Open Research Projects

Adaptive Learning Dynamics of the Immune System

Abstract

The goal of this project is to invert the paradigm of applying extrinsic AI/ML algorithms to practically understand biological data. Instead, we want to study how instrinsic biological learning functions in the immune system work by employing recent theoretical understanding about dynamics of artificial neural networks. The main setting we are going to investigate is the immune system. There has been recent theoretical progress suggesting that self-adaptation capabilities could be key to enhancing its function considerably. Yet, this requires a better understanding of the main mechanisms via a combination of adaptive network models in combination with statistical data assimilation techniques of macroscopic observables. In this project we aim to lay substantial groundwork for this program combining methods from biology, computation, data science, dynamics, and machine learning. We are going to focus on building benchmark models for this context validating them via simulation with forward uncertainty propagation and bifurcation analysis.

AI-Guided Design of Disordered Protein Regions for Regulation of Structured Domains and PROTAC Development

Abstract

Intrinsically disordered protein regions (IDRs) are widespread in the human proteome but defy the classical structure-function paradigm. Unlike structured domains, IDRs do not adopt a single, stable secondary or tertiary conformation but instead exist as dynamic ensembles that enable transient, context-dependent interactions. Most IDRs co-exist in proteins alongside folded (structured) domains, with which they often engage in regulatory intramolecular interactions. These dynamic interfaces can modulate access to functional sites, regulate enzymatic activity, or mediate complex assembly. High conformational plasticity allows IDRs to act as competitive inhibitors or selectivity filters, especially in nucleic acid-binding proteins. The affinity of IDR-mediated interactions is frequently modulated by post-translational modifications such as phosphorylation. Disruption of the IDR-driven regulatory binding mechanisms has been implicated in various pathologies, highlighting their potential as promising, yet underexplored, therapeutic targets. Engineering specific protein-protein interactions within defined affinity ranges, particularly those involving IDRs, remains a critical frontier in computational structural biology and protein design. The goal of the project is to develop novel computational strategies to design IDRs capable of dynamically interacting with structured protein domains, ideally within a range of kinetic and thermodynamic parameters. Focus will be placed on the de novo design of synthetic IDRs that can competitively modulate known endogenous regulatory interactions with structured protein domains. The project will integrate computational structural modeling with advanced generative AI models and protein language models, as well as experimental validation. The overarching goal of the PhD thesis is to develop models that can generate IDR sequences with tunable affinities for structured domains, enabling dynamic regulatory control. To this end, the candidate will evaluate a spectrum of generative strategies, ranging from pretrained language models to diffusion and inpainting approaches, to develop the most suitable model for designing low-to-moderate affinity interactions. Experimental validation will focus on a proof-of-concept system, the interaction between the N-terminal IDR and the DNA-binding domain of the p53 tumor suppressor protein. Biochemical assays and solution NMR spectroscopy will assess binding affinity and specificity of designed IDRs. The experimental tasks will be primarily carried out by a dedicated postdoc, in close collaboration with the PhD candidate. Ultimately, this approach will facilitate the design of IDR-based proteolysis-targeting chimeras (PROTACs), representing a novel therapeutic approach for traditionally "undruggable" proteins. The doctoral candidate will focus primarily on computational work and model development, while integrating experimental feedback to iteratively improve model performance in a "lab-in-the-loop" workflow.

Closed-loop dynamical control of brain activation patterns in mice and neuronal organoids

Abstract

This project will develop a predictive model of brain activation patterns using calcium imaging and electrophysiology data from mouse cortex and human organoids. Combining global pharmacological modulation with precise sensory and optogenetic stimulation will enhance the contrastive learning framework for dynamics identification developed by the Schneider Laboratory to recognize increasingly complex neural patterns (goal 1) to then deploy a predictive model for closed-loop control of targeted brain regions (goal 2). The study will validate the model’s generalizability while developing applications for memory restoration through pattern-specific interventions. The methodology parallels clinical neuroimaging approaches (fMRI/TMS), with the potential for advancing personalized neuromodulation therapies. Additionally, findings will inform human organoid engineering for tissue replacement and brain-machine interface applications.

Statistical Methods for Multi-Modal Data Analysis in Human Disease Research

Abstract

Non-communicable diseases (NCDs) are responsible for 70% of global deaths, with cardiovascular diseases, cancers, respiratory diseases, and diabetes being the most prevalent. The growing burden of these diseases necessitates a shift from reactive treatment to predictive and preventive healthcare. This PhD research aims to develop novel statistical methodologies to analyze multi-modal longitudinal omics data, integrating infrared (IR) molecular fingerprinting, mass spectrometry (MS)-based proteomics, and nuclear magnetic resonance (NMR)-based metabolomics. Using data from the Health for Hungary (H4H) and German National Cohort (NAKO) studies, this research will focus on statistical trajectory modeling and machine learning to detect early disease markers and predict disease onset. The objectives include designing statistical models for disease progression, integrating multi-modal data sources, applying machine learning algorithms for disease prediction, optimizing statistical study design, and validating findings with independent datasets. The methodology involves, among others, mixed-effects models and functional data analysis for trajectory modeling. Multi-modal data integration will leverage dimension reduction techniques. Machine learning approaches such as ensemble learning, and interpretable AI methods will enhance predictive modeling. Statistical study design optimization will include sample size determination, missing data imputation, and cross-validation strategies. This research is expected to contribute novel statistical frameworks for disease trajectory analysis, improve predictive modeling of disease progression, and establish scalable methodologies for large-scale health studies. The outcomes will support early intervention strategies, enhance personalized healthcare, and contribute to global efforts in preventive medicine.

Uncovering the mechanisms of lung remodeling following acute respiratory lung infection using spatial transcriptomics

Abstract

Lung anatomical structures are often severely damaged following an acute respiratory infection, sometimes leading to death in the most extreme cases. Yet, remarkably, the lung demonstrates a significant capacity for regeneration and repair. The mechanisms underlying this resilience remain poorly understood. In this project, we aim to develop computational models that integrate spatial, temporal, and perturbation data to better understand how the lung undergoes remodeling. Situated at the intersection of computational biology, clinical practice, and pathology, this project holds the potential to uncover novel therapeutic strategies to promote lung repair.

Joint analysis of perturbation screen and transcriptional kinetics to dissect infection processes

Abstract

Millions of years of coevolution between humans and pathogens have fostered complex crosstalk strategies that allow for interference between organisms. Therefore, unraveling the mechanisms by which pathogens disrupt human cells is a powerful scientific approach to understanding cellular pathways. In this project, we aim to develop a novel computational model that integrates dynamic gene regulatory pathways, temporal single-cell RNA-seq, and in silico perturbations. Additionally, we aim to enhance our ability to conduct large-scale perturbation screens. Overall, this research has the potential to significantly advance our understanding of the mechanisms underlying host-pathogen interactions.