Biology subjects

Nowak, T.

Publications and source records attributed to Nowak, T..

8 recordsLinked to original sources

bioRxiv2026

Efficient Stochastic Trace Generation for Transcription

Bursty transcription in single cells typically produces over-dispersed, skewed, and sometimes heavy-tailed expression distributions that are explained by two-state Markov models of the promoters. While the gold standard for simulation is exact stochastic sampling with Gillespies algorithm, obtaining thousands of timed traces is computationally costly. Surrogate models based on stochastic differential equations (SDEs) are widely used to speed up this simulation process. An example is the Chemical Langevin Equation based on Gaussian noise, which, however, does not capture heavy-tailed noise. In this work, we present a unified SDE framework that combines deterministic drift, Gaussian fluctuations, and additive sporadic jumps of arbitrary distributions, and provide an open-source Python implementation, bcrnnoise. The framework subsumes standard surrogate models and allows for vectorized generation of batches of transcription traces. We assess computational speed and accuracy of common surrogate models along with new models, showing that high accuracy can be obtained while reducing computational cost up to two orders of magnitude.

bioinformatics↗

bioRxiv2025

A Quasi-Stationary Distribution Bound for Fault Analysis in Gene Regulatory Networks

The inherent stochastic fluctuations in signaling molecules of Gene Regulatory Networks (GRNs) add unpredictability, complicating the design of robust synthetic GRNs that must function within precise ranges. Multi-stable GRNs, such as toggle switches, are central to systems like biosensors and logic gates but can fail due to unintended transitions between stable states caused by the fluctuations. Despite their importance, tools to characterize the probability distributions around stable states remain limited. We present a mathematical framework to analyze these multi-stable systems using continuous-time Markov chains (CTMCs) and quasi-stationary distributions. This framework is broadly applicable, requiring only that the state space is connected, making it applicable to a variety of systems. We then apply the framework to current examples from the literature and conclude that our method provides quantitative design principles for toggle switch design that match current experimental insights, identifying parameter thresholds where systems transition from frequent stochastic switching (hours) to stable operation (years to decades) and demonstrate upper bound calculations for false positive/negative rates in population-level biosensor dynamics.

systems biology↗

bioRxiv2024

Phage-mediated intercellular CRISPRi for biocomputation in bacterial consortia

Coordinated actions of cells in microbial communities and multicellular organisms enable them to perform complex tasks otherwise difficult for single cells. This has inspired biological engineers to build cellular consortia for larger circuits with improved functionalities, while implementing communication systems for coordination among cells. Here, we investigate the signalling dynamics of a phage-mediated synthetic DNA messaging system, and couple it with CRISPR interference to build distributed circuits that perform logic gate operations in multicellular bacterial consortia. We find that growth phases of both sender and receiver cells, as well as resource competition between them, shape communication outcomes. Leveraging the easy programmability of DNA messages, we build 8 orthogonal signals and demonstrate that intercellular CRISPRi (i-CRISPRi) regulates gene expression across cells. Finally, we multiplex the i-CRISPRi system to implement several multicellular logic gates that involve up to 7 cells and take up to 3 inputs simultaneously, with single- and dual-rail encoding: NOT, YES, AND, and AND-AND-NOT. The communication system developed here lays the groundwork for implementing complex biological circuits in engineered bacterial communities, using phage signals for communication.

synthetic biology↗

bioRxiv2023

An Allee-based Distributed Algorithm for Microbial Whole-Cell Sensors

Reliable detection of substances present at potentially low concentrations is a problem common to many biomedical applications. Complementary to well-established enzyme-, antibody-antigen-, and sequencing-based approaches, so-called microbial whole-cell sensors, i.e., synthetically engineered microbial cells that sense and report substances, have been proposed as alternatives. Typically these cells operate independently: a cell reports an analyte upon local detection. In this work, we analyze a distributed algorithm for microbial whole-cell sensors, where cells communicate to coordinate if an analyte has been detected. The algorithm, inspired by the Allee effect in biological populations, causes cells to alternate between a logical 0 and 1 state in response to reacting with the particle of interest. When the cells in the logical 1 state exceed a threshold, the algorithm converts the remaining cells to the logical 1 state, representing an easily-detectable output signal. We validate the algorithm through mathematical analysis and simulations, demonstrating that it works correctly even in noisy cellular environments.

synthetic biology↗

bioRxiv2022

Co-infection of Ehrlichia with B. burgdorferi drives emergency myelopoiesis and promotes Lyme arthritis

Lyme disease is caused by the extracellular pathogen Borrelia burgdorferi (Bb), transmitted by the Ixodes scapularis tick. Approximately one-third of infected individuals develop arthritis of weight-bearing joints, though it is unclear why some patients develop arthritis and severe systemic disease while others do not. C57BL/6 (B6) mice are susceptible to Bb infection but do not develop arthritis, providing an in vivo model to evaluate mechanisms regulating development of Lyme arthritis. We demonstrate here that co-infection of B6 mice with the tick-borne pathogens Bb and Ehrlichia muris (Em) induced significant arthritis. Although co-infection did not impact bacterial burden or growth of either pathogen, the resultant Lyme arthritis in co-infected mice correlated with significant hematologic disturbances. Whereas single Bb infection elicited no overt hematologic changes, Em infection resulted in thrombocytopenia, lymphopenia, monocytosis, and granulocytosis, which was consistently observed in mice co-infected with both Bb and Em. Hematologic changes correlated with profound changes to the hematopoietic stem and progenitor cell (HSPC) populations in Em-infected mice. Most notable were dramatic reductions in populations of HSPCs committed to myeloid-biased differentiation. Co-infection resulted in persistent hematologic changes and bone marrow inflammation. Our data demonstrate for the first time that B6 mice, resistant to developing Lyme arthritis, exhibit severe joint pathology in the presence of a second pathogen, correlating with persistent emergency myelopoiesis. Our data support the conclusion that pathogen burden is not sufficient for disease and specific inflammatory signals and cells regulate the development of Lyme arthritis. ImportanceTick-borne illnesses, historically relegated to specific geographic areas, are increasing in prevalence and distribution. Borrelia burgdorferi causes Lyme disease, the most common tick-borne illness in North America, characterized by debilitating arthritis, carditis, and neurologic complications. It remains unclear why certain infected individuals develop severe disease while others are only mildly symptomatic. Human monocytic ehrlichiosis (HME) is another tick-borne disease that often results in profound illness and severe hematological disturbances. We show here that co-infection of B6 mice, resistant to Lyme arthritis, with Borrelia burgdorferi and Ehrlichia muris, used to model HME, results in the development of severe arthritis and emergency myelopoiesis. Our work suggests that immune activation driven by co-infection contributes to the development of Lyme arthritis.

immunology↗

bioRxiv2022

A synthetic communication system uncovers self-jamming of bacteriophage transmission

Understanding how delivery and exchange of genetic information by bacteriophages shapes bacterial populations is important for designing applications for phage therapy, biocontrol, and microbiome engineering. Here, we present a synthetic intercellular communication system that repurposes phage M13 for genetic exchange between Escherichia coli cells and build mathematical models of the communication behaviour. Our models, based on Chemical Reaction Networks, capture the growth burden, cell density, and growth phase dependence of phage secretion and infection kinetics and predict the stochasticity characterising phage-bacterial interactions at low numbers. In co-cultures of phage sender and receiver cells, resource sharing and selection pressure determine the choice of horizontal versus vertical phage transmission. Surprisingly, we discover that a phage-encoded immunity factor confers extracellular protection to uninfected bacteria, reducing infection rates by 70%. In a simulated gut environment, this novel "self-jamming" mechanism enables the phage to farm uninfected bacteria for future infections, increasing the overall success of both M13 and E. coli. The synthetic system developed here lays the groundwork for implementing population level controls in engineered bacterial communities, using phage signals for communication.

synthetic biology↗

bioRxiv2022

MobsPy: A Meta-Species Language for Chemical Reaction Networks

Chemical reaction networks are widely used to model biochemical systems. However, when the complexity of these systems increases, the chemical reaction networks are prone to errors in the initial modeling and subsequent updates of the model. We present the Meta-species-oriented Biochemical Systems Language (MobsPy), a language designed to simplify the definition of chemical reaction networks in Python. MobsPy is built around the notion of meta-species, which are sets of species that can be multiplied to create higher-dimensional orthogonal characteristics spaces and inheritance of reactions. Reactions can modify these characteristics. For reactants, queries allow to select a subset from a meta-species and use them in a reaction. For products, queries specify the dimensions in which a modification occurs. We demonstrate the simplification capabilities of the MobsPy language at the hand of a running example and a circuit from literature. The MobsPy Python package includes functions to perform both deterministic and stochastic simulations, as well as easily configurable plotting. The MobsPy package is indexed in the Python Package Index and can thus be installed via pip.

bioinformatics↗

bioRxiv2019

Thinking small: next-generation sensor networks close the size gap in vertebrate biologging

Recent advances in animal tracking technology have ushered in a new era in biologging. However, the considerable size of many sophisticated biologging devices restricts their application to larger animals, while old-fashioned techniques often still represent the state-of-the-art for studying small vertebrates. In industrial applications, low-power wireless sensor networks fulfill requirements similar to those needed to monitor animal behavior at high resolution and at low tag weight. We developed a wireless biologging network (WBN), which enables simultaneous direct proximity sensing, high-resolution tracking, and long-range remote data download at tag weights of one to two grams. Deployments to study wild bats created social networks and flight trajectories of unprecedented quality. Our developments highlight the vast capabilities of WBNs and their potential to close an important gap in biologging: fully automated tracking and proximity sensing of small animals, even in closed habitats, at high spatial and temporal resolution.

animal behavior and cognition↗