Effect of acetate on glucose utilization by isolated rat thymocytes

The effects of acetate and ammonium salts on glucose metabolism, aminoisobutyric acid influx, and radioiodinated insulin binding in isolated thymocytes were studied. Acetate in the concentration range, 0.1–30 mm, was found to inhibit basal and insulin-stimulated CO₂ production whereas ammonium chloride at concentrations greater than 0.3 mm was slightly stimulatory. Ammonium salts inhibited glucose incorporation into glycogen and aminoisobutyric acid influx only at high concentration (30 mm). Neither acetate nor ammonium salts had significant effects on glucose incorporation into glycogen or aminoisobutyric acid influx at lower concentrations. No effect on insulin binding was observed. The observation that very low concentrations of acetate can perturb these biological assay systems suggests that other biological functions may be affected by trace amounts of buffer salts carried over from protein isolation steps.     

A fault detection service for wide area distributed computations

The potential for faults in distributed computing systems is a significant complicating factor for application developers. While a variety of techniques exist for detecting and correcting faults, the implementation of these techniques in a particular context can be difficult. Hence, we propose a fault detection service designed to be incorporated, in a modular fashion, into distributed computing systems, tools, or applications. This service uses well-known techniques based on unreliable fault detectors to detect and report component failure, while allowing the user to trade off timeliness of reporting against false positive rates. We describe the architecture of this service, report on experimental results that quantify its cost and accuracy, and describe its use in two applications, monitoring the status of system components of the GUSTO computational grid testbed and as part of the NetSolve network-enabled numerical solver. This revised version was published online in July 2006 with corrections to the Cover Date.     

Signaling cascades as cellular devices for spatial computations

Signaling networks usually include protein-modification cycles. Cascades of such cycles are the backbones of multiple signaling pathways. Protein gradients emerge from the spatial separation of opposing enzymes, such as kinases and phosphatases, or guanine nucleotide exchange factors (GEFs) and GTPase activating proteins (GAPs) for GTPase cycles. We show that different diffusivities of an active protein form and an inactive form leads to spatial gradients of protein abundance in the cytoplasm. For a cascade of cycles, using a discrete approximation of the space, we derive an analytical expression for the spatial gradients and show that it converges to an exact solution with decreasing the size of the quantization. Our results facilitate quantitative analysis of the dependence of spatial gradients on the network topology and reaction kinetics. We demonstrate how different cascade designs filter and process the input information to generate precise, complex spatial guidance for multiple GTPase effector processes. Thus, protein-modification cascades may serve as devices to compute complex spatial distributions of target proteins within intracellular space.     

Structural characterization of the peroxodiiron(III) intermediate generated during oxygen activation by the W48A/D84E variant of ribonucleotide reductase protein R2 from Escherichia coli

The diiron(II) cluster in the R2 subunit of Escherichia coli ribonucleotide reductase (RNR) activates oxygen to generate a mu-oxodiiron(III) cluster and the stable tyrosyl radical that is critical for the conversion of ribonucleotides to deoxyribonucleotides. Like those in other diiron carboxylate proteins, such as methane monooxygenase (MMO), the R2 diiron cluster is proposed to activate oxygen by formation of a peroxodiiron(III) intermediate followed by an oxidizing high-valent cluster. Substitution of key active site residues results in perturbations of the normal oxygen activation pathway. Variants in which the active site ligand, aspartate (D) 84, is changed to glutamate (E) are capable of accumulating a mu-peroxodiiron(III) complex in the reaction pathway. Using rapid freeze-quench techniques, this intermediate in a double variant, R2-W48A/D84E, was trapped for characterization by M?ssbauer and X-ray absorption spectroscopy. These samples contained 70% peroxodiiron(III) intermediate and 30% diferrous R2. An Fe-Fe distance of 2.5 A was found to be associated with the peroxo intermediate. As has been proposed for the structures of the higher valent intermediates in both R2 and MMO, carboxylate shifts to a mu-(eta(1),eta(2)) or a mu-1,1 conformation would most likely be required to accommodate the short 2.5 A Fe-Fe distance. In addition, the diferrous form of the enzyme present in the reacted sample has a longer Fe-Fe distance (3.5 A) than does a sample of anaerobically prepared diferrous R2 (3.4 A). Possible explanations for this difference in detected Fe-Fe distance include an O(2)-induced conformational change prior to covalent chemistry or differing O(2) reactivity among multiple diiron(II) forms of the cluster.     

Cutting the Wires: Modularization of Cellular Networks for Experimental Design

Understanding naturally evolved cellular networks requires the consecutive identification and revision of the interactions between relevant molecular species. In this process, initially often simplified and incomplete networks are extended by integrating new reactions or whole subnetworks to increase consistency between model predictions and new measurement data. However, increased consistency with experimental data alone is not sufficient to show the existence of biomolecular interactions, because the interplay of different potential extensions might lead to overall similar dynamics. Here, we present a graph-based modularization approach to facilitate the design of experiments targeted at independently validating the existence of several potential network extensions. Our method is based on selecting the outputs to measure during an experiment, such that each potential network extension becomes virtually insulated from all others during data analysis. Each output defines a module that only depends on one hypothetical network extension, and all other outputs act as virtual inputs to achieve insulation. Given appropriate experimental time-series measurements of the outputs, our modules can be analyzed, simulated, and compared to the experimental data separately. Our approach exemplifies the close relationship between structural systems identification and modularization, an interplay that promises development of related approaches in the future.     

Two approaches for metabolic pathway analysis?

Metabolic pathway analysis is becoming increasingly important for assessing inherent network properties in (reconstructed) biochemical reaction networks. Of the two most promising concepts for pathway analysis, one relies on elementary flux modes and the other on extreme pathways. These concepts are closely related because extreme pathways are a subset of elementary modes. Here, the common features, differences and applicability of these concepts are discussed. Assessing metabolic systems by the set of extreme pathways can, in general, give misleading results owing to the exclusion of possibly important routes. However, in certain network topologies, the sets of elementary modes and extreme pathways coincide. This is quite often the case in realistic applications. In our opinion, the unification of both approaches into one common framework for metabolic pathway analysis is necessary and achievable.