Toll Based Measures for Dynamical Graphs

Biological networks are one of the most studied object in computational biology. Several methods have been developed for studying qualitative properties of biological networks. Last decade had seen the improvement of molecular techniques that make quantitative analyses reachable. One of the major biological modelling goals is therefore to deal with the quantitative aspect of biological graphs. We propose a probabilistic model that suits with this quantitative aspects. Our model combines graph with several dynamical sources. It emphazises various asymptotic statistical properties that might be useful for giving biological insightsComment: 11 page

Integrating heterogeneous knowledges for understanding biological behaviors: a probabilistic approach

Despite recent molecular technique improvements, biological knowledge remains incomplete. Reasoning on living systems hence implies to integrate heterogeneous and partial informations. Although current investigations successfully focus on qualitative behaviors of macromolecular networks, others approaches show partial quantitative informations like protein concentration variations over times. We consider that both informations, qualitative and quantitative, have to be combined into a modeling method to provide a better understanding of the biological system. We propose here such a method using a probabilistic-like approach. After its exhaustive description, we illustrate its advantages by modeling the carbon starvation response in Escherichia coli. In this purpose, we build an original qualitative model based on available observations. After the formal verification of its qualitative properties, the probabilistic model shows quantitative results corresponding to biological expectations which confirm the interest of our probabilistic approach.Comment: 10 page

Comparing Bacterial Genomes by Searching their Common Intervals

International audienceComparing bacterial genomes implies the use of a dedicated measure. It relies on comparing circular genomes based on a set of conserved genes. Following this assumption, the common interval appears to be a good candidate. For evidences, we propose herein an approach to compute the common intervals between two circular genomes that takes into account duplications. Its application on a concrete case, comparing E. coli and V. cholerae, is accurate. It indeed emphasizes sets of conserved genes that present high impacts on bacterial functions

Probabilistic Modeling of Microbial Metabolic Networks for Integrating Partial Quantitative Knowledge Within the Nitrogen Cycle

Understanding the interactions between microbial communities and their environment sufficiently to predict diversity on the basis of physicochemical parameters is a fundamental pursuit of microbial ecology that still eludes us. However, modeling microbial communities is problematic, because (i) communities are complex, (ii) most descriptions are qualitative, and (iii) quantitative understanding of the way communities interact with their surroundings remains incomplete. One approach to overcoming such complications is the integration of partial qualitative and quantitative descriptions into more complex networks. Here we outline the development of a probabilistic framework, based on Event Transition Graph (ETG) theory, to predict microbial community structure across observed chemical data. Using reverse engineering, we derive probabilities from the ETG that accurately represent observations from experiments and predict putative constraints on communities within dynamic environments. These predictions can feedback into the future development of field experiments by emphasizing the most important functional reactions, and associated microbial strains, required to characterize microbial ecosystems

Gene Expression Analysis of Zobellia galactanivorans during the Degradation of Algal Polysaccharides Reveals both Substrate-Specific and Shared Transcriptome-Wide Responses

International audienceFlavobacteriia are recognized as key players in the marine carbon cycle, due to their ability to efficiently degrade algal polysaccharides both in the open ocean and in coastal regions. The chemical complexity of algal polysaccharides, their differences between algal groups and variations through time and space, imply that marine flavobacteria have evolved dedicated degradation mechanisms and regulation of their metabolism during interactions with algae. In the present study, we report the first transcriptome-wide gene expression analysis for an alga-associated flavobacterium during polysaccharide degradation. Zobellia galactanivorans Dsij(T), originally isolated from a red alga, was grown in minimal medium with either glucose (used as a reference monosaccharide) or one selected algal polysaccharide from brown (alginate, laminarin) or red algae (agar, porphyran, ι- or κ-carrageenan) as sole carbon source. Expression profiles were determined using whole-genome microarrays. Integration of genomic knowledge with the automatic building of a co-expression network allowed the experimental validation of operon-like transcription units. Differential expression analysis revealed large transcriptomic shifts depending on the carbon source. Unexpectedly, transcriptomes shared common signatures when growing on chemically divergent polysaccharides from the same algal phylum. Together with the induction of numerous transcription factors, this hints at complex regulation events that fine-tune the cell behavior during interactions with algal biomass in the marine environment. The results further highlight genes and loci that may participate in polysaccharide utilization, notably encoding Carbohydrate Active enZymes (CAZymes) and glycan binding proteins together with a number of proteins of unknown function. This constitutes a set of candidate genes potentially representing new substrate specificities. By providing an unprecedented view of global transcriptomic responses during polysaccharide utilization in an alga-associated model flavobacterium, this study expands the current knowledge on the functional role of flavobacteria in the marine carbon cycle and on their interactions with algae

KOALAB: A new method for regulatory motif search. Illustration on alternative splicing regulation in HIV-1

Discovering heterogeneous regulatory motifs remains a difficult problem in biological sequence analysis. In this context, statistical learning or pattern search techniques on their own have shown some limitations. However, significant benefits can be taken from their complementarity. We selected two state-of-the-art methods: a multi-class support vector machine (M-SVM) from the statistical learning domain associated with a performant discrete pattern matching algorithm grappe, and in- tegrated them into a web technology based graphical software: KOALAB (KOupled Algorithmic and Learning Approach for Biology)1 . We applied our method on motif discovery within nucleic acid sequences using experimental SELEX results as training database for the M-SVM. An application dealing with the search for splicing regulatory protein binding sites in HIV-1 genome shows the potential of such an approach

Toward systems biology in brown algae to explore acclimation and adaptation to the shore environment.

International audienceBrown algae belong to a phylogenetic lineage distantly related to land plants and animals. They are almost exclusively found in the intertidal zone, a harsh and frequently changing environment where organisms are submitted to marine and terrestrial constraints. In relation with their unique evolutionary history and their habitat, they feature several peculiarities, including at the level of their primary and secondary metabolism. The establishment of Ectocarpus siliculosus as a model organism for brown algae has represented a framework in which several omics techniques have been developed, in particular, to study the response of these organisms to abiotic stresses. With the recent publication of medium to high throughput profiling data, it is now possible to envision integrating observations at the cellular scale to apply systems biology approaches. As a first step, we propose a protocol focusing on integrating heterogeneous knowledge gained on brown algal metabolism. The resulting abstraction of the system will then help understanding how brown algae cope with changes in abiotic parameters within their unique habitat, and to decipher some of the mechanisms underlying their (1) acclimation and (2) adaptation, respectively consequences of (1) the behavior or (2) the topology of the system resulting from the integrative approach

A Multi-Site Constraint Programming Model of Alternative Splicing Regulation

Alternative splicing is a key process in post-transcriptional regulation, by which several kinds of mature RNA can be obtained from the same premessenger RNA. The resulting combinatorial complexity contributes to biological diversity, especially in the case of the human immunodeficiency virus HIV-1. Using a constraint programming approach, we develop a model of the alternative splicing regulation in HIV-1. Our model integrates different scales (single site vs. multiple sites), and thus allows us to exploit several types of experimental data available to us

Linking Spatial and Temporal Dynamic of Bacterioplankton Communities With Ecological Strategies Across a Coastal Frontal Area

Ocean frontal systems are widespread hydrological features defining the transition zone between distinct water masses. They are generally of high biological importance as they are often associated with locally enhanced primary production by phytoplankton. However, the composition of bacterial communities in the frontal zone remains poorly understood. In this study, we investigate how a coastal tidal front in Brittany (France) structures the free-living bacterioplankton communities in a spatio-temporal survey across four cruises, five stations and three depths. We used 16S rRNA gene surveys to compare bacterial community structures across 134 seawater samples and defined groups of co-varying taxa (modules) exhibiting coherent ecological patterns across space and time. We found that bacterial communities composition was strongly associated with the biogeochemical characteristics of the different water masses and that the front act as an ecological boundary for free-living bacteria. Seasonal variations in primary producers and their distribution in the water column appeared as the most salient parameters controlling heterotrophic bacteria which dominated the free-living community. Different dynamics of modules observed in this environment were strongly consistent with a partitioning of heterotrophic bacterioplankton in oligotroph and copiotroph ecological strategies. Oligotroph taxa, dominated by SAR11 Clade members, were relatively more abundant in low phytoplankton, high inorganic nutrients water masses, while copiotrophs and particularly opportunist taxa such as Tenacibaculum sp. or Pseudoalteromonas sp. reached their highest abundances during the more productive period. Overall, this study shows a remarkable coupling between bacterioplankton communities dynamics, trophic strategies, and seasonal cycles in a complex coastal environment

Investigating the microbial ecology of coastal hotspots of marine nitrogen fixation in the western North Atlantic

AbstractVariation in the microbial cycling of nutrients and carbon in the ocean is an emergent property of complex planktonic communities. While recent findings have considerably expanded our understanding of the diversity and distribution of nitrogen (N2) fixing marine diazotrophs, knowledge gaps remain regarding ecological interactions between diazotrophs and other community members. Using quantitative 16S and 18S V4 rDNA amplicon sequencing, we surveyed eukaryotic and prokaryotic microbial communities from samples collected in August 2016 and 2017 across the Western North Atlantic. Leveraging and significantly expanding an earlier published 2015 molecular dataset, we examined microbial community structure and ecological co-occurrence relationships associated with intense hotspots of N2 fixation previously reported at sites off the Southern New England Shelf and Mid-Atlantic Bight. Overall, we observed a negative relationship between eukaryotic diversity and both N2 fixation and net community production (NCP). Maximum N2 fixation rates occurred at sites with high abundances of mixotrophic stramenopiles, notably Chrysophyceae. Network analysis revealed such stramenopiles to be keystone taxa alongside the haptophyte diazotroph host Braarudosphaera bigelowii and chlorophytes. Our findings highlight an intriguing relationship between marine stramenopiles and high N2 fixation coastal sites.</jats:p