Ioannis Ch. Paschalidis

Achieving reliability and high accuracy in automated protein docking: Cluspro, PIPER, SDU, and stability analysis in CAPRI rounds 13–19

Our approach to protein—protein docking includes three main steps. First, we run PIPER, a rigid body docking program based on the Fast Fourier Transform (FFT) correlation approach, extended to use pairwise interactions potentials. Second, the 1000 best energy conformations are clustered, and the 30 largest clusters are retained for refinement. Third, the stability of the clusters is analyzed by short Monte Carlo simulations, and the structures are refined by the medium‐range optimization method SDU. The first two steps of this approach are implemented in the ClusPro 2.0 protein–protein docking server. Despite being fully automated, the last step is computationally too expensive to be included in the server. When comparing the models obtained in CAPRI rounds 13–19 by ClusPro, by the refinement of the ClusPro predictions and by all predictor groups, we arrived at three conclusions. First, for the first time in the CAPRI history, our automated ClusPro server was able to compete with the best human predictor groups. Second, selecting the top ranked models, our current protocol reliably generates high‐quality structures of protein–protein complexes from the structures of separately crystallized proteins, even in the absence of biological information, provided that there is limited backbone conformational change. Third, despite occasional successes, homology modeling requires further improvement to achieve reliable docking results. Proteins 2010.

Asymptotic buffer overflow probabilities in multiclass multiplexers: an optimal control approach

We consider a multiclass multiplexer with support for multiple service classes and dedicated buffers for each service class. Under specific scheduling policies for sharing bandwidth among these classes, we seek the asymptotic (as the buffer size goes to infinity) tail of the buffer overflow probability for each dedicated buffer. We assume dependent arrival and service processes as is usually the case in models of bursty traffic. In the standard large deviations methodology, we provide a lower and a matching (up to first degree in the exponent) upper bound on the buffer overflow probabilities. We introduce a novel optimal control approach to address these problems. In particular, we relate the lower bound derivation to a deterministic optimal control problem, which we explicitly solve. Optimal state trajectories of the control problem correspond to typical congestion scenarios. We explicitly and in detail characterize the most likely modes of overflow. We specialize our results to the generalized processor sharing policy (GPS) and the generalized longest queue first policy (GLQF). The performance of strict priority policies is obtained as a corollary. We compare the GPS and GLQF policies and conclude that GLQF achieves smaller overflow probabilities than GPS for all arrival and service processes for which our analysis holds. Our results have important implications for traffic management of high-speed networks and can be used as a basis for an admission control mechanism which guarantees a different loss probability for each class.

Demand-Side Management for Regulation Service Provisioning Through Internal Pricing

We develop a market-based mechanism that enables a building smart microgrid operator (SMO) to offer regulation service reserves and meet the associated obligation of fast response to commands issued by the wholesale market independent system operator (ISO) who provides energy and purchases reserves. The proposed market-based mechanism allows the SMO to control the behavior of internal loads through price signals and to provide feedback to the ISO. A regulation service reserves quantity is transacted between the SMO and the ISO for a relatively long period of time (e.g., a one-hour-long time-scale). During this period the ISO follows shorter time-scale stochastic dynamics to repeatedly request from the SMO to decrease or increase its consumption. We model the operational task of selecting an optimal short time-scale dynamic pricing policy as a stochastic dynamic program that maximizes average SMO and ISO utility. We then formulate an associated nonlinear programming static problem that provides an upper bound on the optimal utility. We study an asymptotic regime in which this upper bound is shown to be tight and the static policy provides an efficient approximation of the dynamic pricing policy. Equally importantly, this framework allows us to optimize the long time-scale decision of determining the optimal regulation service reserve quantity. We demonstrate, verify and validate the proposed approach through a series of Monte Carlo simulations of the controlled system time trajectories.

Spatio-temporal network anomaly detection by assessing deviations of empirical measures

We introduce an Internet traffic anomaly detection mechanism based on large deviations results for empirical measures. Using past traffic traces we characterize network traffic during various time-of-day intervals, assuming that it is anomaly-free. We present two different approaches to characterize traffic: (i) a model-free approach based on the method of types and Sanovs theorem, and (ii) a model-based approach modeling traffic using a Markov modulated process. Using these characterizations as a reference we continuously monitor traffic and employ large deviations and decision theory results to ldquocomparerdquo the empirical measure of the monitored traffic with the corresponding reference characterization, thus, identifying traffic anomalies in real-time. Our experimental results show that applying our methodology (even short-lived) anomalies are identified within a small number of observations. Throughout, we compare the two approaches presenting their advantages and disadvantages to identify and classify temporal network anomalies. We also demonstrate how our framework can be used to monitor traffic from multiple network elements in order to identify both spatial and temporal anomalies. We validate our techniques by analyzing real traffic traces with time-stamped anomalies.

Large deviations analysis of the generalized processor sharing policy

In this paper we consider a stochastic server (modeling a multiclass communication switch) fed by a set of parallel buffers. The dynamics of the system evolve in discrete-time and the generalized processor sharing (GPS) scheduling policy of [25] is implemented. The arrival process in each buffer is an arbitrary, and possibly autocorrelated, stochastic process. We obtain a large deviations asymptotic for the buffer overflow probability at each buffer. In the standard large deviations methodology, we provide a lower and a matching (up to first degree in the exponent) upper bound on the buffer overflow probabilities. We view the problem of finding a most likely sample path that leads to an overflow as an optimal control problem. Using ideas from convex optimization we analytically solve the control problem to obtain both the asymptotic exponent of the overflow probability and a characterization of most likely modes of overflow. These results have important implications for traffic management of high-speed networks. They extend the deterministic, worst-case analysis of [25] to the case where a detailed statistical model of the input traffic is available and can be used as a basis for an admission control mechanism.

Probabilistic Service Level Guarantees in Make-to-Stock Manufacturing Systems

We consider a model of a multiclass make-to-stock manufacturing system. External demand for each product class is met from the available finished goods inventory; unsatisfied demand is backlogged. The objective is to devise a production policy that minimizes inventory costs subject to guaranteeing stockout probabilities to stay bounded above by given constants e j , for each product classj( service level guarantees). Such a policy determines whether the facility should be producing ( idling decisions), and if it should, which product class ( sequencing decisions). Approximating the original system, we analyze a correspondingfluid model to make sequencing decisions and employlarge deviations techniques to make idling ones. We consider both linear and quadratic inventory cost structures to obtain apriority-based and ageneralized longest queue first-based production policy, respectively. An important feature of our model is that it accommodates autocorrelated demand and service processes, both critical features of modern failure-prone manufacturing systems.

Inventory Control for Supply Chains with Service Level Constraints: A Synergy between Large Deviations and Perturbation Analysis

We consider a model of a supply chain consisting of n production facilities in tandem and producing a single product class. External demand is met from the finished goods inventory maintained in front of the most downstream facility (stage 1); unsatisfied demand is backlogged. We adopt a base-stock production policy at each stage of the supply chain, according to which the facility at stage i produces if inventory falls below a certain level wi and idles otherwise. We seek to optimize the hedging vector w=(w1,...,wn) to minimize expected inventory costs at all stages subject to maintaining the stockout probability at stage 1 below a prescribed level (service level constraint). We make rather general modeling assumptions on demand and production processes that include autocorrelated stochastic processes. We solve this stochastic optimization problem by combining analytical (large deviations) and sample path-based (perturbation analysis) techniques. We demonstrate that there is a natural synergy between these two approaches.

Statistical location detection with sensor networks

The paper develops a systematic framework for designing a stochastic location detection system with associated performance guarantees using a wireless sensor network. To detect the location of a mobile sensor, the system relies on RF-characteristics of the signal transmitted by the mobile sensor, as it is received by stationary sensors (clusterheads). Location detection is posed as a hypothesis testing problem over a discretized space. Large deviations results enable the characterization of the probability of error leading to a placement problem that maximizes an information-theoretic distance (Chernoff distance) among all pairs of probability distributions of observations conditional on the sensor locations. The placement problem is shown to be NP-hard and is formulated as a linear integer programming problem; yet, large instances can be solved efficiently by leveraging special-purpose algorithms from the theory of discrete facility location. The resultant optimal placement is shown to provide asymptotic guarantees on the probability of error in location detection under quite general conditions by minimizing an upper bound of the error-exponent. Numerical results show that the proposed framework is computationally feasible and the resultant clusterhead placement performs near-optimal even with a small number of observation samples in a simulation environment.

Protein docking by the underestimation of free energy funnels in the space of encounter complexes.

Similarly to protein folding, the association of two proteins is driven by a free energy funnel, determined by favorable interactions in some neighborhood of the native state. We describe a docking method based on stochastic global minimization of funnel-shaped energy functions in the space of rigid body motions (SE(3)) while accounting for flexibility of the interface side chains. The method, called semi-definite programming-based underestimation (SDU), employs a general quadratic function to underestimate a set of local energy minima and uses the resulting underestimator to bias further sampling. While SDU effectively minimizes functions with funnel-shaped basins, its application to docking in the rotational and translational space SE(3) is not straightforward due to the geometry of that space. We introduce a strategy that uses separate independent variables for side-chain optimization, center-to-center distance of the two proteins, and five angular descriptors of the relative orientations of the molecules. The removal of the center-to-center distance turns out to vastly improve the efficiency of the search, because the five-dimensional space now exhibits a well-behaved energy surface suitable for underestimation. This algorithm explores the free energy surface spanned by encounter complexes that correspond to local free energy minima and shows similarity to the model of macromolecular association that proceeds through a series of collisions. Results for standard protein docking benchmarks establish that in this space the free energy landscape is a funnel in a reasonably broad neighborhood of the native state and that the SDU strategy can generate docking predictions with less than 5 Å ligand interface Cα root-mean-square deviation while achieving an approximately 20-fold efficiency gain compared to Monte Carlo methods.

Guest Editorial Special Issue on Wireless Sensor and Actuator Networks

The 16 full papers and 7 technical notes in this special issue focus on wireless sensor and actuator networks. The full papers in this issue can be broadly organized into three main categories: (i) control with WSANS, (ii) control of WSANS, and (iii) WSAN node placement/deployment.