Biology subjects

Siebert, H.

Publications and source records attributed to Siebert, H..

6 recordsLinked to original sources

bioRxiv2023

A three-node Turing gene circuit forms periodic spatial patterns in bacteria

Turing patterns1 are well-known self-organising systems that can form spots, stripes, or labyrinths. They represent a major theory of patterning in tissue organisation, due to their remarkable similarity to some natural patterns, such as skin pigmentation in zebrafish2, digit spacing3,4, and many others. The involvement of Turing patterns in biology has been debated because of their stringent fine-tuning requirements, where patterns only occur within a small subset of parameters5,6. This has complicated the engineering of a synthetic gene circuit for Turing patterns from first principles, even though natural genetic Turing networks have been successfully identified4,7. Here, we engineered a synthetic genetic reaction-diffusion system where three nodes interact according to a non-classical Turing network with improved parametric robustness6. The system was optimised in E. coli and reproducibly generated stationary, periodic, concentric stripe patterns in growing colonies. The patterns were successfully reproduced with a partial differential equation model, in a parameter regime obtained by fitting to experimental data. Our synthetic Turing system can contribute to novel nanotechnologies, such as patterned biomaterial deposition8,9, and provide insights into developmental patterning programs10.

synthetic biology↗

bioRxiv2023

Bioregionalization: From Wallace and Humboldt to deep-time paleoregion dynamics

Bioregionalization methods allow us to classify and map biogeographic units using data on species composition and traits. Here, we reviewed the evolution of the field during the last 70 years, seeking to summarize its history, and identify gaps and future avenues for research. Our results show that the aim of the studies using bioregionalization methods changed in time. First, bioregionalization were used to unveil the drivers of the observed spatial patterns of biodiversity on Earth, and to understand the role of dispersal limitations on the evolutionary history of clades, but recently, these methods are mostly used for conservation management. Further, data used to map biodiversity regions, the ones that we are now defining conservation strategies, are taxonomically and geographically biased, with a large percentage of the papers using vertebrate data from developed continents/countries. Finally, we show how key papers in the field, the ones with most citations, heavily depend on expert criteria and non-reproducible workflows, preventing direct comparison of maps of bioregions from different papers. Following our findings, we identified 3 gaps for the advance in the field, 1) We need to move beyond maps of vertebrate composition. Ideally, we need to increase the taxonomic diversity of the studies, but also to add other type of information, like data on species traits, genetic diversity, or phylogenetic distances. 2) we need reproducible and standardized methods 3) we need to further explore the temporal dimension of bioregions, to understand how they evolved through time.

ecology↗

bioRxiv2020

The Design Principles of Discrete Turing Patterning Systems

The formation of spatial structures lies at the heart of developmental processes. However, many of the underlying gene regulatory and biochemical processes remain poorly understood. Turing patterns constitute a main candidate to explain such processes, but they appear sensitive to fluctuations and variations in kinetic parameters, raising the question of how they may be adopted and realised in naturally evolved systems. The vast majority of mathematical studies of Turing patterns have used continuous models specified in terms of partial differential equations. Here, we complement this work by studying Turing patterns using discrete cellular automata models. We perform a large-scale study on all possible two-species networks and find the same Turing pattern producing networks as in the continuous framework. In contrast to continuous models, however, we find these Turing pattern topologies to be substantially more robust to changes in the parameters of the model. We also find that diffusion-driven instabilities are substantially weaker predictors for Turing patterns in the discrete modelling framework in comparison to the continuous case, and show that a more refined criterion constitutes a stronger predictor. The similarity of the results for the two modelling frameworks suggests a deeper underlying principle of Turing mechanisms in nature. Together with the larger robustness in the discrete case this suggests that Turing patterns may be more robust than previously thought.

systems biology↗

bioRxiv2020

A framework for designing miRNA-based distributed cell classifier circuits

MotivationCell classifiers are synthetic bio-devices performing type-specific in vivo classification. The circuits identify a cell state based on its molecular fingerprint. In particular, the classifiers may be designed to recognize cancerous cells and trigger their apoptosis, shaping a novel therapy for cancer patients. Recently, we introduced a new theoretical design of such devices employing distributed classifiers. Here, a group of single-circuit classifiers decides collectively according to a pre-defined threshold function whether a cell is cancerous. The multi-circuit architecture has shown the potential to predict the cell condition with high accuracy. However, lack of far-reaching machinery to design and evaluate distributed cell classifiers, in particular, assessing their robustness to noise and novel information, makes their application limited. ResultsIn this study, we present a comprehensive framework for designing and evaluating miRNA-based distributed cell classifiers comprising data simulation, pre-processing, and an extensive testing scheme. We develop optimization criteria that allow increasing the accuracy and robustness of classifiers to noise and novel information as shown in simulated and real-world data studies. The evaluation performed on cancer data demonstrates that distributed classifiers outperform single-circuit designs in terms of prediction accuracy. Our classifiers include relevant miRNAs previously described in the literature, as well as more complex regulation patterns included in the data. AvailabilityThe code and data are available at: https://github.com/MelaniaNowicka/RAccoon. Contactm.nowicka@fu-berlin.de

bioinformatics↗

bioRxiv2019

Designing Distributed Cell Classifier Circuits using a Genetic Algorithm

Cell classifiers are decision-making synthetic circuits that allow in vivo cell-type classification. Their design is based on finding a relationship between differential expression of miRNAs and the cell condition. Such biological devices have shown potential to become a valuable tool in cancer treatment as a new type-specific cell targeting approach. So far, only single-circuit classifiers were designed in this context. However, reliable designs come with high complexity, making them difficult to assemble in the lab. Here, we apply so-called Distributed Classifiers (DC) consisting of simple single circuits, that decide collectively according to a threshold function. Such architecture potentially simplifies the assembly process and provides design flexibility. Here, we present a genetic algorithm that allows the design and optimization of DCs. Breast cancer case studies show that DCs perform with high accuracy on real-world data. Optimized classifiers capture biologically relevant miRNAs that are cancer-type specific. The comparison to a single-circuit classifier design approach shows that DCs perform with significantly higher accuracy than individual circuits. The algorithm is implemented as an open source tool.

bioinformatics↗

bioRxiv2018

Identifying lncRNA-mediated regulatory modules via ChIA-PET network analysis

BackgroundAlthough several studies have provided insights into the role of long non-coding RNAs (lncRNAs), the majority of them has unknown function. Recent evidence has shown the importance of both lncR-NAs and chromatin interactions in transcriptional regulation. Although network-based methods, mainly exploiting gene-lncRNA co-expression, have been applied to characterize lncRNA of unknown function by means of guilt-by-association strategies, no method exists which combines co-expression analysis with 3D chromatin interaction data.\n\nResultsTo better understand the function of chromatin interactions in the context of lncRNA-mediated gene regulation, we have developed a multi-step graph analysis approach to examine the RNA polymerase II ChIA-PET chromatin interaction network in the K562 human cell line. We have annotated the network with gene and lncRNA coordinates, and chromatin states from the ENCODE project. We used centrality measures, as well as an adaptation of our previously developed Markov State Models (MSM) clustering method, to gain a better understanding of lncRNAs in transcriptional regulation. The novelty of our approach resides into the detection of fuzzy regulatory modules based on network properties and their optimization based on co-expression analysis between genes and gene-lncRNA pairs. This results in our method returning more bona fide regulatory modules than other state-of-the art approaches for clustering on graphs.\n\nConclusionsInterestingly, we find that lncRNA network hubs tend to be significantly enriched in disease association, positional conservation and enhancer-like functions. We validated regulatory functions for well known lncRNAs, such as MALAT1 and the enhancer-like lncRNA FALEC. In addition, by investigating the modular structure of bigger components we show that we can propose regulatory functional mechanisms for uncharacterized lncRNAs, such FLJ37453, RP11442N24 B.1 and LINC00910.

systems biology↗