Global parameter identification of stochastic reaction networks from single trajectories

We consider the problem of inferring the unknown parameters of a stochastic biochemical network model from a single measured time-course of the concentration of some of the involved species. Such measurements are available, e.g., from live-cell fluorescence microscopy in image-based systems biology. In addition, fluctuation time-courses from, e.g., fluorescence correlation spectroscopy provide additional information about the system dynamics that can be used to more robustly infer parameters than when considering only mean concentrations. Estimating model parameters from a single experimental trajectory enables single-cell measurements and quantification of cell--cell variability. We propose a novel combination of an adaptive Monte Carlo sampler, called Gaussian Adaptation, and efficient exact stochastic simulation algorithms that allows parameter identification from single stochastic trajectories. We benchmark the proposed method on a linear and a non-linear reaction network at steady state and during transient phases. In addition, we demonstrate that the present method also provides an ellipsoidal volume estimate of the viable part of parameter space and is able to estimate the physical volume of the compartment in which the observed reactions take place.Comment: Article in print as a book chapter in Springer's "Advances in Systems Biology

‘Glocal’ Robustness Analysis and Model Discrimination for Circadian Oscillators

To characterize the behavior and robustness of cellular circuits with many unknown parameters is a major challenge for systems biology. Its difficulty rises exponentially with the number of circuit components. We here propose a novel analysis method to meet this challenge. Our method identifies the region of a high-dimensional parameter space where a circuit displays an experimentally observed behavior. It does so via a Monte Carlo approach guided by principal component analysis, in order to allow efficient sampling of this space. This ‘global’ analysis is then supplemented by a ‘local’ analysis, in which circuit robustness is determined for each of the thousands of parameter sets sampled in the global analysis. We apply this method to two prominent, recent models of the cyanobacterial circadian oscillator, an autocatalytic model, and a model centered on consecutive phosphorylation at two sites of the KaiC protein, a key circadian regulator. For these models, we find that the two-sites architecture is much more robust than the autocatalytic one, both globally and locally, based on five different quantifiers of robustness, including robustness to parameter perturbations and to molecular noise. Our ‘glocal’ combination of global and local analyses can also identify key causes of high or low robustness. In doing so, our approach helps to unravel the architectural origin of robust circuit behavior. Complementarily, identifying fragile aspects of system behavior can aid in designing perturbation experiments that may discriminate between competing mechanisms and different parameter sets

Inferring causal molecular networks: empirical assessment through a community-based effort.

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

The Pitfalls of Central Clearing in the Presence of Systematic Risk

Through the lens of market participants' objective to minimize counterparty risk, we provide an explanation for the reluctance to clear derivative trades in the absence of a central clearing obligation. We develop a comprehensive understanding of the benefits and potential pitfalls with respect to a single market participant's counterparty risk exposure when moving from a bilateral to a clearing architecture for derivative markets. Previous studies suggest that central clearing is beneficial for single market participants in the presence of a sufficiently large number of clearing members. We show that three elements can render central clearing harmful for a market participant's counterparty risk exposure regardless of the number of its counterparties: 1) correlation across and within derivative classes (i.e., systematic risk), 2) collateralization of derivative claims, and 3) loss sharing among clearing members. Our results have substantial implications for the design of derivatives markets, and highlight that recent central clearing reforms might not incentivize market participants to clear derivatives

The process of selective exposure: Why confirmatory information processing weakens over time

ArticleCopyright © 2010 Elsevier Inc. All rights reserved.The present research investigated whether the tendency to prefer decision-consistent to decision-inconsistent information after making a preliminary choice would vary during the sequential process of searching for additional pieces of decision-relevant information. Specifically, it was tested whether decision makers would be more confirmatory in their information evaluation and search at the commencement rather than end of an information search process. In fact, five studies revealed that participants exhibited stronger confirmatory tendencies in both information evaluation (Studies 2 and 5) and search (Studies 1, 3, and 4) immediately after making a preliminary decision compared to during the later stages of an information search process. With regard to the underlying mechanism, results further revealed that individuals appear to be more motivated to detect the best decision alternative at the beginning (as opposed to the end) of an information search process, which leads to increases in confirmatory information processing during these stages

Report from the HarmoSter study: Impact of calibration on comparability of LC-MS/MS measurement of circulating cortisol, 17OH-progesterone and aldosterone

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is recommended for measuring circulating steroids. However, assays display technical heterogeneity. So far, reproducibility of corticosteroid LC-MS/MS measurements has received scant attention. The aim of the study was to compare LC-MS/MS measurements of cortisol, 17OH-progesterone and aldosterone from nine European centers and assess performance according to external quality assessment (EQA) materials and calibration. Seventy-eight patient samples, EQA materials and two commercial calibration sets were measured twice by laboratory-specific procedures. Results were obtained by in-house (CAL1) and external calibrations (CAL2 and CAL3). We evaluated intra and inter-laboratory imprecision, correlation and agreement in patient samples, and trueness, bias and commutability in EQA materials. Using CAL1, intra-laboratory CVs ranged between 2.8-7.4%, 4.4-18.0% and 5.2-22.2%, for cortisol, 17OH-progesterone and aldosterone, respectively. Trueness and bias in EQA materials were mostly acceptable, however, inappropriate commutability and target value assignment were highlighted in some cases. CAL2 showed suboptimal accuracy. Median inter-laboratory CVs for cortisol, 17OH-progesterone and aldosterone were 4.9, 11.8 and 13.8% with CAL1 and 3.6, 10.3 and 8.6% with CAL3 (all p<0.001), respectively. Using CAL1, median bias vs. all laboratory-medians ranged from -6.6 to 6.9%, -17.2 to 7.8% and -12.0 to 16.8% for cortisol, 17OH-progesterone and aldosterone, respectively. Regression lines significantly deviated from the best fit for most laboratories. Using CAL3 improved cortisol and 17OH-progesterone between-method bias and correlation. Intra-laboratory imprecision and performance with EQA materials were variable. Inter-laboratory performance was mostly within specifications. Although residual variability persists, adopting common traceable calibrators and RMP-determined EQA materials is beneficial for standardization of LC-MS/MS steroid measurements