Systems Modeling at IfADo

In recent years IfADo has established Systems Modeling to understand the complex, time-resolved interplay among numerous components which finally determine human factors relevant for working environments, namely cognition, behavior, emotion, motivation, and also those that determine the dynamics and kinetics of toxins and drugs and finally influence the labile balance of health and disease. This includes bridging the scales from subcellular to cellular, to the organ level all the way up to prediction of human behavior in human machine interaction (Fig. 1). Here, mathematical Systems Modeling helps to clarify the underlying principles. In our recent work, Systems Modeling has been instrumental to elucidate mechanisms and principles that were not accessible by traditional approaches. Moreover, Systems Modeling guides experimentation by choosing the most informative experiment (or data acquisition) resulting in reduction of experiments and financial resources.

Systems Modeling in the previous work of IfADo. @IfADo

Examples at the cellular and molecular level are modeling of control factors of number and size of endosomes at the subcellular level (Zeigerer et al., Nature, 2012); simulations of signaling networks that tip the balance from tolerant to active states of immune cells (Mesecke et al., Science Signaling, 2011); prediction of chemical exposure scenarios, when metabolic detoxifying functions of the liver can no longer be maintained and lead to a functional break down (Ghallab et al., J. Hepatol., 2016); simulations, whether architectural changes of tissues after exposure to workplace chemicals represent adaptive or adverse responses (Vartak et al., Hepatology, 2016; Jansen et al., 2017). At the organ and organism level modeling of and obtaining data about physiological effects of non-invasive brain stimulation on functional connectivity via MRI (Polania et al., Neuroimage 2011, Polania et al. Curr Biology 2012), modeling of physical stimulation effects to target cognitive stimulation (Voss et al., Nature Neurosci., 2014) and prediction of human behavior in machine interaction (Schlagkamp et al., HPCS, 2016) have been important components of our scientific work.

In past years, Systems Modeling had been performed within cooperations and research networks mainly with external partners hence it represents an expertise that is not sufficiently established at IfADo. However, currently more than 5 million Euros of external research grants (BMBF, EU, DFG) are active at IfADo pertaining to our work in the field of Systems Modeling. While these collaborations were successful in the past, it is becoming increasingly clear that the absence of the necessary expertise and infrastructure at IfADo is hampering the future success of our Systems Modeling approaches. In future, it would represent an enormous progress if the Systems Modeling expertise could be established at our institute to reduce dependency on external partners and to take advantage of the synergy potential between modeling and experimentation by directly bringing modelers and experimental partners together at IfADo. Additional advantages can be gained by applying similar modeling methods and concepts to the different possible applications at IfADo, and to prospectively integrate the IfADo expertise on liver, immune system and brain into models studying their interplay.

Here you find more information on future perspective – modeling the brain-liver-immune axis.

References (IfADo scientists underlined)

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