Garcia-Roves 2016a Abstract MitoFit Science Camp 2016
|Design and implementation of systems biology approaches to integrate heterogenic data in biomedical research.|
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:
- 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.
- 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.
- Data Analysis-Correlations and comparisons:
- 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.
- 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.
- 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
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. - email@example.com