Combine Model-driven and multivariate analysis to unravel potential therapeutic targets considering inter-tumoral heterogeneity




Greater Copenhagen area

Constraint-based analysis of genome-scale metabolic models has become a key methodology to gain insights into functions, capabilities, and properties of cellular metabolism. This systems biology tool has been widely used in cancer research to predict potential vulnerabilities in the metabolic network in the form of synthetic lethal. In brief, synthetic lethals are sets of reactions/genes where only the simultaneous removal of all reactions/genes compromises the viability of the tumoral cell. However, the intertumoral heterogeneity between patients )even with the same cancer type at the same stage) represents an important challenge to be overcame in order to apply these computational approaches in systems medicine approaches.

In addition, since their inception, the size and complexity of genome-scale metabolic reconstructions has significantly increase, thus more computational resources are needed to analyze these systems. This fact is enhanced by the exponential increase of simulations required to unravel potential synthetic lethal genes/reactions as a potential anti-tumoral targets.

Thus, the complexity and size of the metabolic networks together with the large number of simulations needed for synthetic lethal analysis and the inter-tumoral heterogeneity between patients with the same tumor type and stage, make, in practice unfeasible an in-deep study of the mechanisms underlying tumor progression and vulnerabilities via model-driven methods as a translational tool in the scope of systems medicine.

Thus, it is necessary to develop a strategy to reduce the dimensionality of the problem and find more effective ways to develop potential multiple target treatments in complex and multi-factorial diseases such as cancer.

The project: This project is aimed to develop a computational approach combining multi-variate data analysis and model-driven methods that will allow an in-deep discovery of multi-taget metabolic and gene regulatory targets with potential anti-tumoral effects. More specifically it will be achieved by applying three strategies:

  1. Computational metabolic model reduction and compaction

  2. Patients clustering in representative groups based on trascriptomic data

  3. Multivariate analysis in combination with algorithm for the rational reduction of redundant simulations to detect pairs, triplets and quadruplets of genes/reactiosn with anti-tumoral effects

The two first approaches will drastically reduce the number of required simulations while the third will permit to further analyze the behavior of the system.

It is expected that these computational approaches will enable a more in-deep understanding of the complex molecular mechanisms underlying tumor progression and the discovery of novel multi-target therapies towards personalized medicine, that otherwise couldn’t be addressed by current approaches.

This project will be carried out in Prof. Lars Keld Nielsen lab (NNF-CFB DTU, Denmark) and will be under the direct supervision of Dr. Igor Marín.

The role: The successful appointee will apply one or several of the strategies previously mentioned. Finally the results will be analyzed and interpreted in order to describe the mechanisms underlying tumor progression and the discovery of potential novel multi-target therapies.

Criteria: 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 exclusive.

Those students who are interested in join this project can contact to Igor Marín (


We are seeking for a highly motivated, independent, and well organized person, who is passionate about computational biology. Background in bioinformatics

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Company / Organization

DTU Biosustain


Igor Batolomé Marín de Mas




Supervisor info

Bachelor in General Engineering

BSc in Biomedical Engineering

BSc in Biotechnology

BSc in Chemistry and Technology

BSc in Human Life Science Engineering

BSc in Mathematics and Technology

BSc in Quantitative biology and disease modelling

Bachelor of Engineering

Bachelor of Engineering

Bachelor of Engineering

Bachelor of Engineering

Bachelor of Engineering,

Bachelor of Engineering,

Bachelor of Engineering,

Bachelor of Engineering, Chemical and Bio Engineering (prev. Chemical and Biochemical Engineering)

Health Care Technology

Kemi og Bioteknologi

Kemi- og Bioteknologi

MSc Eng in Quantitative Biology and Disease Modelling

MSc in Bioinformatics and Systems Biology

MSc in Biomedical Engineering

MSc in Biotechnology

MSc in Chemical and Biochemical Engineering

MSc in Environmental Engineering

MSc in Mathematical Modelling and Computation

Technical University of Denmark

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