Biology subjects

Radde, N.

Publications and source records attributed to Radde, N..

6 recordsLinked to original sources

bioRxiv2025

Defining the importance of the arginine loop region of protegrin-1 for antimicrobial activity towards colistin-resistant Klebsiella pneumoniae.

Carbapenem resistant Klebsiella pneumoniae extreme drug resistance has warranted the use of colistin as a last-resort antibiotic but these isolates are now displaying increasing rates of colistin resistance. This is in large part due to the modifications made to the lipid A by the PhoPQ two component system. Host defense peptides (HDPs) are like colistin in that they are both positively charged and display amphipathic character, however are not impacted by lipid A modifications to the extent of colistin. To understand how HDPs can penetrate colistin resistant membranes we performed a deep mutational scanning analysis of protegrin-1 and revealed that amino acids mutations that resulted in alteration of peptide structure had more impact on antimicrobial activity than a reduction in charge. Probing single and double amino acid variants using membrane analysis and molecular modeling revealed the loss of antimicrobial activity correlated with decreased inner membrane leakage and pore modeling predicting decreased pore size,

microbiology↗

bioRxiv2025

FAIRification of computational models in biology

Computational models are essential for studying complex systems which, particularly in clinical settings, need to be quality-approved and transparent. To enhance the communication of a models features and capabilities, we propose an adaptation of the Findability, Accessibility, Interoperability and Reusability (FAIR) indicators published by the Research Data Alliance to assess models encoded in domain-specific standards, such as those established by COMBINE. The assessments guide FAIRification and add value to models.

systems biology↗

bioRxiv2024

Measuring the burden of hundreds of BioBricks defines an evolutionary limit on constructability in synthetic biology

Engineered DNA will slow the growth of a host cell if it redirects limiting resources or otherwise interferes with homeostasis. Populations of engineered cells can rapidly become dominated by "escape mutants" that evolve to alleviate this burden by inactivating the intended function. Synthetic biologists working with bacteria rely on genetic parts and devices encoded on plasmids, but the burden of different engineered DNA sequences is rarely characterized. We measured how 301 BioBricks on high-copy plasmids affected the growth rate of Escherichia coli. Of these, 59 (19.6%) negatively impacted growth. The burden imposed by engineered DNA is commonly associated with diverting ribosomes or other gene expression factors away from producing endogenous genes that are essential for cellular replication. In line with this expectation, BioBricks exhibiting burden were more likely to contain highly active constitutive promoters and strong ribosome binding sites. By monitoring how much each BioBrick reduced expression of a chromosomal GFP reporter, we found that the burden of most, but not all, BioBricks could be wholly explained by diversion of gene expression resources. Overall, no BioBricks reduced the growth rate of E. coli by >45%, which agreed with a population genetic model that predicts such plasmids should be "unclonable" because escape mutants will take over during growth of a bacterial colony or small laboratory culture from a transformed cell. We made this model available as an interactive web tool for synthetic biology education and added our burden measurements to the iGEM Registry descriptions of each BioBrick.

synthetic biology↗

bioRxiv2023

The impossible challenge of estimating non-existent moments of the Chemical Master Equation

MotivationThe Chemical Master Equation is a set of linear differential equations that describes the evolution of the probability distribution on all possible configurations of a (bio-)chemical reaction system. Since the number of configurations and therefore the dimension of the CME rapidly increases with the number of molecules, its applicability is restricted to small systems. A widely applied remedy for this challenge are moment-based approaches which consider the evolution of the first few moments of the distribution as summary statistics for the complete distribution. Here, we investigate the performance of two moment-estimation methods for reaction systems whose equilibrium distributions encounter heavy-tailedness and hence do not possess statistical moments. ResultsWe show that estimation via Stochastic Simulation Algorithm trajectories lose consistency over time and estimated moment values span a wide range of values even for large sample sizes. In comparison, the Method of Moments returns smooth moment estimates but is not able to indicate the nonexistence of the allegedly predicted moments. We furthermore analyze the negative effect of a CME solutions heavy-tailedness on SSA run times and explain inherent difficulties. While moment estimation techniques are a commonly applied tool in the simulation of (bio-)chemical reaction networks, we conclude that they should be used with care, as neither the system definition nor the moment estimation techniques themselves reliably indicate the potential heavy-tailedness of the CMEs solution.

systems biology↗

bioRxiv2021

Quasi-Entropy Closure: A Fast and Reliable Approach to Close the Moment Equations of the Chemical Master Equation

MotivationThe Chemical Master Equation is the most comprehensive stochastic approach to describe the evolution of a (bio-)chemical reaction system. Its solution is a time-dependent probability distribution on all possible configurations of the system. As the number of possible configurations is typically very large, the Master Equation is often practically unsolvable. The Method of Moments reduces the system to the evolution of a few moments of this distribution, which are described by a system of ordinary differential equations. Those equations are not closed, since lower order moments generally depend on higher order moments. Various closure schemes have been suggested to solve this problem, with different advantages and limitations. Two major problems with these approaches are first that they are open loop systems, which can diverge from the true solution, and second, some of them are computationally expensive. ResultsHere we introduce Quasi-Entropy Closure, a moment closure scheme for the Method of Moments which estimates higher order moments by reconstructing the distribution that minimizes the distance to a uniform distribution subject to lower order moment constraints. Quasi-Entropy closure is similar to Zero-Information closure, which maximizes the information entropy. Results show that both approaches outperform truncation schemes. Moreover, Quasi-Entropy Closure is computationally much faster than Zero-Information Closure. Finally, our scheme includes a plausibility check for the existence of a distribution satisfying a given set of moments on the feasible set of configurations. Results are evaluated on different benchmark problems. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=135 SRC="FIGDIR/small/470753v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@8c2beforg.highwire.dtl.DTLVardef@9876f7org.highwire.dtl.DTLVardef@1aa5db7org.highwire.dtl.DTLVardef@1eed4b4_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2018

The role of stochastic sequestration dynamics for intrinsic noise filtering in signaling network motifs

The relation between design principles of signaling network motifs and their robustness against intrinsic noise still remains illusive. In this work we investigate the role of cascading for coping with intrinsic noise due to stochasticity in molecular reactions. We use stochastic approaches to quantify fluctuations in the terminal kinase of phosphorylation-dephosphorylation cascade motifs and demonstrate that cascading highly affects these fluctuations. We show that this purely stochastic effect can be explained by time-varying sequestration of upstream kinase molecules. In particular, we discuss conditions on time scales and parameter regimes which lead to a reduction of output fluctuations. Our results are put into biological context by adapting rate parameters of our modeling approach to biologically feasible ranges for general binding-unbinding and phosphorylation-dephosphorylation mechanisms. Overall, this study reveals a novel role of stochastic sequestration for dynamic noise filtering in signaling cascade motifs.

systems biology↗