8,595 research outputs found

    Data-driven modelling of biological multi-scale processes

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    Biological processes involve a variety of spatial and temporal scales. A holistic understanding of many biological processes therefore requires multi-scale models which capture the relevant properties on all these scales. In this manuscript we review mathematical modelling approaches used to describe the individual spatial scales and how they are integrated into holistic models. We discuss the relation between spatial and temporal scales and the implication of that on multi-scale modelling. Based upon this overview over state-of-the-art modelling approaches, we formulate key challenges in mathematical and computational modelling of biological multi-scale and multi-physics processes. In particular, we considered the availability of analysis tools for multi-scale models and model-based multi-scale data integration. We provide a compact review of methods for model-based data integration and model-based hypothesis testing. Furthermore, novel approaches and recent trends are discussed, including computation time reduction using reduced order and surrogate models, which contribute to the solution of inference problems. We conclude the manuscript by providing a few ideas for the development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and Multiscale Dynamics (American Scientific Publishers

    Volume of the steady-state space of financial flows in a monetary stock-flow-consistent model

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    We show that a steady-state stock-flow consistent macro-economic model can be represented as a Constraint Satisfaction Problem (CSP).The set of solutions is a polytope, which volume depends on the constraintsapplied and reveals the potential fragility of the economic circuit,with no need to study the dynamics. Several methods to compute the volume are compared, inspired by operations research methods and theanalysis of metabolic networks, both exact and approximate.We also introduce a random transaction matrix, and study the particularcase of linear flows with respect to money stocks

    Bioinformatics Tools for RNA-seq Data Analysis

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    RNA-Seq is a recently developed approach to transcriptome profiling that uses deep-sequencing technologies. The availability of RNA-seq data encouraged computational biologists to develop algorithms to process the data in a statistically disciplinary manner to generate biologically meaningful results. Clustering viral sequences allows us to characterize the composition and structure of intrahost and interhost viral populations, which play a crucial role in disease progression and epidemic spread. In this research, we propose and validate a new entropy-based method for clustering aligned viral sequences considered as categorical data. The method finds a homogeneous clustering by minimizing information entropy rather than the distance between sequences in the same cluster. Moreover in this research, we present a novel pathway analysis method based on Expectation-Maximization (EM) algorithm to study the enzyme expression and pathway activity using meta-transcriptomic data. We will also discuss our approaches to generating unique gene signatures to understand the role of sensory nerve interference in the anti-melanoma immune response and study the racial disparity in Triple-negative breast cancer. Finally, we present our method to detect the retained introns in RNA-seq data to develop a vaccine against cancer having p53 mutations. In summary, this research provides novel approaches to exploring RNA-seq data and their application to real-world biological research

    MATHEMATICAL MODELING OF \u3ci\u3eCLOSTRIDIUM THERMOCELLUM’S\u3c/i\u3e METABOLIC RESPONSES TO ENVIRONMENTAL PERTURBATION

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    Clostridium thermocellum is a thermophilic anaerobe that is capable of producing ethanol directly from lignocellulosic compounds, however this organism suffers from low ethanol tolerance and low ethanol yields. In vivo mathematical modeling studies based on steady state traditional metabolic flux analysis, metabolic control analysis, transient and steady states’ flux spectrum analysis (FSA) were conducted on C. thermocellum’s central metabolism. The models were developed in Matrix Laboratory software ( MATLAB® (The Language of Technical Computing), R2008b, Version 7.7.0.471)) based on known stoichiometry from C. thermocellum pathway and known physical constraints. Growth on cellobiose from Metabolic flux analysis (MFA) and Metabolic control analysis (MCA) of wild type (WT) and ethanol adapted (EA) cells showed that, at lower than optimum exogenous ethanol levels, ethanol to acetate (E/A) ratios increased by approximately 29% in WT cells and 7% in EA cells. Sensitivity analyses of the MFA and MCA models indicated that the effects of variability in experimental data on model predictions were minimal (within ±5% differences in predictions if the experimental data varied up to ±20%). Steady state FSA model predictions showed that, an optimum hydrogen flux of ~5mM/hr in the presence of pressure equal to or above 7MPa inhibits ferrodoxin hydrogenase which causes NAD re-oxidation in the system to increase ethanol yields to about 3.5 mol ethanol/mol cellobiose

    PBPK modelling of inter-individual variability in the pharmacokinetics of environmental chemicals

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    International audienceGeneric PBPK models, applicable to a large number of substances, coupled to parameter databases and QSAR modules, are now available for predictive modelling of inter-individual variability in the absorption, distribution, metabolism and excretion of environmental chemicals. When needed, Markov chain Monte Carlo methods and multilevel population models can be jointly used for a Bayesian calibration of a PBPK model, to improve our understanding of the determinants of population heterogeneity and differential susceptibility. This article reviews those developments and illustrates them with recent applications to environmentally relevant questions
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