In Silico Models of Coupled Biological Systems

Project Network 2

Supporting personalized healthcare or the development of tailor-made biomedical products with computational models requires holistic yet individualized models. They must be holistic to accommodate the multiple interacting phenomena that characterize biological systems. Human variability requires that models have to be also individualized. This does become feasible only by integrating system-specific data. Our overarching goal is to develop detailed in silico models of complex biological systems that couple different scales and heterogeneous data. We concentrate our research on the neuromuscular system and on proliferative and degenerative diseases. Our focus will be both on some of the most pressing and largely unresolved research questions within these fields and on setting up a dedicated experimental platform.

Linking the RQs within PN 2

Research Questions

RQ 1 System models: How can we link multiple, currently separated organ, tissue, or (sub-) cellular models on different length and time scales to more realistic system models?

RQ 2 Knowledge-based and data-driven coupling: How can we describe the emerging dynamical system behavior of cell populations by exploiting large amounts of single cell data and highly detailed biophysical models?

RQ 3 Individualization: How can we exploit data on the distribution of microcomponents to replace generic phenomenological descriptions with individualized models of living matter?

RQ 4 Data and model standards: How can we establish metadata descriptions for coupling different biological models and for sharing system models and their data with peers?

RQ 5 Resource limited simulations: How can we simulate, design, and control system models with limited computational resources?

Project Overview

PN 2-1A Data-integrated Modeling to Provide Novel Solutions for Individualizing Cancer Therapy and Predicting Treatment Success
PN 2-1 (II) Development of a data-integrated ODE and agent-based simulation framework that captures and predicts responsiveness to targeted cancer therapeutics
PN 2-1B Data-integrated Modeling to Provide Novel Solutions for Individualizing Cancer Therapy and Predicting Treatment Success
PN 2-2A Data and Model Driven Multiscale Simulation of Tumor Growth in Liver Cell, Tissue and Organ
PN 2-2 (II) Data-enhanced prediction of organ-specific tumor growth in the liver - a hybrid knowledge and data-driven approach
PN 2-2B Data-Integrated Simulation of Tumour Growth and Regression in Brain Tissue
PN 2-3A Investigating sensorimotor interaction through selective sensory perturbations
PN 2-3 (II) Separating motor and neural effects of somatosensory perturbations
PN 2-3B Enhanced proprioceptor dynamics to predict sensorimotor interaction of a biophysical hand-arm-model
PN 2-4A Machine learning-based decomposition of the activity of individual motor units from synthetic and experimental data
PN 2-4B Machine learning-based decomposition of the activity of individual motor units from synthetic and experimental data
PN 2-5 Molecular Dynamics Simulations of the Substrate Recognition of Protein Lysine Methyltransferases
PN 2-6 Seamlesss data flow by EnzymeML, an SBML-based exchange format for the integration of enzymatic reaction data
PN 2-6 (II) Software-driven RDM (sdRDM), a generic and extensible bottom-up research data management concept and its application in biocatalysis and beyond
PN 2-7 Data-Integrated Training of Surrogate Models for Uncertainty Quantification and Diagnostics of Complex Biological Systems Models
PN 2-8 Modelling of architectural-informed and activation-driven contractions of the human M. tibialis anterior
PN 2-9 Methodology for the calibration and analysis of stochastic models for heterogeneous intracellular processes with applications in cancer development

Associated Projects

PN 2A-1 Validation of recent theories of skeletal muscle contraction: experiments and modelling
PN 2A-2 The Cyber-Physical Twin of the Human Organ
PN 2A-4 Modeling and analysis of synthetic, methylation based, epigenetic gene circuits

Project Network Coordinators

This image shows Nicole Radde

Nicole Radde

Prof. Dr. rer. nat.

[Photo: SimTech/Max Kovalenko]

This image shows Oliver Röhrle

Oliver Röhrle

Prof.

[Photo: SimTech/Max Kovalenko]

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