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bioRxiv2025

Sequestration-Based Neural Networks That Operate Out of Equilibrium

Classification of high-dimensional information is a ubiquitous computing paradigm across diverse biological systems, including organs such as the brain, down to signaling between individual cells. Inspired by the success of artificial neural networks in machine learning, the idea of engineering genetic circuits that operate as neural networks emerges as a strategy to expand the classification capabilities of living systems. In this work, we design these biomolecular neural networks (BNNs) based on the molecular sequestration reaction, and experimentally characterize their behavior as linear classifiers for increasing levels of complexity. Initially, we demonstrate that a static, DNA-based system can effectively prototype a linear classifier, though we also identify its limitations to easily tune the slope of the decision boundary it generates. We then propose and experimentally validate a BNN at the protein level using a cell-free transcription-translation (TXTL) system, which overcomes the DNA-based systems limitation and behaves as a linear classifier even before it reaches its steady state (or, equivalently, out-of-equilibrium). Ultimately, we test a CRISPR-based design and its out-of-equilibrium behavior in a biological context by successfully constructing a linear classifier within mammalian cells. Overall, by leveraging mathematical modeling and experimental automation, we establish molecular sequestration as a universal scheme for implementing neural networks within living systems, paving the way for transformative advances in synthetic biology and programmable biocomputing systems.

synthetic biology↗

bioRxiv2026

A Transferable Genomic Language Model Framework for Fungal Gene Essentiality Prediction

Predicting biological function from genomic sequence remains a major challenge in computational and systems biology. Here, we tested whether the genomic language model Evo2, which encodes context-dependent DNA sequence patterns into embeddings, enables prediction of essential genes in fungi, a phenotype central to fungal biology and antifungal target discovery. We found that model performance was constrained not by the type or complexity of the downstream classifier, but by the biological information contained in Evo2 DNA embeddings. Specifically, the information recoverable from these embeddings progressively declined for biological features further downstream of DNA sequence, revealing a bottleneck for predicting higher-order cellular phenotypes. We alleviated this bottleneck by integrating Evo2 embeddings with two sequence-informed, system-level features: ortholog-based essentiality and protein-protein interactions. The multimodal framework demonstrated consistent performance both within and across three evolutionarily divergent yeasts (Candida albicans, Saccharomyces cerevisiae, and Schizosaccharomyces pombe), and its predictions were supported by published experimental evidence when transferred to the filamentous mold Aspergillus fumigatus. These results establish our framework as a transferrable tool for predicting essential genes across fungal genomes, including species with limited or no experimentally determined essentially data.

bioinformatics↗

bioRxiv2017

A dynamical model of TCRβ gene recombination: Coupling the initiation of Dβ-Jβ rearrangement to TCRβ allelic exclusion

One paradigm of random monoallelic gene expression is that of T-cell receptor (TCR){beta} allelic exclusion in T lymphocytes. However, the dynamics that sustain asymmetric choice in TCR{beta} dual allele usage and the production of TCR{beta} monoallelic expressing T-cells remain poorly understood. Here, we develop a computational model to explore a scheme of TCR{beta} allelic exclusion based on the stochastic initiation of DNA rearrangement [V(D)J recombination] at homologous alleles in T-cell progenitors, and thus account for the genotypic profiles typically associated with allelic exclusion in differentiated T-cells. Disturbances in these dynamics at the level of an individual allele have limited consequences on these pro1les, robust feature of the system that is underscored by our simulations. Our study predicts a biological system in which locus-specific, prime epigenetic allelic activation effects set the stage to both optimize the production of TCR{beta} allelically excluded T-cells and curtail the emergence of their allelically included counterparts.

immunology↗

bioRxiv2019

Analysis of model organism viability through an interspecies pathway comparison pipeline using the dynamic impact approach

BackgroundComputational biologists investigate gene expression time-series data using estimation, clustering, alignment, and enrichment methods to make biological sense of the data and provide compelling visualization. While there is an abundance of microarray and RNA-seq data available, interpreting the data while capturing the dynamism of a time-course experiment remains a difficult challenge. Advancements in RNA-seq technologies have allowed us to collect extensive profiles of diverse developmental processes but also requires additional methods for analysis and data integration to capture the increased dynamism. An approach that can both capture the dynamism and direction of change in a time-course experiment in a holistic manner and simultaneously identify which biological pathways are significantly altered is necessary for the interpretation of systems biology data. In addition, there is a need for a method to evaluate the viability of model organisms across different treatments and conditions. By comparing effects of a specific treatment (e.g., a drug) on the target pathway between multiple species and determining pathways with a similar response to biological cues between organisms, we can determine the best animal model for that treatment for future studies. MethodsHere, we present Dynamic Impact Approach with Normalization (DIA-norm), a dynamic pathway analysis tool for the analysis of time-course data without unsupervised dimensionality reduction. We analyzed five datasets of mesenchymal stem cells retrieved from the Gene Expression Omnibus data repository (3 human, 1 mouse cell line, 1 pig) which were differentiated in vitro towards adipogenesis. In the first step, DIA-norm calculated an impact and flux score for each biological term using p-value and fold change. In the second step, these scores were normalized and interpolated using cubic spline. Cross-correlation was then performed between all the data sets with r[≥]0.6 as a benchmark for high correlation as r = 0.7 is the limit of experimental reproducibility. ResultsDIA-norm predicted that the pig was a better model for humans than a mouse for the study of adipogenesis. The pig model had a higher number of correlating pathways with humans (64.5 to 30.5) and higher average correlation (r = 0.51 vs r = 0.46) as compared to mouse model vs human. While not a definitive conclusion, the results are in accordance with prior phylogenetic and disease studies in which pigs are a good model for studying humans, specifically regarding obesity. In addition, DIA-norm identified a larger number of biologically important pathways (approximately 2x number of pathways) versus a comparable enrichment analysis tool, DAVID. DIA-norm also identified some possible pathways of interests for adipogenesis, namely, nitrogen metabolism (r = 0.86), where there is little to no existing literature. ConclusionDIA-norm captured 80+% of biological important pathways and achieved high pathway correlation between species for the vast majority of important adipogenesis pathways. DIA-norm can be used for both time-series pathway analysis and the determination of a model organism. Our findings indicate that DIA-norm can be used to study the effect of any treatment, including drugs, on specific pathways between multiple species to determine the best animal model for that treatment for future studies. The reliability of DIA-norm to provide biological insights compared to enrichment approach tools has been demonstrated in the selected transcriptomic studies by identifying a higher number of total and biologically relevant pathways. DIA-norms final advantage was its easily interpretable graphical outputs that aid in visualizing dynamic changes in expression.

bioinformatics↗

bioRxiv2020

Transcriptomic analysis of dystonia-associated genes reveals functional convergence within specific cell types and shared neurobiology with psychiatric disorders

Dystonia is a neurological disorder characterized by sustained or intermittent muscle contractions causing abnormal movements and postures, often occurring in absence of any structural brain abnormality. Psychiatric comorbidities, including anxiety, depression, obsessive-compulsive disorder and schizophrenia, are frequent in dystonia patients. While mutations in a fast-growing number of genes have been linked to Mendelian forms of dystonia, the cellular, anatomical, and molecular basis remains unknown for most genetic forms of dystonia, as does its genetic and biological relationship to neuropsychiatric disorders. Here we applied an unbiased systems-biology approach to explore the cellular specificity of all currently known dystonia-associated genes, predict their functional relationships, and test whether dystonia and neuropsychiatric disorders share a genetic relationship. To determine the cellular specificity of dystonia-associated genes in the brain, single-nuclear transcriptomic data derived from mouse brain was used together with expression-weighted cell-type enrichment. To identify functional relationships amongst dystonia-associated genes, we determined the enrichment of these genes in co-expression networks constructed from ten human brain regions. Stratified linkage-disequilibrium score regression was used to test whether co-expression modules enriched for dystonia-associated genes significantly contribute to the heritability of anxiety, major depressive disorder, obsessive-compulsive disorder, schizophrenia, and Parkinsons disease. Dystonia-associated genes were significantly enriched in adult nigral dopaminergic neurons and striatal medium spiny neurons. Furthermore, four of the 220 gene co-expression modules tested were significantly enriched for the dystonia-associated genes. The identified modules were derived from the substantia nigra, putamen, frontal cortex, and white matter, and were all significantly enriched for genes associated with synaptic function. Finally, we demonstrated significant enrichments of the heritability of depression, obsessive-compulsive disorder and schizophrenia, but not anxiety and Parkinsons disease, within the putamen and white matter modules. In conclusion, multiple dystonia-associated genes interact and contribute to pathogenesis likely through dysregulation of synaptic signalling in striatal medium spiny neurons, adult nigral dopaminergic neurons and frontal cortical neurons. Furthermore, the enrichment of the heritability of psychiatric disorders in the co-expression modules enriched for dystonia-associated genes indicates that psychiatric symptoms associated with dystonia are likely to be intrinsic to its pathophysiology.

genetics↗

bioRxiv2020

Phase-dependent closed-loop modulation of neural oscillations in vivo

Normal brain function is associated with an assortment of oscillations of various frequencies, each reflecting the timing of separate computational processes and levels of synchronization within and between brain areas. Stimulation accurately delivered on a specified phase of a given oscillation provides the opportunity to target individual aspects of brain function. To achieve this, we have developed a highly responsive system to produce a continuous online phase-estimate. In addition to stable oscillations, the system accurately tracks the early cycles of short, transient oscillations and can operate across the frequency range of most established neuronal oscillations (4 to 250 Hz). Here we demonstrate bidirectional modulation of the pathologically elevated parkinsonian beta-band oscillation (around 35 Hz) in 6-OHDA hemi-lesioned rats. Beta phase, monitored using a single channel electrocorticogram above secondary motor cortex, was used to drive electrical stimulation of the globus pallidus on one of eight phases spanning the oscillation cycle. Stimulation of the early ascending phase suppressed the oscillation whereas stimulation of the early descending phase was amplifying. By implementing a rule that prevented stimulation when the phase estimate was unstable, we achieved a system that could adapt stimulation rate and pattern to respond to the changes produced in the target oscillation. This allowed the electronic system to create and maintain a state of equilibrium with the biological system resulting in continuous stable modulation of the target oscillation over time. These results demonstrate the feasibility of phase locked stimulation as a more refined strategy for remediation of pathological beta oscillations in the treatment of the motor symptoms of Parkinsons disease. Furthermore, they establish the utility of our algorithm and allow for the potential to assess the contribution of rhythmic activity in neuronal computation across a number of brain systems.

neuroscience↗

bioRxiv2021

Trajectory inference across multiple conditions with condiments: differential topology, progression, differentiation, and expression.

In single-cell RNA-sequencing (scRNA-seq), gene expression is assessed individually for each cell, allowing the investigation of developmental processes, such as embryogenesis and cellular differentiation and regeneration, at unprecedented resolutions. In such dynamic biological systems, grouping cells into discrete groups is not reflective of the biology. Cellular states rather form a continuum, e.g., for the differentiation of stem cells into mature cell types. This process is often represented via a trajectory in a reduced-dimensional representation of the scRNA-seq dataset. While many methods have been suggested for trajectory inference, it is often unclear how to handle multiple biological groups or conditions, e.g., inferring and comparing the differentiation trajectories of wild-type and knock-out stem cell populations. In this manuscript, we present a method for the estimation and downstream interpretation of cell trajectories across multiple conditions. Our framework allows the interpretation of differences between conditions at the trajectory, cell population, and gene expression levels. We start by integrating datasets from multiple conditions into a single trajectory. By comparing the conditions along the trajectorys path, we can detect large-scale changes, indicative of differential progression. We also demonstrate how to detect subtler changes by finding genes that exhibit different behaviors between these conditions along a differentiation path.

bioinformatics↗

bioRxiv2023

Mosaic-PICASSO: accurate crosstalk removal for multiplex fluorescence imaging

Ultra-multiplexed fluorescence imaging has revolutionized our understanding of biological systems, enabling the simultaneous visualization and quantification of multiple targets within biological specimens. A recent breakthrough in this field is PICASSO, a mutual-information-based technique capable of demixing up to 15 fluorophores without their spectra, thereby significantly simplifying the application of ultra-multiplexed fluorescence imaging. However, this study has identified a limitation of mutual information-based techniques. They do not differentiate between spatial colocalization and spectral mixing. Consequently, mutual information-based demixing may incorrectly interpret spatially co-localized targets as non-colocalized, leading to overcorrection. We found that selecting regions within a multiplex image with low spatial similarity for measuring spectroscopic mixing results in more accurate demixing. This method effectively minimizes overcorrections and promises to accelerate the broader adoption of ultra-multiplex imaging.

bioengineering↗

bioRxiv2025

Prediction of metabolic dynamics through deep learning and high-throughput multiomics data

Synthetic biologys remarkable potential to tackle important societal problems is held back by our inability to predictably engineer biological systems. Here, we collected one of the largest public multiomics synthetic biology datasets generated to date, and used it to train a novel deep learning algorithm able to predict product and metabolic dynamics with great accuracy, starting to approach the predictive capabilities found in physics and chemistry. We were able to predict production time series with 90-99% accuracy, and final production with 96% accuracy. Further, we were able to produce good predictions for a majority of extracellular metabolites, and twenty different intracellular metabolites. These predictions were provided for a target of industrial relevance: a non-model yeast (Pichia kudriavzevii) engineered to produce large amounts of malonic acid, a desirable biomanufacturing target. This approach is generally applicable to any host, pathway, and product because all required knowledge is inferred from experimental data.

bioengineering↗

bioRxiv2018

PorSignDB: a database of in vivo perturbation signatures for dissecting clinical outcome of PCV2 infection

Porcine Circovirus Type 2 (PCV2) is a pathogen that has the ability to cause often devastating disease manifestations in pig populations with major economic implications. How PCV2 establishes subclinical persistence and why certain individuals progress to lethal lymphoid depletion remain to be elucidated. Here we present PorSignDB, a gene signature database describing in vivo porcine tissue physiology that we generated from a large compendium of in vivo transcriptional profiles and that we subsequently leveraged for deciphering the distinct physiological states underlying PCV2-affected lymph nodes. This systems biology approach indicated that subclinical PCV2 infections shut down the immune system. A robust signature of PCV2 disease emphasized that immune activation is dysfunctional in subclinical infections, however, in contrast it is promoted in PCV2 patients with clinical manifestations. Functional genomics further uncovered IL-2 as a driver of PCV2-mediated disease and we identified STAT3 as a druggable PCV2 host factor candidate. Our systematic dissection of the mechanistic basis of PCV2 reveals that subclinical and clinical PCV2 display two diametrically opposed immunotranscriptomic recalibrations that represent distinct physiological states in vivo, which suggests a paradigm shift in this field. Finally, our PorSignDB signature database is publicly available as a community resource (http://www.vetvirology.ugent.be/PorSignDB/, included in Gene Sets from Community Contributors http://software.broadinstitute.org/gsea/msigdb/contributed_genesets.jsp) and provides systems biologists with a valuable tool for catalyzing studies of human and veterinary disease.\n\nAuthor SummaryPorcine Circovirus Type 2 (PCV2) is a small but economically important pathogen circulating endemically in pig populations. Although PCV2 causes mostly chronic subclinical infections, many individuals develop a lethal form of circoviral disease consisting of a collapse of lymphoid tissue. In order to provide a fresh look at how PCV2 reprograms host tissue, we created PorSignDB, a compendium of hundreds of transcriptomic gene-expression signatures derived from primary porcine tissue specimens of well over 1500 patients or lab animals. By leveraging PorSignDB on transcriptomic data of PCV2 patients, we uncover that subclinical PCV2 reprograms the host into a striking state of non-infection, which explains its failure to respond to an initial phase of circoviral presence. A PCV2 disease signature further demonstrates that the silenced immune system associated with subclinical PCV2 becomes fully activate in PCV2 patients, triggering severe circoviral disease. Further genomic and functional analysis demonstrate STAT3 as a druggable host factor and IL-2 as a disease driver. Together, this study demonstrates the mechanistic underpinnings of clinical outcome of PCV2 infections: subclinical and clinical PCV2 display two entirely opposing transcriptomic recalibrations of lymphoid tissue.

microbiology↗

bioRxiv2019

The Michaelis-Menten paradox: Km is not an equilibrium constant but a steady-state constant.

Often, in vitro or in vivo enzyme-mediated catalytic events occur far from equilibrium and, therefore, substrate affinity measured as the inverse of ES {rightleftarrows} E+S dissociation equilibrium constant (Kd) has a doubtful physiological meaning; in practice it is almost impossible to determine Kd (except using stopped-flow or other sophisticated methodologies). The Michaelis-Menten constant (Km), the concentration of substrate ([S]) providing half of enzyme maximal activity, is not the (Kd). In the simple E+S {rightleftarrows} ES [->] E+P or in more complex models describing S conversion into P, Km must be considered the constant defining the steady state at any substrate concentration. Enzyme kinetics is based on initial rate determination, i.e. in the linear part of the S to P conversion when the concentration of [ES] remains constant while steady state occurs. We also show that Systems Biology issues such as the time required to respond to a system perturbation, is more dependent on k1, the kinetic constant defining substrateenzyme association, than on Km. Whereas Km is instrumental for biochemical basic and applied approaches, in any physiological condition, an important parameter to be considered is the substrate association rate (k1).

biochemistry↗

bioRxiv2026

On the inseparability of the prior and neural resources in behavioural bias

Sensory decision making depends on environmental expectations and finite neural resources, yet these quantities are often modelled as separable values in biological systems. Here we show that this separation is neither computationally nor biologically necessary for efficient population codes. We demonstrate that prior expectations can be embedded directly in an encoding populations tuning, thereby producing behavioural biases via Bayes-optimal inference with a uniform explicit prior. Re-analysis of V1 physiology provides preliminary support for the predicted organisation of population activity. These results highlight how systematic perceptual bias can stem from the implementation of decoding in an efficiently organised sensory code.

neuroscience↗

bioRxiv2026

Interpretable Machine Learning Links Group-Specific Plasma Proteomics to Regional Structural Neuroimaging in Alzheimer's Disease

Background: Alzheimer's disease (AD) pathology begins years before clinical symptoms, highlighting the need for scalable biomarkers that capture biological changes across the AD continuum. Broader plasma proteomics may capture systemic processes that vary with disease stage, yet their relationship to AD-related brain neurodegeneration remains unclear. We aimed to identify plasma proteomic signatures across cognitively normal (CN), mild cognitive impairment (MCI), and AD and evaluate their neuroanatomical associations. Methods: We analyzed 146 plasma analytes from 565 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants (58 CN, 396 MCI, 111 AD). Group-specific proteomic signatures were identified using pairwise classification models with nested 5*5 cross-validation. SHAP quantified feature importance, with the top 15 proteins per comparison being prioritized for MRI association analysis. We analyzed 103 regional structural MRI volumes harmonized across 57 acquisition sites using ComBat. Separate ordinary least squares regression tested protein-MRI associations, adjusting for age, sex, APOE4, diagnosis, and intracranial volume, followed by Benjamini-Hochberg false discovery rate (FDR) correction. Results: Classification models using proteomic data achieved AUC of 0.956 +- 0.017 for CN vs MCI, 0.963 +- 0.011 for MCI vs. AD, and 0.836 +- 0.085 for CN vs. AD. SHAP identified group-dependent proteomic signatures. No protein-MRI associations survived FDR correction for CN vs. MCI. In MCI vs. AD, higher cortisol was associated with lower left precuneus and superior parietal volumes, while higher osteopontin was associated with lower left paracentral and accumbens volumes. In CN vs. AD, higher vitronectin was associated with lower posterior corpus callosum volume. Conclusion: These findings suggest that plasma proteomics capture group-specific systemic biological signals across the AD continuum. Linking ML-prioritized plasma proteins to regional brain structure provides neuroanatomical context for blood-based biomarker discovery.

neuroscience↗

bioRxiv2021

Immunity Depletion, Telomere Imbalance, and Cancer-associated Metabolism Pathway Aberrations in Intestinal Mucosa upon Caloric Restriction

Systematic analysis of calorie restriction (CR) mechanisms and pathways in cancer biology has not been carried out, leaving therapeutic benefits unclear. Using a systems biology approach and metadata analysis, we studied gene expression changes in the response of normal mouse duodenum mucosa (DM) to short-term (2-weeks) 25% CR as a biological model. We found a high similarity of gene expression profiles in human and mouse DM tissues. Surprisingly, 26% of the 467 CR responding differential expressed genes (DEGs) in mice consist of cancer-associated genes--most never studied in CR contexts. The DEGs were enriched with over-expressed cell cycle, oncogenes, and metabolic reprogramming pathways (MRP) that determine tissue-specific tumorigenesis, cancer, and stem cell activation; tumor suppressors and apoptosis genes were under-expressed. DEG enrichments suggest a misbalance in telomere maintenance and activation of metabolic pathways playing dual (anti-cancer and pro-oncogenic) roles. Immune system genes (ISGs) consist of 37% of the total DEGs; the majority of ISGs are suppressed, including cell-autonomous immunity and tumor immune evasion controls. Thus, CR induces MRP suppressing multiple immune mechanics and activating oncogenic pathways, potentially driving pre-malignant and cancer states. These findings change the paradigm regarding the anti-cancer role of CR and may initiate specific treatment target development.

cancer biology↗

bioRxiv2019

A novel method to detect intracellular metabolite alterations in MCF-7 cells by doxorubicin induced cell death

Metabolic reprogramming within cancer cells is suggested as a potential barrier to chemotherapy. Additionally, metabolic tumor heterogeneity is one of factor behind discernible hallmarks such as drug resistance, relapse of tumor and the formation of secondary tumors. In this paper, cell based assays including PI/annexin V staining and immunoblot assay were performed to show the apoptotic cell death in MCF-7 cells treated with DOX. Further, MCF-7 cells were lysed in hypotonic buffer and whole cell lysate was purified by a novel and specifically designed metabolite (100 to 1000 Da) fractionation system as vertical tube gel electrophoresis (VTGE). Further, purified intracellular metabolites were subjected to identification by LC-HRMS technique. The authors show the presence of cleaved PARP 1 in MCF-7 cells treated with DOX. Concomitantly, data show the absence of active caspase 3 in MCF-7 cells. Novel findings are to identify key intracellular metabolites assisted by VTGE system that include lipid (CDP-DG, phytosphingosine, dodecanamide), non-lipid (N-acetyl-D-glucosamine, N1-acetylspermidine and gamma-L-glutamyl-L-cysteine) and tripeptide metabolites in MCF-7 cells treated by DOX. Interestingly, the authors report a first evidence of doxorubicinone, an aglycone form of DOX in MCF-7 cells that is potentially linked to the mechanism of cell death in MCF-7 cells. This paper reports on novel methods and processes that involve VTGE system based purification of hypotonically lysed novel intracellular metabolites of MCF-7 cells treated by DOX. Here, these identified intracellular metabolites corroborate to caspase 3 independent and mitochondria induced apoptotic cell death in MCF-7 cells.\n\nSIGNIFICANCE STATEMENTMetabolic reprogramming in cancer cells is implicated in various tumor hallmarks. Interestingly, thousands of research have addressed the molecular basis of drug treatment and resistance in chemotherapy. But, there is a significant gap in the precise methodologies and approaches in addressing intracellular metabolite alterations. This paper reports on a novel approach that helped reveal new findings on intracellular metabolite changes in case of doxorubicin (DOX) induced cell death in MCF-7 cells. This paper highlights the additional insights on debatable findings available in literature in the contexts of DOX induced cell death mechanisms. In this paper, novel and specifically designed vertical tube gel electrophoresis (VTGE) system is claimed to purify intracellular metabolites and this method is compatible with other biological system.

cancer biology↗

bioRxiv2017

A Multi-Stage Representation Of Cell Proliferation As A Markov Process

The stochastic simulation algorithm commonly known as Gillespies algorithm (originally derived for modelling well-mixed systems of chemical reactions) is now used ubiquitously in the modelling of biological processes in which stochastic effects play an important role. In well mixed scenarios at the sub-cellular level it is often reasonable to assume that times between successive reaction/interaction events are exponentially distributed and can be appropriately modelled as a Markov process and hence simulated by the Gillespie algorithm. However, Gillespies algorithm is routinely applied to model biological systems for which it was never intended. In particular, processes in which cell proliferation is important (e.g. embryonic development, cancer formation) should not be simulated naively using the Gillespie algorithm since the history-dependent nature of the cell cycle breaks the Markov process. The variance in experimentally measured cell cycle times is far less than in an exponential cell cycle time distribution with the same mean.\n\nHere we suggest a method of modelling the cell cycle that restores the memoryless property to the system and is therefore consistent with simulation via the Gillespie algorithm. By breaking the cell cycle into a number of independent exponentially distributed stages we can restore the Markov property at the same time as more accurately approximating the appropriate cell cycle time distributions. The consequences of our revised mathematical model are explored analytically as far as possible. We demonstrate the importance of employing the correct cell cycle time distribution by recapitulating the results from two models incorporating cellular proliferation (one spatial and one non-spatial) and demonstrating that changing the cell cycle time distribution makes quantitative and qualitative differences to the outcome of the models. Our adaptation will allow modellers and experimentalists alike to appropriately represent cellular proliferation - vital to the accurate modelling of many biological processes - whilst still being able to take advantage of the power and efficiency of the popular Gillespie algorithm.

cell biology↗

bioRxiv2022

Hypergraphs and centrality measures identifying keyfeatures in gene expression data

Multidisciplinary approaches can significantly advance our understanding of complex systems. For instance, gene co-expression networks align prior knowledge of biological systems with studies in graph theory, emphasising pairwise gene to gene interactions. In this paper, we extend these ideas, promoting hypergraphs as an investigative tool for studying multi-way interactions in gene expression data. Additional freedoms are achieved by representing individual genes with hyperedges, and simultaneous testing each gene against many features/vertices. Further gene/hyperedge interactions can be captured and explored using the line graph representations, a techniques that also reduces the complexity of dense hypergraphs. Such an approach provides access to graph centrality measures, which in turn identify salient features within a data set, for instance dominant or hub-like hyperedges leading to key knowledge on gene expression. The validity of this approach is established through the study of gene expression data for the plant species Senecio lautus and results will be interpreted within this biological setting.

bioinformatics↗

bioRxiv2025

Assessing the Rate-Dependence of the First Phase of Glucose-Stimulated Insulin Secretion: Dynamic Perifusion Studies with Isolated Human Pancreatic Islets

Insulin released in response to a stepwise increase in glucose (square wave) is biphasic with a transient first-phase peak of 5-10 min and a more sustained second phase. While it is usually assumed that the first phase is rate- and the second is concentration-dependent, there are no detailed investigations into the rate-sensitivity of the first phase. We performed dynamic perifusion studies with isolated human pancreatic islets in a system that allowed fully customizable glucose ramps and established the corresponding insulin secretion time-profiles with high (once-a-minute) time-resolution. We considered the first-phase release to be the excess amount of insulin in addition to what would be expected from the glucose concentration-dependent second-phase release and examined its dependence on the glucose gradient (rate of increase). The average first-phase insulin release rate calculated this way increased with the gradient and could be fitted well with a Hill-type sigmoid function having a half-maximal value around 1.25 mM/min (nHill=1.8, r2=0.96). This agrees well with our previously introduced glucose-insulin control system built on a general framework of a sigmoid proportional-integral-derivative ({sigma}PID) controller, a generalized PID controller more suitable for biological systems than classic linear ones as biological responses are always limited between zero and a possible maximum. Experimental results obtained here were used to slightly readjust the parameters of this local glucose concentration-based computational model resulting in predictions in good agreement with measured first- and second-phase insulin secretions (r2>0.90). Thus, glucose-stimulated insulin secretion (GSIS) of perifused human islets can be described well as the sum of a rate-sensitive first phase, which is a sigmoid function of the glucose gradient with half-maximal value around 1.25 mM/min, and a concentration-sensitive second phase, which is a sigmoid function of the glucose concentration with half-maximal value around 8 mM.

physiology↗