In silico analysis of microbial communities through constraint-based metabolic modelling
Doctoral thesis, 2019

Microbial communities are involved in many vital biological processes from elemental cycles to sustaining human health. The bacterial assemblages are remarkably under-studied as they are reluctant to grow in the laboratory conditions. Therefore, alternative omics-based approaches and computational modelling methods have been an active area of research to investigate microbial communities physiologically, ecologically and biochemically. In this thesis different microbial consortia involved in food production and also the human gut microbiota have been modelled and investigated. In the case of the human gut microbiota, the effects of malnutrition on the overall health of children from three different countries, namely, Malawi and Bangladesh, and Sweden have been studied. In each of the first two countries, a group of malnourished children going through food therapy as well as a healthy cohort were monitored to investigate the effect of food intervention on malnutrition, with their gut microbiota being the focal point. In this project, using metagenomics data we identified the dominant strains in each cohort, reconstructed genome-scale metabolic models (GEMs) for the most abundant ones and used our models to predict diet-microbe, microbe-microbe, and microbe- host interactions. Based on our results in this project, in addition to being less diverse, the gut microbiota of malnourished children showed a lower potency regarding the production of valuable metabolites. The second investigated microbial consortia were the ones used in fermented milk products. Based on the genome sequence and also experimental data for five selected strains, we reconstructed GEMs, curated the models and performed community modelling to predict their metabolic interactions. Using the simulation outcomes, we could predict a ratio for bacterial strains used in yogurt starter culture to maximise the production of acetaldehyde which is a key contributor to yogurt’s unique taste and aroma.

GEMs are powerful tools to model an organism’s metabolic capabilities, and although numerous GEMs have been reconstructed, their quality control has not gained enough attention. Evaluation of a repository of semi-automatically reconstructed GEMs related to the human gut microbiota and another repository of manually curated ones was performed comparatively. Assessing these models from topological and functional aspects, it was shown that semi-automatically reconstructed models required extensive manual curation before they could be used for target-specific simulations.

In constraint-based modelling, an objective function is usually optimised under particular environmental conditions, however, in case of the microbial communities, there is no distinct and relevant objective function. Therefore, an unbiased uniform randomised sampling algorithm was implemented for microbial communities. The samples acquired from the solution space were analysed statistically to see clustering patterns of the reactions and commensalistic relationships between the community members were identified. Overall, computational modelling paves the way towards gaining a mechanistic understanding of microbial communities and provides us with testable hypotheses and insight.

fermented food production


genome-scale metabolic model

community modelling


Microbial community

systems biology

lactic acid bacteria

gut microbiota

uniform randomised sampling

KA-salen. Kemigården 4, Chalmers.
Opponent: Bas Teusink


Parizad Babaei

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Challenges in modeling the human gut microbiome

Nature Biotechnology,; Vol. 36(2018)p. 682-686

Other text in scientific journal

Parizad Babaei, Christian Chervaux, Chloë Beal, Jean-Michel Faurie, and Jens Nielsen, Computational design of optimum bacterial consortia for milk fermentation using genome- scale metabolic models

Parizad Babaei, Promi Das, Adil Mardinoglu, and Jens Nielsen, Uniform randomised sampling of microbial communities

Promi Das, Parizad Babaei, and Jens Nielsen, Metagenomic analysis of microbe-mediated vitamin metabolism in the human gut microbiome

Human gut microbiota and healthy aging: Recent developments and future prospective

Nutrition and healthy aging,; Vol. 4(2016)p. 3-16

Journal article

An average adult carries about three pounds of microbes which is equal to the weight of the brain. These microorganisms come from all main branches of life; including bacteria, viruses, archaea, and microeukaryotes. More than 70% of these microbial species reside in the human gastrointestinal tract and their incredible diversity and functionality is uncovered with the advances in molecular biology technologies and computational algorithms. They protect us against the pathogens, help us digest otherwise undigestible complex fibres, contribute to the gut homeostasis and maturation of the body’s immune system. Their composition is relatively stable, however, dynamic in response to factors such as diet, genetics, age, and antibiotic usage. Probiotics, beneficial live bacteria in dietary intakes, have also been shown to contribute positively to the gut microbiota. These bacteria are found in some fermented milk products. Although the efficiency and claimed benefits of probiotics are still not clear, the vital role of bacterial consortia in food production and biopreservation is apparent. Long before even discovering these tiny organisms, people have been utilizing them to make their favourite food unknowingly. Complex bacterial consortia are challenging to study as conventional microbiological methods cannot support the survival of most of the bacterial communities occurring in nature. Therefore, bioinformatics methods and biological modelling techniques contribute immensely to the field of microbial ecology and hold the potential for generating hypotheses into strategies to manipulate the natural microbial communities towards specific beneficial goals.

Subject Categories

Bioinformatics (Computational Biology)

Bioinformatics and Systems Biology

Computer Science



Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 4557



KA-salen. Kemigården 4, Chalmers.

Opponent: Bas Teusink

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3/6/2019 8