Reinhard Laubenbacher

Learning dynamical systems with biochemically informed neural ordinary differential equations

The paper introduces BINODEs, a neural-ODE framework that integrates biochemical knowledge into dynamical models to improve interpretability and flexibility in modeling biochemical systems.

Personalizing computational models to construct medical digital twins

This paper develops an algorithm to calibrate agent-based computational models to individual patients using data assimilation techniques for personalized medicine.

The heme-scavenger, hemopexin, protects against fungal lung injury by mitigating NETosis: an experimental and computational study.

This study shows hemopexin reduces fungal lung injury by limiting heme-mediated NETosis in aspergillosis.

Control of Medical Digital Twins with Artificial Neural Networks

This paper introduces dynamics-informed neural-network controllers for medical digital twins and benchmarks them against other methods using agent-based models.

Modularity of biological systems: a link between structure and function

The paper explores the relationship between structural and functional modularity in gene regulatory networks using Boolean models and simulations, showing that structural decomposition correlates with dynamic behavior.

COVID-associated pulmonary aspergillosis in immunocompetent patients

The paper uses a computational model to simulate immune responses and identify factors contributing to CAPA in immunocompetent patients with severe SARS-CoV2.

SteadyCellPhenotype: A web-based tool for the modeling of biological networks with ternary logic

SteadyCellPhenotype models ternary biological networks by finding steady states and simulating trajectories with visualization tools.

Multiscale mechanistic modelling of the host defense in invasiveaspergillosis reveals leukocyte activation and iron acquisition as driversof infection outcome

This study develops a multiscale computational model of invasive aspergillosis to identify key factors determining infection outcome, finding that fungal growth, iron acquisition, and leukocyte activation are critical.

Mathematical modeling of the Candida albicans yeast to hyphal transition reveals novel control strategies

This study builds a Boolean model of the C. albicans yeast-to-hyphal transition, identifies key control strategies, and validates them experimentally.

Control of Intracellular Molecular Networks Using Algebraic Methods

This paper introduces a control method for multistate biological networks using algebraic techniques and applies it to cancer and iron metabolism models to identify intervention strategies.

Sensitivity and robustness of comorbidity network analysis

This study evaluates the robustness and sensitivity of comorbidity network analysis techniques across different data sources and link determination methods.

Comparative Analysis of Linear and Nonlinear Dimension Reduction Techniques on Mass Cytometry Data

The paper compares dimension reduction techniques on mass cytometry data, finding t-SNE and Diffusion Maps best for preserving cell relationships and providing informative visualizations.

The Innate Immune Response to Ischemic Injury: a Multiscale Modeling Perspective

The paper constructs a multiscale model of TLR4 signaling in ischemic injury to predict therapeutic outcomes and identify safe targets.

Addressing current challenges in cancer immunotherapy with mathematical and computational modeling

This review discusses mathematical and computational models addressing challenges in cancer immunotherapy, including tumor classification, treatment scheduling, and combination therapy design.

The Influence of Canalization on the Robustness of Boolean Networks

This paper introduces mathematical tools to analyze robustness in Boolean networks using k-canalizing functions and provides formulas for c-sensitivity and activities.