1. Elisa Domínguez-Hüttinger
“Unravelling the Disease Mechanisms of Atopic Dermatitis through Mathematical Modelling”
Abstract
Atopic dermatitis (AD) is a skin disease that affects almost 20% of the paediatric population worldwide. Furthermore, children affected by AD are predisposed to other atopic diseases such as asthma and hay fever in later stages of life. Despite its high clinical relevance, the causes of AD remain poorly understood, mainly because AD is a complex disease that can be triggered by the combination of several environmental and genetic factors. Early stages of AD are characterized by the interplay between disrupted skin barrier function and impaired innate immune responses, and are followed by chronic stages in which biased adaptive immune responses lead to allergic reactions and further aggravate the disease. Successful therapeutic intervention and diagnostics are hindered by the lack of mechanistic understanding of this developmental process of AD. Systems biology approaches, which combine mathematical modelling and experimentation, allow us to analyse complex biological systems including the regulatory networks that are impaired in AD. In our group, we developed the first mathematical model of AD. It consists of a mathematical representation of the most relevant regulatory interactions that control the normal functioning of the epidermis and that are disrupted in AD. Together, these regulatory interactions of cells and biomolecules form a strongly interconnected network that is targeted by the different genetic and environmental risk factors that predispose to AD. Computer simulations and analysis of our mathematical model allowed us to untangle the individual effects of the different predisposing risk factor for AD, alone or in combination, on the pathogenic process of AD, from a systems-level perspective. Further, dynamical analysis of our mathematical model uncovered the mechanisms that are involved in the transition from a healthy epidermis to epidermal phenotypes that are typical of patients suffering from early or advanced stages of the disease. Our mathematical model uncovers several different risk-factor dependent mechanisms of AD, and successfully reproduces the different clinical stages of the disease. Together, our mathematical modelling results correspond to a mechanistic description of the different phases of the pathogenic process, and elucidate the role of the different triggers of the disease development. This corresponds to patient-specific descriptions of AD that are important to elucidate new and effective personalized treatment strategies for this socially relevant disease.
2. Mariano Beguerisse
“Network analyses of the E. coli core metabolic model”
In this talk I will present our recent work analysing the network of metabolic reactions of E. coli under different biological scenarios using tools from network science. I discuss how we construct the networks from lists of metabolic reactions and the different ways in which this can be done. Using flux-balance analysis (FBA) we can produceversions of the network that incorporate different biological scenarios. I will show a systematic comparison of the structure of the different networks using methods from network science. In particular, we study the community and role structure of the model network and each ofthe FBA-produced networks using flow-based methods such as Markov Stability and Role-based similarity. In each case the networks and their communities display different properties that reflect different aspects of the metabolism. Finally, I will compare the our results to standard classifications of the reactions into subsystems and comment on their similarities and differences.