Modeling metabolic changes in brain cancer

Host laboratory and collaborators

Harold Cremer (IBDM) /

Bianca Habermann (IBDM) /

Marie-Cartherine Tiveron (IBDM) /

Stephen Chapman (IBDM) /


How do cancer cells adapt their metabolism to the needs of rapid and uncontrolled growth? We want to address this important question (Nguyen et al., 2022) by combining an innovative in vivo mouse model of glioblastoma, in which we can follow the formation of glioblastoma in mice in a temporally and spatially highly controlled manner by transformation of neural stem cells (Lee et al., 2018), with a mathematical approach of modelling mitochondrial metabolism using Flux Balance Analysis (FBA; Smith, et al., 2017; Orth et al., 2010). By measuring gene expression changes over time during tumor formation, our mouse model will provide high-resolution insight into the cellular changes during the transformation and tumor growths processes. These expression changes can then be used as constraints for our mathematical model, which allows us to calculate fine-tuned predictions of mito-metabolism during glioblastoma formation and progression, leading to novel testable hypotheses that we can verify experimentally.


Brain Cancer, glioblastoma, neural stem cells, metabolism, mitochondria, modelling, Flux Balance Analysis


1. Objective: To investigate gene expression changes from cell of origin to full blown glioblastoma in an innovative cancer model.

2. Objective: To provide new and testable models, explaining changes in metabolic pathways during early stages of cancer development.

3. To functionally test the developed models in the initial cancer model.

Proposed approach (experimental / theoretical / computational)

We will use postnatal brain electroporation into mouse postnatal neural stem cells to mutate the anti-oncogenes p53 and PTEN and to express a constitutively active form of the EGF-receptor (EGFRvIII). This leads in all cases to the formation of high-grade gliomas in the brain. Microdissection and FACS will be used to isolate cells from developing tumors at different stages and perform single cell as well as batch RNA-seq of isolated samples, to characterize the various emerging cell populations. These high-resolution gene expression data will be used to identify and model changes in metabolic pathways in combination with Flux Balance Analysis, which performs linear programming and optimisation, to predict steady state mitochondrial metabolism. Detailed metabolic states will be generated for each selected stage of glioblastoma development, allowing to identify the key metabolic players driving glioblastoma formation and progression. These will be experimentally verified in the subsequent step.


This project is based on the tight interaction between the experimental neurobiology group of Harold Cremer and the computational biology group of Bianca Habermann, both at the IBDM. The Cremer team is interested in neural stem cell biology and the cellular origin of brain cancer. In the team the PhD candidate will perform all experimental work and generate all biological samples and sequencing data for the joint study, under the day to day supervision of Dr. Marie-Catherine Tiveron. The Habermann team will be instructive for the bioinformatic analyses of the RNA seq results during brain tumor progression, thereby focusing on changes in metabolic pathways over cancer progression. The team will subsequently generate models on how cellular metabolism is altered under non-controlled growth conditions. During this phase the joint PhD student will perform all computational analyses under the supervision of Dr. Stephen Chapmann.

Expected profile

The ideal candidate has a master in biology with a strong background in cell biology, neurobiology and some knowledge in cancer biology. Importantly, the candidate has strong interest and at least basic knowledge in informatics and computational biology.

Is this project the continuation of an existing project or an entirely new one? In the case of an existing project, please explain the links between the two projects

This is an entirely new proposal.

2 to 5 references related to the project

  • T.T.T. Nguyen et al., 2022, Therapeutic Drug-Induced Metabolic Reprogramming in Glioblastoma, Cells,
  • Smith et al., 2017, MitoCore: a curated constraint-based model for simulating human central metabolism, doi: 10.1186/s12918-017-0500-7
  • J.H. Le et al., 2018, Human glioblastoma arises from subventricular zone cells with low-level driver mutations, Nature,
  • Orth, J. D., Thiele, I., & Palsson, B. Ø. (2010). What is flux balance analysis?. Nature biotechnology doi: 10.1038/nbt.1614

3 main publications from each PI over the last 5 years

Harold Cremer

  • Coré N, Erni A, Hoffmann HM, Mellon PL, Saurin AJ, Béclin C, Cremer H. (2020) Stem cell regionalization during olfactory bulb neurogenesis depends on regulatory interactions between Vax1 and Pax6, Elife, doi: 10.7554/Elife.58215
  • PlateL JC, Angelova A, Bugeon S, Wallace j, Ganay T, Chudotvorova I, Deloulme JC, Béclin C, Tiveron MC, Coré N, Murthy VN, Cremer H (2019) Neuronal integration in the adult mouse olfactory bulb is a non-selective addition process, Elife. 2019 Jul 11;8. pii: e44830. doi: 10.7554/eLife.44830
  • Tiveron MC, Beclin C, Murgan S, Wild S, Angelova A, Marc J, Coré N, de Chevigny A, Herrera E, Bosio A, Bertrand V and Cremer H (2017) Zic-proteins are repressors of dopaminergic forebrain fate in mice and C. elegans, J. Neuroscience 29, 3888-16; DOI:


Bianca Habermann

  • Marchiano F, Haering M, Habermann BH. The mitoXplorer 2.0 update: integrating and interpreting mitochondrial expression dynamics within a cellular context. Nucleic Acids Res. 2022 May 7;50(W1):W490-9. doi: 10.1093/nar/gkac306. PMID: 35524562.
  • Haering M and Habermann BH. RNfuzzyApp: an R shiny RNA-seq data analysis app for visualisation, differential expression analysis, time-series clustering and enrichment analysis, 2021, 10:654, PMID: 35186266,
  • Meiler A, Marchiano F, Haering M, Weitkunat M, Schnorrer F, Habermann BH. AnnoMiner is a new web-tool to integrate epigenetics, transcription factor occupancy and transcriptomics data to predict transcriptional regulators. Sci Rep. 2021 Jul 29;11(1):15463. doi: 10.1038/s41598-021-94805-1. PMID: 34326396.