wood has been a part of some of the most memorable storylines on EastEnders for the past decade, performing the role of Max Branning. He impressed the judges and TV audiences alike with his slick dance moves on last year's BBC One series of Strictly Come Dancing, and now he has the chance to show off his skills to arena audiences on the Strictly Come Dancing Live Tour 2016.
Jake says: "I really enjoyed my time on Strictly last year but couldn't do the tour due to EastEnders' filming commitments, so it's great to get the chance to throw myself into the whole experience again as part of the 2016 Strictly tour. I'm so pleased that Frankie is doing the tour as well so we can enjoy reliving it all again together and I'm very happy that I'm being reunited with Janette. I can't wait to relive some of my favourite dances in arenas across the country!"
Tickets for the 2016 Strictly Come Dancing Live UK Tour, which stops off at Sheffield Arena on Tuesday 26th & Wednesday 27th January 2016 are now on sale.
Tickets purchased by telephone on 0114 256 5656 or online at www.sheffieldarena.co.uk are priced £38.50 - £71.50 (including booking fee). Tickets purchased in person at the Arena Box Office are priced £36.75 - £68.25 (including booking fee).


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Hello everyone, my first post on here, and a total technophobe, so please be gentle :-)
Years ago I built loads of small maps (Mac OS9) purely for 3-player battles. I've downloaded the version that runs on OSX, but can only see the original map. How do I get the maps I created to run (along with several others which were old favourites from the Infinity disc) on the OSX version?
Many thanks in advance.


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Advice for a buildPost Date: 2012-05-03
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Topic: Advice for a build
Posted: 03 May 2012 at 1:10pm
Budget: ~$5000 or less
Expectations: Don't care about how it looks. I want awesome performance, but noise is a major concern. Willing to spend more (within reason) to keep noise in check.
Usage: Mostly gaming (30" monitor at 2560x1600), also some general video/photo editing, multipurpose stuff.
Explanation of some of my choices:
- I want to future-proof by buying big now with the expectation that I likely won't be changing any parts for several years. Additional video card or replacing to next-gen (or next next gen) is the one potential exception.
- Unlike most people, I don't attempt to backup the entire Internet locally. So I can actually fit most everything I have on less than 500GB. The single SSD will contain almost everything but my media/data files. I will definitely have to uninstall games I don't play often, but I'm okay with that. I tend to play a game until I'm done with it, then I can turf it.
- I realize 16GB of RAM is not needed for gaming. For the increase in price I'd rather have it now than worry about upgrading it down the road.
Major questions:
1. I think IvyBridge 3770K will suit my needs fine. My one concern is whether the GTX 690 (or possibly x2 690 SLI in the future) will be held back by the platform. Would 3930K allow for greater performance? All the GTX690 reviews I've looked at tested using 3960X. (EDIT: From what I'm reading now, platform isn't an issue)
2. Which case? Hot-swappable bays don't matter. I don't intend to open the case often, so access isn't a big concern. Size and weight are irrelevant. Looks don't matter. Digital Storm has so many options, and it really isn't clear to me what the benefits are to each one. A nice chart with each case side by side would be helpful. Trying to keep the system as quiet as possible is my first concern with the case selection. The Dreadnought, Valkyrie, Slade, and Hailstorm series all seem like options. Apparently the Aventum is SandyBridge-E only? Once selecting Expert Mode for Chassis selection, there are a million more options. I like the idea of the vertical heat dissipation cases I've read about. But again, noise is my biggest concern, so long as the case is large enough to easily hold everything and allow proper heat ventilation. Even a 24" tall case is perfectly fine.
3. Power supply. Is1000W sufficient even if I go x2 GTX 690 SLI in the future? One question I have is whether going with a higher capacity power supply has any benefit? If it is less stressed, does it run quieter? Does a larger PSU actually use more electricity, or is it more about what capacity it can actually handle if you need it?
4. Cooling: The options in the configuration tool provide zero feedback to give any kind of guidance. I am looking for maintenance-free use, with good bang for the buck. Perhaps H20 Stage 3? But I don't really know what that effectively gives me over Stage 2, or whether Stage 4 might be significantly better for the money spent. The costs ramp up pretty quick from there.
5. CPU boost: Will Stage 3 overclocking harm the life of the CPU? I think not provided the cooling is sufficient. In which case, what cooling level is required with 3770K CPU, and with 1 or 2 GTX 690s? Keeping in mind the 690 vents only half the air to the case exterior, and the other half to the interior.
6. Any thoughts on shipping costs to Western Canada?
Here's a build I've put together for input:
Price: $4414
Edited by ChrisM - 03 May 2012 at 1:58pm
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|Quote Reply Posted: 03 May 2012 at 2:31pm
Hi ChrisM....Welcome to the forums. I' m running out of time until tomorrow. Maybe others can help until then. I would make some changes like psu to 750hx for single card and dropping the noise suppresion package and chsnging cpu cooler to D14 or H100. Overall your build looks great. Gotta go.
EDIT.....Hi ChrisM....Looking over things, in addition to changing cooler I would up psu to 1050HX for sli potential in the future that you asked about. A bigger psu will use more juice but only when it needs to. Psu's are quiet and the larger one gives you headroom for the future and when they drop a bit in efficiency with age.
Go with the Dreadnought HafX case. It is the best for cooling. The Cosmo is expensive, huge and doesn't cool as well. It could be made to cool better, but that would be adding fans. The fans in HafX are large and slow moving, meaning less noise then a bunch of smaller fans. It will still be noisier than an older, smaller single fan case from box store, but the noise is usually gpu fans anyways.
Go with 1600Mhz ram,. The difference is nil.
Drop noise suppresion package. A waste.
We would normally say to drop the free oc'ing on video card, but I'm not sure how that would apply to the 690. I would ask DS, No sense stressing the card more than you have to,
I think level 2 oc would do it just fine. Not sure the added is worth it without going to a water cooling solution. Not sure but it plays it a little safer
Hope this helps and good luck
Edited by bprat22 - 04 May 2012 at 4:04am
Joined: 03 May 2012
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Posts: 2
|Quote Reply Posted: 04 May 2012 at 8:53am
Thank you very much bprat22.
Here is a new build, taking your suggestions into account. As well I made a couple of other modifications. For example, I changed the motherboard since the only advantage I could see to the deluxe model is built in WIFI which I'm not interested in.
684067Price: $3,844.00
Chassis Model: Special Deal Hot Seller - Cooler Master 942 HAF X
Exterior Finish: - Standard Factory Finish
Trim Accents: - Standard Factory Finish
Processor: Intel Core i7 3770K 3.50 GHz (Unlocked CPU) (Quad Core) (Requires Z77 Motherboard)
Motherboard: ASUS SABERTOOTH Z77 (Intel Z77 Chipset) (Features Thermal Armor and Thermal Radar)
System Memory: 16GB DDR3 1600MHz Digital Storm Certified Performance Series (Highly Recommended) (Hand Tested)
Power Supply: 1050W Corsair Pro Silver 1050HX (Dual/Triple/Quad SLI Compatible)
Expansion Bay: - No Thanks
Hard Drive Set 1: Operating System: 1x (240GB Solid State (By: Intel) (Model: 520 Series)
Set 1 Raid Options: - No Thanks
Hard Drive Set 2: Multimedia\Data: 1x (500GB Western Digital/Seagate/Hitachi/Samsung (7200 RPM) (16MB Cache)
Hard Drive Set 3: Backup\Misc.: - No Thanks
Optical Drive 1: DVD-R/RW/CD-R/RW (DVD Writer 24x / CD-Writer 48x)
Optical Drive 2: - No Thanks
Internet Access: High Speed Network Port (Supports High-Speed Cable / DSL / Network Connections)
Video Card: 1x NVIDIA GeForce GTX 690 4GB (Includes PhysX)
Add-on Card: - No Thanks
Sound Card: ASUS Xonar Essence STX Virtual 7.1 (PCI-Express Slot Based)
Extreme Cooling: H20: Stage 2: Corsair H100 Liquid CPU Cooler (240mm Radiator) (Extreme-Performance Edition)
H20 Tube Color:- Not Applicable, I do not have a FrostChill or Sub-Zero LCS Cooling System Selected
Chassis Airflow: Standard Factory Chassis Fans
Internal Lighting: - No Thanks
Enhancements: - No Thanks
Chassis Mods: - No Thanks
Noise Reduction: - No Thanks
LaserMark: - No Thanks
Boost Processor: Stage 2: Overclock CPU 4.5GHz to 4.8GHz (Requires Pro/Deluxe/Sabertooth Motherboard)
Boost Video Card: FREE: Yes, Overclock the video card(s) as much as possible with complete stability
Boost Memory: - No Thanks, Please do not overclock my memory
Boost OS: - No Thanks, Please do not tweak the services on the operating system
Windows OS: Microsoft Windows 7 Home Premium (64-Bit Edition)
Recovery Tools: Windows Recovery Toolkit (Bundled with Windows 7 CD)
Virus Protection: - No Thanks
Office: - No Thanks
Game: - No Thanks
Display: - No Thanks
Surge Shield: - No Thanks
Speakers: - No Thanks
Keyboard: - No Thanks
Mouse: - No Thanks
External Storage: - No Thanks
Exclusive T-Shirt: FREE: Digital Storm T-Shirt - Black (Medium)
Priority Build: - No Thanks, Ship Within 10-15 Business Days After Order Is Successfully Processed
Warranty: Life-time Expert Customer Care with 3 Year Limited Warranty
A couple more questions:
Why is the noise suppression package a waste? Is it that the case already includes noise canceling materials in the construction? Or the case is already so silent with the large fans? From what I've read, the H100 can be noisy depending on whether it is set to low/med/hi. I'm not sure how DS decides what to set it to, but I would imagine we can direct them in that regard. The medium/normal/balanced setting seems reasonable to me, provided that will still allow for reasonable overclock rates.
The GTX 690 reviews say that nvidia has selected chips with a fair bit of OC headroom in them. The stock settings are turned down a bit just to keep the TDP under 300W. So that's why I selected the overclocking service. As you say, I don't want to stress the card unnecessarily, but DS can probably provide good info here.
Edited by ChrisM - 04 May 2012 at 8:57am
DigitalStorm East -- (Unofficially!)
Joined: 08 Jun 2011
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Posts: 19950
|Quote Reply Posted: 04 May 2012 at 9:21am
That build look great. I like air cooler on cpu better but the H100 works good.
If the H100 cooler can be kept at low, medium speeds then it's not bad. For that chip we always recommend the D14 or Phanteks. Quiet all the time, cools great and very reliable.
Hope this helps.
Joined: 27 Oct 2011
Online Status: Offline
Posts: 234
|Quote Reply Posted: 04 May 2012 at 10:11am
I agree with bprat on the noise suppression package, especially if you want to OC your GPU.
OCing your GPU requires one to monitor temps closely (of course, if DS is doing this for your, it's OK). The noise suppression won't be as beneficial for your GPU when it is OCed.
Edited by criTalon - 04 May 2012 at 10:11am
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Joined: 22 Feb 2010
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Posts: 2253
|Quote Reply Posted: 05 May 2012 at 9:36am
Hi ChrisM,
I would not OC the 690 with an air cooled GPU. One of the 680s dumps its heat right into the case. The 690 is a good candidate for OC but I would be concerned with doing it with air cooling. Now you could add a 120mm fan behind the drive bay so it blows directly on the GPU pushing the air towards the back of the case. That might be a fix. All of this is opinion right now as there is not enough systems out there to learn from. Hopefully DS will give us some feedback on how they are doing with the 690 and air cooling in the standard HAF-X case.
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|1403 5th Street in Suite B
|Mon-Wed 9am-5pm
|Thu 2:30pm-5pm
|Fri 9am-5pm
Davis Holistic Health Center offers a wide variety of alternative medical services under one roof. Recently moved to 1403 5th Street, the Davis Holistic Health Center has been in Davis since 1992 offering quality service for Davis and the surrounding communities of Sacramento, Vacaville, Dixon, Winters and Fairfield.
"Whether you are looking for acupuncture, massage, counseling, or meditation and yoga classes, our practitioners and teachers bring years of experience to helping you regain health and wholeness."
Services offered through Davis Holistic Health Center include
Many of the Asian Classics Institute classes are taught here.
- Brian Dempsey Licensed Acupuncturist and Herbalist (email Brian)
Brian Dempsey, L.Ac, is the acupuncturist and herbalist for the Davis Holistic Health Center and is the founder and owner of the Davis Holistic Health Center. He has practiced acupuncture and herbal medicine for the past 18 years. After graduating from the American College of Traditional Chinese Medicine and the Cheng Du College of Traditional Chinese Medicine, Dempsey began practice in Davis as director and practitioner of the Davis Holistic Health Center in 1993. Brian is also a founding member of the Sutter Center for Integrative Health and teaches as an adjunct teacher at the Davis Medical School. He has taught hatha yoga since 1979.
Brian treats a variety of health problems including pain conditions such as muscle strain, sciatic, bursitis, numbness and tingling symptoms and shooting pain. He also treats migraine and tension headaches, neck tension, whiplash and low back pain. Brian also follows the NIH protocols on acupuncture treatments for arthritis, including degenerative disc disease to assist patients with pain due to this condition.
In addition to pain conditions, Brian also treats fibromyalgia, chronic fatigue, Irritable bowel, chrones, gastritis and other common GI conditions. He is also completing a book on natural treatments for the common cold including chronic conditions such as a low immune system, chronic cough and chronic allergies. Brian also has experience in weight loss, hormonal problems such as PMS and menopause, liver disorders such as Hepitis or portal hypertension and insomnia. Many of Brian's patients have insurance that covers acupuncture treatment. For an appointment, call 530-758-7525.
- Rebecca Pope, PhD holds the rank of Master of Medical Qigong (a branch of Chinese Medicine). She has practiced qigong and tai chi for decades and trained in Medical Qigong through the International Instutute of Medical Qigong, She teaches public classes and sees private clients who want to use qigong for specific health or emotional issues. She is a member of the practitioner team at the Sutter Center for Integrative Health.
- Janet Papale (RYT 500) has taught yoga since 1994. Reflecting the two traditions in which she has trained, Iyengar and Krishnamacharya, her teaching focuses on respecting the unique needs of every individual, uncovering obstacles, and helping students find ways to honor body, energy, mind, emotion, and spirit. She maintains a close relationship with her principal inspiration and mentor, Israeli teacher Gila Zion. She has completed a 500 hour Iyengar-based teacher training with Donald Moyer and Mary Lou Weprin, focusing on asana and pranayama, and including kinesiology with Judith Lasater and pranayama with Richard Rosen. Mentored by Ann Rogers, she is a certified teacher with the Krishnamacharya Healing Yoga Foundation in Chennai, India, and has attended trainings with Desikachar. She has also recently completed a training is restorative yoga with Judith Lasater and is a certified Relax and Renew trainer.
- Lin Turner has been teaching yoga for over 30 years. She has received training from Ananda Yoga center and has served as an adjunct teacher for their yoga teacher training. Lin Turner has a philosophy of creating a safe class to explore poses in an environment that helps students to cultivate awareness and relaxation. Private sessions available. (530) 757-2780.
- Armita Pishdadi completed a 200-hour training with Dennis Eagan at BO Tree in Davis and specialty training in Prenatal and Postnatal yoga with Jane Austin at Yoga Tree in San Francisco. Armita continues to deepen her teaching through continued study with various teachers, believing we are all lifelong students of yoga.
- Brian Dempsey has been trained since 1981 and has been trained through the Mt. Madonna Center Yoga Teacher Training Program as well as with Jennifer Lynn, Judith Lasiter and many other teachers.
- Jenna McAsey has been certified through Integrative Yoga Therapy and has been teaching Yoga for 12 years in Davis. She studied also with Agi Jacob and Judith Lasater in Iyengar and Restorative Yoga. Her recent certification is in Kundalini Yoga - as taught by Yogi Bhajan and she is enthusiastic to now offer the energetic practice of Kundalini Yoga in Davis. Kundalini yoga classes are suitable for adults , include ecxercise, breathwork, chanting, meditation and relaxation. new students please call for more info: 530 756 4494
- Denise Dempsey and Mindfulness-Based Stress Reduction Programs, Massage and Bodywork, has moved to a new Davis location. Visit http://www.StressReductionPrograms.com for more information or call 530-304-4341 or email Denise at firstname.lastname@example.com.
Tom Richards is a Certified Rolfer and Movement Practitioner. Rolfing (aka Structural Integration) is a type of body manipulation that addresses recent and lifelong patterns of strain in the body and its connective tissues. Postural balance and functional use is restored through hands-on bodywork and gentle exercises. For an appointment call (510)759-2047


text:
FRANKFURT (Reuters) - Quarterly earnings from SAP AG fell short of expectations on Tuesday, showing the German business software maker failed to keep up with arch-rival Oracle Corp and sending its shares sharply lower.
SAP's results, released more than a week before they had been scheduled, showed group revenue rose 12 percent to 5.06 billion euros ($6.8 billion), below an average analyst forecast of 5.17 billion, according to Reuters data.
Operating profit rose more slowly than revenue, gaining 10 percent to 1.96 billion euros, resulting in SAP's operating margin narrowing by 0.8 percentage points to 38.8 percent in the three months through December.
The figures contrasted with those of Oracle, the world's No. 3 software maker and SAP's biggest competitor, which last month forecast strong sales for 2013 after posting a 17 percent jump in quarterly software revenue.
SAP shares were down 3.8 percent and were the biggest decliners in a 2 percent weaker sector index by 1500 GMT. The stock fell as low as 57.77 euros, its lowest in two months.
"Especially after their main competitor Oracle managed to beat estimates last month, many had hoped that SAP would follow suit," said Markus Huber, head of German trading at ETX Capital.
"Even by missing estimates by a very small margin, and despite having had an overall excellent 2012, investors are venting their disappointment by dumping the stock heavily," Huber added.
SAP said its operating profit was affected by investments in web-based software products, also known as "cloud" businesses.
The Walldorf, southern Germany-based company, along with peers such as IBM and Oracle, are betting on faster-growing, web-based software products, which are less vulnerable to the economic downturn as there are no upfront costs for program licenses, dedicated hardware or installation.
SAP last year splashed out $7.7 billion to buy internet-based computing companies Ariba and SuccessFactors.
The web-based services garnered just 342 million euros in 2012 revenues, a fraction of the total 16.3 billion.
With a market capitalization of $100 billion, SAP is the second-most valuable company in the German blue chip index behind Volkswagen, which is worth $105 billion, according to Reuters data.
SAP is due to publish full financial results and a full-year outlook on January 23. Some analysts fear the 2013 outlook could also disappoint.
"Guidance could be slightly below current consensus and expectations may be revised lower," Barclays analyst Gerardus Vos said in a note.
SAP's full-year operating profit at constant currencies reached 5.02 billion euros, missing its own guidance for at least 5.05 billion euros.
($1 = 0.7482 euros)
Additional reporting by Maria Sheahan; Editing by David Holmes


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Aloha and mahalo for visiting Kona Wine Market!
Kona's first specialty Beer, Wine & Spirit source.
Providing top shelf service and products to the westside of Big Island for over 26 years!
Local Delivery and Nationwide Shipping Available
Drink Global...Buy Local!
Our Favorites
Niner Paso Robles Cabernet Sauvignon 2015
- we92
$50.00 $45.00
Bodegas Avancia Cuvée de O Godello 2016Cuvee de O Godello expresses the freshest expression of the Valdeorras' unique slate soils and indigenous clones of Godello. Sur lie ageing and used large format barrel fermentation impart a weight and complexity to Cuvée de O that sets it apart from other young Godellos.
- ws90
- we88
$21.00 $18.90
Orin Swift Machete 2015A brooding hue and viscous consistency, powerful aromas of crème de cassis, boysenberry, and ripe cherries are complemented by a soft minerality and hints of dark chocolate. The entry is smooth with dark plums and a charred meatiness that floods to create a velvety texture that lingers.
- ws92
$67.00 $60.30
Foxen Santa Maria Valley Pinot Noir 2016
- we91
$49.00 $44.10
Tenuta Sant'Antonio Tenuta Santantonio Scaia Bianco 2017Colour: pale straw yellow with greenish glints. Aroma: white acacia and jasmine flowers, bouquet of citrus fruits such as grapefruit, orange and pineapple, apple, pear and mango and a light touch of banana. Flavour: fresh and agreeable with a sustained acidity. Well-balanced softness and tang make it both inviting and intriguing.
$17.00 $15.30
Chateau Musar Red 20072007 was a good year for the Cinsault which displayed red berry and concentrated cherry fruits, reminiscent of Amarone. As the Cabernet Sauvignon rested in vats, it showed intense coffee bean aromas and ripe fruit, typical of a warmer vintage. Nearly 7 years on, upon its' release in the spring of 2014, the wine is showing impressive aromas of blueberries, ripe blackberries, cherries and plum fruitcake. The palate is initially soft and round with primary fruits of blackberry, plum, red cherries and pomegranate with some chocolaty spicy notes in the background. It opens up with mouth filling berry fruit and delicious notes of concentrated dark fruits and molasses. It really begins to impress in the mid palate where it holds its' intensity and structure effortlessly all the way to the end. The alcohol is well integrated with elegant but youthful tannins - it is a well-balanced wine with good underpinning acidity and a compelling structure.
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New Beer (Bottles and Draught for Growler Fills)
North Coast Brewing Co. Barrel Aged Old Rasputin 500ml BottleEvery year we age a special batch of our much-loved Russian Imperial Stout in Bourbon barrels. The depth, intensity, and complexity of the flavor profile of this special release, like its predecessors, make it a worthy tribute to Old Rasputin.
Van Steenberge Sour Power Six Pack Sampler 6 pack 330ml BottleThe Petrus Sour Power Six Pack Sampler features 4 of Global Beer's finest sour ale offerings brewed by Br. De Brabandere. Included in the pack are three Petrus Aged Pale, and one each of Petrus Aged Red, Petrus 50/50, and Petrus Oud Bruin.
Sierra Nevada Hazy Little Thing 6 pack 12oz BottleA hop-heavy, unfiltered, hazy little thing called IPA. As brewers, we get the privilege to sample our beers straight from the tanks in all their raw glory. Some beers need a little polishing to get ready to go out into the world, while others-the hop-heavy, rowdy, crowd-pleasers-should just be left alone. We wanted to share this brewery-only treat with you, so we present this Hazy Little Thing, our unfiltered, unprocessed IPA, straight from the tanks and into the can. Food Pairings: Grilled pork ribs with brown sugar & bourbon BBQ sauce. Sharp white cheddar. Pineapple upside-down cake.


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Toronto 2011: ATO bringing 'The Oranges' to theaters in 2012
Interest in dramatic comedies remains strong at the Toronto International Film Festival, with independent distributor ATO Pictures on Friday acquiring rights to Julian Farino's intergenerational romance "The Oranges." ATO says it will bring the movie to U.S. theaters in 2012.
The movie stars Hugh Laurie (of TV's "House" fame) as a New Jersey father who strikes up a romance with the twentysomething daughter of his best friend, played by Leighton Meester (known for her role on "Gossip Girl"). The affair throws the lives of two families into disarray. The cast also includes Catherine Keener, Oliver Platt and Allison Janney.
The announcement reflects a Toronto deal market that has warmed up as the festival has gone along, particularly for dramatic comedies. CBS Films earlier in the week picked up rights to director Lasse Hallstrom's culture-clash film "Salmon Fishing in the Yemen," IFC on Wednesday bought Lynn Shelton's love-triangle dramedy "Your Sister's Sister," and Jennifer Westfeldt's marital tale "Friends With Kids" has drawn the interest of several buyers.
Also on Friday, the movie arm of World Wrestling Entertainment acquired rights to "The Day," Doug Aarniokoski's post-apocalyptic thriller.
Toronto 2011: 'The Oranges' unpeels laughs and unease
Toronto 2011: Clooney's 'Descendants' is fall's big wild card
Toronto 2011: Woody Harrelson and Oren Moverman build a rampart
-- Steve Zeitchik in Toronto
Photo: The cast of "The Oranges." Credit: Toronto International Film Festival