In recent years, there has been a revolution in the capability to measure and observe the processes of life on many temporal and spatial scales. Simultaneously, our ability to perform extensive simulations of molecular, cellular, and tissue-scale processes has substantially improved and will continue to do so. Research at ZIB is focused on extending this expertise towards spatiotemporal modeling and simulation across the molecular and cellular scales, from pushing time-scale and accuracy barriers, via a combination of the development of novel theoretical and algorithmic techniques for effective molecular or particle dynamics with machine learning, to spatio-temporal hybrid models coupling molecular with extra- and intra-cellular processes. Our projects target seamless integration of new mathematical approaches with incorporation of experimental data, high performance computing, and/or large-scale data analysis, and contribute to finding new strategies in relevant real-world applications like drug design or neurotransmission.

HPC and Machine Learning for Drug Discovery

HPC and ML for Drug Discovery

High-performance computers are required to simulate complex systems that allow to study the behavior of molecules under real conditions. This project combines artificial...

HPC and ML for Drug Discovery
RobustCircuit logo wide

RobustCircuit

RobustCircuit is a collaborative project of 13 research teams pursuing eight neurobiological projects at five different institutions. The goal of the project is to...

RobustCircuit
Model-regularized Learning of Complex Dynamical Behavior

Model-Regularized Learning Of Complex Dynamical Behavior

This project is planned to couple machine learning approaches, especially from the field of Deep Learning, with (reduced) ODE models in the sense that the model becomes... Model-Regularized Learning Of Complex Dynamical Behavior
MATH+ AA1-20

Geometric Learning for Single-Cell RNA Velocity Modeling

Recent advances in Single-Cell RNA sequencing allow to infer both the gene expression of a cell and the so-called "velocity vector" initializing the changes in that... Geometric Learning for Single-Cell RNA Velocity Modeling
Pareto-ML-Optimization-Cycle

AA1-19 Drug Candidates as Pareto Optima in Chemical Space

The search for novel drug candidates that, at the same time, act with high efficacy, comply with defined chemical properties, and also show low off-target effects can be...

AA1-19 Drug Candidates as Pareto Optima in Chemical Space
opinion dynamics

Opinion Dynamics

The literature contains a manifold of fundamental models for the dynamical changes of opinions through social influence during social interaction (personal, social... Opinion Dynamics
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Manifold-Valued Graph Neural Networks

Geometry-aware, data-analytic approaches improve understanding and assessment of pathophysiological processes. We will derive a new theoretical framework for deep neural... Manifold-Valued Graph Neural Networks
Spine_DYN3M

Individualized Morphological Analysis of the Human Spine

The causes of lower back pain (LBP) are still not fully understood. One essential part of a better understanding might be the association of LBP,  spinal morphology, and... Individualized Morphological Analysis of the Human Spine
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MODAL-MedLab

Changes in cells while they are undergoing transformation from "normal" to malignant cells (e.g. during infections) happen on many biological levels, such as genome...

MODAL-MedLab
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Reduced Basis Methods in Orthopedic Hip Surgery Planning

This project aims at the development, analysis and implementation of algorithms for computer-assisted planning in hip surgery and hip joint replacement by fast virtual... Reduced Basis Methods in Orthopedic Hip Surgery Planning