Flagship Project: InterOmics

Transcription

1 Flagship Project: InterOmics Development of an integrated platform for the Omics science applications for biomarker discovery to the definition of diagnosis, predictive and theranostic profiles. Scientific Director: Luciano Milanesi The flagship INTEROMICS research projects is supported by the MIUR (MInistery of University and Research) and coordinated by the CNR (National Research Council).

2 CNR-ITB CNR-IBFM CNR-ICRM CNR-IEIIT CNR-IMATI CNR-ISTI UNI-MI CNR-IBBA UNI-TV ISS CNR-ISA CNR-ITB CNR-IIT CNR-IASI CNR-IAC CNR-ICAR

3 Virologico Clinica Medica ed Oncologica Infezioni Batteriche, Fungine e Protozoarie Bioinformatics System Biology

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7 System Medicine Bioinformatics System Biology Biotechnology System Pathology ICT

8 Next Generation Sequencing

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10 MATHEMATICAL MODELING NETWORK RECONSTRUCTION BIOLOGICAL PROCESSES SIMULATION AND ANALYSIS ANALYSIS OF SYSTEM COMPONENTS

11 Pathways Discovery

12 12 Cell Cycle Database

13 Cell Cycle Model

14 Simulation & Statistics

15 SNP and Biomarkers Analysis GO KEGG RefGENE EnsEMBL CNV PDB Reactome dbsnp HapMap BioGRID Integrated Knowledge Database Ontological Annotations DB Integrated Biological Entities DB SNPs Features List of GENES List of RANKED SNPS

16 microrna Analysis

17 Sensitivity analysis A Sensitivity Analysis Approach for the Study of a Mitochondrial Bioenergetic Model Sensitivity analysis studies how the variation in the output of a mathematical model can be apportioned qualitatively or quantitatively to different sources in the input of the model Sensitivity indexes represent the importance of the model input factors with respect to the model output value Why perform sensitivity analysis? Testing the robustness of a model Searching errors during model development Find the most influential parameters for the model dynamics Analyse the relationship between input and output variables 17/20

18 Mitochondrial bioenergetic model Mitochondria are involved in many pathologies (diabetes, cancer, neurodegenerative disease) The model of Bazil, et al. (2009) describes the mitochondrial bioenergetic in different conditions (based on experimental datasets) 18/20

19 ATP/ADP ratio ATP is the main energy source for the majority of cellular functions Mitochondria are main source of ATP (in aerobic conditions) ATP/ADP ratio regulates mitochondrial reactions 19/20

20 Sensitivity analysis of biological models Identification of the molecular species and the reactions having the strongest influence on model dynamics Many biomedical applications (e.g. identified a potential pharmacological target) 20/20

21 Classification of model behaviour In regionalised sensitivity analysis model output realizations is classified according to a specific system property (e.g. ATP/ADP ratio) OUTPUT Desired -Behavioral (B) Undesired-Not Behavioral (NB) The property is verified The property is not verified 21/20

22 Input variable distributions At each input factor are associated two distributions of values in the set of: Model parameterizations that lead to desired condition Model parameterizations that lead to undesired condition Similar distribution for a non-influential parameter Different distributions for an influential parameter Two sample kolmogorov smirnov test is able to analyse if two distributions come from the same distribution 22/20

23 Analysis steps 1) Generation of a set of model parameterizations (of length 42, the number of adjustable parameters) 2) Simulation of the model until the attainment of a steady state 3) Computation of ATP/ADP ratio and subdivision of model output set into two subsets ([ATP]>[ADP] and [ATP]<[ADP]) 4) Mapping the two subsets of model output into two sets of model parameterizations 5) Compute the two samples kolmogorov smirnov test for each model parameter to verify its influence 23/20

24 Parameters values and model dynamics The model is defined as a set of 73 differential algebric equations (DAEs) Sensitivity analysis is required in order to understand correlations input-output we focused on the ATP/ADP ratio 42 adjustable parameters 24/20

25 Output distributions In the two subsets of model parameterizations: several parameters has similar values distributions a small group of parameters has different values distributions P value Enzyme/Transporter 4,85E-140 ANT 1,62E-65 mkleak 3,70E-62 CI 4,51E-50 CIV 8,15E-38 CIII 6,30E-36 CIII_bPi 4,69E-31 mkhe 1,02E-26 mnce_kna 1,22E-09 CIII_KPi 1,14E-06 mnce_bca 2,14E-06 CaUNI 1,43E-05 akgdh 5,53E-05 mnce 0, mnce_kca 25/20

26 Computational cost Given the high dimensionality of parameter space, a large number of simulations (10^3-10^5) are required to ensure p-value convergence High performance computing is required!!! Since simulations are independent distributed approaches can reduce the computational time 26/20

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28 acknowledgements MIUR Flagship Initiative INTEROMICS ; MIUR ITALBIONET, 28/20