Inferring and comparing metabolism across heterogeneous sets of annotated genomes using AuCoMe.

Comparative analysis of genome-scale metabolic networks (GSMNs) may yield important information on the biology, evolution, and adaptation of species. However, it is impeded by the high heterogeneity of the quality and completeness of structural and functional genome annotations, which may bias the results of such comparisons. To address this issue, we developed AuCoMe, a pipeline to automatically reconstruct homogeneous GSMNs from a heterogeneous set of annotated genomes without discarding available manual annotations. We tested AuCoMe with three data sets, one bacterial, one fungal, and one algal, and showed that it successfully reduces technical biases while capturing the metabolic specificities of each organism. Our results also point out shared and divergent metabolic traits among evolutionarily distant algae, underlining the potential of AuCoMe to accelerate the broad exploration of metabolic evolution across the tree of life.

A rule-based multiscale model of hepatic stellate cell plasticity: Critical role of the inactivation loop in fibrosis progression.

Hepatic stellate cells (HSC) are the source of extracellular matrix (ECM) whose overproduction leads to fibrosis, a condition that impairs liver functions in chronic liver diseases. Understanding the dynamics of HSCs will provide insights needed to develop new therapeutic approaches. Few models of hepatic fibrosis have been proposed, and none of them include the heterogeneity of HSC phenotypes recently highlighted by single-cell RNA sequencing analyses. Here, we developed rule-based models to study HSC dynamics during fibrosis progression and reversion. We used the Kappa graph rewriting language, for which we used tokens and counters to overcome temporal explosion. HSCs are modeled as agents that present seven physiological cellular states and that interact with (TGFß1) molecules which regulate HSC activation and the secretion of type I collagen, the main component of the ECM. Simulation studies revealed the critical role of the HSC inactivation process during fibrosis progression and reversion. While inactivation allows elimination of activated HSCs during reversion steps, reactivation loops of inactivated HSCs (iHSCs) are required to sustain fibrosis. Furthermore, we demonstrated the model's sensitivity to (TGFß1) parameters, suggesting its adaptability to a variety of pathophysiological conditions for which levels of (TGFß1) production associated with the inflammatory response differ. Using new experimental data from a mouse model of CCl4-induced liver fibrosis, we validated the predicted ECM dynamics. Our model also predicts the accumulation of iHSCs during chronic liver disease. By analyzing RNA sequencing data from patients with non-alcoholic steatohepatitis (NASH) associated with liver fibrosis, we confirmed this accumulation, identifying iHSCs as novel markers of fibrosis progression. Overall, our study provides the first model of HSC dynamics in chronic liver disease that can be used to explore the regulatory role of iHSCs in liver homeostasis. Moreover, our model can also be generalized to fibroblasts during repair and fibrosis in other tissues.

Regulus infers signed regulatory relations from few samples' information using discretization and likelihood constraints.

MOTIVATION: Transcriptional regulation is performed by transcription factors (TF) binding to DNA in context-dependent regulatory regions and determines the activation or inhibition of gene expression. Current methods of transcriptional regulatory circuits inference, based on one or all of TF, regions and genes activity measurements require a large number of samples for ranking the candidate TF-gene regulation relations and rarely predict whether they are activations or inhibitions. We hypothesize that transcriptional regulatory circuits can be inferred from fewer samples by (1) fully integrating information on TF binding, gene expression and regulatory regions accessibility, (2) reducing data complexity and (3) using biology-based likelihood constraints to determine the global consistency between a candidate TF-gene relation and patterns of genes expressions and region activations, as well as qualify regulations as activations or inhibitions. RESULTS: We introduce Regulus, a method which computes TF-gene relations from gene expressions, regulatory region activities and TF binding sites data, together with the genomic locations of all entities. After aggregating gene expressions and region activities into patterns, data are integrated into a RDF (Resource Description Framework) endpoint. A dedicated SPARQL (SPARQL Protocol and RDF Query Language) query retrieves all potential relations between expressed TF and genes involving active regulatory regions. These TF-region-gene relations are then filtered using biological likelihood constraints allowing to qualify them as activation or inhibition. Regulus provides signed relations consistent with public databases and, when applied to biological data, identifies both known and potential new regulators. Regulus is devoted to context-specific transcriptional circuits inference in human settings where samples are scarce and cell populations are closely related, using discretization into patterns and likelihood reasoning to decipher the most robust regulatory relations.

Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology.

Computational models are often employed in systems biology to study the dynamic behaviours of complex systems. With the rise in the number of computational models, finding ways to improve the reusability of these models and their ability to reproduce virtual experiments becomes critical. Correct and effective model annotation in community-supported and standardised formats is necessary for this improvement. Here, we present recent efforts toward a common framework for annotated, accessible, reproducible and interoperable computational models in biology, and discuss key challenges of the field.

MERRIN: MEtabolic regulation rule INference from time series data.

MOTIVATION: Many techniques have been developed to infer Boolean regulations from a prior knowledge network (PKN) and experimental data. Existing methods are able to reverse-engineer Boolean regulations for transcriptional and signaling networks, but they fail to infer regulations that control metabolic networks. RESULTS: We present a novel approach to infer Boolean rules for metabolic regulation from time-series data and a PKN. Our method is based on a combination of answer set programming and linear programming. By solving both combinatorial and linear arithmetic constraints, we generate candidate Boolean regulations that can reproduce the given data when coupled to the metabolic network. We evaluate our approach on a core regulated metabolic network and show how the quality of the predictions depends on the available kinetic, fluxomics or transcriptomics time-series data. AVAILABILITY AND IMPLEMENTATION: Software available at https://github.com/bioasp/merrin. SUPPLEMENTARY INFORMATION: Supplementary data are available at https://doi.org/10.5281/zenodo.6670164.

Insights into the potential for mutualistic and harmful host-microbe interactions affecting brown alga freshwater acclimation.

Microbes can modify their hosts' stress tolerance, thus potentially enhancing their ecological range. An example of such interactions is Ectocarpus subulatus, one of the few freshwater-tolerant brown algae. This tolerance is partially due to its (un)cultivated microbiome. We investigated this phenomenon by modifying the microbiome of laboratory-grown E. subulatus using mild antibiotic treatments, which affected its ability to grow in low salinity. Low salinity acclimation of these algal-bacterial associations was then compared. Salinity significantly impacted bacterial and viral gene expression, albeit in different ways across algal-bacterial communities. In contrast, gene expression of the host and metabolite profiles were affected almost exclusively in the freshwater-intolerant algal-bacterial communities. We found no evidence of bacterial protein production that would directly improve algal stress tolerance. However, vitamin K synthesis is one possible bacterial service missing specifically in freshwater-intolerant cultures in low salinity. In this condition, we also observed a relative increase in bacterial transcriptomic activity and the induction of microbial genes involved in the biosynthesis of the autoinducer AI-1, a quorum-sensing regulator. This could have resulted in dysbiosis by causing a shift in bacterial behaviour in the intolerant algal-bacterial community. Together, these results provide two promising hypotheses to be examined by future targeted experiments. Although they apply only to the specific study system, they offer an example of how bacteria may impact their host's stress response.

Discrete modeling for integration and analysis of large-scale signaling networks.

Most biological processes are orchestrated by large-scale molecular networks which are described in large-scale model repositories and whose dynamics are extremely complex. An observed phenotype is a state of this system that results from control mechanisms whose identification is key to its understanding. The Biological Pathway Exchange (BioPAX) format is widely used to standardize the biological information relative to regulatory processes. However, few modeling approaches developed so far enable for computing the events that control a phenotype in large-scale networks. Here we developed an integrated approach to build large-scale dynamic networks from BioPAX knowledge databases in order to analyse trajectories and to identify sets of biological entities that control a phenotype. The Cadbiom approach relies on the guarded transitions formalism, a discrete modeling approach which models a system dynamics by taking into account competition and cooperation events in chains of reactions. The method can be applied to every BioPAX (large-scale) model thanks to a specific package which automatically generates Cadbiom models from BioPAX files. The Cadbiom framework was applied to the BioPAX version of two resources (PID, KEGG) of the Pathway Commons database and to the Atlas of Cancer Signalling Network (ACSN). As a case-study, it was used to characterize sets of biological entities implicated in the epithelial-mesenchymal transition. Our results highlight the similarities between the PID and ACSN resources in terms of biological content, and underline the heterogeneity of usage of the BioPAX semantics limiting the fusion of models that require curation. Causality analyses demonstrate the smart complementarity of the databases in terms of combinatorics of controllers that explain a phenotype. From a biological perspective, our results show the specificity of controllers for epithelial and mesenchymal phenotypes that are consistent with the literature and identify a novel signature for intermediate states.

Cyanorak v2.1: a scalable information system dedicated to the visualization and expert curation of marine and brackish picocyanobacteria genomes.

Cyanorak v2.1 (http://www.sb-roscoff.fr/cyanorak) is an information system dedicated to visualizing, comparing and curating the genomes of Prochlorococcus, Synechococcus and Cyanobium, the most abundant photosynthetic microorganisms on Earth. The database encompasses sequences from 97 genomes, covering most of the wide genetic diversity known so far within these groups, and which were split into 25,834 clusters of likely orthologous groups (CLOGs). The user interface gives access to genomic characteristics, accession numbers as well as an interactive map showing strain isolation sites. The main entry to the database is through search for a term (gene name, product, etc.), resulting in a list of CLOGs and individual genes. Each CLOG benefits from a rich functional annotation including EggNOG, EC/K numbers, GO terms, TIGR Roles, custom-designed Cyanorak Roles as well as several protein motif predictions. Cyanorak also displays a phyletic profile, indicating the genotype and pigment type for each CLOG, and a genome viewer (Jbrowse) to visualize additional data on each genome such as predicted operons, genomic islands or transcriptomic data, when available. This information system also includes a BLAST search tool, comparative genomic context as well as various data export options. Altogether, Cyanorak v2.1 constitutes an invaluable, scalable tool for comparative genomics of ecologically relevant marine microorganisms.

PAX2GRAPHML: a python library for large-scale regulation network analysis using BioPAX.

SUMMARY: PAX2GRAPHML is an open-source Python library that allows to easily manipulate BioPAX source files as regulated reaction graphs described in.graphml format. The concept of regulated reactions, which allows connecting regulatory, signaling and metabolic levels, has been used. Biochemical reactions and regulatory interactions are homogeneously described by regulated reactions involving substrates, products, activators and inhibitors as elements. PAX2GRAPHML is highly flexible and allows generating graphs of regulated reactions from a single BioPAX source or by combining and filtering BioPAX sources. Supported by the graph exchange format .graphml, the large-scale graphs produced from one or more data sources can be further analyzed with PAX2GRAPHML or standard Python and R graph libraries. AVAILABILITY AND IMPLEMENTATION: https://pax2graphml.genouest.org.

Pregnancy Outcomes among Women with Class III Obesity with Pre-diabetic Early Hemoglobin A1C.

OBJECTIVE: We aimed to assess the risk of developing gestational diabetes mellitus (GDM) in women with a normal A1C (<5.7) compared with those with an A1C in the pre-diabetic range (5.7-6.4). STUDY DESIGN: This study comprises of a retrospective cohort of non-anomalous singleton pregnancies with maternal body mass index (BMI) ≥40 at a single institution from 2013 to 2017. Pregnancies with multiple gestation, late entry to care, type 1 or 2 diabetes, and missing diabetes-screening information were excluded. The primary outcome was development of GDM. Secondary outcomes included fetal growth restriction, macrosomia, gestational age at delivery, large for gestational age, delivery BMI at delivery, total weight gain in pregnancy, induction of labor, shoulder dystocia, and cesarean delivery. Bivariate statistics were used to compare demographics, pregnancy complications, and delivery characteristics of women who had an early A1C < 5.7 and A1C 5.7 to 6.4. Multivariable analyses were used to estimate the odds of the primary outcome. RESULTS: Eighty women (68%) had an early A1C <5.7 and 38 (32%) had a A1C 5.7 to 6.4. Women in the lower A1C group were less likely to be Black (45 vs. 74%, p = 0.01). No differences in other baseline demographics were observed. The median A1C was 5.3 for women with A1C < 5.7 and 5.8 for women with A1C 5.7 to 6.4 (p < 0.001). GDM was significantly more common in women with A1C 5.7 to 6.4 (3.8 vs. 24%, p = 0.002). Women with pre-diabetic range A1C had an odd ratio of 11.1 (95% CI 2.49-48.8) for GDM compared with women with a normal A1C. CONCLUSION: Women with class III obesity and a pre-diabetic range A1C are at an increased risk for gestational diabetes when compared with those with a normal A1C in early pregnancy. KEY POINTS: · One in 3 women with class III obesity had a pre-diabetic range early A1C.. · Class III obese women who have a pre-diabetic A1C have a higher risk of gestational diabetes.. · In this high-risk population, early A1C results in the pre-diabetic range are associated with higher rates of gestational diabetes..

Constructing xenobiotic maps of metabolism to predict enzymes catalyzing metabolites capable of binding to DNA.

BACKGROUND: The liver plays a major role in the metabolic activation of xenobiotics (drugs, chemicals such as pollutants, pesticides, food additives...). Among environmental contaminants of concern, heterocyclic aromatic amines (HAA) are xenobiotics classified by IARC as possible or probable carcinogens (2A or 2B). There exist little information about the effect of these HAA in humans. While HAA is a family of more than thirty identified chemicals, the metabolic activation and possible DNA adduct formation have been fully characterized in human liver for only a few of them (MeIQx, PhIP, A[Formula: see text]C). RESULTS: We have developed a modeling approach in order to predict all the possible metabolites of a xenobiotic and enzymatic profiles that are linked to the production of metabolites able to bind DNA. Our prediction of metabolites approach relies on the construction of an enriched and annotated map of metabolites from an input metabolite.The pipeline assembles reaction prediction tools (SyGMa), sites of metabolism prediction tools (Way2Drug, SOMP and Fame 3), a tool to estimate the ability of a xenobotics to form DNA adducts (XenoSite Reactivity V1), and a filtering procedure based on Bayesian framework. This prediction pipeline was evaluated using caffeine and then applied to HAA. The method was applied to determine enzymes profiles associated with the maximization of metabolites derived from each HAA which are able to bind to DNA. The classification of HAA according to enzymatic profiles was consistent with their chemical structures. CONCLUSIONS: Overall, a predictive toxicological model based on an in silico systems biology approach opens perspectives to estimate the genotoxicity of various chemical classes of environmental contaminants. Moreover, our approach based on enzymes profile determination opens the possibility of predicting various xenobiotics metabolites susceptible to bind to DNA in both normal and physiopathological situations.

MiR-202 controls female fecundity by regulating medaka oogenesis.

Female gamete production relies on coordinated molecular and cellular processes that occur in the ovary throughout oogenesis. In fish, as in other vertebrates, these processes have been extensively studied both in terms of endocrine/paracrine regulation and protein expression and activity. The role of small non-coding RNAs in the regulation of animal reproduction remains however largely unknown and poorly investigated, despite a growing interest for the importance of miRNAs in a wide variety of biological processes. Here, we analyzed the role of miR-202, a miRNA predominantly expressed in male and female gonads in several vertebrate species. We studied its expression in the medaka ovary and generated a mutant line (using CRISPR/Cas9 genome editing) to determine its importance for reproductive success with special interest for egg production. Our results show that miR-202-5p is the most abundant mature form of the miRNA and that it is expressed in granulosa cells and in the unfertilized egg. The knock out (KO) of mir-202 gene resulted in a strong phenotype both in terms of number and quality of eggs produced. Mutant females exhibited either no egg production or produced a dramatically reduced number of eggs that could not be fertilized, ultimately leading to no reproductive success. We quantified the size distribution of the oocytes in the ovary of KO females and performed a large-scale transcriptomic analysis approach to identified dysregulated molecular pathways. Together, cellular and molecular analyses indicate that the lack of miR-202 impairs the early steps of oogenesis/folliculogenesis and decreases the number of large (i.e. vitellogenic) follicles, ultimately leading to dramatically reduced female fecundity. This study sheds new light on the regulatory mechanisms that control the early steps of follicular development, including possible targets of miR-202-5p, and provides the first in vivo functional evidence that a gonad-predominant microRNA may have a major role in female reproduction.

Using automated reasoning to explore the metabolism of unconventional organisms: a first step to explore host-microbial interactions.

Systems modelled in the context of molecular and cellular biology are difficult to represent with a single calibrated numerical model. Flux optimisation hypotheses have shown tremendous promise to accurately predict bacterial metabolism but they require a precise understanding of metabolic reactions occurring in the considered species. Unfortunately, this information may not be available for more complex organisms or non-cultured microorganisms such as those evidenced in microbiomes with metagenomic techniques. In both cases, flux optimisation techniques may not be applicable to elucidate systems functioning. In this context, we describe how automatic reasoning allows relevant features of an unconventional biological system to be identified despite a lack of data. A particular focus is put on the use of Answer Set Programming, a logic programming paradigm with combinatorial optimisation functionalities. We describe its usage to over-approximate metabolic responses of biological systems and solve gap-filling problems. In this review, we compare steady-states and Boolean abstractions of metabolic models and illustrate their complementarity via applications to the metabolic analysis of macro-algae. Ongoing applications of this formalism explore the emerging field of systems ecology, notably elucidating interactions between a consortium of microbes and a host organism. As the first step in this field, we will illustrate how the reduction in microbiotas according to expected metabolic phenotypes can be addressed with gap-filling problems.

Assessing patient perceptions of cytomegalovirus infection in pregnancy.

Pregnant women impacted by cytomegalovirus (CMV) make clinical decisions despite uncertain outcomes. Intolerance of uncertainty score (IUS) is a validated measure of tendency for individuals to find unacceptable that a negative event might occur. We investigated patient perceptions of CMV infection during pregnancy and correlated IUS and knowledge with decision-making. Electronic questionnaire was sent to women from July to August 2017. The questionnaire evaluated knowledge of CMV, IUS, and responses regarding management to three clinical scenarios with escalating risk of CMV including choices for no further testing, ultrasound, amniocentesis, or abortion. For each scenario, logistic regression was used to model IUS on responses. A total of 815 women were included. The majority of participants was white (63.1%) and 42% had a postgraduate degree. Over 70% reported that they had not previously heard of CMV. In the scenario with only CMV exposure, participants with increasing IUS were more likely to choose abortion (odds ratio [OR] = 1.04; 95% confidence interval [CI]: 1.01, 1.06) and no further testing (OR = 0.97; 95% CI: 0.95, 0.99). In the scenario with mild ultrasound findings in setting of CMV exposure, increasing IUS was associated with higher odds of choosing no further testing (OR = 0.97; 95% CI, 0.94, 0.99). No significant association was observed between IUS and responses in the scenario with severe ultrasound abnormalities in setting of CMV exposure. The majority of patients had no knowledge of CMV. Higher IUS was associated more intervention in low severity scenarios, but in severe scenarios, IUS was not associated with participants' choices.

Postpartum Weight Loss in Women with Class-III Obesity: Do They Lose What They Gain?

OBJECTIVE: Excessive gestational weight gain (GWG) increases risk of postpartum weight retention in normal and overweight women but little is known about weight retention in morbidly obese women. We evaluated the impact of GWG on postpartum weight retention in women with class-III obesity. STUDY DESIGN: This is a retrospective cohort of pregnancies at a single institution from July 2013 to December 2017 complicated by body mass index (BMI) ≥ 40 at entry to care. Women were classified as GWG within (WITHIN), less than (LESS), or greater than (MORE) Institute of Medicine's (IOM) recommendations. Women were excluded for multiples, late prenatal care, preterm birth, fetal anomalies, intrauterine demise, weight loss, and missing data. Primary outcome was achievement of intake weight at the postpartum visit. Logistic regression was used to adjust for confounding factors. RESULTS: Among 338 women, 93 (28%) gained WITHIN, 129 (38%) LESS, and 144 (43%) MORE. Women in the MORE group were less likely to achieve their intake weight at the postpartum visit (adjusted odds ratio [AOR] = 0.09 95% confidence interval [CI]: 0.05-0.17, p < 0.01). Women gaining MORE were the only group who did not lose weight from intake to postpartum (Median weight change [LESS: -14 lbs (IQR: -20 to -7)] vs. [WITHIN: -7 lbs (IQR: -13 to -1)] vs. [MORE: 5 lbs (IQR: 0-15)]; p < 0.01). CONCLUSION: Excessive GWG in women with class-III obesity is associated with postpartum weight retention.

Gestational Weight Gain and Postpartum Depression in Women with Class III Obesity.

OBJECTIVE: We investigated the association between gestational weight gain (GWG) and postpartum depression (PPD) in women with class III obesity. STUDY DESIGN: This is a retrospective cohort of women with body mass index (BMI) ≥ 40 kg/m2 at entry to care, first prenatal visit ≤14 weeks gestation, with singleton, nonanomalous pregnancies who delivered at term from July 2013 to December 2017. Women missing data regarding PPD were excluded. Primary outcome was PPD; classified as Edinburgh Postnatal Depression Scale (EPDS) score >13/30 or provider's report of depression. Participants were classified, according to Institute of Medicine GWG guidelines (11-20 pounds), as either less than 11 pounds (LT11) or at/more than 11 pounds (GT11). Bivariate statistics compared demographics and pregnancy characteristics. Logistic regression used to estimate odds of primary outcome. RESULTS: Of 275 women, 96 (34.9%) gained LT11 and 179 (65.1%) gained GT11 during pregnancy. The rate of PPD was 8.7% (n = 24), 9 (9.4%) in the LT11 group and 15 (8.4%) in the GT11 group (p = 0.82, odds ratio: 1.13, 95% confidence interval [CI]: 0.48, 2.69). When controlling for entry BMI and multiparity, adjusted odds of PPD was 1.07 (95% CI: 0.44, 2.63). No correlation was found between GWG and EPDS. CONCLUSION: A relationship between GWG and PPD in class III obese women was not found in this cohort.

Scalable and exhaustive screening of metabolic functions carried out by microbial consortia.

Motivation: The selection of species exhibiting metabolic behaviors of interest is a challenging step when switching from the investigation of a large microbiota to the study of functions effectiveness. Approaches based on a compartmentalized framework are not scalable. The output of scalable approaches based on a non-compartmentalized modeling may be so large that it has neither been explored nor handled so far. Results: We present the Miscoto tool to facilitate the selection of a community optimizing a desired function in a microbiome by reporting several possibilities which can be then sorted according to biological criteria. Communities are exhaustively identified using logical programming and by combining the non-compartmentalized and the compartmentalized frameworks. The benchmarking of 4.9 million metabolic functions associated with the Human Microbiome Project, shows that Miscoto is suited to screen and classify metabolic producibility in terms of feasibility, functional redundancy and cooperation processes involved. As an illustration of a host-microbial system, screening the Recon 2.2 human metabolism highlights the role of different consortia within a family of 773 intestinal bacteria. Availability and implementation: Miscoto source code, instructions for use and examples are available at: https://github.com/cfrioux/miscoto.

Formalizing and enriching phenotype signatures using Boolean networks.

In order to predict the behavior of a biological system, one common approach is to perform a simulation on a dynamic model. Boolean networks allow to analyze the qualitative aspects of the model by identifying its steady states and attractors. Each of them, when possible, is associated with a phenotype which conveys a biological interpretation. Phenotypes are characterized by their signatures, provided by domain experts. The number of steady states tends to increase with the network size and the number of simulation conditions, which makes the biological interpretation difficult. As a first step, we explore the use of Formal Concept Analysis as a symbolic bi-clustering technics to classify and sort the steady states of a Boolean network according to biological signatures based on the hierarchy of the roles the network components play in the phenotypes. FCA generates a lattice structure describing the dependencies between proteins in the signature and steady-states of the Boolean network. We use this lattice (i) to enrich the biological signatures according to the dependencies carried by the network dynamics, (ii) to identify variants to the phenotypes and (iii) to characterize hybrid phenotypes. We applied our approach on a T helper lymphocyte (Th) differentiation network with a set of signatures corresponding to the sub-types of Th. Our method generated the same classification as a manual analysis performed by experts in the field, and was also able to work under extended simulation conditions. This led to the identification and prediction of a new hybrid sub-type later confirmed by the literature.

Computational discovery of dynamic cell line specific Boolean networks from multiplex time-course data.

Protein signaling networks are static views of dynamic processes where proteins go through many biochemical modifications such as ubiquitination and phosphorylation to propagate signals that regulate cells and can act as feed-back systems. Understanding the precise mechanisms underlying protein interactions can elucidate how signaling and cell cycle progression occur within cells in different diseases such as cancer. Large-scale protein signaling networks contain an important number of experimentally verified protein relations but lack the capability to predict the outcomes of the system, and therefore to be trained with respect to experimental measurements. Boolean Networks (BNs) are a simple yet powerful framework to study and model the dynamics of the protein signaling networks. While many BN approaches exist to model biological systems, they focus mainly on system properties, and few exist to integrate experimental data in them. In this work, we show an application of a method conceived to integrate time series phosphoproteomic data into protein signaling networks. We use a large-scale real case study from the HPN-DREAM Breast Cancer challenge. Our efficient and parameter-free method combines logic programming and model-checking to infer a family of BNs from multiple perturbation time series data of four breast cancer cell lines given a prior protein signaling network. Because each predicted BN family is cell line specific, our method highlights commonalities and discrepancies between the four cell lines. Our models have a Root Mean Square Error (RMSE) of 0.31 with respect to the testing data, while the best performant method of this HPN-DREAM challenge had a RMSE of 0.47. To further validate our results, BNs are compared with the canonical mTOR pathway showing a comparable AUROC score (0.77) to the top performing HPN-DREAM teams. In addition, our approach can also be used as a complementary method to identify erroneous experiments. These results prove our methodology as an efficient dynamic model discovery method in multiple perturbation time course experimental data of large-scale signaling networks. The software and data are publicly available at https://github.com/misbahch6/caspo-ts.

Traceability, reproducibility and wiki-exploration for "à-la-carte" reconstructions of genome-scale metabolic models.

Genome-scale metabolic models have become the tool of choice for the global analysis of microorganism metabolism, and their reconstruction has attained high standards of quality and reliability. Improvements in this area have been accompanied by the development of some major platforms and databases, and an explosion of individual bioinformatics methods. Consequently, many recent models result from "à la carte" pipelines, combining the use of platforms, individual tools and biological expertise to enhance the quality of the reconstruction. Although very useful, introducing heterogeneous tools, that hardly interact with each other, causes loss of traceability and reproducibility in the reconstruction process. This represents a real obstacle, especially when considering less studied species whose metabolic reconstruction can greatly benefit from the comparison to good quality models of related organisms. This work proposes an adaptable workspace, AuReMe, for sustainable reconstructions or improvements of genome-scale metabolic models involving personalized pipelines. At each step, relevant information related to the modifications brought to the model by a method is stored. This ensures that the process is reproducible and documented regardless of the combination of tools used. Additionally, the workspace establishes a way to browse metabolic models and their metadata through the automatic generation of ad-hoc local wikis dedicated to monitoring and facilitating the process of reconstruction. AuReMe supports exploration and semantic query based on RDF databases. We illustrate how this workspace allowed handling, in an integrated way, the metabolic reconstructions of non-model organisms such as an extremophile bacterium or eukaryote algae. Among relevant applications, the latter reconstruction led to putative evolutionary insights of a metabolic pathway.