{"id":3114,"date":"2023-01-17T20:50:35","date_gmt":"2023-01-18T02:50:35","guid":{"rendered":"https:\/\/kermitmurray.com\/msblog\/?page_id=3114"},"modified":"2023-01-17T20:50:35","modified_gmt":"2023-01-18T02:50:35","slug":"biorxiv-systems-biology","status":"publish","type":"page","link":"https:\/\/kermitmurray.com\/msblog\/links\/journal-feeds\/biochemistry-journal-feeds\/biorxiv\/biorxiv-systems-biology\/","title":{"rendered":"BioRxiv Systems Biology"},"content":{"rendered":"\n<div class=\"wp-block-caxton-grid relative\"><div class=\"absolute absolute--fill\"><div class=\"absolute absolute--fill cover bg-center\" style=\"background-color:;background-image:linear-gradient( );\"><\/div><div class=\"absolute absolute--fill\" style=\"background-color:;background-image:linear-gradient( );opacity:1;\"><\/div><\/div><div class=\"relative caxton-columns caxton-grid-block\" style=\"padding-top:0;padding-left:0;padding-bottom:0;padding-right:0;grid-template-columns:repeat(12, 1fr)\" data-tablet-css=\"padding-left:em;padding-right:em;\" data-mobile-css=\"padding-left:em;padding-right:em;\">\n<div class=\"wp-block-caxton-section relative\" style=\"grid-area:span 1\/span 8\"><div class=\"absolute absolute--fill\"><div class=\"absolute absolute--fill cover bg-center\" style=\"background-color:;background-image:linear-gradient( );\"><\/div><div class=\"absolute absolute--fill\" style=\"background-color:;background-image:linear-gradient( );opacity:1;\"><\/div><\/div><div class=\"relative caxton-section-block\" style=\"padding-top:5px;padding-left:5px;padding-bottom:5px;padding-right:5px\" data-mobile-css=\"padding-left:1em;padding-right:1em;\" data-tablet-css=\"padding-left:1em;padding-right:1em;\">\n<p><strong><a href=\"https:\/\/www.biorxiv.org\/alertsrss\" target=\"_blank\" rel=\"noreferrer noopener\">Journal Home<\/a><\/strong><\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-caxton-section relative\" style=\"grid-area:span 1\/span 4\"><div class=\"absolute absolute--fill\"><div class=\"absolute absolute--fill cover bg-center\" style=\"background-color:;background-image:linear-gradient( );\"><\/div><div class=\"absolute absolute--fill\" style=\"background-color:;background-image:linear-gradient( );opacity:1;\"><\/div><\/div><div class=\"relative caxton-section-block\" style=\"padding-top:5px;padding-left:5px;padding-bottom:5px;padding-right:5px\" data-mobile-css=\"padding-left:1em;padding-right:1em;\" data-tablet-css=\"padding-left:1em;padding-right:1em;\">\n<p><strong><a href=\"http:\/\/connect.biorxiv.org\/biorxiv_xml.php?subject=systems_biology\" target=\"_blank\" rel=\"noreferrer noopener\">RSS<\/a><\/strong><\/p>\n<\/div><\/div>\n<\/div><\/div>\n\n\n<ul class=\"has-dates has-authors has-excerpts wp-block-rss\"><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.07.10.737756v1?rss=1'>Adenylyl cyclases combinatorially integrate opposing dopamine receptor signals<\/a><\/div><time datetime=\"2026-07-13T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">July 13, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Gregrowicz, J., Elowitz, M. B.<\/span><div class=\"wp-block-rss__item-excerpt\">Dopamine receptors are divided into two families which exert opposing effects on the second messenger cyclic AMP (cAMP). While most neuronal cell types express a single receptor subtype, some neurons co-express opposing receptor subtypes. It remains unclear how these cells could resolve simultaneous stimulatory and inhibitory inputs. Here, we introduce a multiplexed assay that quantifies surface receptor abundance and dynamic cAMP output in single cells. Using this assay, together with mathematical modeling, we demonstrate that signals from opposing receptor subtypes [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.07.04.736372v1?rss=1'>Monocyte-amplified transcriptional signatures of human diseases<\/a><\/div><time datetime=\"2026-07-10T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">July 10, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Arrieta-Ortiz, M. L., Wu, W.-J., Baliga, N. S.<\/span><div class=\"wp-block-rss__item-excerpt\">Blood-based biomarkers discovered by machine learning often lack disease specificity and cross-population robustness for clinical applications. We describe a biomarker discovery strategy that exploits monocytes as circulating sentinels to amplify disease-perturbed signals in blood. This strategy leverages monocyteMINER, a mechanistic transcriptional regulatory network inferred from monocyte transcriptomes of 1,202 healthy individuals. As proof-of-concept, we uncovered a 31-gene atherosclerosis-perturbed network that underpins disease etiology, identifying diagnostic signatures for coronary artery disease (ARAP2, P2RY14, FKBP15) and acute myocardial infarction (SERPINA1, ASGR2). For [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.07.03.736393v1?rss=1'>Kinome-wide CRISPR\/Cas9-knockout screening reveals critical protein kinases in vasopressin V2-receptor signaling<\/a><\/div><time datetime=\"2026-07-10T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">July 10, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Park, E., Chen, L., Raghuram, V., Khan, S., Murillo-de-Ozores, A. R., Chou, C.-L., Yang, C.-R., Knepper, M. A.<\/span><div class=\"wp-block-rss__item-excerpt\">Identification of signaling networks is an essential goal in systems biology. Here, we use CRISPR\/Cas9 knockout screening (employing a whole kinome sgRNA library) to identify functionally critical protein kinases in a well-studied Gs-dependent G-protein coupled receptor (GPCR)-signaling model, namely the vasopressin V2 receptor (V2R) pathway. Screening was done using a specially-designed fluorescence-based reporter cell line with green-fluorescent protein (GFP) co-transcribed with Aqp2, a gene whose transcription is dependent on vasopressin-mediated activation of protein kinase A (PKA). Positive regulators (n=14) included [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.07.09.737344v1?rss=1'>Synthesizing Mechanistic Hypotheses from Single-Cell Omics via Discretized Feature Attribution and Empirical Language Model Grounding<\/a><\/div><time datetime=\"2026-07-10T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">July 10, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Chen, J., Hong, Y., Bermudez, A., Hu, J., Hsieh, C.-J., Lin, N.<\/span><div class=\"wp-block-rss__item-excerpt\">Single-cell multimodal omics offer unprecedented resolution of cellular networks, yet translating continuous computational attributions into structured, testable biological mechanisms remains a persistent bottleneck. To address this limitation, we introduce an analytical pipeline employing decision trees to discretize continuous neural network attributions into explicit regulatory thresholds. These boundaries then structurally constrain large language models, enabling them to integrate established literature with empirical data to synthesize context-specific hypotheses. Applying this continuous-to-discrete framework across sparse datasets yielded novel biological mechanisms. Specifically, the framework [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.07.08.737212v1?rss=1'>Nutritional Anthelmintics: Chicory Reconfigures the Equine Holobiont Across Microbial, Parasitic, and Host Scales<\/a><\/div><time datetime=\"2026-07-10T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">July 10, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Mach, N., Mendez, S., Malsa, J., Auclair, J., Bars, D., Sevillia, M.-A., Pot, G., Monie Ibanes, M., Henri, H., Chevalier, O., Regis, C., Beaumelle, C., Velarde, A., Lansade, L., Williams, A., Richard, E., Yannic, G., Bourgoin, G., Fleurance, G.<\/span><div class=\"wp-block-rss__item-excerpt\">Anthelmintic resistance in cyathostomins is escalating worldwide, threatening equine health and highlighting the need for sustainable, ecology based parasite control strategies. Chicory (Cichorium intybus, Puna II) has emerged as a promising antiparasitic forage, yet its broader effects on the equine holobiont, parasites, microbiota, and host physiology remain poorly understood. We conducted a 32 day longitudinal grazing trial in young horses to assess how chicory affects parasitological outcomes, gut microbial ecology, nemabiome composition, behaviour, and host physiological and immune responses. Twenty-six [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.07.09.737453v1?rss=1'>How p53 stress memory could redirect JAK\/STAT1 antiviral signalling: a model-based prediction.<\/a><\/div><time datetime=\"2026-07-10T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">July 10, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Tshianyi Mwana Kalala, f. d., Omana, R. W., Ndondo, A. M., Kumwimba, D., Gonze, D.<\/span><div class=\"wp-block-rss__item-excerpt\">Viral infection can coactivate interferon (IFN)&#8211;JAK\/STAT1 signalling and the p53&#8211;Mdm2 stress-response pathway, two modules that jointly shape antiviral defence and cell-fate decisions. Here, we focus on viral infection contexts capable of inducing genotoxic stress associated with DNA double-strand breaks, thereby triggering oscillatory or sustained p53&#8211;Mdm2 dynamics. Whether p53 acts merely as a parallel stress pathway, or actively reshapes how an activated JAK\/STAT1 response is temporally decoded and functionally routed, remains unclear. We develop a coupled ordinary [ndash]differential-equation model linking an [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.07.05.736581v1?rss=1'>HetNetEX: Exact Asymptotic Inference in Heterogeneous Biomedical Knowledge Graphs<\/a><\/div><time datetime=\"2026-07-10T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">July 10, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Ghosh, T., Gillenwater, L. A., Greene, C. S., Costello, J. C.<\/span><div class=\"wp-block-rss__item-excerpt\">Heterogeneous biomedical knowledge networks (hetnets) integrate disparate data types, drugs, genes, diseases, and pathways, across independent sources; Hetionet (https:\/\/het.io) is a widely used example. A standard approach for assessing connectivity significance is XSwap, which permutes the hetnet P times and fits a gamma-hurdle null model to the degree-weighted path count (DWPC), pooling permuted values across pairs with matching source and target degrees to increase the effective sample size. This permutation approach has been highly successful in practice, but it faces [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.07.05.736551v1?rss=1'>Metabolomic signatures support the diagnostics of peritoneal endometriosis using generalised linear models<\/a><\/div><time datetime=\"2026-07-10T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">July 10, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Cecil, A., Vouk, K., Novak Pusic, M., Vogler, A., Wenzl, R., Prehn, C., Adamski, J., Lanisnik Rizner, T.<\/span><div class=\"wp-block-rss__item-excerpt\">Endometriosis, a common inflammatory gynecological disorder affecting up to 10% of women worldwide, is characterized by the presence of endometrium-like tissue outside the uterus. Current diagnostic methods, such as ultrasound and MRI, effectively detect ovarian and deep endometriosis but fail to detect more common peritoneal type. Diagnosing peritoneal endometriosis currently necessitates invasive laparoscopy and histological confirmation. Despite numerous efforts, no new reliable biomarkers have successfully transitioned into routine clinical use. This study aimed to investigate the use of targeted metabolomics [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.06.28.735086v1?rss=1'>Quantifying the Information Capacity of DNA Methylation as an Epigenetic Memory System<\/a><\/div><time datetime=\"2026-07-10T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">July 10, 2026<\/time> <span class=\"wp-block-rss__item-author\">by De la Fuente, I. M., Carrasco-Pujante, J., Fedetz, M., Legarreta, L., Malaina, I., Camino-Pontes, B., Perez-Yarza, G., Martinez, L., Cortes, J. M., Lopez, J. I.<\/span><div class=\"wp-block-rss__item-excerpt\">The information content of the genome has been extensively analyzed. However, a comparable quantitative framework for DNA methylation is still lacking. Without such quantification, the magnitude of this regulatory and dynamic epigenetic structure remains conceptually imprecise, even though methylation dysregulation is strongly linked to disease-related phenotypes and altered cellular identity. Here we address this gap by applying Shannon information theory to DNA methylation. We first consider methylation marks as binary or probabilistic regulatory states and estimate the theoretical upper-bound information [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.07.02.733740v1?rss=1'>Declines in mRNA synthesis set the rate of organismal aging<\/a><\/div><time datetime=\"2026-07-10T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">July 10, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Del Carmen-Fabregat, A., Oswal, N., Sinha, K., Vicencio, J., Abramowitz, R., Eder, M., Begik, O., Sedlackova, L., Novoa, E. M., Stroustrup, N.<\/span><div class=\"wp-block-rss__item-excerpt\">The abundance of mRNA sets a ceiling on a cells capacity to produce protein and carry out its functions. Here, we describe a pathological decline in absolute mRNA abundance that occurs in most cell types during invertebrate and mammalian aging, caused by decreases in mRNA synthesis capacity. In C. elegans, decreases in mRNA abundance are tightly coupled to declines in RNA Polymerase II (Pol II) protein abundance. Measuring Pol II abundance dynamics in vivo, we find that individuals enter adulthood [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.07.10.737737v1?rss=1'>Regulatory memory and growth-coupled inheritance shape nutrient-dependent flagella number variation in Salmonella<\/a><\/div><time datetime=\"2026-07-10T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">July 10, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Barua, A., Giralt-Zuniga, M., Erhardt, M., Hatzikirou, H.<\/span><div class=\"wp-block-rss__item-excerpt\">Bacteria must balance the advantage of movement against the cost of building flagella, yet how nutrient availability shapes variation in flagellar number across single cells remains unclear. Here, we combine time-resolved basal-body measurements in Salmonella enterica with a mechanistically constrained stochastic model of flagellar remodeling. The model separates two routes from nutrient availability to flagellar number: an RflP-dependent regulatory memory that sets a synthesis target via a latent sensing-memory variable, and a physical inheritance process in which synthesis, binomial partitioning, [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.06.30.735464v1?rss=1'>A Cross-Species Systems Genetics Framework Identifies Causal Genes in Diabetic Nephropathy<\/a><\/div><time datetime=\"2026-07-09T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">July 9, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Mishra, K., Sakban, R. B., Shankar, S., Wong, J., Farah, B. L., Guo, J., Ching, J., Tham, M. S., Kovalik, J.-P., Gurley, S. B., Petretto, E., Tolwinski, N. S., Coffman, T. M., Behmoaras, J.<\/span><div class=\"wp-block-rss__item-excerpt\">Diabetic nephropathy (DN) is the leading cause of kidney failure in the developed world, but the genetic architecture of DN susceptibility is not well characterised. Here we apply a systems genetics approach in a mouse model of DN to discover novel QTLs for clinically relevant phenotypes including albuminuria, glomerulosclerosis, and macrophage infiltration. For context and prioritisation, we combined single-cell-transcriptomics-guided pQTL and eQTL mapping with cell-type-specific co-expression networks, identifying 192 candidate pGenes for albuminuria. While many were novel, 27% had prior [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.06.30.735480v1?rss=1'>Biological Network Organization, Not Generic Graph Topology, Drives Graph-Based Gene Essentiality Prediction<\/a><\/div><time datetime=\"2026-07-09T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">July 9, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Rahimi, S., Bonner, S., Afzal, A., Milo, M., Morrissey, E., Petsalaki, E.<\/span><div class=\"wp-block-rss__item-excerpt\">Predicting gene essentiality across cellular contexts is a central challenge in computational biology, with implications for identifying cancer vulnerabilities. Graph neural networks (GNNs) integrate molecular interaction networks with gene-level features, but it remains unclear whether their performance gains arise from biologically meaningful connectivity or generic graph structure. Here, we systematically evaluate the role of network information in gene essentiality prediction using 2,741 genes across three tissues. We compare GNNs to feature-only baselines, including multilayer perceptron (MLP) and random forest (RF) [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.07.01.735871v1?rss=1'>Shield-4i: A Whole-mount Multiplexed Imaging Platform for Studying Multiscale Information Flow in 3D Multicellular Systems<\/a><\/div><time datetime=\"2026-07-09T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">July 9, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Hornbachner, R., Shamipour, S., Arslan, F. N., Fan, R., Hess, M., Curvaia, F., Lu\u0308thi, J., Oates, A. C., Bedzhov, I., Gilmour, D., Uhlmann, V., Pelkmans, L.<\/span><div class=\"wp-block-rss__item-excerpt\">Self-organization in multicellular systems emerges from reciprocal interactions across spatiotemporal scales. Understanding how subcellular organization, tissue remodeling and developmental outcome are coordinated, thus requires simultaneous profiling of biological processes spanning orders of magnitudes in space and time. Yet, a unified experimental and computational framework for capturing these multiscale properties across in vivo and stem cell-derived systems has been lacking. Here, we introduce Shield-4i, a high-throughput, versatile, and accessible method for automated in toto iterative immunofluorescence imaging of whole-mount structures at [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.07.02.735880v1?rss=1'>Weak form Scientific Machine Learning for Systems Biology: A Tutorial on WENDy<\/a><\/div><time datetime=\"2026-07-09T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">July 9, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Heitzman-Breen, N., Lyons, R., Jain, P., Jolly, M. K., Bortz, D. M.<\/span><div class=\"wp-block-rss__item-excerpt\">Mechanistic ordinary differential equation models are widely used in systems biology to represent biochemical networks, population dynamics, cell-state transitions, and other biological processes; however, their predictive value depends critically on accurate parameter estimation from noisy and often sparse experimental data. In this tutorial, we present the Weak-form Estimation of Nonlinear Dynamics (WENDy) method as a forward-solver-free approach that reformulates parameter estimation as a covariance-corrected weak-form regression problem by integrating the model equations against compactly supported test functions. We present the [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.07.01.735797v1?rss=1'>Identifying Novel Targets of the Stringent Response in Plants and Cyanobacteria using chemoproteomics<\/a><\/div><time datetime=\"2026-07-09T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">July 9, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Karlsson, A., Rillema, R., Sporre, E., Englund, E., Vogiatzi, N., Llavina Ramirez, J., Gurdap, C. O., Sezgin, E., Edfors, F., Blikstad, C., Strand, A., Ducat, D. C., Hudson, E. P.<\/span><div class=\"wp-block-rss__item-excerpt\">Survival in dynamic environments requires photosynthetic organisms to rapidly sense and respond to stress. The stringent response, mediated by the signaling molecule guanosine-3,5-bisdiphosphate (ppGpp), is crucial for acclimation to environmental changes such as darkness and nitrogen limitation. While it has been extensively characterized in heterotrophic bacteria such as Escherichia coli, the molecular mechanisms and regulatory targets of ppGpp in photosynthetic organisms remain less understood. Here, we report large-scale chemoproteomic identification of ppGpp-binding proteins across plant chloroplasts and cyanobacteria, revealing both [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.07.03.736321v1?rss=1'>Scalable biophysical constraints for physiologically consistent metabolic states<\/a><\/div><time datetime=\"2026-07-09T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">July 9, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Toumpe, I., Weilandt, D. R., Narayanan, B., Fengos, G., Hatzimanikatis, V., Miskovic, L.<\/span><div class=\"wp-block-rss__item-excerpt\">Systems biology aims to develop predictive models that connect molecular mechanisms to cellular behavior. Genome-scale metabolic models are among the most widely used frameworks for integrating stoichiometric, thermodynamic, and omics-derived information to predict feasible metabolic phenotypes. However, cellular metabolism operates on timescales governed by enzyme kinetics and by the relationship between metabolic fluxes and metabolite pool sizes. In steady-state metabolic models, this relationship can be expressed in terms of metabolite turnover rates, defined as flux-to-pool-size ratios that quantify how rapidly [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.06.18.733086v1?rss=1'>The Boolean Breast-Cancer Network (BBCN): structural controllability of cell-fate signalling and a bistable resistance-apoptosis switch<\/a><\/div><time datetime=\"2026-07-09T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">July 9, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Bhatti, A. I.<\/span><div class=\"wp-block-rss__item-excerpt\">We model a breast tumour as a Boolean network of signalling pathways and pose therapy as a control problem: find minimal, druggable interventions that drive the network to a desired cell-fate phenotype as a genuine fixed point of the free dynamics, without permanently forcing any node. Two complementary analyses are reported (Figure 1). In the first, a 135-node network is driven by a three-tier capped controller. Individual phenotypes are highly controllable (apoptosis 81-86%, proliferation 94-95% of patients across three cohorts), [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.06.25.734625v1?rss=1'>Cellular Stress Tolerance Governs Genetic Transformability in Recalcitrant Candida Species<\/a><\/div><time datetime=\"2026-07-09T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">July 9, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Cotter, C. J., Carper, D. L., Giannone, R. J., Trinh, C. T.<\/span><div class=\"wp-block-rss__item-excerpt\">Candida species are fungal pathogens whose rapidly increasing antifungal resistance poses a substantial public health challenge. High-throughput CRISPR-based screening could accelerate antifungal target discovery, yet its application in Candida has been limited by low DNA transformation efficiency. Chemical transformation exposes cells to environmental stresses to permit DNA uptake, but the physiological constraints on transformability remain poorly defined. Here, we show that genetic transformability in C. albicans is governed by the cellular capacity to withstand and recover from transformation-induced stress. Nutrient [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.07.08.737208v1?rss=1'>Characterising AlphaFold 3s ability to predict T cellantigen specificity<\/a><\/div><time datetime=\"2026-07-09T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">July 9, 2026<\/time> <span class=\"wp-block-rss__item-author\">by McMaster, B., Elmoselhy, A., Ilievski, I., Thorpe, C. J., La Gupta, N. L., Rossjohn, J., Deane, C., Koohy, H.<\/span><div class=\"wp-block-rss__item-excerpt\">T cells are a key part of the adaptive immune system. Using their surface-bound T cell antigen receptors (TCRs), these cells scan peptides and other antigens presented to them by major histocompatibility complex molecules (MHCs) on the surface of cells, searching for abnormalities. Although determining the map between TCRs and their target antigens is of vital importance for the design of safe and effective T cell-based vaccines and therapeutics, decoding these interactions is challenging. Experimental methods are not scalable, and [&hellip;]<\/div><\/li><\/ul>\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-wide\"\/>\n\n\n\n<h4 class=\"wp-block-heading\">Related Journals<\/h4>\n\n\n<ul class=\"su-siblings\"><li class=\"page_item page-item-3099\"><a href=\"https:\/\/kermitmurray.com\/msblog\/links\/journal-feeds\/biochemistry-journal-feeds\/biorxiv\/biorxiv-biochemistry\/\">BioRxiv Biochemistry<\/a><\/li>\n<li class=\"page_item page-item-3112\"><a href=\"https:\/\/kermitmurray.com\/msblog\/links\/journal-feeds\/biochemistry-journal-feeds\/biorxiv\/biorxiv-bioinformatics\/\">BioRxiv Bioinformatics<\/a><\/li>\n<li class=\"page_item page-item-3132\"><a href=\"https:\/\/kermitmurray.com\/msblog\/links\/journal-feeds\/biochemistry-journal-feeds\/biorxiv\/biorxiv-biophysics\/\">BioRxiv Biophysics<\/a><\/li>\n<li class=\"page_item page-item-3188\"><a href=\"https:\/\/kermitmurray.com\/msblog\/links\/journal-feeds\/biochemistry-journal-feeds\/biorxiv\/biorxiv-cancer-biology\/\">BioRxiv Cancer Biology<\/a><\/li>\n<li class=\"page_item page-item-3190\"><a href=\"https:\/\/kermitmurray.com\/msblog\/links\/journal-feeds\/biochemistry-journal-feeds\/biorxiv\/biorxiv-pharmacology-and-toxicology\/\">BioRxiv Pharmacology and Toxicology<\/a><\/li>\n<li class=\"page_item page-item-3193\"><a href=\"https:\/\/kermitmurray.com\/msblog\/links\/journal-feeds\/biochemistry-journal-feeds\/biorxiv\/biorxiv-zoology\/\">BioRxiv Zoology<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Related Journals<\/p>\n","protected":false},"author":1,"featured_media":2652,"parent":3087,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"class_list":["post-3114","page","type-page","status-publish","has-post-thumbnail","hentry","entry"],"_links":{"self":[{"href":"https:\/\/kermitmurray.com\/msblog\/wp-json\/wp\/v2\/pages\/3114","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/kermitmurray.com\/msblog\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/kermitmurray.com\/msblog\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/kermitmurray.com\/msblog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/kermitmurray.com\/msblog\/wp-json\/wp\/v2\/comments?post=3114"}],"version-history":[{"count":1,"href":"https:\/\/kermitmurray.com\/msblog\/wp-json\/wp\/v2\/pages\/3114\/revisions"}],"predecessor-version":[{"id":3115,"href":"https:\/\/kermitmurray.com\/msblog\/wp-json\/wp\/v2\/pages\/3114\/revisions\/3115"}],"up":[{"embeddable":true,"href":"https:\/\/kermitmurray.com\/msblog\/wp-json\/wp\/v2\/pages\/3087"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/kermitmurray.com\/msblog\/wp-json\/wp\/v2\/media\/2652"}],"wp:attachment":[{"href":"https:\/\/kermitmurray.com\/msblog\/wp-json\/wp\/v2\/media?parent=3114"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}