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bioRxiv2021

Interactive, visual simulation of a spatio-temporal model of gas exchange in the human alveolus

In interdisciplinary fields such as systems biology, close collaboration between experimentalists and theorists is crucial for the success of a project. Theoretical modeling in physiology usually describes complex systems with many interdependencies. On one hand, these models have to be grounded on experimental data. On the other hand, experimenters must be able to penetrate the model in its dependencies in order to correctly interpret the results in the physiological context. When theorists and experimenters collaborate, communicating results and ideas is sometimes challenging. We promote interactive, visual simulations as an engaging way to communicate theoretical models in physiology and to thereby advance our understanding of the process of interest. We defined a new spatio-temporal model for gas exchange in the human alveolus and implemented it in an interactive simulation software named Alvin. In Alvin, the course of the simulation can be traced in a three-dimensional rendering of an alveolus and dynamic plots. The user can interact by configuring essential model parameters. Alvin allows to run and compare multiple simulation instances simultaneously. The mathematical model was developed with the aim of visualization and the simulation software was engineered based on a requirements analysis. Our work resulted in an integrative gas exchange model and an interactive application that exceed the current standards. We exemplified the use of Alvin for research by identifying unknown dependencies in published experimental data. Employing a detailed questionnaire, we showed the benefits of Alvin for education. We postulate that interactive, visual simulation of theoretical models, as we have implemented with Alvin on respiratory processes in the alveolus, can be of great help for communication between specialists and thereby advancing research.

bioinformatics↗

bioRxiv2022

Programmable eukaryotic protein expression with RNA sensors

The diversity of cell types and states can be scalably measured and defined by expressed RNA transcripts. However, approaches to programmably sense and respond to the presence of specific RNAs within living biological systems with high sensitivity are lacking. RNA sensors that gate expression of reporter or cargo genes would have diverse applications for basic biology, diagnostics and therapeutics by enabling cell-state specific control of transgene expression. Here, we engineer a novel programmable RNA-sensing technology, Reprogrammable ADAR Sensors (RADARS), which leverages RNA editing by adenosine deaminases acting on RNA (ADAR) to gate translation of a protein payload on the presence of endogenous RNA transcripts. In mammalian cells, we engineer RADARS with diverse payloads, including luciferase and fluorescent proteins, with up to 164-fold activation and quantitative detection in the presence of target RNAs. We show RADARS are functional either expressed from DNA or as synthetic mRNA. Importantly, RADARS can function with endogenous cellular ADAR. We apply RADARS to multiple contexts, including RNA-sensing induced cell death via caspases, cell type identification, and in vivo control of synthetic mRNA translation, demonstrating RADARS as a tool with significant potential for gene and cell therapy, synthetic biology, and biomedical research. One Sentence SummaryA new technology utilizing ADAR mediated RNA-editing enables robust reprogrammable protein expression based on target RNA transcripts in mammalian cells, leading to broad applications in basic science research, cell engineering, and gene therapy.

genomics↗

bioRxiv2023

Acetone-Butanol-Ethanol (ABE) fermentation with Clostridial Co-cultures for Enhanced Biobutanol Production

This study investigates acetone-butanol-ethanol (ABE) fermentation, a process using solventogenic Clostridium species to produce acetone, butanol, and ethanol. Recent biotechnological advancements, such as omics, systems biology, and metabolic engineering, have reignited interest in butanol production, responding to the increasing gasoline costs and the demand for sustainable energy systems. This study unravels the distinct physiological attributes of C. acetobutylicum (Cac) and C. pasteurianum (Cpa), significantly impacting sustainable bioenergy technologies. Employing response surface methodology (RSM), we embarked on a comprehensive statistical optimization journey in the co-culture system, Cac MTCC 11274 and Cpa MTCC 116, enhancing biobutanol production from mixed substrates. A spectrum of process parameters was scrutinized, encompassing the ratio of Cac and Cpa inoculum, sodium concentration, and the ratio of xylose to glucose. Statistical analysis revealed salt concentrations profound influence on biomass, total alcohol, and butanol production. The culmination of these endeavors yielded highly promising outcomes: a butanol concentration of 12.1 {+/-} 0.45 g L-1 (model prediction: 11.87 g L-1), biomass of 4.15 {+/-} 0.03 (model prediction: 4.06 OD600), and ABE concentration of 23.1 {+/-} 0.55 g L-1 (model prediction: 22.45 g L-1). These results represent a significant leap forward in bioenergy technologies, offering both practical insights and sustainable solutions for enhanced biofuel production.

microbiology↗

bioRxiv2024

Interpretable Multi-Omics Data Integration with Deep Archetypal Analysis

High-throughput multi-omic molecular profiling allows probing biological systems at unprecedented resolution. However, the integration and interpretation of high-dimensional, sparse, and noisy multimodal datasets remains challenging. Deriving new biology using current methods is particularly difficult because they are not based on biological principles, but instead focus exclusively on a dimensionality reduction task. Here we introduce MIDAA (Multiomic Integration with Deep Archetypal Analysis), a framework that combines archetypal analysis, an approach grounded in biological principles, with deep learning. Using the concept of archetypes that are based on evolutionary trade-offs and Pareto optimality - MIDAA finds extreme data points that define the geometry of the latent space, preserving the complexity of biological interactions while retaining an interpretable output. We demonstrate that indeed these extreme points represent cellular programmes reflecting the underlying biology. We show on real and simulated multi-omics data how MIDAA outperforms state-of-the-art methods in identifying parsimonious, interpretable, and biologically relevant patterns.

bioinformatics↗

bioRxiv2025

Post-Inhibitory Rebound by δ-Cells Transforms Inhibition into Excitationand Redefines Islet Plasticity.

The coordination of hormone secretion within the pancreatic islet remains incompletely understood. Here we identify post-inhibitory rebound (PIR), a transient excitatory overshoot of glucoregulatory hormones following inhibitory signaling, as a fundamental and previously unrecognized feature of islet physiology. Human islets from donors with type 2 diabetes displayed concomitantly elevated insulin and somatostatin secretion with tightly correlated dynamics, a pattern reproduced in islets from high-fat-diet-fed mice. Using a combination of cell-type-specific chemogenetic and optogenetic approaches, high-resolution triple-hormone perifusion, Ca{superscript 2} imaging, in vivo islet grafts, and physiological ligands, we demonstrate that transient {delta}-cell activation or inhibition consistently triggers rebound excitation of {beta}- and -cells. These responses generate glucose-dependent, synchronized oscillations of insulin and glucagon secretion that are partially mediated by somatostatin receptor signaling. Mechanistic insights obtained in mouse islets were recapitulated using translatable approaches in human islets, establishing the relevance of this dynamic signaling framework across species. In vivo, selective {delta}-cell modulation bidirectionally regulated systemic glucose tolerance, confirming {delta}-cell control over endocrine output. These findings support a model in which somatostatin signaling encodes hormone output through temporal dynamics rather than tonic inhibition. By reframing somatostatin as a dynamic regulator of excitability, we identify {delta}-cell-driven rebound as a mechanism that converts inhibition into coordinated endocrine output. Together, this work establishes a revised framework for intra-islet paracrine crosstalk in which temporal dynamics, rather than static inhibition, govern endocrine coordination. Significance StatementHormone secretion in the pancreatic islet has long been explained by inhibitory feedback loops, with somatostatin viewed as a static suppressor of insulin and glucagon release. Our study overturns this paradigm by identifying post-inhibitory rebound (PIR) as a dynamic property of intra-islet signaling. {delta}-cell-driven PIR synchronizes - and {beta}-cell activity, enabling adaptive, glucose-dependent hormone release and revealing that somatostatin confers system plasticity when needed. This work establishes a revised model of intra-islet paracrine crosstalk in which {delta}-cell-driven rebound dynamics, rather than static inhibition, determine endocrine coordination and system plasticity. Our discovery reframes inhibition as a rhythmic and regenerative force, positioning PIR as a potentially generalizable mechanism of excitability across biological systems. HighlightsO_LI{delta}-cells convert inhibition into excitation. Transient somatostatin signals trigger post-inhibitory rebound (PIR) in {beta}- and -cells, generating glucose-dependent synchronized hormone oscillations. C_LIO_LIProvides a mechanistic explanation for a long-standing paradox in human type 2 diabetes. Elevated basal insulin and somatostatin secretion are positively correlated; PIR explains this co-hypersecretion. C_LIO_LIAsymmetric {delta}-cell control confers islet plasticity. Stronger inhibition of -cells versus {beta}-cells enables balanced insulin/glucagon output during feeding or glucagon-skewed surges for hypoglycemia protection. C_LIO_LIMulti-scale validation across species. Chemogenetics, optogenetics, native ligands, Ca{superscript 2} imaging, in vivo grafts, and a data-informed phenomenological model demonstrate conservation from mouse to human islets. C_LIO_LIInhibition is instructive. A revised model of intra-islet paracrine crosstalk in which cell-type-specific temporal dynamics encode hormone output through rebound. C_LI Graphical AbstractSomatostatin, long considered a static inhibitor of growth and secretion, instead acts as a dynamic modulator that transforms inhibition into robust, context-dependent excitation. In the islet, amplified SST pulses drive rebound excitation to promote balanced insulin and glucagon output during anabolic demand, while dampening produces glucagon-skewed surges for protection against hypoglycemia. Inhibition in the islet is not merely suppressive but instructive, encoding future hormone output through rebound dynamics. The same pulse-rebound principle may operate in SST-rich systems such as the brain, gut, and tumors, where dynamic modulation of SST signaling confers adaptability and robustness rather than tonic suppression. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/687745v2_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@1a88336org.highwire.dtl.DTLVardef@3e618aorg.highwire.dtl.DTLVardef@175d94forg.highwire.dtl.DTLVardef@decf4_HPS_FORMAT_FIGEXP M_FIG C_FIG

physiology↗

bioRxiv2019

Integration of Structured Biological Data Sources using Biological Expression Language

BackgroundThe integration of heterogeneous, multiscale, and multimodal knowledge and data has become a common prerequisite for joint analysis to unravel the mechanisms and aetiologies of complex diseases. Because of its unique ability to capture this variety, Biological Expression Language (BEL) is well suited to be further used as a platform for semantic integration and harmonization in networks and systems biology.\n\nResultsWe have developed numerous independent packages capable of downloading, structuring, and serializing various biological data sources to BEL. Each Bio2BEL package is implemented in the Python programming language and distributed through GitHub (https://github.com/bio2bel) and PyPI.\n\nConclusionsThe philosophy of Bio2BEL encourages reproducibility, accessibility, and democratization of biological databases. We present several applications of Bio2BEL packages including their ability to support the curation of pathway mappings, integration of pathway databases, and machine learning applications.\n\nTweetA suite of independent Python packages for downloading, parsing, warehousing, and converting multi-modal and multi-scale biological databases to Biological Expression Language

bioinformatics↗

bioRxiv2019

The Degree Distribution of Human Brain Functional Connectivity is Generalized Pareto: A Multi-Scale Analysis

Are degree distributions of human brain functional connectivity networks heavy-tailed? Initial claims based on least-square fitting suggested that brain functional connectivity networks obey power law scaling in their degree distributions. This interpretation has been challenged on methodological grounds. Subsequently, estimators based on maximum-likelihood and non-parametric tests involving surrogate data have been proposed. No clear consensus has emerged as results especially depended on data resolution. To identify the underlying topological distribution of brain functional connectivity calls for a closer examination of the relationship between resolution and statistics of model fitting. In this study, we analyze high-resolution functional magnetic resonance imaging (fMRI) data from the Human Connectome Project to assess its degree distribution across resolutions. We consider resolutions from one thousand to eighty thousand regions of interest (ROIs) and test whether they follow a heavy or short-tailed distribution. We analyze power law, exponential, truncated power law, log-normal, Weibull and generalized Pareto probability distributions. Notably, the Generalized Pareto distribution is of particular interest since it interpolates between heavy-tailed and short-tailed distributions, and it provides a handle on estimating the tails heaviness or shortness directly from the data. Our results show that the statistics support the short-tailed limit of the generalized Pareto distribution, rather than a power law or any other heavy-tailed distribution. Working across resolutions of the data and performing cross-model comparisons, we further establish the overall robustness of the generalized Pareto model in explaining the data. Moreover, we account for earlier ambiguities by showing that down-sampling the data systematically affects statistical results. At lower resolutions models cannot easily be differentiated on statistical grounds while their plausibility consistently increases up to an upper bound. Indeed, more power law distributions are reported at low resolutions (5K) than at higher ones (50K or 80K). However, we show that these positive identifications at low resolutions fail cross-model comparisons and that down-sampling data introduces the risk of detecting spurious heavy-tailed distributions. This dependence of the statistics of degree distributions on sampling resolution has broader implications for neuroinformatic methodology, especially, when several analyses rely on down-sampled data, for instance, due to a choice of anatomical parcellations or measurement technique. Our findings that node degrees of human brain functional networks follow a short-tailed distribution have important implications for claims of brain organization and function. Our findings do not support common simplistic representations of the brain as a generic complex system with optimally efficient architecture and function, modeled with simple growth mechanisms. Instead these findings reflect a more nuanced picture of a biological system that has been shaped by longstanding and pervasive developmental and architectural constraints, including wiring-cost constraints on the centrality architecture of individual nodes.

neuroscience↗

bioRxiv2025

Persist or Give up? Fire ants motivated to search for a high-quality food source even if they don't know how to find it

Finding resources for the colony is one of the most difficult and risky tasks for a social insect worker. A worker on a foraging trip can face a number of challenges, including interference from other individuals, her own errors, and environmental disturbances. Collectively, colonies may use a variety of strategies to minimize the impact of such perturbations on the foraging process. Here, we investigated how individual Solenopsis xyloni ant workers react to perturbation of an established pheromone trail. We trained foragers from colonies in the field to either a low or high concentration sucrose solution in a feeder on a T-maze setup, then replaced a section of floor covering, removing a section of the pheromone trail previously laid. We found that while ants made correct choices on the T-maze when the trail was intact, their choices did not differ from chance when the trail was absent, indicating strong reliance on a pheromone trail (and not, for example, memory) to return to the resource. Moreover, when the trail was absent, we found that a majority of ants abandoned the resource, and that even the ants that were able to reach the resource did not repair the perturbed trail. However, with a high-quality resource, more ants persisted in attempting to reach it (instead of abandoning). We interpret these responses in the framework of robustness mechanisms discussed in systems biology. Our study thus links individual and collective responses to perturbations, and provides an empirical example of how information use interacts with system robustness. Statements and declarationsThe authors have no competing interests to declare that are relevant to the content of this article.

animal behavior and cognition↗

bioRxiv2026

A neural moonshine conjecture. The representation of error-correcting codes in 3D symmetries invites the prospect of a Golay in the machine.

Conant-Ashbys ( good regulator) theorem states that a simple regulator of a system must behave as an image of it. Notably, evolutionary features present in living beings often mirror natural processes, yielding symmetries between biological structures and the external environment. For instance, nervous cells can represent abstract entities through coordinated activities of neural networks. Central pattern generators (CPGs) used in locomotion display cyclic group symmetries (e.g. Zn), while the visual cortex and hyperbolic geometries in the hippocampus are respectively connected with Euclidean (SE(2), SE(3)) and Special linear (SL(2, R)) groups. This study renders conceivable a very efficient instance of the perfect error-correcting Golay code, whose automorphism group is the Mathieu sporadic simple group M24. We demonstrate that a set of 24 neurons is naturally organizable in a fully symmetric 3D polygonal network that embodies corruption-resistant dynamics. The existence of this configuration in biological systems is yet to be observed.

neuroscience↗

bioRxiv2026

PSoup: an R package for simulating biological networks from a qualitative perspective

Mathematical modelling is essential for understanding how complex biological systems respond to genetic, physiological, and environmental changes. Existing approaches, however, often require trade-offs between mechanistic detail, model size, parameter uncertainty, and interpretability. Ordinary differential equation (ODE) models capture biochemical processes with quantitative precision but can demand extensive parameterisation. In contrast, large statistical and machine-learning models rely on substantial datasets and frequently lack mechanistic transparency. Qualitative approaches such as Boolean networks improve scalability but may oversimplify biological behaviour. To address some of these limitations, we present PSoup, an R package that automatically converts knowledge graphs into transparent, parameter-free, qualitative models. PSoup uses algebraic update rules designed around a fixed, biologically interpretable baseline, enabling predictions of relative change across diverse perturbations without requiring kinetic parameters. This design allows PSoup to integrate information across biological scales and from heterogeneous experimental sources. We evaluated PSoup using the well-studied shoot branching network of Bertheloot et al. (2019), which incorporates hormonal (auxin, strigolactone, cytokinin) and metabolic (sucrose) regulation. Across 78 experimental conditions, PSoup correctly predicted 88.5%of perturbation outcomes, including 89.5%accuracy for unique, biologically consistent comparisons. We further demonstrate how PSoup can distinguish among alternative plausible network topologies, revealing how structural differences influence emergent system behaviour. PSoup offers an intuitive, accessible, and mathematically transparent framework for exploring biological networks. Its capacity to integrate diverse knowledge and test alternative hypotheses positions it as a powerful tool for biological discovery and a valuable complement to existing modelling approaches.

plant biology↗

bioRxiv2026

Label free multimodal optical imaging of metabolic heterogeneity in aging by integrating SRS, MPF, FLIM, and SHG

Cellular metabolism is governed by the coordinated organization of macromolecules, including lipids and proteins, together with redox-active cofactors such as NADH and FAD. However, resolving these biochemical features quantitatively and spatially at subcellular resolution remains challenging because no single imaging modality can capture molecular composition, redox state, and tissue architecture simultaneously without labeling. Here, we present MANIFEST (Multimodal Analysis of Nonlinear Imaging for Functional Endogenous Spatial correlaTion), a label-free imaging platform that integrates stimulated Raman scattering (SRS), second harmonic generation (SHG), multiphoton fluorescence (MPF), and fluorescence lifetime imaging microscopy (FLIM) with their spatial correlation. MANIFEST combines chemical imaging of lipids with autofluorescence- and lifetime-based quantification of NADH and FAD metabolism, enabling spatially resolved analysis of metabolic heterogeneity at organelle and tissue-compartment levels. We apply this framework to four distinct aging or disease models: amyloid-beta-treated tri-cultured brain cells, high-fat diet mouse liver, human non-ischemic cardiomyopathy tissue, and aging mouse retina. Across these systems, MANIFEST reveals disease-associated lipid remodeling, redox imbalance, disrupted metabolic zonation, collagen reorganization, and layer-specific metabolic changes. By integrating complementary nonlinear optical modalities into a single label-free platform, MANIFEST provides an extensible, proof-of-concept platform for high-resolution metabolic phenotyping in complex biological systems and offers new opportunities for studying disease mechanisms, aging biology, and metabolism-driven tissue pathology.

bioengineering↗

bioRxiv2023

Selective activation of intracellular β1AR using a spatially restricted antagonist

G-protein-coupled receptors (GPCRs) regulate several physiological and pathological processes and represent the target of approximately 30% of FDA-approved drugs. GPCR-mediated signaling was thought to occur exclusively at the plasma membrane. However, recent studies have unveiled their presence and function at subcellular membrane compartments. There is a growing interest in studying compartmentalized signaling of GPCRs. This requires development of novel tools to separate GPCRs signaling at the plasma membrane from the ones initiated at intracellular compartments. We took advantage of the structural and pharmacological information available for {beta}1-adrenergic receptor ({beta}1AR), an exemplary GPCR that functions at subcellular compartments, and rationally designed spatially restricted antagonists. We generated a cell impermeable {beta}1AR antagonist by conjugating a suitable pharmacophore to a sulfonate-containing fluorophore. This cell-impermeable antagonist only inhibited {beta}1AR on the plasma membrane. In contrast, a cell permeable {beta}1AR agonist containing a non-sulfonated fluorophore, efficiently inhibited both the plasma membrane and Golgi pools of {beta}1ARs. Furthermore, the cell impermeable antagonist selectively inhibited the phosphorylation of downstream effectors of PKA proximal to the plasma membrane in adult cardiomyocytes while {beta}1AR intracellular pool remained active. Our tools offer promising avenues for investigating compartmentalized {beta}1AR signaling in various context, potentially advancing our understanding of {beta}1AR-mediated cellular responses in health and disease. They also offer a general strategy to study compartmentalized signaling for other GPCRs in various biological systems.

cell biology↗

bioRxiv2024

DigiLoCS: A Leap Forward in Predictive Organ-on-Chip Simulations

Digital twins, driven by data and mathematical modelling, have emerged as powerful tools for simulating complex biological systems. In this work, we focus on modelling the clearance on a liver-on-chip as a digital twin that closely mimics the clearance functionality of the human liver. Our approach involves the creation of a compartmental physiological model of the liver using ordinary differential equations (ODEs) to estimate pharmacokinetic (PK) parameters related to on-chip liver clearance. The objectives of this study were twofold: first, to predict human clearance values, and second, to propose a framework for bridging the gap between in vitro findings and their clinical relevance. The methodology integrated quantitative Organ-on-Chip (OoC) and cell-based assay analyses of drug depletion kinetics and is further enhanced by incorporating an OoC-digital twin model to simulate drug depletion kinetics in humans. The in vitro liver clearance for 32 drugs was predicted using a digital-twin model of the liver-on-chip and in vitro to in vivo extrapolation (IVIVE) was assessed using time series PK data. Three ODEs in the model define the drug concentrations in media, interstitium and intracellular compartments based on biological, hardware, and physicochemical information. A key issue in determining liver clearance appears to be the insufficient drug concentration within the intracellular compartment. The digital twin establishes a connection between the hardware chip structure and an advanced mapping of the underlying biology, specifically focusing on the intracellular compartment. Our modelling offers the following benefits: i) better prediction of intrinsic liver clearance of drugs compared to the state-of-the-art model and ii) explainability of behaviour based on physiological parameters. Finally, we illustrate the clinical significance of this approach by applying the findings to humans, utilising propranolol as a proof-of-concept example. This study stands out as the biggest cross-organ-on-chip platform investigation to date, systematically analysing and predicting human clearance values using data obtained from various in vitro liver-on-chip systems. Author summaryAccurate prediction of in vivo clearance from in vitro data is important as inadequate understanding of the clearance of a compound can lead to unexpected and undesirable outcomes in clinical trials, ranging from underdosing to toxicity. Physiologically based pharmacokinetic (PBPK) model estimation of liver clearance is explored. The aim is to develop digital twins capable of determining better predictions of clinical outcomes, ultimately reducing the time, cost, and patient burden associated with drug development. Various hepatic in vitro systems are compared and their effectiveness for predicting human clearance is investigated. The developed tool, DigiLoCs, focuses explicitly on accurately describing complex biological processes within liver-chip systems. ODE-constrained optimisation is applied to estimate the clearance of compounds. DigiLoCs enable differentiation between active biological processes (metabolism) and passive processes (permeability and partitioning) by incorporating detailed information on compound-specific characteristics and hardware-specific data. These findings signify a significant stride towards more accurate and efficient drug development methodologies.

pharmacology and toxicology↗

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↗

bioRxiv2020

Multi-BIRW:a network-based algorithm for drug repurposing

This paper is based on Computational Biology with an eye towards medicine. In fact our aim is to identify new indications for existing drugs with a computational approach using Systems Biology, in order to speed up the market entry, avoiding some phases of drug development (for example toxicity tests) and with a lower cost. The method that we will discuss exploits the concept of Random Walk in a heterogeneous network, formed by a drug-drug and disease-disease similarity and known drug-disease associations. This is done by combining different types of data in order to connect more effectively two drugs or two diseases with a similarity score. At the end we integrate the known associations between drugs and diseases with the purpose of finding similarity values of new couples.

bioinformatics↗

bioRxiv2021

Quantifying biochemical reaction rates from static population variability within complex networks

Quantifying biochemical reaction rates within complex cellular processes remains a key challenge of systems biology even as high-throughput single-cell data have become available to characterize snapshots of population variability. That is because complex systems with stochastic and non-linear interactions are difficult to analyze when not all components can be observed simultaneously and systems cannot be followed over time. Instead of using descriptive statistical models, we show that incompletely specified mechanistic models can be used to translate qualitative knowledge of interactions into reaction rate functions from covariability data between pairs of components. This promises to turn a globally intractable problem into a sequence of solvable inference problems to quantify complex interaction networks from incomplete snapshots of their stochastic fluctuations.

biophysics↗

bioRxiv2024

Chronic circadian disruption in adolescent mice impairs hippocampal memory disrupting gene expression oscillations

Chronodisruption, the misalignment of internal biological systems with external environmental changes, leads to adverse health effects. Particularly, Social Jet Lag (SJL) is defined as the discrepancy between social and biological time and it exemplifies this misalignment, affecting a large part of the young population and impacting cognitive function. Despite its prevalence, our understanding of how developmental chronodisruption ultimately translates into morbidity is limited. To address this, we implemented a chronic chronodisruption protocol in adolescent mice consisting of light/dark cycle manipulation. We employed a comprehensive battery of established behavioral tests alongside an in-depth analysis of the oscillatory expression of the molecular clock and other genes involved in relevant physiological function. Our results show that chronic circadian disruption during adolescence induces impairments in short-term, social, and spatial memory without prompting anxiety-like behavior. Additionally, we report altered gene expression patterns of circadian clock genes per1, per2, cry2 and npas2 in the hypothalamus and the hippocampus. Lastly, we observed a disruption of hippocampal gene expression oscillations which may underlie the hippocampal memory impairments. Overall, this work underscores the critical role of adolescent circadian rhythms in maintaining cognitive function, the relevance of circadian control of hippocampal homeostasis, and the importance of further research into the mechanisms of chronodisruption, particularly during adolescence, to better understand its long-term implications for cognitive function and overall health.

neuroscience↗

bioRxiv2024

Modelling the prebiotic origins of regulation & agency in evolving protocell ecologies

How did natural systems develop the first mechanisms of regulation and what for? How could they turn into adaptive agents in a minimal (though deeply meaningful) biological sense? A novel simulation platform, Araudia, has been implemented to address these questions, which are deeply interrelated, in a prebiotic scenario where metabolically diverse protocells are allowed to modify their dynamic behaviour in response to changes in their boundary conditions (e.g., nutrient concentrations in the medium) and/or in the activity of other protocells, including cross-feeding relationships. We extend consumer-resource models (CRMs) to a stochastic evolutionary framework in which novelty appears bottom-up (i.e., from small changes at the individual protocell level), and a short-term memory may also come forth and spread in the population, to demonstrate that simple adaptive/learning processes can have relevant effects at somatic times (i.e., within the lifetime of single protocells). Our interest in exploring the interplay between metabolic-physiological aspects and ecological-evolutionary ones derives from the fact that it provides a complex causal domain, where various spatial and temporal scales intermingle, and where both the actual and the potential (pathways/behaviours) must be considered. It is in such a complex domain where the appearance of regulation acquires full significance, as an effective means to navigate phenotypic spaces that become too big for unguided exploration, given the large number of possible functionalities (or functional states) accessible to each proto-cellular agent. The results obtained for several minimalist cases below show that, despite the non-trivial combination of --both endogenous and exogenous--factors involved in the constitution of regulatory mechanisms, these are bound to play a critical role in the survival of infra-biological systems, especially under rapidly changing environmental conditions that longer-term adaptive mechanisms would not be able to cope with.

ecology↗