Biology subjects

Andrews, S. S.

Publications and source records attributed to Andrews, S. S..

10 recordsLinked to original sources

bioRxiv2026

Effects of dose-response relationships on cellular information transmission

Cell signaling systems can transmit information with high precision, despite relying on complex reaction networks and inherently noisy biochemical reactions. To better understand how they achieve this precision, this work focuses on how cells can tune their quantitative steady-state dose-response relationships to improve signaling fidelity. Using a combination of analytic and numerical approaches, we quantify the maximum information that can be transmitted in single-step and multi-step signaling systems under several biologically realistic noise distributions: Gaussian, gamma, constant coeffcient of variation, and binomial. We find that signaling systems transmit more information if their dose-response curves have low baselines, large dynamic ranges, and curve shapes that are well matched to the natural distribution of input signals. For multi-step systems, linear transfer functions typically improve signaling fidelity for all noise levels, consistent with previous work on "dose-response alignment". We also find that switchlike transfer functions transmit a maximum of one bit and are robust to low levels of noise, but fail rapidly with high levels of noise. Simulations of toy signaling systems corroborate these results. Together, these findings provide insight into some cellular design principles that have arisen through evolution and that are widely observed in nature.

systems biology↗

bioRxiv2026

Plasma membrane nanoscale dynamics of Arabidopsis leucine-rich repeat receptor kinase complexes

Plasma membrane-localized receptors operate as dynamic signaling complexes and integrative networks1-3, yet the spatial and temporal regulation of these interactions remain largely unknown. Here, by analyzing the components of a minimal Arabidopsis leucine-rich repeat receptor kinase network, we describe the differential diffusion and organization of receptor complex components and unveil the nanoscale spatial and temporal logic underlying the formation of receptor kinase complexes. The ligand-binding receptors FLS2 and BRI1, and the accessory receptor BIR3, are organized in plasma membrane nanodomains, within which the co-receptor BAK1 diffuses and is spatially arrested upon ligand perception. BAK1s spatial arrest relies on extracellular domain (ECD)-ECD interactions but does not require receptor complex activation. Mathematical modelling, single molecule imaging and bio-assays infer that accessory receptors maintain a dynamic pool of co-receptors in the vicinity of ligand-binding receptors to promote ligand-induced complex formation and signaling. We propose that ligand-induced receptor kinase complex formation is a deterministic process defined by the relative nanoscale spatial positioning of individual signaling and regulatory components.

cell biology↗

bioRxiv2024

The turbulent soundscape of intertidal oyster reefs

Turbulence and sound are important cues for oyster reef larval recruitment. Numerous studies have found a relationship between turbulence intensity and swimming behaviors of marine larvae, while others have documented the importance of sounds in enhancing larval recruitment to oyster reefs. However, the relationship between turbulence and the reef soundscape is not well understood. In this study we made side-by-side acoustic Doppler velocimeter turbulence measurements and hydrophone soundscape recordings over 2 intertidal oyster reefs (1 natural and 1 restored) and 1 adjacent bare mudflat as a reference. Sound pressure levels (SPL) were similar across all three sites, although SPL > 2000 Hz was highest at the restored reef, likely due to its larger area that contained a greater number of sound-producing organisms. Flow noise (FN), defined as the mean of pressure fluctuations recorded by the hydrophone at f < 100 Hz, was significantly related to mean flow speed, turbulent kinetic energy, and turbulence dissipation rate ({varepsilon}), agreeing with theoretical calculations for turbulence. Our results also show a similar relationship between {varepsilon} and FN to what has been previously reported for {varepsilon} vs. downward larval swimming velocity (wb), with both FN and wb demonstrating rapid growth at {varepsilon} > 0.1 cm2 s-3. These results suggest that reef turbulence and sounds may attract oyster larvae in complementary and synergistic ways.

biophysics↗

bioRxiv2024

Signal integration and integral feedback control with biochemical reaction networks

Biochemical reaction networks perform a variety of signal processing functions, one of which is computing the integrals of signal values. This is often used in integral feedback control, where it enables a systems output to respond to changing inputs, but to then return exactly back to some pre-determined setpoint value afterward. To gain a deeper understanding of how biochemical networks are able to both integrate signals and perform integral feedback control, we investigated these abilities for several simple reaction networks. We found imperfect overlap between these categories, with some networks able to perform both tasks, some able to perform integration but not integral feedback control, and some the other way around. Nevertheless, networks that could either integrate or perform integral feedback control shared key elements. In particular, they included a chemical species that was neutrally stable in the open loop system (no feedback), meaning that this species does not have a unique stable steady-state concentration. Neutral stability could arise from zeroth order decay reactions, binding to a partner that was produced at a constant rate (which occurs in antithetic control), or through a long chain of covalent cycles. Mathematically, it arose from rate equations for the reaction network that were underdetermined when evaluated at steady-state.

systems biology↗

bioRxiv2020

Python Inferfaces for the Smoldyn Simulator

MotivationSmoldyn is a particle-based biochemical simulator that is frequently used for systems biology and biophysics research. Previously, users could only define models using text-based input or a C/C++ applicaton programming interface (API), which were convenient, but limited extensibility. ResultsWe added a Python API to Smoldyn to improve integration with other software tools such as Jupyter notebooks, other Python code libraries, and other simulators. It includes low-level functions that closely mimic the existing C/C++ API and higher-level functions that are more convenient to use. These latter functions follow modern object-oriented Python conventions. AvailabilitySmoldyn is open source and free, available at http://www.smoldyn.org, and can be installed with the Python package manager pip. It runs on Mac, Windows, and Linux. Contactsteven.s.andrews@gmail.com Supplementary informationDocumentation is available at http://www.smoldyn.org/SmoldynManual.pdf and https://smoldyn.readthedocs.io/en/latest/python/api.html.

bioinformatics↗

bioRxiv2020

Individual yeast cells signal at different levels but each with good precision

Different isogenic cells have been shown to exhibit widely varying responses to the same extracellular signal. Based on the assumption that this variation arises from noise in the signaling pathways that cells use to transmit information from surface to nucleus, recent publications asserted that single cells cannot detect their surroundings accurately. Here, we analyze existing data on gene expression induced by the Saccharomyces cerevisiae pheromone response system, finding that individual cells signal consistently over time, implying that response variation arises primarily from stable cell-to-cell differences rather than signaling noise. Individual cells transmit at least 2.7 bits of information through the pheromone response system, enabling each cell to distinguish between at least 6 pheromone concentrations. In principle, cells can gain further precision by internally referencing these responses with measurements of constitutively expressed genes. Combination with prior results shows that only about 6% of total response variation arises from signaling pathway noise. One-sentence summarySingle yeast cells signal consistently over time, indicating that their signaling pathways transmit information precisely.

systems biology↗

bioRxiv2019

Effects of surfaces and macromolecular crowding on bimolecular reaction rates

Biological cells are complex environments that are densely packed with macromolecules and subdivided by membranes, both of which affect the rates of chemical reactions. It is well known that crowding reduces the volume available to reactants, which increases reaction rates, and also inhibits reactant diffusion, which decreases reaction rates. This work investigates these effects quantitatively using analytical theory and particle-based simulations. A reaction rate equation based on only these two processes turned out to be inconsistent with simulation results. However, accounting for diffusion inhibition by the surfaces of nearby obstacles, which affects access to reactants, led to perfect agreement for reactions near impermeable planar membranes and improved agreement for reactions in crowded spaces. A separate model that quantified reactant occlusion by crowders, and extensions to a thermodynamic "cavity" model proposed by Berezhkovskii and Szabo (J. Phys. Chem. B 120:5998, 2016), were comparably successful. These results help elucidate reaction dynamics in confined spaces and improve prediction of in vivo reaction rates from in vitro ones.

biophysics↗

bioRxiv2018

High resolution protein-protein interaction mapping using all-versus-all sequencing (AVA-seq)

Two-hybrid systems test for protein-protein interactions and can provide important information for genes with unknown function. Despite their success, two-hybrid systems have remained mostly untouched by improvements from next-generation DNA sequencing. Here we present a method for all-versus-all protein interaction mapping (AVA-seq) that utilizes next-generation sequencing to remove multiple bottlenecks of the two-hybrid process. The method allows for high resolution protein-protein interaction mapping of a small set of proteins, or the potential for lower-resolution mapping of entire proteomes. Features of the system include open-reading frame selection to improve efficiency, high bacterial transformation efficiency, a convergent fusion vector to allow paired-end sequencing of interactors, and the use of protein fragments rather than full-length genes to better resolve specific protein contact points. We demonstrate the systems strengths and limitations on a set of proteins known to interact in humans and provide a framework for future large-scale projects.

genomics↗

bioRxiv2018

Silencing of ANKRD12 circRNA induces molecular and functional changes associated with invasive phenotypes

Circular RNAs (circRNA) that form through non-canonical backsplicing events of pre-mRNA transcripts are evolutionarily conserved and abundantly expressed across species. However, the functional relevance of circRNAs remains a topic of debate. In this study, we identified and characterized a circular RNA derived from Exon 2 and Exon 8 of the ANKRD12 gene, termed here as circANKRD12. We show that this circRNA is abundantly expressed in breast and ovarian cancers. The circANKRD12 is RNase R resistant and predominantly localized in the cytoplasm in contrast to its source gene mRNA. We confirmed the expression of this circRNA across a variety of cancer cell lines and provide evidence for its functional relevance through downstream regulation of several tumor invasion genes. We show that silencing of circANKRD12 induces a phenotypic change by significantly regulating cell cycle, increasing invasion and migration, altering the metabolism in cancer cells. These results reveal the functional significance of circANKRD12 and provide evidence of a regulatory role for this circRNA in cancer progression.

cancer biology↗

bioRxiv2017

Rule-based modeling using wildcards

Many biological molecules exist in multiple variants, such as proteins with different post-translational modifications, DNAs with different sequences, and phospholipids with different chain lengths. Representing these variants as distinct species, as most biochemical simulators do, leads to the problem that the number of species, and chemical reactions that interconvert them, typically increase combinatorially with the number of ways that the molecules can vary. This can be alleviated by \"rule-based modeling methods,\" in which software generates the chemical reaction network from relatively simple \"rules.\" This article presents a new approach to rule-based modeling. It is based on wildcards that match to species names, much as wildcards can match to file names in computer operating systems. It is much simpler to use than the formal rule-based modeling approaches developed previously but can also lead to unintended consequences if not used carefully. This article demonstrates rule-based modeling with wildcards through examples for: signaling systems, protein complexation, polymerization, nucleic acid sequence copying and mutation, the \"SMILES\" chemical notation, and others. The method is implemented in Smoldyn, a spatial and stochastic biochemical simulator, for both the generate-first and on-the-fly expansion, meaning whether the reaction network is generated before or during the simulation.

systems biology↗