Garcia-Roves 2016a Abstract MitoFit Science Camp 2016

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Design and implementation of systems biology approaches to integrate heterogenic data in biomedical research.

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Gonzalez-Franquesa A, Gama-Perez P, Aguilar A, Yanes O, Martin-Subero JI, Sales-Pardo M, Guimera R, Garcia-Roves PM (2016)

Event: MitoFit Science Camp 2016 Kuehtai AT

Our current research project aims to design and implement systems biology approaches to integrate heterogenic data (phenotypical, functional, molecular and omics data) in order to obtain a holistic view of the metabolic plasticity during diet-induced obesity, and after a lifestyle intervention was performed. For such purpose an integrative approach was designed and implemented. Our workflow was divided in several tasks:

  1. The design and structure of a data base (DB). A Content Management System (CMS) DB was used in order to dump all the data obtained in this study. A CMS needs two elements: a content management application (CMA), that is a website in which the user can dump the data; and the Content Delivery Application (CDA) that compiles all the information dumped through the CMA and updates the website.
  2. Data acquisition from the DB. Python was used for computing the codes for the correlations and comparisons analysis. The data from the DB was called from the code through a pandas. DataFrame data structure.
  3. Data Analysis-Correlations and comparisons:
    1. Inter-group correlations and comparisons. Among other analyses our approach attempted to compare the three experimental groups at the same time. This was achieved by defining a 3-point vector for each parameter/attribute in which the 3 points correlated to the mean of this given parameter/attribute for 1) Control group 2) HFD-pathological group and 3) Intervention group. For the correlation and comparison analysis to compare these 3-point vectors attribute cosine similarity was used.
    2. Across-groups patterns definition. The across-groups approach attempted to visually show how much some parameters were reversed after the intervention was performed. For this purpose, the vectors aforementioned were used (ie. attribute=[Ctrl mean, HFD mean, Int mean]). Mann-Whitney comparisons were used to compare if the means among groups were significantly different or not (ie. Ctrl mean vs HFD mean for a given parameter). Once the significant differences were calculated, a pattern was defined and all parameters were assigned to one of the different categories: “no-changed”, “reverted” or “non-reverted”. These patterns were defined for gene expression and metabolites parameters, and mitochondrial respiratory performance data.
  4. Detection of non-reverted gene clusters by using a protein-protein interaction (PPI) network approach.


Our intention is to use this study design to open a discussion and show potential tools and strategies that could be useful for the MITOEAGLE cost action. The final goal is to work on a database on mt-respiratory physiology.


O2k-Network Lab: ES Barcelona Garcia-Roves PM


Labels: MiParea: Respiration, Exercise physiology;nutrition;life style  Pathology: Obesity 





Event: D1  MitoFit Science Camp 2016 

Affiliations

1-Joslin Diabetes Center, Boston, MA, USA; 2-Dept Physiol Sc, Univ Barcelona, Spain; 3-Dept Chem Engineering, Univ Rovira i Virgili, Tarragona, Spain; 4-Centre Omic Sc, Univ Rovira i Virgili, Reus, Spain; 5-Dept Anatomic Pathology, Pharmacol Microbiol, Univ Barcelona, Spain. - pgarciaroves@ub.edu