This paper proposes an iterative Maximum Satisfiability (MaxSAT) approach designed to solve train scheduling optimization problems. The generation of railway timetables is known to be intractable for a single track. We consider hundreds of trains on interconnected multi-track railway networks with complex connections between trains. Furthermore, the proposed algorithm is incremental to reduce the impact of time discretization. The performance of our approach is evaluated with the real-world Swiss Federal Railway (SBB) Crowd Sourcing Challenge benchmark and Periodic Event Scheduling Problems benchmark (PESPLib). The execution time of the proposed approach is shown to be, on average, twice as fast as the best existing solution for the SBB instances. In addition, we achieve a significant improvement over SAT-based solutions for solving the PESPLib instances. We also analyzed real schedule data from Switzerland and the Netherlands to create a disruption generator based on probability distributions. The novel incremental algorithm allows solving the train scheduling problem under disruptions with better performance than traditional algorithms.

Numerous genomes are sequenced and made available to the community through the NCBI portal. However, and, unlike what happens for gene function annotation, annotation of promoter sequences and the underlying prediction of regulatory associations is mostly unavailable, severely limiting the ability to interpret genome sequences in a functional genomics perspective. Here we present an approach where one can download a genome of interest from NCBI in the GenBank Flat File (.gbff) format and, with a minimum set of commands, have all the information parsed, organized and made available through the platform web interface. Also, the new genomes are compared with a given genome of reference in search of homologous genes, shared regulatory elements and predicted transcription associations. We present this approach within the context of Community YEASTRACT of the YEASTRACT + portal, thus benefiting from immediate access to all the comparative genomics queries offered in the YEASTRACT + portal. Besides the yeast community, other communities can install the platform independently, without any constraints. In this work, we exemplify the usefulness of the presented tool, within Community YEASTRACT, in constructing a dedicated database and analysing the genome of the highly promising oleaginous red yeast species Rhodotorula toruloides currently poorly studied at the genome and transcriptome levels and with limited genome editing tools. Regulatory prediction is based on the conservation of promoter sequences and available regulatory networks. The case-study examined is focused on the Haa1 transcription factor-a key regulator of yeast resistance to acetic acid, an important inhibitor of industrial bioconversion of lignocellulosic hydrolysates. The new tool described here led to the prediction of a RtHaa1 regulon with expected impact in the optimization of R. toruloides robustness for lignocellulosic and pectin-rich residue biorefinery processes.

The identification of large regulatory and signalling networks involved in the control of crucial cellular processes calls for proper modelling approaches. Indeed, models can help elucidate properties of these networks, understand their behaviour and provide (testable) predictions by performing in silico experiments. In this context, qualitative, logical frameworks have emerged as relevant approaches, as demonstrated by a growing number of published models, along with new methodologies and software tools. This productive activity now requires a concerted effort to ensure model reusability and interoperability between tools. Following an outline of the logical modelling framework, we present the most important achievements of the Consortium for Logical Models and Tools, along with future objectives. Our aim is to advertise this open community, which welcomes contributions from all researchers interested in logical modelling or in related mathematical and computational developments.

BACKGROUND: With the decrease of DNA sequencing costs, sequence-based typing methods are rapidly becoming the gold standard for epidemiological surveillance. These methods provide reproducible and comparable results needed for a global scale bacterial population analysis, while retaining their usefulness for local epidemiological surveys. Online databases that collect the generated allelic profiles and associated epidemiological data are available but this wealth of data remains underused and are frequently poorly annotated since no user-friendly tool exists to analyze and explore it. RESULTS: PHYLOViZ is platform independent Java software that allows the integrated analysis of sequence-based typing methods, including SNP data generated from whole genome sequence approaches, and associated epidemiological data. goeBURST and its Minimum Spanning Tree expansion are used for visualizing the possible evolutionary relationships between isolates. The results can be displayed as an annotated graph overlaying the query results of any other epidemiological data available. CONCLUSIONS: PHYLOViZ is a user-friendly software that allows the combined analysis of multiple data sources for microbial epidemiological and population studies. It is freely available at http://www.phyloviz.net.

MOTIVATION: Investigating the relation between the structure and behavior of complex biological networks often involves posing the question if the hypothesized structure of a regulatory network is consistent with the observed behavior, or if a proposed structure can generate a desired behavior. RESULTS: The above questions can be cast into a parameter search problem for qualitative models of regulatory networks. We develop a method based on symbolic model checking that avoids enumerating all possible parametrizations, and show that this method performs well on real biological problems, using the IRMA synthetic network and benchmark datasets. We test the consistency between IRMA and time-series expression profiles, and search for parameter modifications that would make the external control of the system behavior more robust. AVAILABILITY: GNA and the IRMA model are available at http://ibis.inrialpes.fr/.

BACKGROUND: The study of biological networks has led to the development of increasingly large and detailed models. Computer tools are essential for the simulation of the dynamical behavior of the networks from the model. However, as the size of the models grows, it becomes infeasible to manually verify the predictions against experimental data or identify interesting features in a large number of simulation traces. Formal verification based on temporal logic and model checking provides promising methods to automate and scale the analysis of the models. However, a framework that tightly integrates modeling and simulation tools with model checkers is currently missing, on both the conceptual and the implementational level. RESULTS: We have developed a generic and modular web service, based on a service-oriented architecture, for integrating the modeling and formal verification of genetic regulatory networks. The architecture has been implemented in the context of the qualitative modeling and simulation tool GNA and the model checkers NUSMV and CADP. GNA has been extended with a verification module for the specification and checking of biological properties. The verification module also allows the display and visual inspection of the verification results. CONCLUSIONS: The practical use of the proposed web service is illustrated by means of a scenario involving the analysis of a qualitative model of the carbon starvation response in E. coli. The service-oriented architecture allows modelers to define the model and proceed with the specification and formal verification of the biological properties by means of a unified graphical user interface. This guarantees a transparent access to formal verification technology for modelers of genetic regulatory networks.

Formal verification based on model checking provides a powerful technology to query qualitative models of dynamical systems. The application of model-checking approaches is hampered, however, by the difficulty for non-expert users to formulate appropriate questions in temporal logic. In order to deal with this problem, we propose the use of patterns, that is, high-level query templates capturing recurring questions which can be automatically translated to temporal logic. We develop a set of patterns for the analysis of qualitative models of genetic regulatory networks, which are sufficiently generic though to be useful in other application domains. The applicability of the patterns has been investigated by the analysis of a model of the network of global regulators controlling the carbon starvation response in Escherichia coli.