Biology subjects

Kollmann, M.

Publications and source records attributed to Kollmann, M..

8 recordsLinked to original sources

bioRxiv2023

Self-sustained rhythmic behavior of Synechocystis PCC 6803 under continuous light conditions in the absence of light-dark entrainment

Circadian clocks regulate biological activities, providing organisms a fitness advantage under diurnal changing conditions by allowing them to anticipate and adapt to recurring external changes. In recent years attention was drawn to the entrainment by intracellular cycles. Photosynthetic Cyanobacteria coordinate their gene expression, metabolism, and other activities in a circadian fashion. Solely, three proteins, KaiA, KaiB, and KaiC, constitute the well-studied circadian clock of the cyanobacterial model, Synechococcus elongatus PCC 7942. It remained inconclusive for a long time whether Synechocystis sp. PCC 6803, an important organism for biotechnological applications, can also maintain circadian rhythms under continuous illumination. Using an approach, which does not require genetic modification, we investigated the growth behavior of Synechocystis via non-invasive online backscattering measurement and verified all three criteria for true circadian oscillators: temperature compensation, entrainment by external stimuli, and a self-sustained freerunning period of about 24 hours. Since manipulation of the circadian clock (Synechocystis {Delta}kaiA1B1C1) led to a significant reduction in glycogen content, disruption of glycogen synthesis (Synechocystis {Delta}glgC) entirely inhibited glycogen formation and both mutants lost oscillations, we hypothesize that the oscillations reflect glycogen metabolism. Significance StatementMonitoring circadian rhythms in cyanobacteria usually requires genetically modified reporter strains or intensive sampling for downstream analysis. Even for the main cyanobacterial model Synechocystis sp. PCC 6803 it was debated for years to which extent undamped circadian oscillations are really present until a suitable reporter strain was developed. We applied online backscatter measurements as an alternative readout to monitor circadian oscillations in cyanobacteria. In Synechocystis the temperature-compensated kaiA1B1C1-driven 24 h metabolic oscillations did not require light-dark entrainment, highlighting the relevance of the clock for the carbon metabolism even under continuous light, an aspect which should be considered for industrial set-ups. Our method opens the possibility to extend circadian analysis to non-GMO and monitor metabolic rhythmicity during high-density cultivation.

systems biology↗

bioRxiv2023

Abnormality Detection for Medical Images UsingSelf-Supervision and Negative Samples

Recent progress in computer-aided technologies has had a considerable impact on helping experts with a reliable and fast diagnosis of abnormal samples. In particular, self-supervised and self-distillation techniques have advanced automated out-of-distribution (OOD) detection in the image domain. Further improvements in OOD detection have been observed by including negative samples derived from shifting transformations of real images. In this work, we study different ways of creating negative samples for medical images and how effective they are when leveraging them in a self-supervised self-distillation framework. We investigate the impact of various types of negative examples by applying different shifting transformations on samples when they are derived from in-distribution training data, from an auxiliary dataset, or a combination of both. For the case of the auxiliary dataset, we compare the OOD detection performance when auxiliary samples are extracted from an indomain or an out-domain. Our approach uses only data belonging to healthy people during the training procedure and does not require any additional information from labels. We demonstrate the efficiency of our technique by comparing abnormality detection performance on diverse medical datasets, setting new benchmarks for pneumonia, polyp, and glaucoma detection from X-ray, colonoscopy, and ophthalmology images.

bioinformatics↗

bioRxiv2023

Superposition of target structures enables design of bi-stable RNA molecules with deep learning

The ability to design RNA molecules with specific structures and functions could facilitate research and developments in biotechnology, biology and pharmacy. Here we present a flexible RNA design framework based on deep learning that locally optimizes sequences by gradient-guided search methods. We demonstrate its effectiveness by designing bi-stable RNA molecules by superimposing conformer target structures.

bioinformatics↗

bioRxiv2021

Pneumonia Detection with Semantic Similarity Scores

X-ray images have been widely used for medical diagnoses of cardiothoracic and pulmonary abnormalities due to its noninvasiveness. Advancement in computer-aided diagnostic technologies, such as deep supervised methods, can help radiologists with a reliable early treatment and reduce diagnosis time. Nevertheless, these methods are prone to the small number of labeled samples and are limited to a specific abnormality. In this paper we combined a self-supervised contrastive method with a Mahalanobis distance score to develope an abnormality detection method that uses only healthy images during the training procedure. We were able to outperform previous unsupervised methods for the task of Pneumonia detection. We show that representation learned by the self-supervised method improves the supervised tasks for Pneumonia detection.

bioinformatics↗

bioRxiv2021

De Novo Prediction of RNA 3D Structures with Deep Learning

We present a Deep Learning approach to predict 3D folding structures of RNAs from their nucleic acid sequence. Our approach combines an autoregressive Deep Generative Model, Monte Carlo Tree Search, and a Score Model to find and rank the most likely folding structures for a given RNA sequence. We confirm the predictive power of our approach by setting new benchmarks for some longer sequences in a simulated blind test of the RNA Puzzles prediction challenge.

bioinformatics↗

bioRxiv2018

Circularly shifted filters enable data efficient sequence motif inference with neural networks

MotivationNucleic acids and proteins often have localized sequence motifs that enable highly specific interactions. Due to the biological relevance of sequence motifs, numerous inference methods have been developed. Recently, convolutional neural networks (CNNs) achieved state of the art performance because they can approximate complex motif distributions. These methods were able to learn transcription factor binding sites from ChIP-seq data and to make accurate predictions. However, CNNs learn filters that are difficult to interpret, and networks trained on small data sets often do not generalize optimally to new sequences.\n\nResultsHere we present circular filters, a novel convolutional architecture, that contains all circularly shifted variants of the same filter. We motivate circular filters by the observation that CNNs frequently learn filters that correspond to shifted and truncated variants of the true motif. Circular filters enable learning of non-truncated motifs and allow easy interpretation of the learned filters. We show that circular filters improve motif inference performance over a wide range of hyperparameters. Furthermore, we show that CNNs with circular filters perform better at inferring transcription factor binding motifs from ChIP-seq data than conventional CNNs.\n\nContactmarkus.kollmann@hhu.de

bioinformatics↗

bioRxiv2016

Predicting gene expression level in E. coli from mRNA sequence information

MotivationThe accurate characterization of the translational mechanism is crucial for enhancing our understanding of the relationship between genotype and phenotype. In particular, predicting the impact of the genetic variants on gene expression will allow to optimize specific pathways and functions for engineering new biological systems. In this context, the development of accurate methods for predicting translation efficiency from the nucleotide sequence is a key challenge in computational biology.\n\nMethodsIn this work we present PGExpress, a binary classifier to discriminate between mRNA sequences with low and high translation efficiency in E. coli. PGExpress algorithm takes as input 12 features corresponding to RNA folding and anti-Shine-Dalgarno hybridization free energies. The method was trained on a set of 1,772 sequence variants (WT-High) of 137 essential E. coli genes. For each gene, we considered 13 sequence variants of the first 33 nucleotides encoding for the same amino acids followed by the superfolder GFP. Each gene variant is represented sequence blocks that include the Ribosome Binding Site (RBS), the first 33 nucleotides of the coding region (C33), the remaining part of the coding region (CC), and their combinations.\n\nResultsOur logistic regression-based tool (PGExpress) was trained using a 20-fold gene-based cross-validation procedure on the WT-High dataset. In this test PGExpress achieved an overall accuracy of 74%, a Matthews correlation coefficient 0.49 and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.81. Tested on 3 sets of sequences with different Ribosome Binding Sites, PGExpress reaches similar AUC. Finally, we validated our method by performing in-house experiments on five newly generated mRNA sequence variants. The predictions of the expression level of the new variants are in agreement with our experimental results in E. coli.\n\nAvailabilityhttp://folding.biofold.org/pgexpress\n\nContactmarkus.kollmann@hhu.de, emidio.capriotti@unibo.it

bioinformatics↗

bioRxiv2016

Inferability of transcriptional networks from large scalegene deletion studies

Generating a comprehensive map of molecular interactions in living cells is difficult and great efforts are undertaken to infer molecular interactions from large scale perturbation experiments. Here, we develop the analytical and numerical tools to quantify the fundamental limits for inferring transcriptional networks from gene knockout screens and introduce a network inference method that is unbiased and scalable to large network sizes. We show that it is possible to infer gene regulatory interactions with high statistical significance, even if prior knowledge about potential regulators is absent.

systems biology↗