Decision Diagram-Based Branch-and-Bound with Caching for Dominance and Suboptimality Detection

The branch-and-bound algorithm based on decision diagrams introduced by Bergman et al. in 2016 is a framework for solving discrete optimization problems with a dynamic programming formulation. It works by compiling a series of bounded-width decision diagrams that can provide lower and upper bounds for any given subproblem. Eventually, every part of the search space will be either explored or pruned by the algorithm, thus proving optimality. This paper presents new ingredients to speed up the search by exploiting the structure of dynamic programming models. The key idea is to prevent the repeated expansion of nodes corresponding to the same dynamic programming states by querying expansion thresholds cached throughout the search. These thresholds are based on dominance relations between partial solutions previously found and on the pruning inequalities of the filtering techniques introduced by Gillard et al. in 2021. Computational experiments show that the pruning brought by this caching mechanism allows significantly reducing the number of nodes expanded by the algorithm. This results in more benchmark instances of difficult optimization problems being solved in less time while using narrower decision diagrams.Comment: Accepted to INFORMS Journal on Computin

Verification of interlocking systems using statistical model checking

In the railway domain, an interlocking is the system ensuring safe train traffic inside a station by controlling its active elements such as the signals or points. Modern interlockings are configured using particular data, called application data, reflecting the track layout and defining the actions that the interlocking can take. The safety of the train traffic relies thereby on application data correctness, errors inside them can cause safety issues such as derailments or collisions. Given the high level of safety required by such a system, its verification is a critical concern. In addition to the safety, an interlocking must also ensure that availability properties, stating that no train would be stopped forever in a station, are satisfied. Most of the research dealing with this verification relies on model checking. However, due to the state space explosion problem, this approach does not scale for large stations. More recently, a discrete event simulation approach limiting the verification to a set of likely scenarios, was proposed. The simulation enables the verification of larger stations, but with no proof that all the interesting scenarios are covered by the simulation. In this paper, we apply an intermediate statistical model checking approach, offering both the advantages of model checking and simulation. Even if exhaustiveness is not obtained, statistical model checking evaluates with a parametrizable confidence the reliability and the availability of the entire system.Comment: 12 pages, 3 figures, 2 table

Transcription factor LSF facilitiates lysine methylation of α-tubulin by microtubule-associated SET8

Microtubules are critical for mitosis, cell motility, and protein and organelle transport, and are a validated target for anticancer drugs. However, tubulin regulation and recruitment in these cellular processes is less understood. Post-translational modifications of tubulin are proposed to regulate microtubule functions and dynamics. Although many such modifications have been investigated, tubulin methylations and enzymes responsible for methylation have only recently begun to be described. Here we report that N-lysine methyl transferase KMT5A (SET8/PR-Set7), which methylates histone H4K20, also methylates α-tubulin. Furthermore, the transcription factor LSF binds both tubulin and SET8, and enhances α-tubulin methylation in vitro, countered by FQI1, a specific small molecule inhibitor of LSF. Thus, the three proteins SET8, LSF, and tubulin, all essential for mitotic progression, interact with each other. Overall, these results point to dual functions for both SET8 and LSF not only in chromatin regulation, but also for cytoskeletal modification.First author draf

Transcription factor LSF-DNMT1 complex dissociation by FQI1 leads to aberrant DNA methylation and gene expression

The transcription factor LSF is highly expressed in hepatocellular carcinoma (HCC) and promotes oncogenesis. Factor quinolinone inhibitor 1 (FQI1), inhibits LSF DNA-binding activity and exerts anti-proliferative activity. Here, we show that LSF binds directly to the maintenance DNA (cytosine-5) methyltransferase 1 (DNMT1) and its accessory protein UHRF1 both in vivo and in vitro. Binding of LSF to DNMT1 stimulated DNMT1 activity and FQI1 negated the methyltransferase activation. Addition of FQI1 to the cell culture disrupted LSF bound DNMT1 and UHRF1 complexes, resulting in global aberrant CpG methylation. Differentially methylated regions (DMR) containing at least 3 CpGs, were significantly altered by FQI1 compared to control cells. The DMRs were mostly concentrated in CpG islands, proximal to transcription start sites, and in introns and known genes. These DMRs represented both hypo and hypermethylation, correlating with altered gene expression. FQI1 treatment elicits a cascade of effects promoting altered cell cycle progression. These findings demonstrate a novel mechanism of FQI1 mediated alteration of the epigenome by DNMT1-LSF complex disruption, leading to aberrant DNA methylation and gene expression.We would like to thank Drs. Donald Comb, Rich Roberts, William Jack and Clotilde Carlow at New England Biolabs Inc. for research support and encouragement. The authors thank Dr. Lauren Brown (Boston University Center for Molecular Discovery) for the preparation of FQI1. UH research on this project was supported by Ignition Awards from Boston University and a Johnson & Johnson Clinical Innovator's Award through Boston University. SES research is supported by the NIH (P50 GM067041 & R24 GM111625). Research performed by HGC was partly a requirement for the MCBB graduate program at Boston University and supported by NEB. (Boston University; Johnson & Johnson Clinical Innovator's Award through Boston University; P50 GM067041 - NIH; R24 GM111625 - NIH; NEB)Published versio

Cardinality Reasoning for Bin-Packing Constraint: Application to a Tank Allocation Problem

International audienceFlow reasoning has been successfully used in CP for more than a decade. It was originally introduced by Régin in the well-known Alldifferent and Global Cardinality Constraint (GCC) available in most of the CP solvers. The BinPacking constraint was introduced by Shaw and mainly uses an independent knapsack reasoning in each bin to filter the possible bins for each item. This paper considers the use of a cardinal-ity/flow reasoning for improving the filtering of a bin-packing constraint. The idea is to use a GCC as a redundant constraint to the BinPacking that will count the number of items placed in each bin. The cardinality variables of the GCC are then dynamically updated during the propagation. The cardinality reasoning of the redundant GCC makes deductions that the bin-packing constraint cannot see since the placement of all items into every bin is considered at once rather than for each bin individually. This is particularly well suited when a minimum loading in each bin is specified in advance. We apply this idea on a Tank Allocation Problem (TAP). We detail our CP model and give experimental results on a real-life instance demonstrating the added value of the cardinality reasoning for the bin-packing constraint. This constraint enforces the relation L j = i (X i = j) · w i , ∀j. It makes the link between n weighted items (item i has a weight w i) and the m different capacitated bins in which they are to be put. Only the weights of the items are integers, the other arguments of the constraints are finite domain (f.d.) variables. Note that in this formulation, Lj is a variable which is bounded by the maximal capacity of the bin j. Without loss of generality we assume the item variables and their weights are sorted such that w i ≤ w i+1. Example: BinP acking([1, 4, 1, 2, 2], [2, 3, 3, 3, 4], [5, 7, 0, 3])