Integration of protein binding interfaces and abundance data reveals evolutionary pressures in protein networks

Networks of protein-protein interactions have received considerable interest in the past two decades for their insights about protein function and evolution. Traditionally, these networks only map the functional partners of proteins; they lack further levels of data such as binding affinity, allosteric regulation, competitive vs noncompetitive binding, and protein abundance. Recent experiments have made such data on a network-wide scale available, and in this thesis I integrate two extra layers of data in particular: the binding sites that proteins use to interact with their partners, and the abundance or “copy numbers” of the proteins. By analyzing the networks for the clathrin-mediated endocytosis (CME) system in yeast and the ErbB signaling pathway in humans, I find that this extra data reveals new insights about the evolution of protein networks. The structure of the binding site or interface interaction network (IIN) is optimized to allow higher binding specificity; that is, a high gap in strength between functional binding and nonfunctional mis-binding. This strongly implies that mis-binding is an evolutionary error-load constraint shaping protein network structure. Another method to limit mis-binding is to balance protein copy numbers so that there are no “leftover” proteins available for mis-binding. By developing a new method to quantify balance in IINs, I show that the CME network is significantly balanced when compared to randomly sampled sets of copy numbers. Furthermore, IINs with a biologically realistic structure produce less mis-binding under balanced concentrations, when compared to random networks, but more mis-binding under unbalanced concentrations. This implies strong pressure for copy number balance and that any imbalance should occur for functional reasons. I thus explore some functional consequences of imbalance by constructing dynamic models of two poorly balanced subnetworks of the larger CME network. In general, I find that balanced copy numbers provide higher protein complex yield (number of complete complexes), but imbalance may allow cells to “bottleneck” a functional process, effectively turning complex formation on or off via spatial localization of subunits. Finally, I find that strongly binding proteins are more likely to be balanced, as these “sticky” proteins would be more likely to engage in mid-binding otherwise

Pre-dialysis clinic attendance improves quality of life among hemodialysis patients

BACKGROUND: Although previous research has demonstrated that referral to pre-dialysis clinics is associated with favourable objective outcomes, the benefit of a pre-dialysis clinic from the perspective of patient-perceived subjective outcomes, such as quality of life (QOL), is less well defined. METHODS: A retrospective incident cohort study was conducted to determine if pre-dialysis clinic attendance was a predictor of better QOL scores measured within the first six months of hemodialysis (HD) initiation. Inclusion criteria were HD initiation from January 1 1998 to January 1 2000, diagnosis of chronic renal failure, and completion of the QOL questionnaire within six months of HD initiation. Patients receiving HD for less than four weeks were excluded. An incident cohort of 120 dialysis patients was identified, including 74 patients who attended at least one pre-dialysis clinic and 46 patients who did not. QOL was measured using the SF 36-Item Health Survey. Independent variables included age, sex, diabetes, pre-dialysis clinic attendance and length of attendance, history of ischemic heart disease, stroke, peripheral vascular disease, heart failure, malignancy, and chronic lung disease, residual creatinine clearance at dialysis initiation, and kt/v, albumin and hemoglobin at the time of QOL assessment. Bivariate and multivariate linear regression analyses were used to identify predictors of QOL scores. RESULTS: Multivariate analysis suggested that pre-dialysis clinic attendance was an independent predictor of higher QOL scores in four of eight health domains (physical function, p < 0.01; emotional role limitation, p = 0.01; social function, p = 0.01; and general health, p = 0.03), even after statistical adjustment for age, sex, residual renal function, kt/v, albumin, and co-morbid disease. Pre-dialysis clinic attendance was also an independent predictor of the physical component summary score (p = 0.03). CONCLUSIONS: We conclude that pre-dialysis clinic attendance favourably influences patient-perceived quality of life within six months of dialysis initiation

Nonlinear lattice model of viscoelastic Mode III fracture

We study the effect of general nonlinear force laws in viscoelastic lattice models of fracture, focusing on the existence and stability of steady-state Mode III cracks. We show that the hysteretic behavior at small driving is very sensitive to the smoothness of the force law. At large driving, we find a Hopf bifurcation to a straight crack whose velocity is periodic in time. The frequency of the unstable bifurcating mode depends on the smoothness of the potential, but is very close to an exact period-doubling instability. Slightly above the onset of the instability, the system settles into a exactly period-doubled state, presumably connected to the aforementioned bifurcation structure. We explicitly solve for this new state and map out its velocity-driving relation

Detection statistics in the micromaser

We present a general method for the derivation of various statistical quantities describing the detection of a beam of atoms emerging from a micromaser. The user of non-normalized conditioned density operators and a linear master equation for the dynamics between detection events is discussed as are the counting statistics, sequence statistics, and waiting time statistics. In particular, we derive expressions for the mean number of successive detections of atoms in one of any two orthogonal states of the two-level atom. We also derive expressions for the mean waiting times between detections. We show that the mean waiting times between de- tections of atoms in like states are equivalent to the mean waiting times calculated from the uncorrelated steady state detection rates, though like atoms are indeed correlated. The mean waiting times between detections of atoms in unlike states exhibit correlations. We evaluate the expressions for various detector efficiencies using numerical integration, reporting re- sults for the standard micromaser arrangement in which the cavity is pumped by excited atoms and the excitation levels of the emerging atoms are measured. In addition, the atomic inversion and the Fano-Mandel function for the detection of de-excited atoms is calculated for compari- son to the recent experimental results of Weidinger et al. [1], which reports the first observation of trapping states.Comment: 26 pages, 11 figure

Sequence Analysis of the Cloned mRNA Coding for Glyceraldehyde-3-Phosphate Dehydrogenase from Chicken Heart Muscle

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66279/1/j.1432-1033.1983.tb07239.x.pd

A Cognitive Architecture Based on a Learning Classifier System with Spiking Classifiers

© 2015, Springer Science+Business Media New York. Learning classifier systems (LCS) are population-based reinforcement learners that were originally designed to model various cognitive phenomena. This paper presents an explicitly cognitive LCS by using spiking neural networks as classifiers, providing each classifier with a measure of temporal dynamism. We employ a constructivist model of growth of both neurons and synaptic connections, which permits a genetic algorithm to automatically evolve sufficiently-complex neural structures. The spiking classifiers are coupled with a temporally-sensitive reinforcement learning algorithm, which allows the system to perform temporal state decomposition by appropriately rewarding “macro-actions”, created by chaining together multiple atomic actions. The combination of temporal reinforcement learning and neural information processing is shown to outperform benchmark neural classifier systems, and successfully solve a robotic navigation task

Spectral Energy Distributions of type 2 QSOs: obscured star formation at high redshifts

We present new mid-infrared and submillimetre observations for a sample of eight high redshift type-2 QSOs located in the Chandra Deep Field South. The sources are X-ray absorbed with luminosities in excess of 10^44 erg/s. Two of the targets have robust detections, S/N > 4, while a further three targets are marginally detected with S/N > =2.5. All sources are detected in multiple mid-infrared bands with the Spitzer Space Telescope. The multiwavelength spectral energy distributions (SEDs) of the type-2 QSOs are compared to those of two local ultraluminous galaxies (Arp220 and IR22491) in order to assess contributions from a star-forming component in various parts of the SED. We suggest that their submillimetre emission is possibly due to a starburst while a large fraction of the mid-infrared energy is likely to originate in the obscured central quasar. Using the mid-infrared and submm observations we derive infrared luminosities which are found to be in excess of L>10^12Lsun. The submillimetre (850micron) to X-ray (2 keV) spectral indices (alpha_SX) span a wide range. About half of the type-2 QSOs have values typical for a Compton-thick AGN with only 1 per cent of the nuclear emission seen through scattering and, the remaining with values typical of submm-bright galaxies. Combining the available observational evidence we outline a possible scenario for the early stages of evolution of these sources.Comment: Accepted for publication in MNRA