# Ivo F. SbalzariniTechnische Universität Dresden | TUD · Faculty of Computer Science

Ivo F. Sbalzarini

Prof. Dr. sc. techn. Dipl. Masch.-Ing. ETH

https://sbalzarini-lab.org -- we are hiring!

## About

258

Publications

94,210

Reads

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6,029

Citations

Citations since 2016

Introduction

I develop computational methods, software, and algorithms for numerical simulations of biological processes in image-derived geometries and for biological image processing ("image-based computational biology").
Follow us on twitter: @MOSAICgroup1

Additional affiliations

October 2014 - present

July 2012 - present

June 2012 - present

Education

June 2002 - March 2006

October 1997 - May 2002

## Publications

Publications (258)

We investigate a class of DNA mixture deconvolution algorithms based on variational inference, and we show that this can significantly reduce computational runtimes with little or no effect on the accuracy and precision of the result. In particular, we consider Stein Variational Gradient Descent (SVGD) and Variational Inference (VI) with an evidenc...

The CellxGene project has enabled access to single-cell data in the scientific community, providing tools for browsed-based no-code analysis of more than 500 annotated datasets. However, single-cell data requires dimensional reduction to visualize, and 2D embedding does not take full advantage of three-dimensional human spatial understanding and co...

We propose a Gaussian jump process model on a regular Cartesian lattice for the diffusion part of the Reaction–Diffusion Master Equation (RDME). We derive the resulting Gaussian RDME (GRDME) formulation from analogy with a kernel-based discretization scheme for continuous diffusion processes and quantify the limits of its validity relative to the c...

Cell migration is crucial for organismal development and shapes organisms in health and disease. Although a lot of research has revealed the role of intracellular components and extracellular signaling in driving single and collective cell migration, the influence of physical properties of the tissue and the environment on migration phenomena in vi...

Motivation:
Access to unprecedented amounts of quantitative biological data allows us to build and test biochemically accurate reaction-diffusion models of intracellular processes. However, any increase in model complexity increases the number of unknown parameters and, thus, the computational cost of model analysis. To efficiently characterize th...

We provide an internal validation study of a recently published precise DNA mixture algorithm based on Hamiltonian Monte Carlo sampling. We provide results for all 428 mixtures analysed by Riman et al. and compare the results with two state-of-the-art software products: STRmix v2.6 and Euroformix v3.4.0. The comparison shows that the Hamiltonian Mo...

Motivation: Analysing mixed DNA profiles is a common task in forensic genetics. Due to the complexity of the data, such analysis is often performed using Markov Chain Monte Carlo (MCMC)-based genotyping algorithms. These trade off precision against execution time. When default settings (including default chain lengths) are used, as large as a 10-fo...

We present a content-adaptive generation and parallel compositing algorithm for view-dependent explorable representations of large three-dimensional volume data. Large distributed volume data are routinely produced in both numerical simulations and experiments, yet it remains challenging to visualize them at smooth, interactive frame rates. Volumet...

We present an efficient raycasting-based rendering algorithm for view-dependent piecewise constant representations of volumetric data. Our algorithm leverages the properties of perspective projection to simplify intersections of rays with the view-dependent frustums that form part of these representations. It also leverages spatial homogeneity in t...

We present data structures and algorithms for native implementations of discrete convolution operators over Adaptive Particle Representations (APR) of images on parallel computer architectures. The APR is a content-adaptive image representation that locally adapts the sampling resolution to the image signal. It has been developed as an alternative...

Front Cover In article number 2200149, Colin‐York, Fritzsche, and co‐workers found that by combining lattice light sheet microscopy (LLSM), fluorescence recovery after photo‐bleaching (FRAP), and numerical simulations, LLSM‐FRAP provides a means of quantifying molecular dynamics within complex 3D cell morphologies.

We present a statistical learning framework for robust identification of differential equations from noisy spatio-temporal data. We address two issues that have so far limited the application of such methods, namely their robustness against noise and the need for manual parameter tuning, by proposing stability-based model selection to determine the...

We present a mesh-free collocation scheme to discretize intrinsic surface differential operators over surface point clouds with given normal vectors. The method is based on Discretization-Corrected Particle Strength Exchange (DC-PSE), which generalizes finite difference methods to mesh-free point clouds and moving Lagrangian particles. The resultin...

Quantifying molecular dynamics within the context of complex cellular morphologies is essential toward understanding the inner workings and function of cells. Fluorescence recovery after photobleaching (FRAP) is one of the most broadly applied techniques to measure the reaction diffusion dynamics of molecules in living cells. FRAP measurements typi...

Motivation
Analysis of mixed DNA profiles is one of the common tasks for the forensic practitioners. Due to the complexity of the data the analysis is often performed with Bayesian probabilistic genotyping algorithms. These trade off the precision of the results against the execution time. When the default settings are used, as large as a 10-fold c...

Diverse modes of cell migration shape organisms in health and disease and much research has focused on the role of intracellular and extracellular components in different cell migration phenomena. What is less explored, however, is how the arrangement of the underlying extracellular matrix that many cells move upon in vivo influences migration.
Com...

We present a principled data-driven strategy for learning deterministic hydrodynamic models directly from stochastic non-equilibrium active particle trajectories. We apply our method to learning a hydrodynamic model for the propagating density lanes observed in self-propelled particle systems and to learning a continuum description of cell dynamics...

We present data structures and algorithms for native implementations of discrete convolution operators over Adaptive Particle Representations (APR) of images on parallel computer architectures. The APR is a content-adaptive image representation that locally adapts the sampling resolution to the image signal. It has been developed as an alternative...

We show how the finite sizes of unordered defect cores in discretized orientation and vector fields can reliably be estimated using a robustness measure for topological defects. Topological defects, or singular points, in vector and orientation fields are considered in applications from material science to life sciences to fingerprint recognition....

We characterize and remedy a failure mode that may arise from multi-scale dynamics with scale imbalances during training of deep neural networks, such as Physics Informed Neural Networks (PINNs). PINNs are popular machine-learning templates that allow for seamless integration of physical equation models with data. Their training amounts to solving...

We present an autotuning approach for compile-time optimization of numerical discretization methods in simulations of partial differential equations. Our approach is based on data-driven regression of performance models for numerical methods. We use these models at compile time to automatically determine the parameters (e.g., resolution, time step...

We present a user-friendly and intuitive C++ expression system to implement numerical simulations of continuum biological hydrodynamics. The expression system allows writing simulation programs in near-mathematical notation and makes codes more readable, more compact, and less error-prone. It also cleanly separates the implementation of the partial...

We characterize and remedy a failure mode that may arise from multi-scale dynamics with scale imbalances during training of deep neural networks, such as Physics Informed Neural Networks (PINNs). PINNs are popular machine-learning templates that allow for seamless integration of physical equation models with data. Their training amounts to solving...

We present a design and implementation of distributed sparse block grids that transparently scale from a single CPU to multi-GPU clusters. We support dynamic sparse grids as, e.g., occur in computer graphics with complex deforming geometries and in multi-resolution numerical simulations. We present the data structures and algorithms of our approach...

We introduce OpenPME, the Open Particle-Mesh Environment, a problem solving environment that provides a Domain Specific Language (DSL) for numerical simulations in scientific computing. It is built atop a domain metamodel that is general enough to cover the main types of numerical simulations: simulations using particles, meshes, and hybrid combina...

We provide a formal definition for a class of algorithms known as "particle methods". Particle methods are used in scientific computing. They include popular simulation methods, such as Discrete Element Methods (DEM), Molecular Dynamics (MD), Particle Strength Exchange (PSE), and Smoothed Particle Hydrodynamics (SPH), but also particle-based image...

We propose a statistical learning framework based on group-sparse regression that can be used to (i) enforce conservation laws, (ii) ensure model equivalence, and (iii) guarantee symmetries when learning or inferring differential-equation models from data. Directly learning interpretable mathematical models from data has emerged as a valuable model...

Numerical methods for approximately solving partial differential equations (PDE) are at the core of scientific computing. Often, this requires high-resolution or adaptive discretization grids to capture relevant spatio-temporal features in the PDE solution, e.g., in applications like turbulence, combustion, and shock propagation. Numerical approxim...

We develop a theoretical foundation for a time-series analysis method suitable for revealing the spectrum of diffusion coefficients in mixed Brownian systems, where no prior knowledge of particle distinction is required. This method is directly relevant for particle tracking in biological systems, where diffusion processes are often non-uniform. We...

Topological defects are singular points in vector fields, important in applications ranging from fingerprint detection to liquid crystals to biomedical imaging. In discretized vector fields, topological defects and their topological charge are identified by finite differences or finite-step paths around the tentative defect. As the topological char...

The identification of singular points or topological defects in discretized vector fields occurs in diverse areas ranging from the polarization of the cosmic microwave background to liquid crystals to fingerprint recognition and bio‐medical imaging. Due to their discrete nature, defects and their topological charge cannot depend continuously on eac...

Numerical methods for approximately solving partial differential equations (PDE) are at the core of scientific computing. Often, this requires high-resolution or adaptive discretization grids to capture
relevant spatio-temporal features in the PDE solution, e.g., in applications like turbulence, combustion, and shock propagation. Numerical approxim...

Bile, the central metabolic product of the liver, is secreted by hepatocytes into bile canaliculi (BC), tubular subcellular structures of 0.5-2 $\mu$m diameter which are formed by the apical membranes of juxtaposed hepatocytes. BC interconnect to build a highly ramified 3D network that collects and transports bile towards larger interlobular bile d...

We propose a statistical learning framework based on group-sparse regression that can be used to 1) enforce conservation laws, 2) ensure model equivalence, and 3) guarantee symmetries when learning or inferring differential-equation models from measurement data. Directly learning $\textit{interpretable}$ mathematical models from data has emerged as...

We present generalizations of the classic Newton and Lagrange interpolation schemes to arbitrary dimensions. The core contribution that enables this new method is the notion of unisolvent nodes, i.e., nodes on which the multivariate polynomial interpolant of a function is unique. We prove that by choosing these nodes in a proper way, the resulting...

The identification of singular points or topological defects in discretized vector fields occurs in diverse areas ranging from the polarization of the cosmic microwave background to liquid crystals to fingerprint recognition and bio-medical imaging. Due to their discrete nature, defects and their topological charge cannot depend continuously on eac...

Proteins can self-organize into spatial patterns via non-linear dynamic interactions on cellular membranes. Modelling and simulations have shown that small GTPases can generate patterns by coupling guanine nucleotide exchange factors (GEF) to effectors, generating a positive feedback of GTPase activation and membrane recruitment. Here, we reconstit...

Proteins can self-organize into spatial patterns via non-linear dynamic interactions on cellular membranes. Modelling and simulations have shown that small GTPases can generate patterns by coupling guanine nucleotide exchange factors (GEF) to effectors, generating a positive feedback of GTPase activation and membrane recruitment. Here, we reconstit...

Proteins can self-organize into spatial patterns via non-linear dynamic interactions on cellular membranes. Modelling and simulations have shown that small GTPases can generate patterns by coupling guanine nucleotide exchange factors (GEF) to effectors, generating a positive feedback of GTPase activation and membrane recruitment. Here, we reconstit...

We present Bionic Tracking, a novel method for solving biological cell tracking problems with eye tracking in virtual reality using commodity hardware. Using gaze data, and especially smooth pursuit eye movements, we are able to track cells in time series of 3D volumetric datasets. The problem of tracking cells is ubiquitous in developmental biolog...

We present Bionic Tracking, a novel method for solving biological cell tracking problems with eye tracking in virtual reality using commodity hardware. Using gaze data, and especially smooth pursuit eye movements, we are able to track cells in time series of 3D volumetric datasets. The problem of tracking cells is ubiquitous in developmental biolog...

Proteins can self-organize into spatial patterns via non-linear dynamic interactions on cellular membranes. Modelling and simulations have shown that small GTPases can generate patterns by coupling guanine nucleotide exchange factors (GEF) to effector binding, generating a positive feedback of GTPase activation and membrane recruitment. Here, we co...

The cell cortex, a thin film of active material assembled below the cell membrane, plays a key role in cellular symmetry-breaking processes such as cell polarity establishment and cell division. Here, we present a minimal model of the self-organization of the cell cortex that is based on a hydrodynamic theory of curved active surfaces. Active stres...

The cell cortex, a thin film of active material assembled below the cell membrane, plays a key role in cellular symmetry breaking processes such as cell polarity establishment and cell division. Here, we present a minimal model of the self-organization of the cell cortex that is based on a hydrodynamic theory of curved active surfaces. Active stres...

As computer simulations progress to increasingly complex, non-linear, and three-dimensional systems and phenomena, intuitive and immediate visualization of their results is becoming crucial. While Virtual Reality (VR) and Natural User Interfaces (NUIs) have been shown to improve understanding of complex 3D data, their application to live in situ vi...

We present a statistical learning framework for robust identification of partial differential equations from noisy spatiotemporal data. Extending previous sparse regression approaches for inferring PDE models from simulated data, we address key issues that have thus far limited the application of these methods to noisy experimental data, namely the...

Life science today involves computational analysis of a large amount and variety of data, such as volumetric data acquired by state-of-the-art microscopes, or mesh data resulting from analysis of such data or simulations. Visualisation is often the first step in making sense of the data, and a crucial part of building and debugging analysis pipelin...

In scientific computing, the problem of finding an analytical representation of a given function \(f: \Omega \subseteq \mathbb {R}^m \longrightarrow \mathbb {R},\mathbb {C}\) is ubiquitous. The most practically relevant representations are polynomial interpolation and Fourier series. In this article, we address both problems in high-dimensional spa...

Significance
Morphogenesis, the emergence of shape and form in biological systems, is a process that is fundamentally mechanochemical: Shape changes of material are driven by active mechanical forces that are generated by chemical processes, which in turn can be affected by the deformations and flows that occur. We provide a framework that integrat...

For $m,n \in \mathbb{N}$, $m\geq 1$ and a given function $f : \mathbb{R}^m\longrightarrow \mathbb{R}$, the \emph{polynomial interpolation problem} (PIP) is to determine a \emph{unisolvent node set} $P_{m,n} \subseteq \mathbb{R}^m$ of $N(m,n):=|P_{m,n}|=\binom{m+n}{n}$ points and the uniquely defined polynomial $Q_{m,n,f}\in \Pi_{m,n}$ in $m$ variab...

Modern microscopes create a data deluge with gigabytes of data generated each second, and terabytes per day. Storing and processing this data is a severe bottleneck, not fully alleviated by data compression. We argue that this is because images are processed as grids of pixels. To address this, we propose a content-adaptive representation of fluore...

For a given multi-digraph G = (V, E) we generalize Biggs Theorem to the case of directed cycles, which allows to compute the R–dimension, R = Z,Z2, of the directed cycle space of G in quadratic time. For a set of elementary directed (connected) cycles O we further consider the CW complex of elementary cycles X(G,O), given by interpreting the vertic...

An implicit aim in cellular infection biology is to understand the mechanisms how viruses, microbes, eukaryotic parasites, and fungi usurp the functions of host cells and cause disease. Mechanistic insight is a deep understanding of the biophysical and biochemical processes that give rise to an observable phenomenon. It is typically subject to fals...

Quantitative analysis of microscopy images is ideally suited for understanding the functional biological correlates of individual molecular species identified by one of the several available 'omics' techniques. Due to advances in fluorescent labeling, microscopy engineering and image processing, it is now possible to routinely observe and quantitat...

Quantitative analysis of microscopy images is ideally suited for understanding the functional biological correlates of individual molecular species identified by one of the several available “omics” techniques. Due to advances in fluorescent labeling, microscopy engineering and image processing, it is now possible to routinely observe and quantitat...

The Adaptive Particle Representation (APR) is novel method for sparse representation of a
signal. It is based on a linear-time algorithm to construct an optimal local resolution
function for each point in the domain. This choice of local resolution provides rigorous
point-wise error bounds for the function value and any derivative approximation.

Scalable and efficient numerical simulations continue to gain importance, as computation is firmly established as the third pillar of discovery, alongside theory and experiment. Meanwhile, the performance of computing hardware grows through increasingly heterogeneous parallelism, enabling simulations of ever more complex models. However, efficientl...

Modern microscopy modalities create a data deluge with gigabytes of data generated each second, or terabytes per day. Storing and processing these data is a severe bottleneck, not fully alleviated by data compression. We argue that this is because images are processed as regular grids of pixels. To address the root of the problem, we here propose a...