Identifying regulational alterations in gene regulatory networks by state space representation of vector autoregressive models and variational annealing

Background: In the analysis of effects by cell treatment such as drug dosing, identifying changes on gene network structures between normal and treated cells is a key task. A possible way for identifying the changes is to compare structures of networks estimated from data on normal and treated cells separately. However, this approach usually fails to estimate accurate gene networks due to the limited length of time series data and measurement noise. Thus, approaches that identify changes on regulations by using time series data on both conditions in an efficient manner are demanded. Methods: We propose a new statistical approach that is based on the state space representation of the vector autoregressive model and estimates gene networks on two different conditions in order to identify changes on regulations between the conditions. In the mathematical model of our approach, hidden binary variables are newly introduced to indicate the presence of regulations on each condition. The use of the hidden binary variables enables an efficient data usage; data on both conditions are used for commonly existing regulations, while for condition specific regulations corresponding data are only applied. Also, the similarity of networks on two conditions is automatically considered from the design of the potential function for the hidden binary variables. For the estimation of the hidden binary variables, we derive a new variational annealing method that searches the configuration of the binary variables maximizing the marginal likelihood. Results: For the performance evaluation, we use time series data from two topologically similar synthetic networks, and confirm that our proposed approach estimates commonly existing regulations as well as changes on regulations with higher coverage and precision than other existing approaches in almost all the experimental settings. For a real data application, our proposed approach is applied to time series data from normal Human lung cells and Human lung cells treated by stimulating EGF-receptors and dosing an anticancer drug termed Gefitinib. In the treated lung cells, a cancer cell condition is simulated by the stimulation of EGF-receptors, but the effect would be counteracted due to the selective inhibition of EGF-receptors by Gefitinib. However, gene expression profiles are actually different between the conditions, and the genes related to the identified changes are considered as possible off-targets of Gefitinib. Conclusions: From the synthetically generated time series data, our proposed approach can identify changes on regulations more accurately than existing methods. By applying the proposed approach to the time series data on normal and treated Human lung cells, candidates of off-target genes of Gefitinib are found. According to the published clinical information, one of the genes can be related to a factor of interstitial pneumonia, which is known as a side effect of Gefitinib.

Cost Management Practices and Strategic Perspectives: a study in the construction industry of the State of Parana

The purpose of this paper is to investigate the cost management practices of building industry companies of Parana that follow the typology of Porter's strategies. The sample comprises member companies of the Association of Construction Industries of the State of Parana (PR-SINDUSCON) operating in the segment of residential buildings. The data were collected by means of questionnaires sent to 317 SINDUSCON members. 69 were returned and 54 used for our research. Exploratory Factorial Analysis of the data allowed us to identify two groups of cost management practices. Analyses suggest equality between the adopted cost management practices and the Cost Control Planning (CCP) practices among the companies of the Group 1, regardless of the generic strategy adopted. The companies of the Group 2 that adopted the differentiation strategy seem to use mainly the ACR cost management practice. Our findings differ from those obtained by Chenhall insofar as companies that adopt low cost strategies tend to use managerial controls focused on cost control and rigid budgetary controls.

MATHEMATICAL MODELS AND POLYHEDRAL STUDIES FOR INTEGRAL SHEET METAL DESIGN

We deal with the optimization of the production of branched sheet metal products. New forming techniques for sheet metal give rise to a wide variety of possible profiles and possible ways of production. In particular, we show how the problem of producing a given profile geometry can be modeled as a discrete optimization problem. We provide a theoretical analysis of the model in order to improve its solution time. In this context we give the complete convex hull description of some substructures of the underlying polyhedron. Moreover, we introduce a new class of facet-defining inequalities that represent connectivity constraints for the profile and show how these inequalities can be separated in polynomial time. Finally, we present numerical results for various test instances, both real-world and academic examples.

Management practices and influences on IT architecture decisions: a case study in a telecom company

The study aims to analyze the IT architecture management practices associated with their degree of maturity and the influence of institutional and strategic factors on the decisions involved through a case study in a large telecom organization. The case study allowed us to identify practices that led the company to its current stage of maturity and identify practices that can lead the company to the next stage. The strategic influence was mentioned by most respondents and the institutional influence was present in decisions related to innovation and those dealing with a higher level of uncertainties.