Therapeutic target discovery using Boolean network attractors: avoiding pathological phenotypes

Target identification, one of the steps of drug discovery, aims at identifying biomolecules whose function should be therapeutically altered in order to cure the considered pathology. This work proposes an algorithm for in silico target identification using Boolean network attractors. It assumes that attractors of dynamical systems, such as Boolean networks, correspond to phenotypes produced by the modeled biological system. Under this assumption, and given a Boolean network modeling a pathophysiology, the algorithm identifies target combinations able to remove attractors associated with pathological phenotypes. It is tested on a Boolean model of the mammalian cell cycle bearing a constitutive inactivation of the retinoblastoma protein, as seen in cancers, and its applications are illustrated on a Boolean model of Fanconi anemia. The results show that the algorithm returns target combinations able to remove attractors associated with pathological phenotypes and then succeeds in performing the proposed in silico target identification. However, as with any in silico evidence, there is a bridge to cross between theory and practice, thus requiring it to be used in combination with wet lab experiments. Nevertheless, it is expected that the algorithm is of interest for target identification, notably by exploiting the inexpensiveness and predictive power of computational approaches to optimize the efficiency of costly wet lab experiments.Comment: Since the publication of this article and among the possible improvements mentioned in the Conclusion, two improvements have been done: extending the algorithm for multivalued logic and considering the basins of attraction of the pathological attractors for selecting the therapeutic bullet

Reliable ABC model choice via random forests

Approximate Bayesian computation (ABC) methods provide an elaborate approach to Bayesian inference on complex models, including model choice. Both theoretical arguments and simulation experiments indicate, however, that model posterior probabilities may be poorly evaluated by standard ABC techniques. We propose a novel approach based on a machine learning tool named random forests to conduct selection among the highly complex models covered by ABC algorithms. We thus modify the way Bayesian model selection is both understood and operated, in that we rephrase the inferential goal as a classification problem, first predicting the model that best fits the data with random forests and postponing the approximation of the posterior probability of the predicted MAP for a second stage also relying on random forests. Compared with earlier implementations of ABC model choice, the ABC random forest approach offers several potential improvements: (i) it often has a larger discriminative power among the competing models, (ii) it is more robust against the number and choice of statistics summarizing the data, (iii) the computing effort is drastically reduced (with a gain in computation efficiency of at least fifty), and (iv) it includes an approximation of the posterior probability of the selected model. The call to random forests will undoubtedly extend the range of size of datasets and complexity of models that ABC can handle. We illustrate the power of this novel methodology by analyzing controlled experiments as well as genuine population genetics datasets. The proposed methodologies are implemented in the R package abcrf available on the CRAN.Comment: 39 pages, 15 figures, 6 table

Dynamique hydrogéomorphologique et diversité végétale dans le delta du Rhône (France) de -10 000 ans à demain

International audienceLe delta du Rhône représente aujourd’hui une vaste plaine de niveau de base dont la surface est estimée à 1742 km2(fig. 1). Ancrée entre les deux bras du Rhône, la « Grande Camargue » n’en constitue à peine que la moitié (850 km2).La présente étude vise à retracer l’histoire hydrogéomorphologique et végétale du « grand delta » à l’échelle del’Holocène1.Dans le delta du Rhône, les écosystèmes végétaux sont très bien connus. La diversité végétale y est élevée malgrél’absence de relief : plus de 1000 espèces recensées parmi les plantes à fleurs pour un total de 4700 espèces habitant laFrance. La végétation actuelle est fortement dépendante non seulement des paramètres climatiques globaux mais aussidu milieu local, en particulier de la teneur en sel des eaux de surface et de sub-surface, et des actions humaines2.L’évolution de la plaine deltaïque, dont les principaux prismes sédimentaires ont été édifiés en moins de 6000 ans, dansun contexte de fluctuations plus ou moins marquées du niveau de la mer, des apports alluvionnaires etd’occupation/utilisation des sols, a forcément eu une incidence sur la dynamique végétale et la biodiversité en général.Il nous a donc paru utile de reprendre les étapes de l’édification du delta du Rhône, à la lumière des travaux dequelques-uns de nos prédécesseurs3, enrichis et complétés par les données stratigraphiques acquises plus récemment, etsurtout grâce aux jalons chronologiques apportés par les trouvailles archéologiques et les datations 14C, qui souventavaient fait défaut dans les études antérieures

A Lognormal Central Limit Theorem for Particle Approximations of Normalizing Constants

International audienceThis paper deals with the numerical approximation of normalizing constants produced by particle methods, in the general framework of Feynman-Kac sequences of measures. It is well-known that the corresponding estimates satisfy a central limit theorem for a fixed time horizon n as the number of particles N goes to infinity. Here, we study the situation where both n and N go to infinity in such a way that lim n→∞ . In this context, Pitt et al. \cite{pitt2012} recently conjectured that a lognormal central limit theorem should hold. We formally establish this result here, under general regularity assumptions on the model. We also discuss special classes of models (time-homogeneous environment and ergodic random environment) for which more explicit descriptions of the limiting bias and variance can be obtained

Efficient validation and construction of border arrays

In this article we present an on-line linear time and space algorithm to check if an integer array f is the border array of at least one string w built on a bounded or unbounded size alphabet Σ. We first show some relations between the border array of some string w and the skeleton of the DFA recognizing Σ ∗ · w, independently of the explicit knowledge of w. This enables us to design algorithms for validating and generating border arrays that outperform existing ones [4, 3]. The validating algorithm lowers the delay (time spent on one element of the array) from O(|w|) to O(min{|Σ|, |w|}) comparing to algorithms in [4, 3]. Finally we give some results on the numbers of distinct border arrays on some alphabet sizes.

Migraine management: current trends and future prospects

peer reviewedDespite its high prevalence and individual as well as societal burden, migraine remains underdiagnosed and undertreated. In recent years, the options for the management of migraine patients have greatly expanded. A number of drugs belonging to various pharmacological classes and deliverable by several routes are now available both for the acute and the preventive treatments of migraine. Nevertheless, disability and satisfaction remain low in many subjects because treatments are not accessible, not optimized, not effective or simply not tolerated. There is thus still considerable room for better education and for more efficient therapies. In spite of useful internationally accepted guidelines, anti-migraine treatment has to be individually tailored to each patient taking into account the migraine subtype, the ensuing disability, the patient's previous history and present expectations, and the comorbid disorders. In this article we will summarize the phenotypic presentations of migraine and review recommendations for acute and preventive treatment, highlighting recent advances which are relevant for clinical practice in terms of both diagnosis and management

ABC random forests for Bayesian parameter inference

This preprint has been reviewed and recommended by Peer Community In Evolutionary Biology (http://dx.doi.org/10.24072/pci.evolbiol.100036). Approximate Bayesian computation (ABC) has grown into a standard methodology that manages Bayesian inference for models associated with intractable likelihood functions. Most ABC implementations require the preliminary selection of a vector of informative statistics summarizing raw data. Furthermore, in almost all existing implementations, the tolerance level that separates acceptance from rejection of simulated parameter values needs to be calibrated. We propose to conduct likelihood-free Bayesian inferences about parameters with no prior selection of the relevant components of the summary statistics and bypassing the derivation of the associated tolerance level. The approach relies on the random forest methodology of Breiman (2001) applied in a (non parametric) regression setting. We advocate the derivation of a new random forest for each component of the parameter vector of interest. When compared with earlier ABC solutions, this method offers significant gains in terms of robustness to the choice of the summary statistics, does not depend on any type of tolerance level, and is a good trade-off in term of quality of point estimator precision and credible interval estimations for a given computing time. We illustrate the performance of our methodological proposal and compare it with earlier ABC methods on a Normal toy example and a population genetics example dealing with human population evolution. All methods designed here have been incorporated in the R package abcrf (version 1.7) available on CRAN.Comment: Main text: 24 pages, 6 figures Supplementary Information: 14 pages, 5 figure