Stochastic simulations underpin computational research in fields from systems biology and epidemiology to finance and physics-informed machine learning, yet their reproducibility is hard to quantify because each run yields different outcomes. Existing practices to improve computational reproducibility focus on sharing code, simulation seeds, data, and software environments but do not ensure independent reproduction of results, limiting scientific rigor. Here we introduce the Empirical Characteristic Function Equality Convergence Test (EFECT), a universal computational framework for quantifying the statistical reproducibility of stochastic simulations based on empirical characteristic functions. EFECT defines a normalized EFECT error that measures distributional differences between two sets of simulation outputs over a model- and scale-independent range, and an EFECT convergence point that specifies the minimum sample size required to achieve a desired reproducibility threshold at a chosen significance level. EFECT applies to any simulation whose results can be represented as bounded, real-valued data, regardless of modeling formalism or source of stochasticity. To facilitate widespread adoption and data exchange, we implemented EFECT in an open-source software library and a compact, machine-readable standardized report. We rigorously evaluated the framework across more than 40 test cases spanning stochastic differential equations, agent-based models, Boolean networks, and partial differential equations. Applying EFECT to published models in pandemic epidemiology, financial stochastic volatility, and physics-informed neural networks reveals that commonly used sample sizes are often underpowered: substantial parameter differences can go undetected unless thousands of replicates are simulated. By providing a domain-agnostic statistical reproducibility metric, reusable software libraries, and a standardized report format, EFECT offers a practical framework for embedding quantitative reproducibility and replicability checks into stochastic simulation workflows across data-intensive disciplines. Thus, EFECT delivers the capability to make scientific inferences and policy decisions on the basis of reliable, reproducible stochastic simulations.

Abstract In early intestinal carcinogenesis, adenoma formation is commonly initiated by loss-of-function mutations in a tumor suppressor that lead to oncoprotein gain-of-function, like in the tumor suppressor–oncoprotein pair APC–β-catenin. Small intestinal organoids provide an in vitro system to study consequences of such mutations on tissue organization. However, conventional genetic manipulations do not allow precise control over the onset and duration of oncoprotein activity to study their influence on tissue transformation. Furthermore, homogenous tissues of clonal genetic models do not readily capture cellular interaction among mutated and neighboring wildtype tissue during early transformation. To mimic oncogenic activation of β-catenin, we established a chemical-(opto)genetic approach to gain bio-orthogonal acute, spatial and temporal control over β-catenin oncoprotein stability and relate oncoprotein levels to morphological development of mouse small intestinal organoids. We identified aberrant phenotypes that result from bio-orthogonally induced oncoprotein activity but persist even after oncoprotein depletion. Furthermore, local activation of oncomimetic β-catenin activity within the stem cell niche leads to aberrant differentiation during homeorhesis and homeostasis, recapitulating early events of tissue transformation.

Obsolescence of proprietary systems can render valuable scientific instruments useless. Here we refurbished and enhanced a spectrofluorometer, enabling full optical characterization of upconversion nanoparticles.

David Fabricius, a Reformed pastor in Ostfriesland, was highly regarded by Kepler as an exceptional observer, second only to Tycho Brahe. From 1596 to 1609, Fabricius engaged in extensive correspondence, exchanging numerous letters with Brahe and subsequently with Kepler. These communications also provided values for direct observations on meridian altitudes of planets and stars, as well as elongations between a planet and a star or between two stars. We provide a detailed summary of Fabricius’s observations and compare them with the prediction of twenty-first-century models. The analysis indicates that under specific conditions, his observations exhibit sub-arcminute deviations in relation to those calculated from modern theories. Our findings preliminarily indicate that Fabricius’ astronomical observational abilities were comparable to, an occasionally superior to, those of Brahe himself. We provide machine-readable tables of his observations.

As monitoring multiple signals becomes more cost-effective, combining them through a data fusion-aware denoising method can produce a more robust estimation of the underlying process. Here, we present a method based on the Haar wavelet transform that trades off resolution against accuracy based on statistical significance. By taking advantage of correlations between channels, it offers a superior performance compared to denoising each channel separately. It outperforms standard wavelet methods when the magnitude of interest in the data-fusion process involves a non-linear transformation or reduction of a multichannel signal. We demonstrate its efficacy by benchmarking our method against standard wavelet thresholding for synthetic single and multichannel time series, and a multichannel two-dimensional image. The method has a simple interpretation as an adaptive binning of the signal, and neither requires training data nor specialized hardware to run fast. In addition, a reference Python implementation is available on GitHub and PyPI, making it simple to integrate into any analysis pipeline.

In September 2023, the two largest bioimaging networks in the Americas, Latin America Bioimaging (LABI) and BioImaging North America (BINA), came together during a 1-week meeting in Mexico. This meeting provided opportunities for participants to interact closely with decision-makers from imaging core facilities across the Americas. The meeting was held in a hybrid format and attended in-person by imaging scientists from across the Americas, including Canada, the United States, Mexico, Colombia, Peru, Argentina, Chile, Brazil and Uruguay. The aims of the meeting were to discuss progress achieved over the past year, to foster networking and collaborative efforts among members of both communities, to bring together key members of the international imaging community to promote the exchange of experience and expertise, to engage with industry partners, and to establish future directions within each individual network, as well as common goals. This meeting report summarises the discussions exchanged, the achievements shared, and the goals set during the LABIxBINA2023: Bioimaging across the Americas meeting.

Alamandine (ALA) exerts protective effects similar to angiotensin (Ang) (1-7) through Mas-related G protein-coupled receptor type D receptor (MrgDR) activation, distinct from Mas receptor (MasR). ALA induces anti-inflammatory effects in mice but its impact in human macrophages remains unclear. We aimed to investigate the anti-inflammatory effects of ALA in human macrophages. Interleukin (IL)-6 and IL-1β were measured by ELISA in human THP-1 macrophages and human monocyte-derived macrophages exposed to lipopolysaccharide (LPS). Consequences of MasR-MrgDR heteromerization were investigated in transfected HEK293T cells. ALA decreased IL-6 and IL-1β secretion in LPS-activated THP-1 macrophages. The ALA-induced decrease in IL-6 but not in IL-1β was prevented by MasR blockade and MasR downregulation, suggesting MasR-MrgDR interaction. In human monocyte-derived M1 macrophages, ALA decreased IL-1β secretion independently of MasR. MasR-MrgDR interaction was confirmed in THP-1 macrophages, human monocyte-derived macrophages, and transfected HEK293T cells. MasR and MrgDR formed a constitutive heteromer that was not influenced by ALA. ALA promoted Akt and ERK1/2 activation only in cells expressing MasR-MrgDR heteromers, and this effect was prevented by MasR blockade. While Ang-(1-7) reduced cellular proliferation in MasR -but not MrgDR- expressing cells, ALA antiproliferative effect was elicited in cells expressing MasR-MrgDR heteromers. ALA also induced an antiproliferative response in THP-1 cells and this effect was abolished by MasR blockade, reinforcing MasR-MrgDR interaction. MasR-MrgDR heteromerization is crucial for ALA-induced anti-inflammatory and antiproliferative responses in human macrophages. This study broaden our knowledge of the protective axis of the RAS, thus enabling novel therapeutic approaches in inflammatory-associated diseases.

MOTIVATION:Chemical reaction networks (CRNs) play a pivotal role in diverse fields such as systems biology, biochemistry, chemical engineering, and epidemiology. High-level definitions of CRNs enables to use various simulation approaches, including deterministic and stochastic methods, from the same model. However, existing Python tools for simulation of CRN typically wrap external C/C++ libraries for model definition, translation into equations and/or numerically solving them, limiting their extensibility and integration with the broader Python ecosystem. RESULTS:In response, we developed Poincaré and SimBio, two novel Python packages for simulation of dynamical systems and CRNs. Poincaré serves as a foundation for dynamical systems modeling, while SimBio extends this functionality to CRNs, including support for the Systems Biology Markup Language (SBML). Poincaré and SimBio are developed as pure Python packages enabling users to easily extend their simulation capabilities by writing new or leveraging other Python packages. Moreover, this does not compromise the performance, as code can be just-in-time compiled with Numba. Our benchmark tests using curated models from the BioModels repository demonstrate that these tools may provide a potentially superior performance advantage compared to other existing tools. In addition, to ensure a user-friendly experience, our packages use standard typed modern Python syntax that provides a seamless integration with integrated development environments. Our Python-centric approach significantly enhances code analysis, error detection, and refactoring capabilities, positioning Poincaré and SimBio as valuable tools for the modeling community. AVAILABILITY AND IMPLEMENTATION:Poincaré and SimBio are released under the MIT license. Their source code is available on GitHub (https://github.com/maurosilber/poincare and https://github.com/hgrecco/simbio) and can be installed from PyPI or conda-forge.

Viral factories of liquid-like nature host transcription and replication in most viruses. The syncytial respiratory virus factories include gene function proteins, brought together by the phosphoprotein (P) RNA polymerase cofactor, present across non-segmented negative stranded RNA viruses. Homotypic liquid-liquid phase separation of RSV-P is governed by an α-helical molten globule domain, and strongly self-downmodulated by adjacent sequences. Condensation of P with the nucleoprotein N is stoichiometrically tuned, defining aggregate-droplet and droplet-dissolution boundaries. Time course analysis show small N-P nuclei gradually coalescing into large granules in transfected cells. This behavior is recapitulated in infection, with small puncta evolving to large viral factories, strongly suggesting that P-N nucleation-condensation sequentially drives viral factories. Thus, the tendency of P to undergo phase separation is moderate and latent in the full-length protein but unleashed in the presence of N or when neighboring disordered sequences are deleted. This, together with its capacity to rescue nucleoprotein-RNA aggregates suggests a role as a “solvent-protein” and possible fluidity tuner of viral factories.

Data-driven science requires reliable data generation, and in modern instrumentation software plays a central role to achieve this goal.Computer-controlled experiments allow for complex synchronization of sensors and actuators.Moreover, as it was already recognized decades ago, they enable on-line analysis routines to steer the experiment in real time (Enke, 1982).When combined with programming best practices, instrumentation software fosters reproducible, traceable, and open science.

Background estimation is the first step in quantitative analysis of images. It has an impact on all subsequent analyses, in particular for segmentation and calculation of ratiometric quantities. Most methods recover only a single value such as the median or yield a biased estimation in non-trivial cases. We introduce, to our knowledge, the first method to recover an unbiased estimation of background distribution. It leverages the lack of local spatial correlation in background pixels to robustly select a subset that accurately represents the background. The resulting background distribution can be used to test for foreground membership of individual pixels or estimate confidence intervals in derived quantities.

Significance Estimating neighboring cells by using only nuclear markers is crucial in many biological applications. Although several strategies have been used for this purpose, most published methods lack a rigorous characterization of their efficiencies. Remarkably, previously described methods are not automatic and depend only on cell-cell distance, neglecting the importance of pair-neighborhood interaction. Aim To develop a robust and automatic method for assessing cell local neighborhood, while analyzing the impact of the physical variables involved in this task. Approach We inferred neighbors from images with nuclei labeling by approximating the cell-cell interaction graph by the Delaunay triangulation of nuclei centroids. Each edge of this graph was filtered by thresholding in cell-cell distance and the maximum angle that each pair subtends with shared neighbors (pair-neighborhood interaction). Thresholds were calculated by maximizing a new robust statistic that measures the communicability efficiency of the cell graph. Using a variety of images of diverse tissues with additional membrane labeling to find the ground truth, we characterized the assessment performance. Results On average, our method detected 95% of true neighbors, with only 6% of false discoveries. Even though our method’s performance and tissue regularity are correlated, it works with performance metrics over 86% in very different organisms, including Drosophila melanogaster , Tribolium castaneum , Arabidopsis thaliana and C. elegans . Conclusions We automatically estimated neighboring relationships between cells in 2D and 3D using only nuclear markers. To achieve this goal, we filtered the Delaunay triangulation of nuclei centroids with a new measure of graph communicability efficiency. In addition, we found that taking pair-neighborhood interactions into account, in contrast to considering only cell-cell distances, leads to significant performance improvements. This becomes more notorious when the number of cells is low or the geometry of the cell graph is highly complex.

Smaug is a conserved translational regulator that binds numerous mRNAs, including nuclear transcripts that encode mitochondrial enzymes. Smaug orthologs form cytosolic membrane-less organelles (MLOs) in several organisms and cell types. We have performed single-molecule fluorescence in situ hybridization (FISH) assays that revealed that SDHB and UQCRC1 mRNAs associate with Smaug1 bodies in U2OS cells. Loss of function of Smaug1 and Smaug2 (also known as SAMD4A and SAMD4B, respectively) affected both mitochondrial respiration and morphology of the mitochondrial network. Phenotype rescue by Smaug1 transfection depends on the presence of its RNA-binding domain. Moreover, we identified specific Smaug1 domains involved in MLO formation, and found that impaired Smaug1 MLO condensation correlates with mitochondrial defects. Mitochondrial complex I inhibition upon exposure to rotenone, but not strong mitochondrial uncoupling upon exposure to CCCP, rapidly induced the dissolution of Smaug1 MLOs. Metformin and rapamycin elicited similar effects, which were blocked by pharmacological inhibition of AMP-activated protein kinase (AMPK). Finally, we found that Smaug1 MLO dissolution weakens the interaction with target mRNAs, thus enabling their release. We propose that mitochondrial respiration and the AMPK-mTOR balance controls the condensation and dissolution of Smaug1 MLOs, thus regulating nuclear mRNAs that encode key mitochondrial proteins. This article has an associated First Person interview with the first authors of the paper.

In recent years, Fourier Ptychographic Microscopy (FPM) has been demonstrated for a wide variety of applications including wide field of view pathology and quantitative phase imaging. Briefly, this technique produces a high resolution reconstruction by computationally combining a set of diversely acquired captures of the same sample. However, the nonlinear aspects of the algorithm and the noise in a typical dataset affect the reconstruction quality. The ability to weight differentially each image within an entire FPM capture would allow to device an optimal design of an acquisition platform and to potentially avoid such reconstruction problems. Currently there is no method to calculate the information content of each capture and therefore to weight it accordingly. In this work, we propose such a method based on the concept of Fisher Information, not available until now for FPM acquisitions. This criterion depends on the selection of a sample model and the parameters to be measured in an FPM experiment. We show the application of our technique to two common problems: volume quantification using phase images and distance determination between two particles, both revealing interesting internal aspects of Ptychography. The approach used here could lead to an improvement of the speed in the acquisition and reconstruction process by making a more efficient selection of the illumination angles.

Understanding signal propagation across biological networks requires to simultaneously monitor the dynamics of several nodes to uncover correlations masked by inherent intercellular variability. To monitor the enzymatic activity of more than two components over short time scales has proven challenging. Exploiting the narrow spectral width of homo-FRET-based biosensors, up to three activities can be imaged through fluorescence polarization anisotropy microscopy. We introduce Caspase Activity Sensor by Polarization Anisotropy Multiplexing (CASPAM) a single-plasmid triple-modality reporter of key nodes of the apoptotic network. Apoptosis provides an ideal molecular framework to study interactions between its three composing pathways (intrinsic, extrinsic, and effector). We characterized the biosensor performance and demonstrated the advantages that equimolar expression has in both simplifying experimental procedure and reducing observable variation, thus enabling robust data-driven modeling. Tools like CASPAM become essential to analyze molecular pathways where multiple nodes need to be simultaneously monitored.

Apoptosis is a mechanism of programmed cell death in which cells engage in a controlled demolition and prepare to be digested without damaging their environment. In normal conditions, apoptosis is repressed until it is irreversibly induced by an appropriate signal. In adult organisms, apoptosis is a natural way to dispose of damaged cells and its disruption or excess is associated with cancer and autoimmune diseases. Apoptosis is regulated by a complex signaling network controlled by caspases, specialized enzymes that digest essential cellular components and promote the degradation of genomic DNA. In this work, we propose an effective description of the signaling network focused on caspase-3 as a readout of cell fate. We integrate intermediate network interactions into a nonlinear feedback function acting on caspase-3 and introduce the effect of pro-apoptotic stimuli and regulatory elements as a saturating activation function. We show that activation dynamics in the theory is similar to previously reported experimental results. We compute bifurcation diagrams and obtain cell fate maps describing how stimulus intensity and feedback strength affect cell survival and death fates. These fates overlap within a bistable region that depends on total caspase concentration, regulatory elements, and feedback nonlinearity. We study a strongly nonlinear regime to obtain analytical expressions for bifurcation curves and fate map boundaries. For a broad range of parameters, strong stimuli can induce an irreversible switch to the death fate. We use the theory to explore dynamical stimulation conditions and determine how cell fate depends on stimulation temporal patterns. This analysis predicts a critical relation between transient stimuli intensity and duration to trigger irreversible apoptosis. We derive an analytical expression for this critical relation, valid for short stimuli. Our description provides distinct predictions and offers a framework to study how this signaling network processes different stimuli to make a cell fate decision.

Gastrulation is a key event in animal embryogenesis during which germ layer precursors are rearranged and the embryonic axes are established. Cell polarization is essential during gastrulation, driving asymmetric cell division, cell movements, and cell shape changes. The furry ( fry ) gene encodes an evolutionarily conserved protein with a wide variety of cellular functions, including cell polarization and morphogenesis in invertebrates. However, little is known about its function in vertebrate development. Here, we show that in Xenopus, Fry plays a role in morphogenetic processes during gastrulation, in addition to its previously described function in the regulation of dorsal mesoderm gene expression. Using morpholino knock-down, we demonstrate a distinct role for Fry in blastopore closure and dorsal axis elongation. Loss of Fry function drastically affects the movement and morphological polarization of cells during gastrulation and disrupts dorsal mesoderm convergent extension, responsible for head-to-tail elongation. Finally, we evaluate a functional interaction between Fry and NDR1 kinase, providing evidence of an evolutionarily conserved complex required for morphogenesis.

Apoptosis, a form of programmed cell death central to all multicellular organisms, plays a key role during organism development and is often misregulated in cancer. Devising a single model applicable to distinct stimuli and conditions has been limited by lack of robust observables. Indeed, previous numerical models have been tailored to fit experimental datasets in restricted scenarios, failing to predict response to different stimuli. We quantified the activity of three caspases simultaneously upon intrinsic or extrinsic stimulation to assemble a comprehensive dataset. We measured and modeled the time between maximum activity of intrinsic, extrinsic and effector caspases, a robust observable of network dynamics, to create the first integrated Apoptotic Reaction Model (ARM). Observing how effector caspases reach maximum activity first irrespective of stimuli used, led us to identify and incorporate a missing feedback into a successful model for extrinsic stimulation. By simulating different recently performed experiments, we corroborated that ARM adequately describes them. This integrated model provides further insight into the indispensable feedback from effector caspase to initiator caspases.

Fluorescence imaging under cryo-arrest allows resolution of dynamic signaling system features that are otherwise obscured.