Community Systems Modeling: From Complexity to Optimality
Examensarbete för masterexamen
Complex adaptive systems (MPCAS), MSc
Ghaffari Nouran, Pouyan
Biological systems, whether they are community of species in an ecosystem, circuits of neurons in the brain or molecular mechanisms inside a cell, are inherently dynamic and complex. This complexity is enclosed not only in number and characteristics of constituent components of the system, but rather in the non-uniform and intricate way of connections between the components to form an emergent phenotype. Human microbiota is not an exception and embodies a typical example of complex biological system. The structure, assembly and dynamics of microbiota along with its contribution to the diet, physiology and development of the host are affected by the diversity of species and genome. In addition, interactions between the various species have an important role in the metabolism of ecosystems. Recently genome-scale metabolic models (GEMs) have been used to model the between three predominant species in human gut microbiota and have been explored the interactions between microbes in simplified community. Despite recent progresses, we still have very limited knowledge about the contribution of individual microorganisms within the communities and the interactions between them. This calls for the development of system-level methods and modeling frameworks to discover details of the complex microbial communities. Here, we introduce a multidimensional distributed model to study and analyze the microbiota as whole, encompassing species with interlaced metabolic interactions in different levels. The model is developed as a comprehensive, integrated and predictive framework and can be used for different complex ecosystems with any number of interacting species. We applied our method to analyze six representative species from abundant phyla in human gut microbiota. The outputs of specialized simulations validated based on SCAFs secretion profile and species abundances for lean and obese subjects.
Livsvetenskaper , Bioinformatik och systembiologi , Life Science , Bioinformatics and Systems Biology