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Results for “systems biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2025

A community-consensus reconstruction of Chinese Hamster metabolism enables structural systems biology analyses to decipher metabolic rewiring in lactate-free CHO cells

Genome-scale metabolic models (GEMs) are indispensable for studying and engineering cellular metabolism. Here, we present iCHO3K, a community-consensus, manually-curated reconstruction of the Chinese Hamster metabolic network. In addition to accounting for 11004 reactions associated with 3597 genes, iCHO3K includes 3489 protein structures and structural descriptors for >70% of its 7377 metabolites, enabling deeper exploration of the link between molecular structure and cellular metabolism. We used iCHO3K to contextualize transcriptomics and metabolomics data from a CHO cell line in which lactate secretion is abolished. We found the reduced glycolytic flux and enhanced TCA cycle flux were accompanied by an elevated NADH and PEP levels in these cells, consistent with experimental measurements. Leveraging iCHO3Ks structural annotations, we identified candidate binding interactions of NADH and PEP with glycolytic enzymes showing model-predicted differential flux, suggesting novel allosteric regulation associated with the observed decrease in glucose uptake and glycolysis. Overall, iCHO3K offers a valuable framework for systematic integration of omics data, improved flux predictions, and structure-guided insights, thus advancing CHO cell engineering and enhancing biomanufacturing efficiency.

systems biology↗

bioRxiv2019

Detecting and Isolating Mass Balance Errors in Reaction Based Models in Systems Biology

MotivationThe growing complexity of reaction-based models necessitates early detection and resolution of model errors. This paper addresses mass balance errors, discrepancies between the mass of reactants and products in reaction specifications. One approach to detection is atomic mass analysis, which uses meta-data to expose atomic formulas of chemical species. Atomic mass analysis isolates errors to individual reactions. However, this approach burdens modelers with expressing model meta-data and writing excessively detailed reactions that include implicit chemical species (e.g., water). Moreover, atomic mass analysis has the shortcoming of not being applicable to large molecules because of the limitations of current annotation techniques. The second approach, Linear Programming (LP) analysis, avoids using model meta-data by checking for a weaker condition, stoichiometric inconsistency. But this approach suffers from false negatives and has large isolation sets (the set of reactions implicated in the stoichiometric inconsistency).\n\nResultsWe propose alternatives to both approaches. Our alternative to atomic mass analysis is moiety analysis. Moiety analysis uses model meta-data in the form of moieties present in chemical species. Moiety analysis avoids excessively detailed reaction specifications, and can be used with large molecules. Our alternative to LP analysis is Graphical Analysis of Mass Equivalence Sets (GAMES). GAMES has a slightly higher false negative rate than LP analysis, but it provides much better error isolation. In our studies of the BioModels Repository, the average size of isolation sets for LP analysis is 55.5; for GAMES, it is 5.4. We have created open source codes for moiety analysis and GAMES.\n\nAvailability and ImplementationOur project is hosted at https://github.com/ModelEngineering/ SBMLLint, which contains examples, documentation, source code files, and build scripts used to create SBMLLint. Our source code is licensed under the MIT open source license.\n\nContact: jlheller@uw.edu\n\nSupplementary information: None.

systems biology↗

bioRxiv2021

Optimized cDICE for efficient reconstitution of biological systems in giant unilamellar vesicles

Giant unilamellar vesicles (GUVs) are often used to mimic biological membranes in reconstitution experiments. They are also widely used in research on synthetic cells as they provide a mechanically responsive reaction compartment that allows for controlled exchange of reactants with the environment. However, while many methods exist to encapsulate functional biomolecules in GUVs, there is no one-size-fits-all solution and reliable GUV fabrication still remains a major experimental hurdle in the field. Here, we show that defect-free GUVs containing complex biochemical systems can be generated by optimizing a double-emulsion method for GUV formation called continuous droplet interface crossing encapsulation (cDICE). By tightly controlling environmental conditions and tuning the lipid-in-oil dispersion, we show that it is possible to significantly improve the reproducibility of high-quality GUV formation as well as the encapsulation efficiency. We demonstrate efficient encapsulation for a range of minimal systems including a minimal actin cytoskeleton, membrane-anchored DNA nanostructures, and a functional PURE (Protein synthesis Using Recombinant Elements) system. Our optimized cDICE method displays promising potential to become a standard method in biophysics and bottom-up synthetic biology.

synthetic biology↗

bioRxiv2022

Noise source importance in linear stochastic models of biological systems that grow, shrink, wander, or persist

While noise is an important factor in biology, biological processes often involve multiple noise sources, whose relative importance can be unclear. Here we develop tools that quantify the importance of noise sources in a network based on their contributions to variability in a quantity of interest. We generalize the edge importance measures proposed by Schmidt and Thomas [1] for first-order reaction networks whose steady-state variance is a linear combination of variance produced by each directed edge. We show that the same additive property extends to a general family of stochastic processes subject to a set of linearity assumptions, whether in discrete or continuous state or time. Our analysis applies to both expanding and contracting populations, as well as populations obeying a martingale ("wandering") at long times. We show that the original Schmidt-Thomas edge importance measure is a special case of our more general measure, and is recovered when the model satisfies a conservation constraint ("persists"). In the growing and wandering cases we show that the choice of observables (measurements) used to monitor the process does not influence which noise sources are important at long times. In contrast, in the shrinking or persisting case, which noise sources are important depends on what is measured. We also generalize our measures to admit models with affine moment update equations, which admit additional limiting scenarios, and arise naturally after linearization. We illustrate our results using examples from cell biology and ecology: (i) a model for the dynamics of the inositol trisphospate receptor, (ii) a model for an endangered population of white-tailed eagles, and (iii) a model for wood frog dispersal. Author summaryBiological processes are frequently subject to an ensemble of independent noise sources. Noise sources produce fluctuations that propagate through the system, driving fluctuations in quantities of interest such as population size or ion channel configuration. We introduce a measure that quantifies how much variability each noise source contributes to any given quantity of interest. Using these methods, we identify which binding events contribute significantly to fluctuations in the state of a molecular signalling channel, which life history events contribute the most variability to an eagle population before and after a successful conservation effort rescued the population from the brink of extinction, and which dispersal events, at what times, matter most to variability in the recolonization of a series of ponds by wood frogs after a drought.

ecology↗

bioRxiv2018

A note on stochastic modeling of biological systems: automatic generation of an optimized Gillespie algorithm

Signaling pathways and gene regulatory networks (GRNs) play a central role in the signal trans-duction and regulation of biochemical processes occurring within the cellular environment. Under-standing their mechanisms and dynamics is of major interest in various areas of life sciences and biological sciences. For example controlling stem cell fate decision requires a comprehension of the dynamical behavior of the networks involved in stem cell differentiation and pluripotency mainte-nance. In addition to analytical mathematical methods which are applicable for small or medium sized systems, there are many computational approaches to model and analyze the behavior of larger systems. However, from a dynamical point of view, modeling a combination of signaling pathways and GRNs present several challenges. Indeed, in addition to being of large dimensionality, these systems have specific dynamical features. Among the most commonly encountered is that the signal transduction controlled by the signaling pathways occurs at a different time scale than the transcription and translation processes. Also, stochasticity is known to strongly impact the regulation of gene expression. In this paper, we describe a simple implementation of an optimized version of the Gille-spie algorithm for simulating relatively large biological networks which include delayed reactions. The implementation presented herein comes with a script for automatically generating the different data structures and source files of the algorithm using standardized input files.\n\nCode availabilityThe Fortran90 implementation of the code and the R script described here as well as the tutorial with practical instructions are stored on the following github repository qvhaelen/ typhon

systems biology↗

bioRxiv2022

Influenza A virus agnostic receptor tropism revealed using a novel biological system with terminal sialic acid-knockout cells

Avian or human influenza A viruses bind preferentially to avian- or human-type sialic acid receptors, respectively, indicating that receptor tropism is an important factor for determining the viral host range. However, there are currently no reliable methods for analyzing receptor tropism biologically under physiological conditions. Here, we established a novel system using MDCK cells with avian- or human-type sialic acid receptors and with both sialic acid receptors knocked out (KO). When we examined the replication of human and avian influenza viruses in these KO cells, we observed unique viral receptor tropism that could not be detected using a conventional solid-phase sialylglycan binding assay, which directly assesses physical binding between the virus and sialic acids. Furthermore, we serially passaged an engineered avian-derived H4N5 influenza virus, whose PB2 gene was deleted, in avian-type receptor-KO cells stably expressing PB2 to select a mutant with enhanced replication in KO cells; however, its binding to human-type sialylglycan was undetectable using the solid-phase binding assay. These data indicate that a panel of sialic acid receptor-KO cells could be a useful tool for determining the biological receptor tropism of influenza A viruses. Moreover, the PB2-KO virus experimental system could help to safely and efficiently identify the mutations required for avian influenza viruses to adapt to human cells that could trigger a new influenza pandemic. Author summaryInfluenza A virus initiates infection via hemagglutinin by binding to avian- or human-type receptors. The acquisition of mutations that allow avian virus hemagglutinins (HAs) to recognize human-type receptors is mandatory for the transmission of avian influenza viruses to humans, which could lead to a pandemic. Therefore, it is important to detect such mutation(s) in animal influenza viruses for pandemic surveillance and risk assessment. In this study, we established a novel system using a set of genetically engineered MDCK cells with knocked out sialic acid receptors to biologically evaluate the receptor tropism for influenza A viruses. Using this system, we observed unique receptor tropism in several virus strains that was undetectable using conventional solid-phase binding assays that measure physical binding between the virus and artificially synthesized sialylglycans. This study makes a significant contribution to the literature because our findings suggest the pitfall of conventional receptor binding assay and the existence of a sialic acid-independent pathway for viral infection. In addition, our system could be safely used to identify mutations that could acquire human-type receptor tropism. Thus, this system could contribute not only toward basic analyses, such as elucidating the mechanism of influenza virus host range determination, but also the surveillance of viruses of animal origin that could be capable of infecting via human-type receptors, triggering a new influenza pandemic.

microbiology↗

bioRxiv2023

Comprehensive Approach to Simulating Large Scale Conformational Changes in Biological Systems Utilizing a Path Collective Variable and New Barrier Restraint

Conformational sampling of complex biomolecules is an emerging frontier in drug discovery. Advances in lab-based structural biology and related computational approaches like AlphaFold have made great strides in obtaining static protein structures for biologically relevant targets. However, biology is in constant motion and many important biological processes rely on conformationally-driven events. Conventional molecular dynamics (MD) simulations run on standard hardware, are impractical for many drug design projects, where conformationally-driven biological events can take microseconds to milliseconds or longer. An alternative approach is to focus the search on a limited region of conformational space defined by a putative reaction coordinate (i.e. path collective variable). The search space is typically limited by applying restraints, which can be guided by insights about the underlying biological process of interest. The challenge is striking a balance between the degree to which the system is constrained while still allowing for natural motions along the path. A plethora of restraints exist to limit the size of conformational search space, although each has drawbacks when simulating complex biological motions. In this work, we present a three-stage procedure to construct realistic path collective variables (PCV), and introduce a new kind of barrier restraint that is particularly well suited for complex conformationally-driven biological events, such as allosteric modulations and conformational signalling. The PCV presented here is all-atom (as opposed to C-alpha or backbone only) and is derived from all-atom MD trajectory frames. The new restraint relies on a barrier function (specifically, the scaled reciprocal function), which we show is particularly beneficial in the context of molecular dynamics, where near-hard-wall restraints are needed with zero tolerance to restraint violation. We have implemented our PCV and barrier restraint within a hybrid sampling framework that combines well-tempered meta-dynamics and extended-Lagrangian adaptive biasing force (meta-eABF). We use three particular examples of high pharmaceutical interest to demonstrate the value of this approach: (1) sampling the distance from ubiquitin to a protein of interest within the supramolecular Cullin-RING ligase complex, (2) stabilizing the wild-type conformation of the oncogenic mutant JAK2-V617F pseudokinase domain, and (3) inducing an activated state of the stimulator of interferon genes (STING) protein observed upon ligand binding. For (2) and (3), we present statistical analysis of meta-eABF free energy estimates and for each case, code for reproducing this work.

biophysics↗

bioRxiv2018

A theoretical approach for discriminating accurately intrinsic pattern of biological systems and recognizing three kind soybean proteomes

proteomics is able to reveal plentiful information related to different physiological and pathological states of biology. Further, the determination of accurately proteomic pattern is the essential platform for deeply proteomic research. While this has been somewhat ignored so far. In this article the quantitative standard Pg=61%, a biological similarity constant for discriminating accurately intrinsic proteomic patterns was established depending on biological common heredity and variation information equation in symmetric variation state. On the other hand, a novel theoretical method was proposed for linearly dividing nonlinear data sequence into linear segments. The proteomes of three kind soybeans were precisely distinguished from one another by analyzing their infrared fingerprint spectra relying on this theoretically systemic approach. Additionally, methods employed in this paper enable us to quickly, accurately and quantitatively determine the proteomic patterns without using any prior knowledge and learning samples, and without using electrophoresis, high performance liquid chromatography-mass spectrometry techniques, which are high cost, time-consuming. This approach provide us with an excellent one for quickly accurate determining biological species, physiological states and diagnosing pathological states based on proteomes.

bioinformatics↗

bioRxiv2025

Quantum-Coherent Identity Preservation and Substrate-Invariant Embodiment: A Theoretical Framework for Sustained Pure-State Dynamics in Complex Biological Systems

Living systems exhibit extraordinary resilience, adaptability, and identity preservation despite continuous atomic turnover. Traditional physics explains this persistence through biochemical stability, but a deeper quantuminformational description remains elusive. Here, we introduce a theoretical framework where a sustaining superoperator ([S]) exactly cancels environmental decoherence ([D]) within the Lindblad formalism, maintaining quantum coherence indefinitely. The resulting sustained pure-state system exhibits vanishing entropy production, stable informational identity, and finite tunneling amplitude under sublinear effective-mass scaling (Meff = m Na) with (0 < < 1). Numerical simulations confirm entropy cancellation, identity invariance under substrate replacement, and anomalous tunneling consistent with coherence-preserving collectivity. These findings propose mathematically consistent conditions for substrate-independent identity persistence and coherent embodiment, connecting concepts from quantum biology, information theory, and open-system thermodynamics.

biophysics↗

bioRxiv2019

Electrical Energy Storage with Engineered Biological Systems

The availability of renewable energy technologies is increasing dramatically across the globe thanks to their growing maturity. However, large scale electrical energy storage and retrieval will almost certainly be a required in order to raise the penetration of renewable sources into the grid. No present energy storage technology has the perfect combination of high power and energy density, low financial and environmental cost, lack of site restrictions, long cycle and calendar lifespan, easy materials availability, and fast response time. Engineered electroactive microbes could address many of the limitations of current energy storage technologies by enabling rewired carbon fixation, a process that spatially separates reactions that are normally carried out together in a photosynthetic cell and replaces the least efficient with non-biological equivalents. If successful, this could allow storage of renewable electricity through electrochemical or enzymatic fixation of carbon dioxide and subsequent storage as carbon-based energy storage molecules including hydrocarbon and non-volatile polymers at high efficiency. In this article we compile performance data on biological and non-biological component choices for rewired carbon fixation systems and identify pressing research and engineering challenges.

synthetic biology↗

bioRxiv2024

Neural Ordinary Differential Equations Inspired Parameterization of Kinetic Models

MotivationMetabolic kinetic models are widely used to model biological systems. Despite their widespread use, it remains challenging to parameterize these Ordinary Differential Equations (ODE) for large scale kinetic models. Recent work on neural ODEs has shown the potential for modeling time-series data using neural networks, and many methodological developments in this field can similarly be applied to kinetic models. ResultsWe have implemented a simulation and training framework for Systems Biology Markup Language (SBML) models using JAX/Diffrax, which we named jaxkineticmodel. JAX allows for automatic differentiation and just-in-time compilation capabilities to speed up the parameterization of kinetic models. We show the robust capabilities of training kinetic models using this framework on a large collection of SBML models with different degrees of prior information on parameter initialization. Finally, we showcase the training framework implementation on a complex model of glycolysis. These results show that our framework can be used to fit large metabolic kinetic models efficiently and provides a strong platform for modeling biological systems. ImplementationImplementation of jaxkineticmodel is available as a Python package at https://github.com/AbeelLab/jaxkineticmodel. Author summaryUnderstanding how metabolism works from a systems perspective is important for many biotechnological applications. Metabolic kinetic models help in achieving understanding, but there construction and parametrization has proven to be complex, especially for larger metabolic networks. Recent success in the field of neural ordinary differential equations in combination with other mathematical/computational techniques may help in tackling this issue for training kinetic models. We have implemented a Python package named jaxkineticmodel that can be used to build, simulate and train kinetic models, as well as compatibility with the Systems Biology Markup Language. This framework allows for efficient training of kinetic models on time-series concentration data using a neural ordinary differential equation inspired approach. We show the convergence properties on a large collection of SBML models, as well as experimental data. This shows a robust training process for models with hundreds of parameters, indicating that it can be used for large-scale kinetic model training.

systems biology↗

bioRxiv2020

Expansion Sequencing: Spatially Precise In Situ Transcriptomics in Intact Biological Systems

AbstractMethods for highly multiplexed RNA imaging are limited in spatial resolution, and thus in their ability to localize transcripts to nanoscale and subcellular compartments. We adapt expansion microscopy, which physically expands biological specimens, for long-read untargeted and targeted in situ RNA sequencing. We applied untargeted expansion sequencing (ExSeq) to mouse brain, yielding readout of thousands of genes, including splice variants and novel transcripts. Targeted ExSeq yielded nanoscale-resolution maps of RNAs throughout dendrites and spines in neurons of the mouse hippocampus, revealing patterns across multiple cell types; layer-specific cell types across mouse visual cortex; and the organization and position-dependent states of tumor and immune cells in a human metastatic breast cancer biopsy. Thus ExSeq enables highly multiplexed mapping of RNAs, from nanoscale to system scale. One Sentence SummaryIn situ sequencing of physically expanded specimens enables multiplexed mapping of RNAs at nanoscale, subcellular resolution.

genomics↗

bioRxiv2024

Interspecies systems biology links bacterial metabolic pathways to nematode gene expression, behavior, and survival

All animals live in tight association with complex microbial communities, yet studying the effects of individual bacteria remains challenging. Here, we exploit the system of the bacterial feeding nematode Pristionchus pacificus, where specific interactions can be studied in isolation. We sequenced the genomes of 84 Pristionchus-associated bacteria to investigate how differences in bacterial metabolic potential affect nematode traits. Specifically, we identified the Paerucumarin biosynthesis pathway as a strong candidate for nematode survival. Hypothesizing that bacterial production of coumarin derivatives contributed to increased lethality, we demonstrated nematicidal effects of two related compounds using supplementation assays. To explore metabolic interactions at a broader scale, we generated nematode transcriptomes for 38 bacterial diets and constructed a coexpression network that revealed distinct developmental and environmental signatures. Interspecies association studies resulted in a bipartite network with more than 2,800 interactions which may assist future studies to identify metabolites affecting various biological processes.

microbiology↗

bioRxiv2021

Long non-coding RNAs bind to proteins relevant to the ethanol tolerance in yeast: a systems biology view

The ethanol disturbs the cell cycle, transcription, translation, protein folding, cell wall, membranes, and many Saccharomyces cerevisiae metabolic processes. Long non-coding RNAs (lncRNAs) are regulatory molecules binding onto the genome or proteins. The number of lncRNAs described for yeast is still scarce, and little is known concerning their roles in the system. There is a lack of knowledge concerning how lncRNAs are responsive to the ethanol tolerance in yeast and whether they act in this tolerance. Hence, by using RNA-Seq data from S. cerevisiae strains with different ethanol tolerance phenotypes, we found the severe ethanol responsive lncRNAs. We modeled how they participate in the ethanol tolerance by analyzing lncRNA-protein interactions. The results showed that the EtOH tolerance responsive lncRNAs, in both higher tolerant and lower tolerant phenotypes, work on different pathways: cell wall, cell cycle, growth, longevity, cell surveillance, ribosome biogenesis, intracellular transport, trehalose metabolism, transcription, and nutrient shifts. In summary, lncRNAs seems to interconnect essential systems modules to overcome the ethanol stress. Finally, here we also found the most extensive catalog of lncRNAs in yeast.

microbiology↗

bioRxiv2025

Scaling in Biological Systems: A Molecular-Ensemble Duality

We have observed in muscle the statistical mechanics of irreversible chemical thermodynamics, revealing the solution to multiple seemingly unrelated paradoxes in science. Analogous to Boltzmanns H theorem, we observe that chemical reaction energy landscapes (ensemble entropic wells) irreversibly evolve over time, pulling reversible chemical reactions forward in time. Loschmidts paradox assumes that reversible molecular reactions scale up to irreversible changes in an ensemble, and many mathematical constructs have been created to satisfy this assumption (Boltzmanns H-function, chemical activities, the kinetics theory of gases, molecular mechanisms of biological function, etc.). However, using a simple statistical argument, here we show that the irreversible time evolutions of molecular and ensemble states are described by two different non-scalable entropies, creating a molecular-ensemble duality in any system on any scale. This inverts common understandings of mechanistic agency and the arrow of time and disproves all molecular mechanisms of irreversible ensemble processes. Significance StatemenThis statistical analysis inverts common understandings of mechanistic agency, entropy, and the arrow of time; it solves several paradoxes in physics; and it disproves molecular mechanisms of irreversible processes.

biophysics↗

bioRxiv2026

Integrated Pangenomic and Systems Biology Analyses Reveal the Genomic Basis of Virulence and Adaptation in Bipolaris sorokiniana

Bipolaris sorokiniana is a hemibiotrophic fungal pathogen responsible for foliar and root diseases of cereals, causing annual yield losses of 10-50%. The recurrent breakdown of host resistance and the emergence of fungicide-resistant pathogen populations underscore the urgent need to understand the genomic mechanisms underpinning pathogen adaptation and virulence. Here, we present the first comprehensive species-wide pangenomic and systems-level analyses of B. sorokiniana based on 19 genomes of globally distributed strains. Orthology-based analyses revealed an open pangenome comprising 16,981 orthogroups, partitioned into a conserved core genome (60.8%) and a highly dynamic accessory genome (39.2%), consisting of soft-core (8.5%), shell (19.7%), and cloud (11.0%) compartments. The core genes were predominantly associated with essential cellular and metabolic functions, while the accessory fractions were enriched in regulatory, stress-responsive, and adaptive processes. Secondary metabolite profiling identified 39-54 biosynthetic gene clusters per genome and revealed a largely conserved metabolic repertoire. Gene family evolution analyses revealed an excess of gene loss over expansion, indicating ongoing genome streamlining and lineage-specific adaptation. The core interactome comprised four densely connected functional communities governing genome maintenance, ribosome biogenesis, cellular bioenergetics, and protein translation. Collectively, this study elevates B. sorokiniana research from single-genome analyses to a population-scale analysis, providing vital insights into the evolutionary architecture of pathogenicity, adaptation, and genome diversification. These findings provide a valuable genomic resource for disease surveillance and functional characterization of virulence determinants, as well as the development of durable resistance strategies and next-generation antifungals for sustainable disease management in cereals.

bioinformatics↗

bioRxiv2023

High Efficiency Rare Earth Element Biomining with Systems Biology Guided Engineering of Gluconobacter oxydans

The global demand for critical rare earth elements (REE) is rising1 with the increase in demand for sustainable energy technologies like wind turbines2,3, electric vehicles2,3, and high efficiency lighting4. Current processes for producing REE require high energy inputs and can produce disproportionate amounts of hazardous waste. Biological methods for REE production are a promising solution to this problem. In earlier work we identified the most important genetic mechanisms contributing to the REE-bioleaching capability of Gluconobacter oxydans B585. Here we have targeted two of these mechanisms to generate a high-efficiency bio-mining strain of G. oxydans. Disruption of the phosphate-specific transport system through a clean deletion of pstS constitutively turns on the phosphate starvation response, yielding a much more acidic biolixiviant, and increasing bioleaching by up to 30%. Coupling knockout of pstS with the over-expression of the mgdh membrane-bound glucose dehydrogenase gene, results in up to 73% improvement of REE-bioleaching.

synthetic biology↗

bioRxiv2025

Cerebral blood perfusion across biological systems and the human lifespan

Blood perfusion delivers oxygen and nutrients to all cells, making it a fundamental feature of brain organization. How cerebral blood perfusion maps onto micro-, meso- and macro-scale brain structure and function is therefore a key question in neuroscience. Here we analyze pseudo-continuous arterial spin labeling (ASL) data from 1 305 healthy individuals in the HCP Lifespan studies (5-100 years) to reconstruct a high-resolution normative cerebral blood perfusion map. At the cellular and molecular level, cerebral blood perfusion co-localizes with granular layer IV, biological pathways for maintenance of cellular relaxation potential and mitochondrial organization, and with neurotransmitter and neuropeptide receptors involved in vasomodulation. At the regional level, blood perfusion aligns with cortical arealization and is greatest in regions with high metabolic demand and resting-state functional hubs. Looking across individuals, blood perfusion is dynamic throughout the lifespan, follows microarchitectural changes in development, and maps onto individual differences in physiological changes in aging. In addition, we find that cortical atrophy in multiple neurodegenerative diseases (late-onset Alzheimers disease, TDP-43C, and dementia with Lewy bodies) is most pronounced in regions with lower perfusion, highlighting the utility of perfusion topography as an indicator of transdiagnostic vulnerability. Finally, we show that ASL-derived perfusion can be used to delineate arterial territories in a data-driven manner, providing insights into how the vascular system is linked to human brain function. Collectively, this work highlights how cerebral blood perfusion is central to, and interlinked with, multiple structural and functional systems in the brain.

neuroscience↗