is increasingly clear that significant alterations in the lipid
profile of cancer cells accompany tumor progression and metastasis.
These changes are induced by a metabolic reprogramming which is aimed
to enhance malignant phenotype in cancer cells.
this context, genome-scale metabolic models (GSMM) have emerged as a
valuable platform to integrate different omic data to study cancer
metabolism from a holistic perspective. However, far too often lipid
associated pathways are poorly annotated in these metabolic networks
which limits the scope of GSMM-based methods to study the altered
it is imperative to develop novel computational tools that allowing a
better integration of high-throughtput lipidomic data into the
current GSMM reconstruction analyses. It is expected that these
computational tools will enable a more in-dept understanding of the
metabolic mechanisms underlying lipid profiles alterations of
multifactorial diseases with a strong metabolic component such as
cancer with potential clinical applications
project is aimed to develop and test a computational framework for
automatically expand and improve the lipid-associated metabolic
pathways of the current computational models of cell metabolism.
a case of concept we will study the metabolic alterations associated
to the chronic exposure to Endocrine disruptors (ED) in prostate
cancer . To achieve this aim the student will apply different
strategies to integrate transcriptomic, metabolomic and lipidomic
data into a computational analysis of the whole metabolism. This
study will provide a holistic view of the molecular processes and
mechanisms underlying tumor progression and metastasis associated to
the chronic exposure to EDs in prostate cancer which ultimately can
unveil potential therapeutic targets. This project is a joint venture
between Prof. Lars Keld Nielsen lab (DTU, Denmark) and Prof. Romà
Tauler's group (IDAEA-CSIC, Spain) that will be under the direct
supervision of Dr. Igor Marín.
successful appointee will develop and apply a pipeline based on
constraint-based methods to expand and improve the lipid-associated
metabolic pathways of a current GSMM. Secondly, the student will
integrate and analyze transcriptomic, metabolomic and lipidomic data
from DU145 before and after a chronic exposure to different EDs.
Finally the results will be analyzed and interpreted in order to
describe the evolutionary mechanisms underlying the metabolic
reprogramming associated to the chronic exposure to EDs in prostate
We are seeking for a highly motivated, independent, and well
organized person, who is passionate about computational biology.
Background on biostatistics and previous knowledge of some
programming language (R, Matlab, Python, ...) are desirable but not
students who are interested in join this project can contact to Igor
We are seeking for a highly motivated, independent, and well organized person, who is passionate about computational biology. Background on bioinformatics