Cost Function Networks to Solve Large Computational Protein Design Problems

International audienc

Cost Function Networks to Solve Large Computational Protein Design Problems

International audienc

The combined immunodetection of AP-2α and YY1 transcription factors is associated with ERBB2 gene overexpression in primary breast tumors

INTRODUCTION: Overexpression of the ERBB2 oncogene is observed in about 20% of human breast tumors and is the consequence of increased transcription rates frequently associated with gene amplification. Several studies have shown a link between activator protein 2 (AP-2) transcription factors and ERBB2 gene expression in breast cancer cell lines. Moreover, the Yin Yang 1 (YY1) transcription factor has been shown to stimulate AP-2 transcriptional activity on the ERBB2 promoter in vitro. In this report, we examined the relationships between ERBB2, AP-2alpha, and YY1 both in breast cancer tissue specimens and in a mammary cancer cell line. METHODS: ERBB2, AP-2alpha, and YY1 protein levels were analyzed by immunohistochemistry in a panel of 55 primary breast tumors. ERBB2 gene amplification status was determined by fluorescent in situ hybridization. Correlations were evaluated by a chi2 test at a p value of less than 0.05. The functional role of AP-2alpha and YY1 on ERBB2 gene expression was analyzed by small interfering RNA (siRNA) transfection in the BT-474 mammary cancer cell line followed by real-time reverse transcription-polymerase chain reaction and Western blotting. RESULTS: We observed a statistically significant correlation between ERBB2 and AP-2alpha levels in the tumors (p < 0.01). Moreover, associations were found between ERBB2 protein level and the combined high expression of AP-2alpha and YY1 (p < 0.02) as well as between the expression of AP-2alpha and YY1 (p < 0.001). Furthermore, the levels of both AP-2alpha and YY1 proteins were inversely correlated to ERBB2 gene amplification status in the tumors (p < 0.01). Transfection of siRNAs targeting AP-2alpha and AP-2gamma mRNAs in the BT-474 breast cancer cell line repressed the expression of the endogenous ERBB2 gene at both the mRNA and protein levels. Moreover, the additional transfection of an siRNA directed against the YY1 transcript further reduced the ERBB2 protein level, suggesting that AP-2 and YY1 transcription factors cooperate to stimulate the transcription of the ERBB2 gene. CONCLUSION: This study highlights the role of both AP-2alpha and YY1 transcription factors in ERBB2 oncogene overexpression in breast tumors. Our results also suggest that high ERBB2 expression may result either from gene amplification or from increased transcription factor levels

Parallel Hybrid Best-First Search

While processor frequency has stagnated over the past two decades, the number of available cores in servers or clusters is still growing, offering the opportunity for significant speed-up in combinatorial optimization. Parallelization of exact methods remains a difficult challenge. We revisit the concept of parallel Branch-and-Bound in the framework of Cost Function Networks. We show how to adapt the anytime Hybrid Best-First Search algorithm in a Master-Worker protocol. The resulting parallel algorithm achieves good load-balancing without introducing new parameters to be tuned as is the case, for example, in Embarrassingly Parallel Search (EPS). It has also a small overhead due to its light communication messages. We performed an experimental evaluation on several benchmarks, comparing our parallel algorithm to its sequential version. We observed linear speed-up in some cases. Our approach compared favourably to the EPS approach and also to a state-of-the-art parallel exact integer programming solver

Parallel Hybrid Best-First Search

International audienceWhile processor frequency has stagnated over the past two decades, the number of available cores in servers or clusters is still growing, offering the opportunity for significant speed-up in combinatorial optimization. Parallelization of exact methods remains a difficult challenge. We revisit the concept of parallel Branch-and-Bound in the framework of Cost Function Networks. We show how to adapt the anytime Hybrid Best-First Search algorithm in a Master-Worker protocol. The resulting parallel algorithm achieves good load-balancing without introducing new parameters to be tuned as is the case, for example, in Embarrassingly Parallel Search (EPS). It has also a small overhead due to its light communication messages. We performed an experimental evaluation on several benchmarks, comparing our parallel algorithm to its sequential version. We observed linear speed-up in some cases. Our approach compared favourably to the EPS approach and also to a state-of-the-art parallel exact integer programming solver

Recent algorithmic advances for combinatorial optimization in graphical models

International audienc

Iterative Decomposition Guided Variable Neighborhood Search for Graphical Model Energy Minimization

International audienceGraphical models factorize a global probability distribution/energy function as the prod-uct/sum of local functions. A major inference task, known as MAP in Markov Random Fields and MPE in Bayesian Networks, is to find a global assignment of all the variables with maximum a posteriori probabil-ity/minimum energy. A usual distinction on MAP solving methods is complete/incomplete, i.e. the ability to prove optimality or not. Most complete methods rely on tree search, while incomplete methods rely on local search. Among them, we study Variable Neighborhood Search (VNS) for graphical models. In this paper, we propose an iterative approach above VNS which uses (partial) tree search inside its local neighborhood exploration. The resulting hybrid method offers a good compromise between completeness and anytime behavior than existing tree search methods while still being competitive for proving optimality. We further propose a parallel version of our method improving its anytime behavior on difficult instances coming from a large graphical model benchmark. Last we experiment on the challenging minimum energy problem found in Computational Protein Design, showing the practical benefit of our parallel version. Solver at www.inra.fr/mia/T/toulbar2 v1.0

Recent algorithmic advances for combinatorial optimization in graphical models

International audienc

Recent algorithmic advances for combinatorial optimization in graphical models

International audienc

Variable neighborhood search for graphical model energy minimization

International audienceGraphical models factorize a global probability distribution/energy function as the product/ sum of local functions. A major inference task, known as MAP in Markov Random Fields and MPE in Bayesian Networks, is to find a global assignment of all the variables with maximum a posteriori probability/minimum energy. A usual distinction on MAP solving methods is complete/incomplete, i.e. the ability to prove optimality or not. Most complete methods rely on tree search, while incomplete methods rely on local search. Among them, we study Variable Neighborhood Search (VNS) for graphical models. In this paper, we propose an iterative approach above VNS that uses (partial) tree search inside its local neighborhood exploration. The proposed approach performs several neighborhood explorations of increasing search complexity, by controlling two parameters, the discrepancy limit and the neighborhood size. Thus, optimality of the obtained solutions can be proven when the neighborhood size is maximal and with unbounded tree search. We further propose a parallel version of our method improving its anytime behavior on difficult instances coming from a large graphical model benchmark. Last we experiment on the challenging minimum energy problem found in Computational Protein Design, showing the practical benefit of our parallel version. A solver is available at https:// github .com /toulbar2 /toulbar2