Stochastic and epistemic uncertainty propagation in LCA

Purpose: When performing uncertainty propagation, most LCA practitioners choose to represent uncertainties by single probability distributions and to propagate them using stochastic methods. However the selection of single probability distributions appears often arbitrary when faced with scarce information or expert judgement (epistemic uncertainty). Possibility theory has been developed over the last decades to address this problem. The objective of this study is to present a methodology that combines probability and possibility theories to represent stochastic and epistemic uncertainties in a consistent manner and apply it to LCA. A case study is used to show the uncertainty propagation performed with the proposed method and compare it to propagation performed using probability and possibility theories alone. Methods: Basic knowledge on the probability theory is first recalled, followed by a detailed description of hal-00811827, version 1- 11 Apr 2013 epistemic uncertainty representation using fuzzy intervals. The propagation methods used are the Monte Carlo analysis for probability distribution and an optimisation on alpha-cuts for fuzzy intervals. The proposed method (noted IRS) generalizes the process of random sampling to probability distributions as well as fuzzy intervals, thus making the simultaneous use of both representations possible

The logic of transcriptional regulator recruitment architecture at cis-regulatory modules controlling liver functions.

Control of gene transcription relies on concomitant regulation by multiple transcriptional regulators (TRs). However, how recruitment of a myriad of TRs is orchestrated at cis-regulatory modules (CRMs) to account for coregulation of specific biological pathways is only partially understood. Here, we have used mouse liver CRMs involved in regulatory activities of the hepatic TR, NR1H4 (FXR; farnesoid X receptor), as our model system to tackle this question. Using integrative cistromic, epigenomic, transcriptomic, and interactomic analyses, we reveal a logical organization where trans-regulatory modules (TRMs), which consist of subsets of preferentially and coordinately corecruited TRs, assemble into hierarchical combinations at hepatic CRMs. Different combinations of TRMs add to a core TRM, broadly found across the whole landscape of CRMs, to discriminate promoters from enhancers. These combinations also specify distinct sets of CRM differentially organized along the genome and involved in regulation of either housekeeping/cellular maintenance genes or liver-specific functions. In addition to these TRMs which we define as obligatory, we show that facultative TRMs, such as one comprising core circadian TRs, are further recruited to selective subsets of CRMs to modulate their activities. TRMs transcend TR classification into ubiquitous versus liver-identity factors, as well as TR grouping into functional families. Hence, hierarchical superimpositions of obligatory and facultative TRMs bring about independent transcriptional regulatory inputs defining different sets of CRMs with logical connection to regulation of specific gene sets and biological pathways. Altogether, our study reveals novel principles of concerted transcriptional regulation by multiple TRs at CRMs

Chimiothèque : vers une approche rationnelle pour la sélection de sous-chimiothèques

The selection of diverse molecules'subsets is a very important stake in the pharmaceutical research. Indeed, the effective discovery of a drug will depend of the quality of this selection. Several methods exist to address this problem. Some of them are based on the creation of groups of molecules, the others on the principle of dissimilarity between chemical compounds. In this work, we propose a new technique, between these two concepts, which allows to obtain subsets, at the same time, diverse in the space and representative from the initial set which they are extracted. First of all, to create this selection method, we defined and formalized mathematically a diversity criterion, then we used heuristics known in machine learning to conceive our algorithm. This one was compared with the other types of diversity selections usually used in chemoinformatic such as k-medoïds, Maximum-Dissimilarity, Sphere-Exclusion. The formalization of the diversity criterion finally allowed us to propose a new criterion of evaluation of the quality of the selections. The algorithm and the criterion presented in this work give diverse and representative samples of a chemical space.La sélection de sous-ensembles de molécules diverses est un enjeu très important de la recherche pharmaceutique. En effet, de la qualité de cette sélection, dépendra la découverte efficace d'un médicament. De nombreuses méthodes existent pour répondre à cette demande. Certaines sont basées sur la création de groupes de molécules, d'autres sur le principe de dissimilarité inter-moléculaire. Nous proposons dans ce travail, une nouvelle technique à la croisée de ces méthodes, qui permet d'obtenir des sous-ensembles à la fois divers dans l'espace et représentatifs de l'ensemble initial duquel ils sont extraits. Pour créer cette méthode de sélection, nous avons tout d'abord défini et formalisé mathématiquement un critère de diversité, puis nous nous sommes appuyés sur des heuristiques connues en apprentissage artificiel pour concevoir l'algorithme. Celui-ci a été comparé à d'autres types de sélections de diversité couramment utilisées en chémoinformatique telles que les k-medoïds, Maximum-Dissimilarity, Sphere-Exclusion. La formalisation du critère de diversité nous a enfin permis de proposer un nouveau critère d'évaluation de la qualité des sélections. La méthode et le critère présentées dans ce travail donnent des échantillons divers et représentatifs d'un espace chimique

Chemical librairies : towards a rational approach for the selection of sub-libraries

La sélection de sous-ensembles de molécules diverses est un enjeu très important de la recherche pharmaceutique. En effet, de la qualité de cette sélection, dépendra la découverte efficace d'un médicament. De nombreuses méthodes existent pour répondre à cette demande. Certaines sont basées sur la création de groupes de molécules, d'autres sur le principe de dissimilarité inter-moléculaire. Nous proposons dans ce travail, une nouvelle technique à la croisée de ces méthodes, qui permet d'obtenir des sous-ensembles à la fois divers dans l'espace et représentatifs de l'ensemble initial duquel ils sont extraits. Pour créer cette méthode de sélection, nous avons tout d'abord défini et formalisé mathématiquement un critère de diversité, puis nous nous sommes appuyés sur des heuristiques connues en apprentissage artificiel pour concevoir l'algorithme. Celui-ci a été comparé à d'autres types de sélections de diversité couramment utilisées en chémoinformatique telles que les k-medoïds, Maximum-Dissimilarity, Sphere-Exclusion. La formalisation du critère de diversité nous a enfin permis de proposer un nouveau critère d'évaluation de la qualité des sélections. La méthode et le critère présentées dans ce travail donnent des échantillons divers et représentatifs d'un espace chimique.The selection of diverse molecules'subsets is a very important stake in the pharmaceutical research. Indeed, the effective discovery of a drug will depend of the quality of this selection. Several methods exist to address this problem. Some of them are based on the creation of groups of molecules, the others on the principle of dissimilarity between chemical compounds. In this work, we propose a new technique, between these two concepts, which allows to obtain subsets, at the same time, diverse in the space and representative from the initial set which they are extracted. First of all, to create this selection method, we defined and formalized mathematically a diversity criterion, then we used heuristics known in machine learning to conceive our algorithm. This one was compared with the other types of diversity selections usually used in chemoinformatic such as k-medoïds, Maximum-Dissimilarity, Sphere-Exclusion. The formalization of the diversity criterion finally allowed us to propose a new criterion of evaluation of the quality of the selections. The algorithm and the criterion presented in this work give diverse and representative samples of a chemical space

Chimiothèques ; vers une approche rationnelle de la sélection de sous-chimiothèques

The selection of diverse molecules'subsets is a very important stake in the pharmaceutical research. Indeed, the e ective discovery of a drug will depend of the quality of this selection. Several methods exist to address this problem. Some of them are based on the creation of groups of molecules, the others on the principle of dissimilarity between chemical compounds. In this work, we propose a new technique, between these two concepts, which allows to obtain subsets, at the same time, diverse in the space and representative from the initial set which they are extracted. First of all, to create this selection method, we de ned and formalized mathematically a diversity criterion, then we used heuristics known in machine learning to conceive our algorithm. This one was compared with the other types of diversity selections usually used in chemoinformatic such as k-medoïds, Maximum-Dissimilarity, Sphere-Exclusion. The formalization of the diversity criterion nally allowed us to propose a new criterion of evaluation of the quality of the selections. The algorithm and the criterion presented in this work give diverse and representative samples of a chemical space.La sélection de sous-ensembles de molécules diverses est un enjeu très important de la recherche pharmaceutique. En effet, de la qualité de cette sélection, dépendra la découverte efficace d'un médicament. De nombreuses méthodes existent pour répondre à cette demande. Certaines sont basées sur la création de groupes de molécules, d'autres sur le principe de dissimilarité inter-moléculaire. Nous proposons dans ce travail, une nouvelle technique à la croisée de ces méthodes, qui permet d'obtenir des sous-ensembles à la fois divers dans l'espace et représentatifs de l'ensemble initial duquel ils sont extraits. Pour créer cette méthode de sélection, nous avons tout d'abord défini et formalisé mathématiquement un critère de diversité, puis nous nous sommes appuyés sur des heuristiques connues en apprentissage artificiel pour concevoir l'algorithme. Celui-ci a été comparé à d'autres types de sélections de diversité couramment utilisées en chémoinformatique telles que les k-medoïds, Maximum-Dissimilarity, Sphere-Exclusion. La formalisation du critère de diversité nous a enfin permis de proposer un nouveau critère d'évaluation de la qualité des sélections. La méthode et le critère présentés dans ce travail donnent des échantillons divers et représentatifs d'un espace chimique

Perspectives on the use of super-enhancers as a defining feature of cell/tissue-identity genes

Super-enhancers (SE) have become a popular concept and are widely used as a feature defining key identity genes. Here, we provide perspectives on the use of SE to define and identify cell/tissue-identity genes. By mining SE and their associated genes using murine functional genomics data, we highlight and discuss current limitations and open questions regarding both the sensitivity and specificity of identity genes/transcription factors predicted by SE. In this context, we point to cell/tissue-specific promoters as an important additional level of information, which we propose to combine with SE when aiming to define potential identity genes

PERSPECTIVES ON THE USE OF SUPER-ENHANCERS AS A DEFINING FEATURE OF CELL/TISSUE- IDENTITY GENES

International audienceSuper-enhancers (SE) have become a popular concept and are widely used as a feature defining key identity genes. Here, we provide perspectives on the use of SE to define and identify cell/tissue-identity genes. By mining SE and their associated genes using murine functional genomics data, we highlight and discuss current limitations and open questions regarding both the sensitivity and specificity of identity genes/TFs predicted by SE. In this context, we point to cell/tissue-specific promoters as an important additional level of information, which we propose to combine with SE when aiming to define potential identity genes

Chimiothèque (vers une approche rationnelle pour la sélection de sous-chimiothèques)

La sélection de sous-ensembles de molécules diverses est un enjeu très important de la recherche pharmaceutique. En effet, de la qualité de cette sélection, dépendra la découverte efficace d'un médicament. De nombreuses méthodes existent pour répondre à cette demande. Certaines sont basées sur la création de groupes de molécules, d'autres sur le principe de dissimilarité inter-moléculaire. Nous proposons dans ce travail, une nouvelle technique à la croisée de ces méthodes, qui permet d'obtenir des sous-ensembles à la fois divers dans l'espace et représentatifs de l'ensemble initial duquel ils sont extraits. Pour créer cette méthode de sélection, nous avons tout d'abord défini et formalisé mathématiquement un critère de diversité, puis nous nous sommes appuyés sur des heuristiques connues en apprentissage artificiel pour concevoir l'algorithme. Celui-ci a été comparé à d'autres types de sélections de diversité couramment utilisées en chémoinformatique telles que les k-medoïds, Maximum-Dissimilarity, Sphere-Exclusion. La formalisation du critère de diversité nous a enfin permis de proposer un nouveau critère d'évaluation de la qualité des sélections. La méthode et le critère présentées dans ce travail donnent des échantillons divers et représentatifs d'un espace chimique.The selection of diverse molecules'subsets is a very important stake in the pharmaceutical research. Indeed, the effective discovery of a drug will depend of the quality of this selection. Several methods exist to address this problem. Some of them are based on the creation of groups of molecules, the others on the principle of dissimilarity between chemical compounds. In this work, we propose a new technique, between these two concepts, which allows to obtain subsets, at the same time, diverse in the space and representative from the initial set which they are extracted. First of all, to create this selection method, we defined and formalized mathematically a diversity criterion, then we used heuristics known in machine learning to conceive our algorithm. This one was compared with the other types of diversity selections usually used in chemoinformatic such as k-medoïds, Maximum-Dissimilarity, Sphere-Exclusion. The formalization of the diversity criterion finally allowed us to propose a new criterion of evaluation of the quality of the selections. The algorithm and the criterion presented in this work give diverse and representative samples of a chemical space.ORLEANS-SCD-Bib. electronique (452349901) / SudocSudocFranceF

The ubiquitous transcription factor CTCF promotes lineage-specific epigenomic remodeling and establishment of transcriptional networks driving cell differentiation

International audienceCell differentiation relies on tissue-specific transcription factors (TFs) that cooperate to establish unique transcriptomes and phenotypes. However, the role of ubiquitous TFs in these processes remains poorly defined. Recently, we have shown that the CCCTC-binding factor (CTCF) is required for adipocyte differentiation through epigenomic remodelling of adipose tissue-specific enhancers and transcriptional activation of Peroxisome proliferator-activated receptor gamma (PPARG), the main driver of the adipogenic program (PPARG), and its target genes. Here, we discuss how these findings, together with the recent literature, illuminate a functional role for ubiquitous TFs in lineage-determining transcriptional networks

Organizing combinatorial transcription factor recruitment at cis -regulatory modules

International audienceGene transcriptional regulation relies on cis-regulatory DNA modules (CRMs), which serve as nexus sites for integration of multiple transcription factor (TF) activities. Here, we provide evidence and discuss recent literature indicating that TF recruitment to CRMs is organized into combinations of trans-regulatory protein modules (TRMs). We propose that TRMs are functional entities composed of TFs displaying the most highly interdependent chromatin binding which are, in addition, able to modulate their recruitment to CRMs through inter-TRM effects. These findings shed light on the architectural organization of TF recruitment encoded by their recognition motifs within CRMs