Reactmine: a search algorithm for inferring chemical reaction networks from time series data

Inferring chemical reaction networks (CRN) from time series data is a challenge encouraged by the growing availability of quantitative temporal data at the cellular level. This motivates the design of algorithms to infer the preponderant reactions between the molecular species observed in a given biochemical process, and help to build CRN model structure and kinetics. Existing ODE-based inference methods such as SINDy resort to least square regression combined with sparsity-enforcing penalization, such as Lasso. However, when the input time series are only available in wild type conditions in which all reactions are present, we observe that current methods fail to learn sparse models. Results: We present Reactmine, a CRN learning algorithm which enforces sparsity by inferring reactions in a sequential fashion within a search tree of bounded depth, ranking the inferred reaction candidates according to the variance of their kinetics, and re-optimizing the CRN kinetic parameters on the whole trace in a final pass to rank the inferred CRN candidates. We first evaluate its performance on simulation data from a benchmark of hidden CRNs, together with algorithmic hyperparameter sensitivity analyses, and then on two sets of real experimental data: one from protein fluorescence videomicroscopy of cell cycle and circadian clock markers, and one from biomedical measurements of systemic circadian biomarkers possibly acting on clock gene expression in peripheral organs. We show that Reactmine succeeds both on simulation data by retrieving hidden CRNs where SINDy fails, and on the two real datasets by inferring reactions in agreement with previous studies

The Logarithmic Sobolev Constant of The Lamplighter

We give estimates on the logarithmic Sobolev constant of some finite lamplighter graphs in terms of the spectral gap of the underlying base. Also, we give examples of application

Entanglement of two-mode Gaussian states: characterization and experimental production and manipulation

A powerful theoretical structure has emerged in recent years on the characterization and quantification of entanglement in continuous-variable systems. After reviewing this framework, we will illustrate it with an original set-up based on a type-II OPO with adjustable mode coupling. Experimental results allow a direct verification of many theoretical predictions and provide a sharp insight into the general properties of two-mode Gaussian states and entanglement resource manipulation

Accelerating metabolic models evaluation with statistical metamodels: application to Salmonella infection models

Mathematical and numerical models are increasingly used in microbial ecology to model the fate of microbial communities in their ecosystem. These models allow to connect in a mechanistic framework species-level informations, such as the microbial genomes, with macro-scale features, such as species spatial distributions or metabolite gradients. Numerous models are built upon species-level metabolic models that predict the metabolic behaviour of a microbe by solving an optimization problem knowing its genome and its nutritional environment. However, screening the community dynamics with these metabolic models implies to solve such an optimization problem by species at each time step, leading to a significant computational load further increased by several orders of magnitude when spatial dimensions are added. In this paper, we propose a statistical framework based on Reproducing Kernel Hilbert Space (RKHS) metamodels that are used to provide fast approximations of the original metabolic model. The metamodel can replace the optimization step in the system dynamics, providing comparable outputs at a much lower computational cost. We will first build a system dynamics model of a simplified gut microbiota composed of a unique commensal bacterial strain in interaction with the host and challenged by a Salmonella infection. Then, the machine learning method will be introduced, and particularly the ANOVA-RKHS that will be exploited to achieve variable selection and model parsimony. A training dataset will be constructed with the original system dynamics model and hyper-parameters will be carefully chosen to provide fast and accurate approximations of the original model. Finally, the accuracy of the trained metamodels will be assessed, in particular by comparing the system dynamics outputs when the original model is replaced by its metamodel. The metamodel allows an overall relative error of 4.71% but reducing the computational load by a speed-up factor higher than 45, while correctly reproducing the complex behaviour occurring during Salmonella infection. These results provide a proof-of-concept of the potentiality of machine learning methods to give fast approximations of metabolic model outputs and pave the way towards PDE-based spatio-temporal models of microbial communities including microbial metabolism and host-microbiota-pathogen interactions

Late-Holocene climatic variability south of the Alps as recorded by lake-level fluctuations at Lake Ledro, Trentino, Italy

International audienceA lake-level record for the late Holocene at Lake Ledro (Trentino, northeastern Italy) is presented. It is based on the sediment and pollen analysis of a 1.75 m high stratigraphic section observed on the southern shore (site Ledro I) and a 3.2 m long sediment core taken from a littoral mire on the southeastern shore (site Ledro II). The chronology is derived from 15 radiocarbon dates and pollen stratigraphy. The late-Holocene composite record established from these two sediment sequences gives evidence of centennial-scale fluctuations with highstands at c. 3400, 2600, 1700, 1200 and 400 cal. BP, in agreement with various palaeohydro-logical records established in central and northern Italy, as well as north of the Alps. In addition, high lake-level conditions at c. 2000 cal. BP may be the equivalent of stronger river discharge observed at the same time in Central Italy's rivers. In agreement with the lake-level record of Accesa (Tuscany), the Ledro record also suggests a relatively complex palaeohydrological pattern for the period around 4000 cal. BP. On a millennial scale, sediment hiatuses observed in the lower part of the Ledro I sediment sequence indicate that, except for a high-stand occurring just after 7500 cal. BP, lower lake levels generally prevailed rather before c. 4000 cal. BP than afterwards. Finally, the lake-level data obtained at Lake Ledro indicate that the relative continuity of settlements in humid areas of northern Italy during the Bronze Age (in contrast to their general abandonment north of the Alps between c. 3450 and 3150 cal. BP), does not reflect different regional patterns of climatic and palaeohy-drological conditions. In contrast, the rise in lake level dated to c. 3400 cal. BP at Ledro appears to coincide with a worldwide climate reversal, observed in both the hemispheres, while palaeoenvironmental and archaeological data collected at Lake Ledro may suggest, as a working hypothesis, a relative emancipation of proto-historic societies from climatic conditions

Peripheral blood biomarkers in multiple sclerosis.

Multiple sclerosis is the most common autoimmune disorder affecting the central nervous system. The heteroge-neity of pathophysiological processes in MS contributes to the highly variable course of the disease and unpre-dictable response to therapies. The major focus of the research on MS is the identification of biomarkers inbiologicalfluids, such as cerebrospinalfluid or blood, to guide patient management reliably. Because of the diffi-culties in obtaining spinalfluid samples and the necessity for lumbar puncture to make a diagnosis has reduced,the research of blood-based biomarkers may provide increasingly important tools for clinical practice. However,currently there are no clearly established MS blood-based biomarkers. The availability of reliable biomarkerscould radically alter the management of MS at critical phases of the disease spectrum, allowing for interventionstrategies that may prevent evolution to long-term neurological disability. This article provides an overview ofthis researchfield and focuses on recent advances in blood-based biomarker researc

Innate Sensing of HIV-Infected Cells

Cell-free HIV-1 virions are poor stimulators of type I interferon (IFN) production. We examined here how HIV-infected cells are recognized by plasmacytoid dendritic cells (pDCs) and by other cells. We show that infected lymphocytes are more potent inducers of IFN than virions. There are target cell-type differences in the recognition of infected lymphocytes. In primary pDCs and pDC-like cells, recognition occurs in large part through TLR7, as demonstrated by the use of inhibitors and by TLR7 silencing. Donor cells expressing replication-defective viruses, carrying mutated reverse transcriptase, integrase or nucleocapsid proteins induced IFN production by target cells as potently as wild-type virus. In contrast, Env-deleted or fusion defective HIV-1 mutants were less efficient, suggesting that in addition to TLR7, cytoplasmic cellular sensors may also mediate sensing of infected cells. Furthermore, in a model of TLR7-negative cells, we demonstrate that the IRF3 pathway, through a process requiring access of incoming viral material to the cytoplasm, allows sensing of HIV-infected lymphocytes. Therefore, detection of HIV-infected lymphocytes occurs through both endosomal and cytoplasmic pathways. Characterization of the mechanisms of innate recognition of HIV-infected cells allows a better understanding of the pathogenic and exacerbated immunologic events associated with HIV infection

Protein tyrosine phosphatases in glioma biology

Gliomas are a diverse group of brain tumors of glial origin. Most are characterized by diffuse infiltrative growth in the surrounding brain. In combination with their refractive nature to chemotherapy this makes it almost impossible to cure patients using combinations of conventional therapeutic strategies. The drastically increased knowledge about the molecular underpinnings of gliomas during the last decade has elicited high expectations for a more rational and effective therapy for these tumors. Most studies on the molecular pathways involved in glioma biology thus far had a strong focus on growth factor receptor protein tyrosine kinase (PTK) and phosphatidylinositol phosphatase signaling pathways. Except for the tumor suppressor PTEN, much less attention has been paid to the PTK counterparts, the protein tyrosine phosphatase (PTP) superfamily, in gliomas. PTPs are instrumental in the reversible phosphorylation of tyrosine residues and have emerged as important regulators of signaling pathways that are linked to various developmental and disease-related processes. Here, we provide an overview of the current knowledge on PTP involvement in gliomagenesis. So far, the data point to the potential implication of receptor-type (RPTPδ, DEP1, RPTPμ, RPTPζ) and intracellular (PTP1B, TCPTP, SHP2, PTPN13) classical PTPs, dual-specific PTPs (MKP-1, VHP, PRL-3, KAP, PTEN) and the CDC25B and CDC25C PTPs in glioma biology. Like PTKs, these PTPs may represent promising targets for the development of novel diagnostic and therapeutic strategies in the treatment of high-grade gliomas