Robustness analysis of genetic regulatory networks affected by model uncertainty

A fundamental problem in systems biology consists of investigating robustness properties of genetic regulatory networks (GRNs) with respect to model uncertainty. This paper addresses this problem for GRNs where the coefficients are rationally affected by polytopic uncertainty, and where the saturation functions are not exactly known. First, it is shown that a condition for ensuring that the GRN has a globally asymptotically stable equilibrium point for all admissible uncertainties can be obtained in terms of a convex optimization problem with linear matrix inequalities (LMIs), hence generalizing existing results that mainly consider only the case of GRNs where the coefficients are linearly affected by the uncertainty and the regulatory functions are in SUM form. Second, the problem of estimating the worst-case convergence rate of the trajectories to the equilibrium point over all admissible uncertainties is considered, and it is shown that a lower bound of this rate can be computed by solving a quasi-convex optimization problem with LMIs. Third, the paper considers the problem of estimating the set of uncertainties for which the GRN has a globally asymptotically stable equilibrium point. This problem is addressed, first, by showing how one can compute estimates with fixed shape by solving a quasi-convex optimization problem with LMIs, and second, by deriving a procedure for computing estimates with variable shape. Numerical examples illustrate the use of the proposed techniques.     

Decision-theoretic planning with generalized first-order decision diagrams

Many tasks in AI require representation and manipulation of complex functions. First-Order Decision Diagrams (FODD) are a compact knowledge representation expressing functions over relational structures. They represent numerical functions that, when constrained to the Boolean range, use only existential quantification. Previous work has developed a set of operations for composition and for removing redundancies in FODDs, thus keeping them compact, and showed how to successfully employ FODDs for solving large-scale stochastic planning problems through the formalism of relational Markov decision processes (RMDP). In this paper, we introduce several new ideas enhancing the applicability of FODDs. More specifically, we first introduce Generalized FODDs (GFODD) and composition operations for them, generalizing FODDs to arbitrary quantification. Second, we develop a novel approach for reducing (G)FODDs using model checking. This yields – for the first time – a reduction that maximally reduces the diagram for the FODD case and provides a sound reduction procedure for GFODDs. Finally we show how GFODDs can be used in principle to solve RMDPs with arbitrary quantification, and develop a complete solution for the case where the reward function is specified using an arbitrary number of existential quantifiers followed by an arbitrary number of universal quantifiers.     

Virtual Reference Feedback Tuning for non-minimum phase plants

Model reference control design methods fail when the plant has one or more non-minimum phase zeros that are not included in the reference model, leading possibly to an unstable closed loop. This is a very serious problem for data-based control design methods, where the plant is typically unknown. In this paper, we extend the Virtual Reference Feedback Tuning method to non-minimum phase plants. This extension is based on the idea proposed in Lecchini and Gevers (2002) for Iterative Feedback Tuning. We present a simple two-step procedure that can cope with the situation where the unknown plant may or may not have non-minimum phase zeros.     

Explicit/multi-parametric model predictive control (MPC) of linear discrete-time systems by dynamic and multi-parametric programming

This work presents a new algorithm for solving the explicit/multi-parametric model predictive control (or mp-MPC) problem for linear, time-invariant discrete-time systems, based on dynamic programming and multi-parametric programming techniques. The algorithm features two key steps: (i) a dynamic programming step, in which the mp-MPC problem is decomposed into a set of smaller subproblems in which only the current control, state variables, and constraints are considered, and (ii) a multi-parametric programming step, in which each subproblem is solved as a convex multi-parametric programming problem, to derive the control variables as an explicit function of the states. The key feature of the proposed method is that it overcomes potential limitations of previous methods for solving multi-parametric programming problems with dynamic programming, such as the need for global optimization for each subproblem of the dynamic programming step.     

Identification and data-driven model reduction of state-space representations of lossless and dissipative systems from noise-free data

We illustrate procedures to identify a state-space representation of a lossless or dissipative system from a given noise-free trajectory; important special cases are passive systems and bounded-real systems. Computing a rank-revealing factorization of a Gramian-like matrix constructed from the data, a state sequence can be obtained; the state-space equations are then computed by solving a system of linear equations. This idea is also applied to perform model reduction by obtaining a balanced realization directly from data and truncating it to obtain a reduced-order model.     

Stabilization of linear systems over networks with bounded packet loss and its use in model predictive control

This paper considers stabilization of discrete-time linear systems, where network exists for transmitting the sensor and controller information, and arbitrary bounded packet loss occurs in the sensor–controller link and the controller–actuator link. The stabilization of this system is transformed into the robust stabilization of a set of systems. The stability result for this system is specially applied on model predictive control (MPC) that explicitly considers the satisfaction of input and state constraints. Two synthesis approaches of MPC are presented, one parameterizing the infinite horizon control moves into a single state feedback law, the other into a free control move followed by the single state feedback law. Two simulation examples are given to illustrate the effectiveness of the proposed techniques.     

A global piecewise smooth Newton method for fast large-scale model predictive control

In this paper, the strictly convex quadratic program (QP) arising in model predictive control (MPC) for constrained linear systems is reformulated as a system of piecewise affine equations. A regularized piecewise smooth Newton method with exact line search on a convex, differentiable, piecewise-quadratic merit function is proposed for the solution of the reformulated problem. The algorithm has considerable merits when applied to MPC over standard active set or interior point algorithms. Its performance is tested and compared against state-of-the-art QP solvers on a series of benchmark problems. The proposed algorithm is orders of magnitudes faster, especially for large-scale problems and long horizons. For example, for the challenging crude distillation unit model of Pannocchia, Rawlings, and Wright (2007) with 252 states, 32 inputs, and 90 outputs, the average running time of the proposed approach is 1.57 ms.     

HiggsBounds 2.0.0: Confronting neutral and charged Higgs sector predictions with exclusion bounds from LEP and the Tevatron

HiggsBounds 2.0.0 is a computer code which tests both neutral and charged Higgs sectors of arbitrary models against the current exclusion bounds from the Higgs searches at LEP and the Tevatron. As input, it requires a selection of model predictions, such as Higgs masses, branching ratios, effective couplings and total decay widths. HiggsBounds 2.0.0 then uses the expected and observed topological cross section limits from the Higgs searches to determine whether a given parameter scenario of a model is excluded at the 95% C.L. by those searches. Version 2.0.0 represents a significant extension of the code since its first release (1.0.0). It includes now 28/53 LEP/Tevatron Higgs search analyses, compared to the 11/22 in the first release, of which many of the ones from the Tevatron are replaced by updates. As a major extension, the code allows now the predictions for (singly) charged Higgs bosons to be confronted with LEP and Tevatron searches. Furthermore, the newly included analyses contain LEP searches for neutral Higgs bosons (H) decaying invisibly or into (non-flavour tagged) hadrons as well as decay-mode independent searches for neutral Higgs bosons, LEP searches via the production modes τ⁺τ⁻H and , and Tevatron searches via . Also, all Tevatron results presented at the ICHEP?10 are included in version 2.0.0. As physics applications of HiggsBounds 2.0.0 we study the allowed Higgs mass range for model scenarios with invisible Higgs decays and we obtain exclusion results for the scalar sector of the Randall–Sundrum model using up-to-date LEP and Tevatron direct search results.

Program summary

Program title: HiggsBoundsCatalogue identifier: AEFF_v2_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEFF_v2_0.htmlProgram obtainable from: CPC Program Library, Queen?s University, Belfast, N. IrelandLicensing provisions: GNU General Public Licence version 3No. of lines in distributed program, including test data, etc.: 74 005No. of bytes in distributed program, including test data, etc.: 1 730 996Distribution format: tar.gzProgramming language: Fortran 77, Fortran 90 (two code versions are offered).Classification: 11.1.Catalogue identifier of previous version: AEFF_v1_0Journal reference of previous version: Comput. Phys. Comm. 181 (2010) 138External routines: HiggsBounds requires no external routines/libraries. Some sample programs in the distribution require the programs FeynHiggs 2.7.1 or CPsuperH2.2 to be installed.Does the new version supersede the previous version?: YesNature of problem: Determine whether a parameter point of a given model is excluded or allowed by LEP and Tevatron neutral and charged Higgs boson search results.Solution method: The most sensitive channel from LEP and Tevatron searches is determined and subsequently applied to test this parameter point. The test requires as input, model predictions for the Higgs boson masses, branching ratios and ratios of production cross sections with respect to reference values.Reasons for new version: This version extends the functionality of the previous version.Summary of revisions: List of included Higgs searches has been expanded, e.g. inclusion of (singly) charged Higgs boson searches. The input required from the user has been extended accordingly.Restrictions: Assumes that the narrow width approximation is applicable in the model under consideration and that the model does not predict a significant change to the signature of the background processes or the kinematical distributions of the signal cross sections.Running time: About 0.01 seconds (or less) for one parameter point using one processor of an Intel Core 2 Quad Q6600 CPU at 2.40 GHz for sample model scenarios with three Higgs bosons. It depends on the complexity of the Higgs sector (e.g. the number of Higgs bosons and the number of open decay channels) and on the code version.     

Efficient parallel implementation of Ewald summation in molecular dynamics simulations on multi-core platforms

We present a multi-step computation method to implement the Ewald summation for long-range electrostatic interactions in molecular dynamic simulations on a multi-core machine. Our methodology is based on the OpenMP programming model. It partitions computations of real-space summation among threads so that the global force of a single particle cannot be modified by more than one thread simultaneously. It requires neither a private copy of the force array for each thread nor an inspector at runtime. Compared with some other methods that can parallelise reduction operations on a force-array, our method achieves relatively higher speedups and lower L2 cache miss and bus utilisation ratios.     

A time-efficient pattern reduction algorithm for k-means clustering

This paper presents an efficient algorithm, called pattern reduction (PR), for reducing the computation time of k-means and k-means-based clustering algorithms. The proposed algorithm works by compressing and removing at each iteration patterns that are unlikely to change their membership thereafter. Not only is the proposed algorithm simple and easy to implement, but it can also be applied to many other iterative clustering algorithms such as kernel-based and population-based clustering algorithms. Our experiments—from 2 to 1000 dimensions and 150 to 10,000,000 patterns—indicate that with a small loss of quality, the proposed algorithm can significantly reduce the computation time of all state-of-the-art clustering algorithms evaluated in this paper, especially for large and high-dimensional data sets.