A5001364288- Gene Regulatory Network Analysis
- Bioinformatics and Genomic Networks
- Computational Drug Discovery Methods
- Quantum Computing Algorithms and Architecture
- Receptor Mechanisms and Signaling
- Microbial Metabolic Engineering and Bioproduction
- Scientific Computing and Data Management
- Neuroendocrine Tumor Research Advances
- Neural Networks and Reservoir Computing
- Lung Cancer Research Studies
- Pancreatic and Hepatic Oncology Research
- Single-cell and spatial transcriptomics
- Viral Infectious Diseases and Gene Expression in Insects
- Molecular Communication and Nanonetworks
- Genetics, Bioinformatics, and Biomedical Research
- Neural dynamics and brain function
Universität Ulm
2021-2024
Regulatory dependencies in molecular networks are the basis of dynamic behaviors affecting phenotypical landscape. With advance high throughput technologies, detail omics data has arrived at single-cell level. Nevertheless, new strategies required to reconstruct regulatory based on populations data. Here, we present a approach generate gene from RNA-sequencing (scRNA-seq) Our exploits heterogeneity pseudo-timepoints. This allows for first time uncouple network reconstruction direct...
The dynamics of cellular mechanisms can be investigated through the analysis networks. One simplest but most popular modeling strategies involves logic-based models. However, these models still face exponential growth in simulation complexity compared with a linear increase nodes. We transfer this approach to quantum computing and use upcoming technique field simulate resulting Leveraging logic has many benefits, including reduction algorithms for systems biology tasks. To showcase...
Abstract Motivation Boolean networks can serve as straightforward models for understanding processes such gene regulation, and employing logical rules. These rules either be derived from existing literature or by data-driven approaches. However, in the context of large networks, exhaustive search intervention targets becomes challenging due to exponential expansion a network’s state space multitude potential target candidates, along with their various combinations. Instead, we employ...
Abstract Pancreatic neuroendocrine tumors (PanNETs) are a rare tumor entity with largely unpredictable progression and increasing incidence in developed countries. Molecular pathways involved PanNETs development still not elucidated, specific biomarkers missing. Moreover, the heterogeneity of makes their treatment challenging most approved targeted therapeutic options for lack objective responses. Here, we applied systems biology approach integrating dynamic modeling strategies, foreign...
Interaction graphs are able to describe regulatory dependencies between compounds without capturing dynamics. In contrast, mathematical models that based on interaction allow investigate the dynamics of biological systems. However, since dynamic complexity these grows exponentially with their size, exhaustive analyses and consequently screening all possible interventions eventually becomes infeasible. Thus, we designed an approach identify dynamically relevant static network topology.Here,...
Boolean networks are commonly used in systems biology to dynamically model gene regulatory interactions. Here, we present a protocol for implementing network dynamics as quantum circuits. We describe steps accessing cloud-based processing units offered by IBM and IonQ downloading parsing logic networks. then detail procedures performing simulations of circuits on local devices visualizing measurement results. For complete details the use execution this protocol, please refer Weidner et al.1
Controlling phenotypical landscapes is of vital interest to modern biology. This task becomes highly demanding because cellular decisions involve complex networks engaging in crosstalk interactions. Previous work on control theory indicates that small sets compounds can single phenotypes. However, a dynamic approach missing determine the drivers whole network dynamics. By analyzing 35 biologically motivated Boolean networks, we developed method identify sufficient decide entire landscape....
The description of gene interactions that constantly occur in the cellular environment is an extremely challenging task due to immense number degrees freedom and incomplete knowledge about microscopic details. Hence, a coarse-grained rather powerful modeling such dynamics provided by Boolean Networks (BNs). BNs are dynamical systems composed agents record their possible over time. Stable states these called attractors which closely related expression biological phenotypes. Identifying full...
Abstract Motivation Biological processes are complex systems with distinct behaviour. Despite the growing amount of available data, knowledge is sparse and often insufficient to investigate regulatory behaviour these systems. Moreover, different cellular phenotypes possible under varying conditions. Mathematical models attempt unravel mechanisms by investigating dynamics networks. Therefore, a major challenge combine regulations phenotypical information as well underlying mechanisms. To...