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.

Different approaches for dealing with rare variants in family-based genetic studies: application of a Genetic Analysis Workshop 17 problem

Rare variants are becoming the new candidates in the search for genetic variants that predispose individuals to a phenotype of interest. Their low prevalence in a population requires the development of dedicated detection and analytical methods. A family-based approach could greatly enhance their detection and interpretation because rare variants are nearly family specific. In this report, we test several distinct approaches for analyzing the information provided by rare and common variants and how they can be effectively used to pinpoint putative candidate genes for follow-up studies. The analyses were performed on the mini-exome data set provided by Genetic Analysis Workshop 17. Eight approaches were tested, four using the trait’s heritability estimates and four using QTDT models. These methods had their sensitivity, specificity, and positive and negative predictive values compared in light of the simulation parameters. Our results highlight important limitations of current methods to deal with rare and common variants, all methods presented a reduced specificity and, consequently, prone to false positive associations. Methods analyzing common variants information showed an enhanced sensibility when compared to rare variants methods. Furthermore, our limited knowledge of the use of biological databases for gene annotations, possibly for use as covariates in regression models, imposes a barrier to further research.

Robotic-assisted laparoscopic partial nephrectomy: initial experience in Brazil and a review of the literature

CONTEXT AND PURPOSE: Partial nephrectomy has become the standard of care for renal tumors less than 4 cm in diameter. Controversy still exists, however, regarding the best surgical approach, especially when minimally invasive techniques are taken into account. Robotic-assisted laparoscopic partial nephrectomy (RALPN) has emerged as a promising technique that helps surgeons achieve the standards of open partial nephrectomy care while offering a minimally invasive approach. The objective of the present study was to describe our initial experience with robotic-assisted laparoscopic partial nephrectomy and extensively review the pertinent literature. MATERIALS AND METHODS: Between August 2009 and February 2010, eight consecutive selected patients with contrast enhancing renal masses observed by CT were submitted to RALPN in a private institution. In addition, we collected information on the patients' demographics, preoperative tumor characteristics and detailed operative, postoperative and pathological data. In addition, a PubMed search was performed to provide an extensive review of the robotic-assisted laparoscopic partial nephrectomy literature. RESULTS: Seven patients had RALPN on the left or right sides with no intraoperative complications. One patient was electively converted to a robotic-assisted radical nephrectomy. The operative time ranged from 120 to 300 min, estimated blood loss (EBL) ranged from 75 to 400 mL and, in five cases, the warm ischemia time (WIT) ranged from 18 to 32 min. Two patients did not require any clamping. Overall, no transfusions were necessary, and there were no intraoperative complications or adverse postoperative clinical events. All margins were negative, and all patients were disease-free at the 6-month follow-up. CONCLUSIONS: Robotic-assisted laparoscopic partial nephrectomy is a feasible and safe approach to small renal cortical masses.Further prospective studies are needed to compare open partial nephrectomy with its minimally invasive counterparts.

Numeric reconstruction of cytoskeleton with finite element method and topology optimization method

The importance of mechanical aspects related to cell activity and its environment is becoming more evident due to their influence in stem cell differentiation and in the development of diseases such as atherosclerosis. The mechanical tension homeostasis is related to normal tissue behavior and its lack may be related to the formation of cancer, which shows a higher mechanical tension. Due to the complexity of cellular activity, the application of simplified models may elucidate which factors are really essential and which have a marginal effect. The development of a systematic method to reconstruct the elements involved in the perception of mechanical aspects by the cell may accelerate substantially the validation of these models. This work proposes the development of a routine capable of reconstructing the topology of focal adhesions and the actomyosin portion of the cytoskeleton from the displacement field generated by the cell on a flexible substrate. Another way to think of this problem is to develop an algorithm to reconstruct the forces applied by the cell from the measurements of the substrate displacement, which would be characterized as an inverse problem. For these kind of problems, the Topology Optimization Method (TOM) is suitable to find a solution. TOM is consisted of an iterative application of an optimization method and an analysis method to obtain an optimal distribution of material in a fixed domain. One way to experimentally obtain the substrate displacement is through Traction Force Microscopy (TFM), which also provides the forces applied by the cell. Along with systematically generating the distributions of focal adhesion and actin-myosin for the validation of simplified models, the algorithm also represents a complementary and more phenomenological approach to TFM. As a first approximation, actin fibers and flexible substrate are represented through two-dimensional linear Finite Element Method. Actin contraction is modeled as an initial stress of the FEM elements. Focal adhesions connecting actin and substrate are represented by springs. The algorithm was applied to data obtained from experiments regarding cytoskeletal prestress and micropatterning, comparing the numerical results to the experimental ones