Project members

Jens Nielsen, PhD,
E-mail: nielsenj [at]
Please read more here

Prof. Jens Nielsen, is project director. His research group has extensive experience with metabolic modelling and database construction and will carry out most research activities for the project.

Human nutrition

Adil Mardinoglu, PhD.

E-mail: adilm [at]

Dr. Adil Mardinoglu is project manager. He will be involved in daily project management and will reconstruct functional genome-scale metabolic model for bone tissue. He will also simulate the interactions between liver, adipose, muscle and bone tissues in subjects having cachexia.

Partho Sarathi Sen, PhD

E-mail: sarathi [at]

Dr.Partho involves in computational modelling of malnutrition and its underlying mechanism in infants, Malawian subjects. Using various genome wide integrated ‘Omics’ approaches gives a deep insight into the life-cycle and adaption of these subjects. These approaches would be applied to understand Protein-energy malnutrition (PEM) such as Kwashiorkor and Marasmus.

Avlant Nilsson

E-mail: avlant [at]

Avlant is developing a toolbox for the study of tissue interactions based on models of the individual tissues. The toolbox uses the distribution of cell types and constraints in form of nutrient and oxygen uptake. This will make it possible to estimate how well different diets will be at fulfilling biological tasks such as: maintenance, growth, activity and storage.

Leif Väremo

E-mail: varemo [at]

Leif is working on the development of a simulation-ready genome-scale metabolic model (GEM) for skeletal muscle cells (myocytes). Further on, he will take part in the integration with other models, including a blood compartment, as well as the integration of omics data to assess the metabolic changes associated with diet, in different tissues.

Gut microbiome

Boyang Ji

E-mail: boyang.ji [at]

Dr. Ji is involved in mathematical modeling of gut microbiota in healthy versus undernourished children. With genome-scale metabolic model and various OMICS (mainly metagenomics) data, we try to quantitatively infer the metabolic interactions between gut microbial species or between gut microbiota and host. Focusing on the effects of dietary interventions on microbiota and host metabolisms, we try to illustrate how food interventions contribute to the microbial alterations in healthy and malnourished children. Altogether, such metabolic modeling approach will provide a way to develop microbiota-based nutritional supplements/therapies based on locally available food in Asia and Africa, which will be optimal for the children gut microbiota.

Manish Kumar

E-mail: manishk [at]

Dr. Manish Kumar is involved in generating genome-scale metabolic models of most abundant bacterial species in gut microbiota of healthy and malnourished children from different countries of Asia, Africa, and Europe. These models will be simulated for quantifying the biosynthesis of short chain fatty acids, amino acids, vitamins, and secondary metabolites from gut microbes. This analysis will help us to predict the correlation in the metabolic variations in gut microbiota between health and malnutrition. These models also will be used to explore the effect of diet alteration on metabolic capabilities of bacterial species in gut microbiota.

Jun Geng

E-mail: gejun [at]

Dr. Jun Geng’s part in this project is to construct kinetic models of infant’s gut microbiota to investigate the microbe-microbe interactions as well as host-microbe interactions which would help to decipher the relationship between gut microbiota and some related diseases.

Parizad Babaei

E-mail: parizad [at]

Parizad is involved in reconstruction and curation of genome-scale metabolic models (GEM) of the most abundant bacterial species in the gut microbiota of malnourished and healthy children. She is also working on developing a toolbox to simulate the microbe-microbe, diet-microbe, and host-microbe interactions in the gut microbial community.

IT support

Shaq Hosseini

E-mail: Shaghayegh.Hosseini [at]

Shaq is working on designing and developing online repository with the ability to host genome-scale metabolic models (GEM).