Constraint-based analysis of gene interactions using restricted boolean networks and time-series data

Abstract Background A popular model for gene regulatory networks is the Boolean network model. In this paper, we propose an algorithm to perform an analysis of gene regulatory interactions using the Boolean network model and time-series data. Actually, the Boolean network is restricted in the sense that only a subset of all possible Boolean functions are considered. We explore some mathematical properties of the restricted Boolean networks in order to avoid the full search approach. The problem is modeled as a Constraint Satisfaction Problem (CSP) and CSP techniques are used to solve it. Results We applied the proposed algorithm in two data sets. First, we used an artificial dataset obtained from a model for the budding yeast cell cycle. The second data set is derived from experiments performed using HeLa cells. The results show that some interactions can be fully or, at least, partially determined under the Boolean model considered. Conclusions The algorithm proposed can be used as a first step for detection of gene/protein interactions. It is able to infer gene relationships from time-series data of gene expression, and this inference process can be aided by a priori knowledge available.

Linear matrix inequality-based robust model predictive control for time-delayed systems

This work addresses the solution to the problem of robust model predictive control (MPC) of systems with model uncertainty. The case of zone control of multi-variable stable systems with multiple time delays is considered. The usual approach of dealing with this kind of problem is through the inclusion of non-linear cost constraint in the control problem. The control action is then obtained at each sampling time as the solution to a non-linear programming (NLP) problem that for high-order systems can be computationally expensive. Here, the robust MPC problem is formulated as a linear matrix inequality problem that can be solved in real time with a fraction of the computer effort. The proposed approach is compared with the conventional robust MPC and tested through the simulation of a reactor system of the process industry.

Modified constraint-induced movement therapy and modified forced-use therapy for stroke patients are both effective to promote balance and gait improvements

Background: Previous studies show that chronic hemiparetic patients after stroke, presents inabilities to perform movements in paretic hemibody. This inability is induced by positive reinforcement of unsuccessful attempts, a concept called learned non-use. Forced use therapy (FUT) and constraint induced movement therapy (CIMT) were developed with the goal of reversing the learned non-use. These approaches have been proposed for the rehabilitation of the paretic upper limb (PUL). It is unknown what would be the possible effects of these approaches in the rehabilitation of gait and balance. Objectives: To evaluate the effect of Modified FUT (mFUT) and Modified CIMT (mCIMT) on the gait and balance during four weeks of treatment and 3 months follow-up. Methods: This study included thirty-seven hemiparetic post-stroke subjects that were randomly allocated into two groups based on the treatment protocol. The non-paretic UL was immobilized for a period of 23 hours per day, five days a week. Participants were evaluated at Baseline, 1st, 2nd, 3rd and 4th weeks, and three months after randomization. For the evaluation we used: The Stroke Impact Scale (SIS), Berg Balance Scale (BBS) and Fugl-Meyer Motor Assessment (FM). Gait was analyzed by the 10-meter walk test (T10) and Timed Up & Go test (TUG). Results: Both groups revealed a better health status (SIS), better balance, better use of lower limb (BBS and FM) and greater speed in gait (T10 and TUG), during the weeks of treatment and months of follow-up, compared to the baseline. Conclusion: The results show mFUT and mCIMT are effective in the rehabilitation of balance and gait. Trial Registration ACTRN12611000411943.

Improving Planting Stocks for the Brazilian Atlantic Forest Restoration through Community-Based Seed Harvesting Strategies

High-diversity reforestation can help jumpstart tropical forest restoration, but obtaining viable seedlings is a major constraint: if nurseries do not offer them, it is hard to plant all the species one would like. From 2007 to 2009, we investigated five different seed acquisition strategies employed by a well-established tree nursery in southeastern Brazil, namely (1) in-house seed harvesters; (2) hiring a professional harvester; (3) amateur seed harvesters; or (4) a seed production cooperative, as well as (5) participating in a seed exchange program. In addition, we evaluated two strategies not dependent on seeds: harvesting seedlings from native tree species found regenerating under Eucalyptus plantations, and in a native forest remnant. A total of 344 native tree and shrub species were collected as seeds or seedlings, including 2,465 seed lots. Among these, a subset of 120 species was obtained through seed harvesting in each year. Overall, combining several strategies for obtaining planting stocks was an effective way to increase species richness, representation of some functional groups (dispersal syndromes, planting group, and shade tolerance), and genetic diversity of seedlings produced in forest tree nurseries. Such outcomes are greatly desirable to support high-diversity reforestation as part of tropical forest restoration. In addition, community-based seed harvesting strategies fostered greater socioeconomic integration of traditional communities in restoration projects and programs, which is an important bottleneck for the advance of ecological restoration, especially in developing countries. Finally, we discuss some of the limitations of the various strategies for obtaining planting stocks and the way forward for their improvement.

A comparison of deterministic, reliability-based and risk-based structural optimization under uncertainty

In this paper, the effects of uncertainty and expected costs of failure on optimum structural design are investigated, by comparing three distinct formulations of structural optimization problems. Deterministic Design Optimization (DDO) allows one the find the shape or configuration of a structure that is optimum in terms of mechanics, but the formulation grossly neglects parameter uncertainty and its effects on structural safety. Reliability-based Design Optimization (RBDO) has emerged as an alternative to properly model the safety-under-uncertainty part of the problem. With RBDO, one can ensure that a minimum (and measurable) level of safety is achieved by the optimum structure. However, results are dependent on the failure probabilities used as constraints in the analysis. Risk optimization (RO) increases the scope of the problem by addressing the compromising goals of economy and safety. This is accomplished by quantifying the monetary consequences of failure, as well as the costs associated with construction, operation and maintenance. RO yields the optimum topology and the optimum point of balance between economy and safety. Results are compared for some example problems. The broader RO solution is found first, and optimum results are used as constraints in DDO and RBDO. Results show that even when optimum safety coefficients are used as constraints in DDO, the formulation leads to configurations which respect these design constraints, reduce manufacturing costs but increase total expected costs (including expected costs of failure). When (optimum) system failure probability is used as a constraint in RBDO, this solution also reduces manufacturing costs but by increasing total expected costs. This happens when the costs associated with different failure modes are distinct. Hence, a general equivalence between the formulations cannot be established. Optimum structural design considering expected costs of failure cannot be controlled solely by safety factors nor by failure probability constraints, but will depend on actual structural configuration. (c) 2011 Elsevier Ltd. All rights reserved.

TWO NEW WEAK CONSTRAINT QUALIFICATIONS AND APPLICATIONS

We present two new constraint qualifications (CQs) that are weaker than the recently introduced relaxed constant positive linear dependence (RCPLD) CQ. RCPLD is based on the assumption that many subsets of the gradients of the active constraints preserve positive linear dependence locally. A major open question was to identify the exact set of gradients whose properties had to be preserved locally and that would still work as a CQ. This is done in the first new CQ, which we call the constant rank of the subspace component (CRSC) CQ. This new CQ also preserves many of the good properties of RCPLD, such as local stability and the validity of an error bound. We also introduce an even weaker CQ, called the constant positive generator (CPG), which can replace RCPLD in the analysis of the global convergence of algorithms. We close this work by extending convergence results of algorithms belonging to all the main classes of nonlinear optimization methods: sequential quadratic programming, augmented Lagrangians, interior point algorithms, and inexact restoration.

Column generation for a multitrip vehicle routing problem with time windows, driver work hours, and heterogeneous fleet

This study addresses a vehicle routing problem with time windows, accessibility restrictions on customers, and a fleet that is heterogeneous with regard to capacity and average speed. A vehicle can performmultiple routes per day, all starting and ending at a single depot, and it is assigned to a single driverwhose totalwork hours are limited.Acolumn generation algorithmis proposed.The column generation pricing subproblem requires a specific elementary shortest path problem with resource constraints algorithm to address the possibility for each vehicle performingmultiple routes per day and to address the need to set the workday’s start time within the planning horizon. A constructive heuristic and a metaheuristic based on tabu search are also developed to find good solutions.