Biology subjects

An, G.

Publications and source records attributed to An, G..

At least 19 recordsLinked to original sources

bioRxiv2026

Species and Accession Diversity of Secondary Metabolites and Antioxidant Activity in Legume Sprouts

Legume sprouts contain diverse bioactive phytochemicals, yet species- and accession-level comparisons of antioxidant properties and secondary metabolites remain limited. Antioxidant capacity, total phenolic content (TPC), total flavonoid content (TFC), and 19 secondary metabolites were evaluated in sprouts of soybean (Glycine max (L.) Merr.), mungbean (Vigna radiata (L.) R. Wilczek), cowpea (Vigna unguiculata (L.) Walp.), and peanut (Arachis hypogaea L.), using ten accessions per species under standardized conditions. Mungbean and cowpea sprouts showed significantly higher antioxidant activity, TPC, and TFC than soybean and peanut; across accessions, ABTS and DPPH activities ranged from 13.67 to 49.33% and 7.91 to 55.16%, and TPC and TFC from 3.91 to 13.81 mg GAE/g and 0.05 to 1.04 mg QE/g, respectively. Metabolite profiling revealed species-specific patterns, including isoflavones in soybean, rutin in mungbean, coumestrol in cowpea and resveratrol in peanut. Both antioxidant and phytochemical profiles varied with species and accession, so both can be selected to obtain sprouts with targeted properties.

plant biology↗

bioRxiv2026

Dynamic knowledge representation of blood brain barrier activation, injury and restitution with a novel agent-based model

The blood brain barrier (BBB) tightly regulates the interface between the central nervous system and the systemic circulation. The function of the BBB is the output of an architecturally complex, multicellular neurovascular unit that consists of brain microvascular endothelial cells, pericytes, astrocytes, microglia and parenchymal neurons. Dysfunction of the BBB has been linked to numerous neurological diseases, such as multiple sclerosis, Alzheimers Disease, traumatic brain injury/chronic traumatic encephalopathy and stroke. Also, the control of the BBB over permeability makes it an ongoing interest and target for pharmaceutical development. Herein we present Blood Brain Barrier Agent-based Model (BBBABM), the first computational model that mechanistically represents the cellular components of the neurovascular unit and the molecular interactions that govern the response of the BBB to injury/activation, including the restorative functions that provide baseline homeostasis regarding the health of the BBB. Simulation experiments with the BBBABM replicate expected dynamics of disruption and restoration that demonstrate a dose responsiveness to the severity of the initial insult. The development of the BBBABM provided insight into the contribution of various forms of cellular-molecular responses to overall BBB dysfunction, including integrating across time scales from minutes to weeks. This initial implementation of the BBBABM offers numerous future paths for development, including being able to mechanistically represent the long time scales (years/decades) present in the pathophysiology of chronic neurological diseases and providing a platform to enhance therapeutic development via in silico trials and as the basis for mechanistic cellular-molecular Digital Twins.

systems biology↗

bioRxiv2025

Labile iron overload reprograms microglia and neurons for lipid droplet synthesis in the aging brain

Lipid-droplet (LD) accumulation emerges in microglia and neurons with age. Because neither cell type is specialized for lipid storage, LDs are linked to dysfunction. The upstream drivers of LD formation and their effects on neighboring cells remain unclear. Here, we identify a mitochondrial-iron axis that promotes LD formation in aging microglia via reactive oxygen species (ROS), which secondarily reshapes neuronal iron handling. LD-enriched microglia show reduced mitochondrial mass and increased labile iron, ROS, and lipid peroxidation. Chelating labile iron or scavenging ROS suppresses LD formation. Conditioned media from iron-stressed microglia alter neuronal iron homeostasis, indicating transcellular coupling. In primary neurons, iron overload increases LDs and activates coordinated iron, ROS, and lipogenesis programs, whereas antioxidant treatment attenuates iron-driven LD accumulation. Together, these findings position iron overload as an upstream regulator of ROS-dependent LD biogenesis in microglia and neurons and reveal a microglia-neuron axis that regulates neuronal iron metabolism during aging.

immunology↗

bioRxiv2024

Modelling molecular differences in the innate immune system responses of chickens and ducks to highly pathogenic avian influenza virus

Highly pathogenic avian influenza virus (HPAIV) presents a global threat to chicken livestock; chickens infected by HPAIV tend to show severe symptoms and high mortality rates. In 2022, the largest recorded outbreak of HPAIV in Europe resulted in millions of chickens being culled in the UK alone to try to prevent further spread. Unlike chickens, mallard ducks show reduced symptom severity and lower mortality rates to HPAIV infection. Research into the immune system responses of these two species shows they differ in their molecular outputs: chickens produce a pro-inflammatory response; mallards produce an anti-viral response. These differences in immune responses are thought to be in part due to chickens missing pattern recognition receptor retinoic acid-inducible gene-I (RIG-I). This project aimed to model the innate immune systems of chickens and mallard ducks to an abstracted molecular level. A literature search was conducted, and the immune systems were modelled in NetLogo as an avian innate immune response agent-based model (AIIRABM). The AIIRABM enabled examination of the relative importance of molecular differences between the chicken and mallard duck innate immune systems and produced similar differences in chicken and mallard duck molecular outputs to those observed in vitro and in vivo. Simulation experiments with the AIIRABM supported the molecular difference RIG-I as key in causing the differences in the chicken and mallard duck innate immune responses to HPAIV. The AIIRABM will be used in further research on the chicken and mallard duck immune responses to HPAIV as the baseline in an iterative modelling cycle.

immunology↗

bioRxiv2024

The Wound Environment Agent-based Model (WEABM): a digital twin platform for characterization and complex therapeutic discovery for volumetric muscle loss

Volumetric Muscle Loss (VML) injuries are characterized by significant loss of muscle mass, usually due to trauma or surgical resection, often with a residual open wound in clinical settings and subsequent loss of limb function due to the replacement of the lost muscle mass with non-functional scar. Being able to regrow functional muscle in VML injuries is a complex control problem that needs to override robust, evolutionarily conserved healing processes aimed at rapidly closing the defect in lieu of restoration of function. We propose that discovering and implementing this complex control can be accomplished by the development of a Medical Digital Twin of VML. Digital Twins (DTs) are the subject of a recent report from the National Academies of Science, Engineering and Medicine (NASEM), which provides guidance as to the definition, capabilities and research challenges associated with the development and implementation of DTs. Specifically, DTs are defined as dynamic computational models that can be personalized to an individual real world "twin" and are connected to that twin via an ongoing data link. DTs can be used to provide control on the real-world twin that is, by the ongoing data connection, adaptive. We have developed an anatomic scale cell-level agent-based model of VML termed the Wound Environment Agent Based Model (WEABM) that can serve as the computational specification for a DT of VML. Simulations of the WEABM provided fundamental insights into the biology of VML, and we used the WEABM in our previously developed pipeline for simulation-based Deep Reinforcement Learning (DRL) to train an artificial intelligence (AI) to implement a robust generalizable control policy aimed at increasing the healing of VML with functional muscle. The insights into VML obtained include: 1) a competition between fibrosis and myogenesis due to spatial constraints on available edges of intact myofibrils to initiate the myoblast differentiation process, 2) the need to biologically "close" the wound from atmospheric/environmental exposure, which represents an ongoing inflammatory stimulus that promotes fibrosis and 3) that selective, multimodal and adaptive local mediator-level control can shift the trajectory of healing away from a highly evolutionarily beneficial imperative to close the wound via fibrosis. Control discovery with the WEABM identified the following design principles: 1) multimodal adaptive tissue-level mediator control to mitigate pro-inflammation as well as the pro-fibrotic aspects of compensatory anti-inflammation, 2) tissue-level mediator manipulation to promote myogenesis, 3) the use of an engineered extracellular matrix (ECM) to functionally close the wound and 4) the administration of an anti-fibrotic agent focused on the collagen-producing function of fibroblasts and myofibroblasts. The WEABM-trained DRL AI integrates these control modalities and provides design specifications for a potential device that can implement the required wound sensing and intervention delivery capabilities needed. The proposed cyber-physical system integrates the control AI with a physical sense-and-actuate device that meets the tenets of DTs put forth in the NASEM report and can serve as an example schema for the future development of Medical DTs.

bioengineering↗

bioRxiv2023

Microsporidia Ser/Thr Protein Phosphatase PP1 Targets DCs MAPK Pathway and Impair Immune Functions

Microsporidia are difficult to completely eliminate. Their persistence may disrupt host cell functions. Here in this study, we aimed to elucidate the impairing effects and consequences of microsporidia infection upon dendritic cells (DCs). We used the zoonotic microsporidia species, Enterocytozoon hellem, in our studies. In vivo experiments showed that E. hellem-infected mice were more susceptible to further pathogenic challenges. DCs were identified as the most affected group of cells. In vitro assays revealed that E. hellem infection impaired the immune functions of DCs as reflected by down-regulation of cytokine expression, lower extent of maturation and antigen presentation. E. hellem infection decreased the ability of DCs to prime and stimulate T cells, thereby hampering host immune cell functions. We further demonstrate that E. hellem Ser/Thr protein phosphatase PP1 directly interacts with host p38(MAPK14) to manipulate the p38 (MAPK14)/NFAT-5 axis of the MAPK pathway. Our study is the first to elucidate the molecular mechanisms of the impairing effects of microsporidia on host DCs immune functions. The emerging of microsporidiosis may be great threat to public health. HighlightsO_LIPersistence of Microsporidia within host impairs dendritic cell functions such as phagocytosis, maturation, antigen presentation and T cell priming, thereby disrupting both innate and adaptive immunities and making the host more vulnerable to secondary infections C_LIO_LIMicrosporidia impairs DCs function via Serine/Threonine Protein Phosphatase PP1 directly targeting DCs p38/MAPK pathway C_LIO_LILatent Microsporidia infection and persistence is a great threat to public health when assessing acute and emerging pathogen risk C_LI

microbiology↗

bioRxiv2023

Unravelling cancer subtype-specific driver genes in single-cell transcriptomics data with CSDGI

Cancer is known as a heterogeneous disease. Cancer driver genes (CDGs) need to be inferred for understanding tumor heterogeneity in cancer. However, the existing computational methods have identified many common CDGs. A key challenge exploring cancer progression is to infer cancer subtype-specific driver genes (CSDGs), which provides guidane for the diagnosis, treatment and prognosis of cancer. The significant advancements in single-cell RNA-sequencing (scRNA-seq) technologies have opened up new possibilities for studying human cancers at the individual cell level. In this study, we develop a novel unsupervised method, CSDGI (Cancer Subtype-specific Driver Gene Inference), which applies Encoder-Decoder-Framework consisting of low-rank residual neural networks to inferring driver genes corresponding to potential cancer subtypes at single-cell level. To infer CSDGs, we apply CSDGI to the tumor single-cell transcriptomics data. To filter the redundant genes before driver gene inference, we perform the differential expression genes (DEGs). The experimental results demonstrate CSDGI is effective to infer driver genes that are cancer subtype-specific. Functional and disease enrichment analysis shows these inferred CSDGs indicate the key biological processes and disease pathways. CSDGI is the first method to explore cancer driver genes at the cancer subtype level. We believe that it can be a useful method to understand the mechanisms of cell transformation driving tumours. Author summaryCancer is recognized as a complex disease with diverse characteristics. In order to comprehend the diversity within tumors, it is essential to infer cancer subtype-specific driver genes (CSDGs), which offer valuable insights for investigating cancer progression and treatment. The remarkable progress made in single-cell RNA-sequencing (scRNA-seq) technologies has ushered in new prospects for studying human cancers at the cellular level. Cancer Subtype-specific Driver Gene Inference (CSDGI) is a novel unsupervised method proposed. In our study, we use Encoder-Decoder-Framework to infer driver genes specific to cancer subtypes in the CSDGI. We apply CSDGI to three tumor single-cell transcriptomics data. The experimental results have shown the effectiveness of CSDGI. Furthermore, functional and disease enrichment analyses illustrate that these inferred CSDGs shed light on crucial biological processes and disease pathways. Our collection of driver genes will serve as a valuable resource in unraveling the mechanisms driving cell transformation in tumors.

bioinformatics↗

bioRxiv2022

Generating synthetic data with a mechanism-based Critical Illness Digital Twin: Demonstration for Post Traumatic Acute Respiratory Distress Syndrome

Machine learning (ML) and Artificial Intelligence (AI) approaches are increasingly applied to predicting the development of sepsis and multiple organ failure. While there has been success in demonstrating the clinical utility of such systems in terms of affecting various outcomes, there are fundamental challenges to the ML/AI approach in terms of improving the actual predictive performance and future robustness of such systems. Given that one of the primary proposed avenues for improving algorithmic performance is the addition of molecular/biomarker/genetic features to the data used to train these systems, the overall sparsity of such available data suggests the need to generate synthetic data to aid in training, as has been the case in numerous other ML/AI tasks, such as image recognition/generation and text analysis/generation. We propose the need to generate synthetic molecular/mediator time series data coincides with the advent of the concept of medical digital twins, specifically related to interpretations of medical digital twins that hew closely to the original description and use of industrial digital twins, which involve simulating multiple individual twins from a common computational model specification. Herein we present an example of generating synthetic time series data of a panel of pro- and anti-inflammatory cytokines using the Critical Illness Digital Twin (CIDT) regarding the development of post-traumatic acute respiratory distress syndrome.

systems biology↗

bioRxiv2022

Optical Biopsy using a neural network to predict functional state from photos of wounds

BackgroundThe clinical characterization of the functional status of active wounds remains a considerable challenge that at present, requires excision of a tissue biopsy. In this pilot study, we use a convolutional Siamese neural network architecture to predict the functional state of a wound using digital photographs of wounds in a canine model of volumetric muscle loss (VML). Materials and MethodsImages of volumetric muscle loss injuries and tissue biopsies were obtained in a canine model of VML. Gene expression profiles for each image were obtained using RNAseq. These profiles were then converted to functional profiles using a manual review of validated gene ontology databases. A Siamese neural network was trained to regress functional profile expression values as a function of the data contained in an extracted image segment showing the surface of a small tissue biopsy. Network performance was assessed in a test set of images using Mean Absolute Percentage Error (MAPE). ResultsThe network was able to predict the functional expression of a range of functions based with a MAPE ranging from [~]5% to [~]50%, with functions that are most closely associated with the early-state of wound healing to be those best-predicted. ConclusionsThese initial results suggest promise for further research regarding this novel use of ML regression on medical images. The regression of functional profiles, as opposed to specific genes, both addresses the challenge of genetic redundancy and gives a deeper insight into the mechanistic configuration of a region of tissue in wounds. As this preliminary study focuses on the first 14 days of wound healing, future work will focus on extending the training data to include longer time periods which would result in additional functions, such as tissue remodeling, having a larger presence in the training data.

bioinformatics↗

bioRxiv2022

Preparing for the next Pandemic: Simulation-based Deep Reinforcement Learning to discover and test multimodal control of systemic inflammation using repurposed immunomodulatory agents

BackgroundPreparation to address the critical gap in a future pandemic between non-pharmacological measures and the deployment of new drugs/vaccines requires addressing two factors: 1) finding virus/pathogen-agnostic pathophysiological targets to mitigate disease severity and 2) finding a more rational approach to repurposing existing drugs. It is increasingly recognized that acute viral disease severity is heavily driven by the immune response to the infection ("cytokine storm"). There exist numerous clinically available biologics that suppress various pro-inflammatory cytokines/mediators, but it is extremely difficult to identify clinically effective treatment regimens with these agents. We propose that this is a complex control problem that resists standard methods of developing treatment regimens and accomplishing this goal requires the application of simulation-based, model-free deep reinforcement learning (DRL) in a fashion akin to training successful game-playing artificial intelligences (AIs). This proof-of-concept study determines if simulated sepsis (e.g. infection-driven cytokine storm) can be controlled in the absence of effective antimicrobial agents by targeting cytokines for which FDA-approved biologics currently exist. MethodsWe use a previously validated agent-based model, the Innate Immune Response Agent-based Model (IIRABM), for control discovery using DRL. DRL training used a Deep Deterministic Policy Gradient (DDPG) approach with a clinically plausible control interval of 6 hours with manipulation of six cytokines for which there are existing drugs: Tumor Necrosis Factor (TNF), Interleukin-1 (IL-1), Interleukin-4 (IL-4), Interleukin-8 (IL-8), Interleukin-12 (IL-12) and Interferon-{gamma} (IFNg). ResultsDRL trained an AI policy that could improve outcomes from a baseline mortality rate of 41% (= recovery rate of 59%) to one with a recovery rate of 82.3% over 42 days simulated time. DiscussionThe current proof-of-concept study demonstrates that significant disease severity mitigation can potentially be accomplished with existing anti-mediator drugs, but only through a multi-modal, adaptive treatment policy requiring implementation with an AI. While the actual clinical implementation of this approach is a projection for the future, the current goal of this work is to inspire the development of a research ecosystem that marries what is needed to improve the simulation models with the development of the sensing/assay technologies to collect the data needed to iteratively refine those models.

systems biology↗

bioRxiv2022

SRSF10 is essential for progenitor spermatogonia expansion by regulating alternative splicing

Alternative splicing expands the transcriptome and proteome complexity and plays essential roles in tissue development and human diseases. However, how alternative splicing regulates spermatogenesis remains largely unknown. Here, using germ cell-specific knockout mouse model, we demonstrated that the splicing factor Srsf10 is essential for spermatogenesis and male fertility. Depletion of Srsf10 in germ cells had little effect on the formation of SSCs but impeded the expansion of progenitor spermatogonia, leading to the failure of spermatogonia differentiation and meiosis initiation. This was further evidenced by the decreased expression of progenitor cell markers in bulk RNA-seq, and much less progenitor and differentiating spermatogonia in single-cell RNA-seq data. Furthermore, the expression of genes involved in cell cycle was abnormal in all subtypes of spermatogonia identified in single-cell RNA-seq data. Notably, using isolated spermatogonia, we found that Srsf10 depletion disturbed the alternative splicing of hundreds of genes, which were preferentially associated with cell cycle, mitotic cell cycle checkpoint and germ cell development, including Dazl, Kit, Ret, Sycp1, Nasp and Bora. These data suggest that SRSF10 is critical for the expansion of progenitor spermatogonia by regulating alternative splicing, expanding our understanding of the mechanism underlying spermatogenesis.

developmental biology↗

bioRxiv2022

Preparing for the next COVID: Deep Reinforcement Learning trained Artificial Intelligence discovery of multi-modal immunomodulatory control of systemic inflammation in the absence of effective anti-microbials

BackgroundDespite a great deal of interest in the application of artificial intelligence (AI) to sepsis/critical illness, most current approaches are limited in their potential impact: prediction models do not (and cannot) address the lack of effective therapeutics and current approaches to enhancing the treatment of sepsis focus on optimizing the application of existing interventions, and thus cannot address the development of new treatment options/modalities. The inability to test new therapeutic applications was highlighted by the generally unsatisfactory results from drug repurposing efforts in COVID-19. HypothesisAddressing this challenge requires the application of simulation-based, model-free deep reinforcement learning (DRL) in a fashion akin to training the game-playing AIs. We have previously demonstrated the potential of this method in the context of bacterial sepsis in which the microbial infection is responsive to antibiotic therapy. The current work addresses the control problem of multi-modal, adaptive immunomodulation in the circumstance where there is no effective anti-pathogen therapy (e.g., in a novel viral pandemic or in the face of resistant microbes). MethodsThis is a proof-of-concept study that determines the controllability of sepsis without the ability to pharmacologically suppress the pathogen. We use as a surrogate system a previously validated agent-based model, the Innate Immune Response Agent-based Model (IIRABM), for control discovery using DRL. The DRL algorithm trains an AI on simulations of infection where both the control and observation spaces are limited to operating upon the defined immune mediators included in the IIRABM (a total of 11). Policies were learned using the Deep Deterministic Policy Gradient approach, with the objective function being a return to baseline system health. ResultsDRL trained an AI policy that improved system mortality from 85% to 10.4%. Control actions affected every one of the 11 targetable cytokines and could be divided into those with static/unchanging controls and those with variable/adaptive controls. Adaptive controls primarily targeted 3 different aspects of the immune response: 2nd order pro-inflammation governing TH1/TH2 balance, primary anti-inflammation, and inflammatory cell proliferation. DiscussionThe current treatment of sepsis is hampered by limitations in therapeutic options able to affect the biology of sepsis. This is heightened in circumstances where no effective antimicrobials exist, as was the case for COVID-19. Current AI methods are intrinsically unable to address this problem; doing so requires training AIs in contexts that fully represent the counterfactual space of potential treatments. The synthetic data needed for this task is only possible through the use of high-resolution, mechanism-based simulations. Finally, being able to treat sepsis will require a reorientation as to the sensing and actuating requirements needed to develop these simulations and bring them to the bedside.

immunology↗

bioRxiv2022

A potent SARS-CoV-2 neutralizing antibody recognizing a conserved epitope with broad mutant variant and SARS-CoV activity.

COVID-19 is the deadliest respiratory virus pandemic since 1918 and the latest of several coronavirus epidemics and pandemics in recent years. Despite the unprecedented response by both the government and private sectors to develop vaccines and therapies, the evolution of SARS-CoV-2 variants resistant to these interventions reveals a crucial need for therapeutics that maintain their efficacy against current and future mutant variants. Here we describe a SARS-CoV-2 neutralizing antibody, ABP-310, with potent activity against all variants tested including the Omicron variant. ABP-310 also displays potent neutralizing activity against SARS-CoV, highlighting the conserved nature of the ABP-310 epitope. By targeting a conserved epitope, we believe that ABP-310 has therapeutic promise not only against the current SARS-CoV-2 variants but would be expected to maintain efficacy against future variants and possibly even novel coronaviruses.

microbiology↗

bioRxiv2021

Quantifying the size and healing of volumetric muscle wounds using 3D Slicer on CT scans in a canine model

Volumetric soft tissue and muscle wounds can arise from trauma or necrotizing soft tissue infection. Quantifying the size of these wounds can be challenging, as they often have irregular borders and contours and invariably involve skin loss. 3-dimensional Computed Tomography (3dCT) has been used to characterize the volume of numerous tissue structures, but these use cases invariably involve structures for which clear anatomic borders exist. This is not the case for volumetric soft tissue or muscle wounds, where the volume of the wound being assessed, which is actually a void representing the absence of tissue, does not contain an explicit border at the superficial surface. We present a method that allows quantification of the void size of volumetric muscle wounds using CT scans processed with the software package 3D Slicer. This quantification allows us to chart the progression of healing in such wounds with sequential scans. The development of a means to quantify wound size and healing rate is a necessary capability in order to assess the efficacy of potential therapeutic interventions aimed at enhancing healing of such wounds.

physiology↗

bioRxiv2021

Comparative computational modeling of the bat and human immune response to viral infection with the Comparative Biology Immune Agent Based Model

Given the impact of pandemics due to viruses of bat origin there is increasing interest in comparative investigation into the differences between bat and human immune responses. The practice of comparative biology can be enhanced by computational methods used for dynamic knowledge representation to visualize and interrogate the putative differences between the two systems. We present an agent-based model that encompasses and bridges the differences between bat and human responses to viral infection: the Comparative Biology Immune Agent-based Model, or CBIABM. The CBIABM examines differences in innate immune mechanisms between bats and humans, specifically regarding inflammasome activity and Type 1 Interferon dynamics, in terms of tolerance to viral infection. Simulation experiments with the CBIABM demonstrate the efficacy of bat-related features in conferring viral tolerance and also suggest a crucial role for endothelial inflammasome activity as a mechanism for bat systemic viral tolerance and affecting the severity of disease in human viral infections. We hope that this initial study will inspire additional comparative modeling projects to link, compare, and contrast immunological functions shared across different species, and in so doing, provide insight and aid in the preparation for future viral pandemics of zoonotic origin.

immunology↗

bioRxiv2021

The Rice propiconazole resistant 1-D mutant, with activated expression of a DPb transcription factor gene, exhibits increased seed yields

Mutants defective in brassinosteroid (BR) biosynthesis or signaling pathways often display semi-dwarfism, as do the highly productive gibberellin mutants that enabled the Green Revolution. However, reduced vegetative growth in BR mutants does not necessarily correspond to increased seed yields. To better understand the mode of action of BR, we isolated a rice propiconazole resistant1-D (pzr1-D) mutant by screening an activation-tagging mutant population in the presence of the BR biosynthesis inhibitor propiconazole (Pcz). The expression of a putative transcription factor gene homologous to Arabidopsis Dimerization Partner (DPb) was activated in pzr1-D. pzr1-D exhibited characteristic phenotypes such as reduced height, and increased seed yields and tiller numbers. Like Arabidopsis DPb, rice PZR1 is expressed differentially in the tissues examined. Furthermore, pzr1-D displayed altered cell division phenotypes, including the production of small calli. In addition, the cell number and size in mutant roots and leaves differed from those in wild-type plants of the same age. RNA sequencing revealed that the promoters of differentially expressed genes are enriched with cognate sequences for both BZR1 and EF-DPb transcription factors, suggesting that PZR1 functions in BR-mediated cell division in rice. PZR1 expression may thus be manipulated to increase seed yield in economically important rice varieties. SIGNIFICANCE STATEMENTMutants defective in brassinosteroid biosynthesis or signaling pathways often display semi-dwarfism. However, in many cases these mutants do not necessarily produce increased seed yields. We show a rice propiconazole resistant1-D (pzr1-D) mutant which exhibits reduced height phenotypes along with increase tiller number and seed yields. pzr1-D shows activation of a putative homologous to Arabidopsis Dimerization Partner (DPb) with functions in cell division, pointing that PZR1 may function in BR-mediated cell division in rice.

plant biology↗

bioRxiv2020

Rapid community-driven development of a SARS-CoV-2 tissue simulator

The 2019 novel coronavirus, SARS-CoV-2, is a pathogen of critical significance to international public health. Knowledge of the interplay between molecular-scale virus-receptor interactions, single-cell viral replication, intracellular-scale viral transport, and emergent tissue-scale viral propagation is limited. Moreover, little is known about immune system-virus-tissue interactions and how these can result in low-level (asymptomatic) infections in some cases and acute respiratory distress syndrome (ARDS) in others, particularly with respect to presentation in different age groups or pre-existing inflammatory risk factors. Given the nonlinear interactions within and among each of these processes, multiscale simulation models can shed light on the emergent dynamics that lead to divergent outcomes, identify actionable "choke points" for pharmacologic interventions, screen potential therapies, and identify potential biomarkers that differentiate patient outcomes. Given the complexity of the problem and the acute need for an actionable model to guide therapy discovery and optimization, we introduce and iteratively refine a prototype of a multiscale model of SARS-CoV-2 dynamics in lung tissue. The first prototype model was built and shared internationally as open source code and an online interactive model in under 12 hours, and community domain expertise is driving regular refinements. In a sustained community effort, this consortium is integrating data and expertise across virology, immunology, mathematical biology, quantitative systems physiology, cloud and high performance computing, and other domains to accelerate our response to this critical threat to international health. More broadly, this effort is creating a reusable, modular framework for studying viral replication and immune response in tissues, which can also potentially be adapted to related problems in immunology and immunotherapy.

systems biology↗

bioRxiv2019

Genetic Algorithms for model refinement and rule discovery in a high-dimensional agent-based model of inflammation

IntroductionAgent-based modeling frequently used modeling method for multi-scale mechanistic modeling. However, the same properties that make agent-based models (ABMs) well suited to representing biological systems also present significant challenges with respect to their construction and calibration, particularly with respect to the selection of potential mechanistic rules and the large number of free parameters often present in these models. We have proposed that various machine learning approaches (such as genetic algorithms (GAs)) can be used to more effectively and efficiently deal with rule selection and parameter space characterization; the current work applies GAs to the challenge of calibrating a complex ABM to a specific data set, while preserving biological heterogeneity. MethodsThis project uses a GA to augment the rule-set for a previously validated ABM of acute systemic inflammation, the Innate Immune Response ABM (IIRABM) to clinical time series data of systemic cytokine levels from a population of burn patients. The genome for the GA is a vector generated from the IIRABMs Model Rule Matrix (MRM), which is a matrix representation of not only the constants/parameters associated with the IIRABMs cytokine interaction rules, but also the existence of rules themselves. Capturing heterogeneity is accomplished by a fitness function that incorporates the sample value range ("error bars") of the clinical data. ResultsThe GA-enabled parameter space exploration resulted in a set of putative MRM rules and associated parameterizations which closely match the cytokine time course data used to design the fitness function. The number of non-zero elements in the MRM increases significantly as the model parameterizations evolve towards a fitness function minimum, transitioning from a sparse to a dense matrix. This results in a model structure that more closely resembles (at a superficial level) the structure of data generated by a standard differential gene expression experimental study. ConclusionWe present an HPC-enabled evolutionary computing approach to calibrate a complex ABM to clinical data while preserving biological heterogeneity. The integration of machine learning, HPC, and multi-scale mechanistic modeling provides a pathway forward to effectively represent the heterogeneity of clinical populations and their data. Author SummaryIn this work, we utilize genetic algorithms (GA) to operate on the internal rule set of a computational of the human immune response to injury, the Innate Immune Response Agent-Based Model (IIRABM), such that it is iteratively refined to generate cytokine time series that closely match what is seen in a clinical cohort of burn patients. At the termination of the GA, there exists an ensemble of candidate model rule-sets/parameterizations which are validated by the experimental data;

systems biology↗