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| Open AccessA generalizable Cas9/sgRNA prediction model using machine transfer learning with small high-quality datasets
Current bacterial sgRNA activity models struggle with accurate predictions and generalizations. Here the authors report crisprHAL, a machine learning architecture that can be trained on existing datasets, and shows good sgRNA activity prediction accuracy can generalize predictions to different bacteria.
- Dalton T. Ham
- , Tyler S. Browne
- & David R. Edgell
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Article
| Open AccessInterpreting biologically informed neural networks for enhanced proteomic biomarker discovery and pathway analysis
Deep neural networks hold significant promise in capturing the complexity of biological systems. However, they suffer from a lack of interpretability. Here, authors present a generalizable method for developing, interpreting, and visualizing biologically informed neural networks for proteomics data.
- Erik Hartman
- , Aaron M. Scott
- & Johan Malmström
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Article
| Open AccessAddressing mechanism bias in model-based impact forecasts of new tuberculosis vaccines
The complex transmission chain of tuberculosis (TB) forces mathematical modelers to make mechanistic assumptions when modelling vaccine effects. Here, authors posit a Bayesian formalism that unlocks mechanism-agnostic impact forecasts for TB vaccines.
- M. Tovar
- , Y. Moreno
- & J. Sanz
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Article
| Open AccessEffects of public-health measures for zeroing out different SARS-CoV-2 variants
China maintained a ‘zero-COVID’ policy from early in the pandemic until late 2022 that employed various public health interventions with the aim of COVID-19 containment. Here, the authors use data from 131 outbreaks in China to estimate the effects of a range of interventions against different SARS-CoV-2 variants in diverse settings.
- Yong Ge
- , Xilin Wu
- & Shengjie Lai
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Article
| Open AccessThermodynamic principle to enhance enzymatic activity using the substrate affinity
Currently, there is no well-defined strategy to increase the activity of enzymes. Here, the authors provide mathematical evidence that adjusting the Michaelis-Menten constant to the substrate concentration maximizes enzymatic activity.
- Hideshi Ooka
- , Yoko Chiba
- & Ryuhei Nakamura
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Article
| Open AccessCD98hc is a target for brain delivery of biotherapeutics
New delivery platforms are needed to allow broader application of biotherapeutics for CNS diseases. Here, the authors show enhanced CNS delivery with a transport vehicle engineered to bind CD98hc, a highly expressed target at the blood-brain barrier.
- Kylie S. Chew
- , Robert C. Wells
- & Mihalis S. Kariolis
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Article
| Open AccessA deep learning method for replicate-based analysis of chromosome conformation contacts using Siamese neural networks
Siamese neural networks are a powerful deep learning approach for image analysis. Here, the authors adapt this method to the replicate-based analysis of Hi-C data and find that it successfully discriminates technical noise from biological variation.
- Ediem Al-jibury
- , James W. D. King
- & Daniel Rueckert
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Article
| Open AccessWhole genome deconvolution unveils Alzheimer’s resilient epigenetic signature
The authors present a deep learning method that deconvolutes ATAC-seq samples into cell type-specific chromatin accessibility profiles. Applied on 191 samples, the method unveils cell type-specific pathways and nominates potential epigenetic mediators underlying resilience to Alzheimer’s disease.
- Eloise Berson
- , Anjali Sreenivas
- & Thomas J. Montine
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Article
| Open AccessCOMPASS: joint copy number and mutation phylogeny reconstruction from amplicon single-cell sequencing data
Understanding the evolution of a tumor is important for predicting its resistance to treatment. This paper presents a new computational method, COMPASS, for inferring the joint phylogeny of single nucleotide variants and copy number alterations from targeted scDNAseq data.
- Etienne Sollier
- , Jack Kuipers
- & Katharina Jahn
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Article
| Open AccessFast kernel-based association testing of non-linear genetic effects for biobank-scale data
We have developed FastKAST, a highly-scalable algorithm to identify non-linear genetic effects on complex traits in large datasets. Applied to 300K UK Biobank individuals, we successfully detected significant non-linear effects across 53 traits.
- Boyang Fu
- , Ali Pazokitoroudi
- & Sriram Sankararaman
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Article
| Open AccessCharacterizing cancer metabolism from bulk and single-cell RNA-seq data using METAFlux
Metabolic reprogramming is a common indicator of the tumour microenvironment. Here the authors develop the METAflux framework to predict metabolic fluxes from single cell RNA-seq data.
- Yuefan Huang
- , Vakul Mohanty
- & Ken Chen
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Article
| Open AccessCell-type-specific co-expression inference from single cell RNA-sequencing data
Inferring co-expressions with scRNA-seq data is challenging, and existing methods suffer from inflated false positives and biases. Here, the authors proposed CS-CORE, which yields unbiased estimates and identifies co-expressions that are more reproducible and biologically relevant for scRNA-seq data.
- Chang Su
- , Zichun Xu
- & Jingfei Zhang
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Article
| Open AccessA machine-learning approach to human ex vivo lung perfusion predicts transplantation outcomes and promotes organ utilization
Ex vivo perfusion is a unique platform to study isolated human lungs. Here, authors show that a machine learning model, InsighTx, derived from data generated during ex vivo lung perfusion can accurately predict transplant outcomes and increase organ utilization rates.
- Andrew T. Sage
- , Laura L. Donahoe
- & Shaf Keshavjee
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Article
| Open AccessProteomics and constraint-based modelling reveal enzyme kinetic properties of Chlamydomonas reinhardtii on a genome scale
Closing a major gap in photosynthetic metabolic modelling, the authors provide over 500 estimates of in vivo enzyme catalytic rate in C. reinhardtii, which considerably improves predictions on how enzyme mass is allocated to different pathways.
- Marius Arend
- , David Zimmer
- & Zoran Nikoloski
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Article
| Open AccessExperimental validation of the free-energy principle with in vitro neural networks
Empirical applications of the free-energy principle entail a commitment to a particular process theory. Here, the authors reverse engineered generative models from neural responses of in vitro networks and demonstrated that the free-energy principle could predict how neural networks reorganized in response to external stimulation.
- Takuya Isomura
- , Kiyoshi Kotani
- & Karl J. Friston
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Article
| Open AccessSONAR enables cell type deconvolution with spatially weighted Poisson-Gamma model for spatial transcriptomics
Spatial transcriptomics reveal cellular profiles with spatial context. Here the authors present SONAR, a computational model that utilizes spatial information to decipher cell types in tissues and validate on various spatial patterns and fine-mapped cell types in complex tissues.
- Zhiyuan Liu
- , Dafei Wu
- & Liang Ma
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Article
| Open AccessFunctional annotation of proteins for signaling network inference in non-model species
An artificial-intelligence network is used to generate highly accurate predictions of proteins’ functionality. The predictions on the identity of regulatory proteins is used to create regulatory networks and make discoveries about complex biological systems.
- Lisa Van den Broeck
- , Dinesh Kiran Bhosale
- & Rosangela Sozzani
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Article
| Open AccessA multiplexed bacterial two-hybrid for rapid characterization of protein–protein interactions and iterative protein design
Protein-protein interactions (PPIs) are crucial for biological functions and have applications ranging from drug design to synthetic cell circuits. Here the authors develop an assay and computational methods to identify more orthogonal coiled-coil pairs, critical for biological processes and drug design.
- W. Clifford Boldridge
- , Ajasja Ljubetič
- & Sriram Kosuri
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Article
| Open AccessPreventing antimalarial drug resistance with triple artemisinin-based combination therapies
Triple artemisinin-based combination therapies have shown high efficacy for treatment of malaria in preliminary studies. Here, the authors use mathematical modelling to assess whether these therapies could also delay the emergence and spread of antimalarial drug resistance when compared against frontline therapies.
- Tran Dang Nguyen
- , Bo Gao
- & Ricardo Aguas
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Article
| Open AccessPartition complex structure can arise from sliding and bridging of ParB dimers
In many bacteria and plasmids, DNA segregation is controlled by the ParABS system, an essential component of which is the formation of a nucleoprotein complex. Here, making use of recent discoveries, the authors develop a sliding and bridging model to predict the fine structure of this complex.
- Lara Connolley
- , Lucas Schnabel
- & Seán M. Murray
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Article
| Open AccessMacrocyclization of linear molecules by deep learning to facilitate macrocyclic drug candidates discovery
Macrocyclization of bioactive acyclic molecules provides a potential avenue to yield novel chemical scaffolds with improved pharmacological properties. Here, the authors propose a deep learning based macrocyclization method to generate diverse macrocycles from a given acyclic molecule.
- Yanyan Diao
- , Dandan Liu
- & Honglin Li
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Article
| Open AccessNutritional redundancy in the human diet and its application in phenotype association studies
Studying human diet may help us identify measures to treat or prevent chronic diseases. Here, the authors discover the nutritional redundancy phenomenon in human diet and demonstrate its association with cardiovascular disease and type 2 diabetes.
- Xu-Wen Wang
- , Yang Hu
- & Yang-Yu Liu
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Article
| Open AccessEpidemiological drivers of transmissibility and severity of SARS-CoV-2 in England
The COVID-19 pandemic has been characterised by periods of dominance of different SARS-CoV-2 variants. In this mathematical modelling study, the authors investigate the epidemiological properties of successive variants in England until early 2022 and quantify the impacts of control measures.
- Pablo N. Perez-Guzman
- , Edward Knock
- & Marc Baguelin
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| Open AccessSequence-based drug design as a concept in computational drug design
Conventional structure-based drug design pipeline is a complex, human-engineered process with multiple independently optimized steps. Here, the authors report a sequence-to-drug concept that discovers drug-like small molecule modulators directly from protein sequences.
- Lifan Chen
- , Zisheng Fan
- & Mingyue Zheng
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Article
| Open AccessImpact of misclassified defective proviruses on HIV reservoir measurements
Quantifying intact proviruses is key to understanding decreases in HIV reservoirs but results can differ depending on the method. To balance sensitivity and specificity of two assays, the authors use mathematical models and measurements of intact and defective proviruses to assess how misclassification can impact estimates of natural and therapeutic reservoir reduction.
- Daniel B. Reeves
- , Christian Gaebler
- & Michel C. Nussenzweig
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Article
| Open AccessA model for organization and regulation of nuclear condensates by gene activity
Through a physics-based model framework, the authors propose a central role for the nonequilibrium processes underling gene activity in shaping morphology, dynamics, and regulation of diverse nuclear condensates.
- Halima H. Schede
- , Pradeep Natarajan
- & Krishna Shrinivas
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Article
| Open AccessDNA 5-methylcytosine detection and methylation phasing using PacBio circular consensus sequencing
Existing methods for detecting DNA methylation (5mC) are less accurate and robust. Here, the authors develop a deep learning tool ccsmeth and a Nextflow pipeline ccsmethphase for genome-wide 5mCpG detection and phasing with high accuracy from CCS reads in human.
- Peng Ni
- , Fan Nie
- & Jianxin Wang
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Article
| Open AccessLearning-induced reorganization of number neurons and emergence of numerical representations in a biologically inspired neural network
How the brain represents numbers remains poorly understood. Here, the authors uncover the emergence of absolute and relative magnitude representations of quantity in a biologically-inspired neural network, mirroring observations in children during numerical skill acquisition.
- Percy K. Mistry
- , Anthony Strock
- & Vinod Menon
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Article
| Open AccessCell facilitation promotes growth and survival under drug pressure in breast cancer
In cancer, interactions between treatment-sensitive and resistant cells can influence the effectiveness of therapies. Here, the authors use experimental and mathematical models to explore interactions between ER+ breast cancer cell lineages that are sensitive or resistant to CDK4/6 inhibition, revealing the role of facilitative growth.
- Rena Emond
- , Jason I. Griffiths
- & Andrea H. Bild
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Article
| Open AccessPredicting in-hospital outcomes of patients with acute kidney injury
Early prediction of AKI-related clinical events and timely intervention for high-risk patients could improve outcomes. Here, the authors show a deep learning model that can identify patients with acute kidney injury (AKI) who are at high risk of death or dialysis at certain time points.
- Changwei Wu
- , Yun Zhang
- & Guisen Li
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Article
| Open AccessJoint inference of exclusivity patterns and recurrent trajectories from tumor mutation trees
Understanding cancer evolution is crucial for developing effective therapies. Here, authors present TreeMHN, a probabilistic model for inferring exclusivity patterns of genomic events and evolutionary trajectories from intra-tumor phylogenetic trees.
- Xiang Ge Luo
- , Jack Kuipers
- & Niko Beerenwinkel
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Article
| Open AccessBiomedical knowledge graph learning for drug repurposing by extending guilt-by-association to multiple layers
Computational drug repurposing models that leverage biomedical knowledge graphs to associate drugs to diseases, are biased to genes. Here, the authors present DREAMwalk, which extends guilt-by-association for multi-layer knowledge graph learning using a semantic information-guided random walk.
- Dongmin Bang
- , Sangsoo Lim
- & Sun Kim
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Article
| Open AccessA convolutional neural network STIFMap reveals associations between stromal stiffness and EMT in breast cancer
The link between stiffness heterogeneity and tumor cell heterogeneity remains poorly understood. Here, authors propose an AI-informed method that reveals correlations between stromal stiffness and breast cancer cells with a heterogeneous EMT phenotype.
- Connor Stashko
- , Mary-Kate Hayward
- & Valerie M. Weaver
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Article
| Open AccessMechanistic studies of a lipase unveil effect of pH on hydrolysis products of small PET modules
Plastic-degrading enzymes can be utilized for plastic recycling. Here, QM/MM molecular dynamics and experimental Michaelis–Menten kinetics provide insight into PETase/MHTase activities of the lipase B from Candida antartica.
- Katarzyna Świderek
- , Susana Velasco-Lozano
- & Vicent Moliner
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| Open AccessModelling genetic stability in engineered cell populations
Predicting the evolution of engineered cell populations is an increasingly popular topic in biotechnology. Here the authors build a model that explores evolution in engineered cell populations which can generate hypotheses that could lead to important insights into strategies for assessing and mitigating the effects of evolution.
- Duncan Ingram
- & Guy-Bart Stan
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Article
| Open AccessAnalysis and modeling of cancer drug responses using cell cycle phase-specific rate effects
Understanding the impact of anti-cancer therapies on cell cycle progression could contribute to the discovery of effective therapeutic treatments. Here, the authors use genetically engineered breast cancer cell lines and computational models to analyse drug effects on specific cell cycle phases and identify effective combination treatments.
- Sean M. Gross
- , Farnaz Mohammadi
- & Laura M. Heiser
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Article
| Open AccessInflationary theory of branching morphogenesis in the mouse salivary gland
The authors show that the ramified ductal network of the mouse salivary gland develops from a set of simple probabilistic rules, where ductal elongation and branching are driven by the persistent expansion of the surrounding tissue.
- Ignacio Bordeu
- , Lemonia Chatzeli
- & Benjamin D. Simons
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Article
| Open AccessNo evidence of sustained nonzoonotic Plasmodium knowlesi transmission in Malaysia from modelling malaria case data
Plasmodium knowlesi is a zoonotic malaria parasite that can infect humans, but whether human-mosquito-human transmission occurs is not known. Here, the authors use data from Malaysia and show, through mathematical modelling, that sustained non-zoonotic transmission is unlikely to be occurring in this setting.
- Kimberly M. Fornace
- , Hillary M. Topazian
- & Chris Drakeley
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| Open AccessEpidemiological inference for emerging viruses using segregating sites
Epidemiological models are commonly fit to case and pathogen sequence data to estimate parameters and to reconstruct disease dynamics. Here, the authors present an inference approach based on sequence data that is well suited for model fitting early on during the expansion of a viral lineage.
- Yeongseon Park
- , Michael A. Martin
- & Katia Koelle
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Article
| Open AccessLearning how network structure shapes decision-making for bio-inspired computing
Better understanding of a trade-off between the speed and accuracy of decision-making is relevant for mapping biological intelligence to machines. The authors introduce a brain-inspired learning algorithm to uncover dependencies in individual fMRI networks with features of neural activity and predict inter-individual differences in decision-making.
- Michael Schirner
- , Gustavo Deco
- & Petra Ritter
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Article
| Open AccessEnabling accurate and early detection of recently emerged SARS-CoV-2 variants of concern in wastewater
Sapoval et al. introduce QuaID, a bioinformatics tool for SARS-CoV-2 variant detection based on quasi-unique mutations. QuaID leverages all mutations, including insertions and deletions, and provides precise detection of variants early in their spread.
- Nicolae Sapoval
- , Yunxi Liu
- & Todd J. Treangen
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Article
| Open AccessModelling the impact of interventions on imported, introduced and indigenous malaria infections in Zanzibar, Tanzania
Malaria elimination is defined by WHO as the absence of recent indigenous cases in an area. In this study, the authors develop a metapopulation model that identifies indigenous cases and use it to investigate the likelihood of malaria elimination in Zanzibar under different intervention scenarios.
- Aatreyee M. Das
- , Manuel W. Hetzel
- & Nakul Chitnis
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Article
| Open AccessOptimal enzyme utilization suggests that concentrations and thermodynamics determine binding mechanisms and enzyme saturations
One of the main challenges hampering the development of kinetic models is the lack of kinetic parameters for many enzymatic reactions. Here, the authors introduce a framework to explore the catalytically optimal operating conditions of any complex enzyme mechanism from an evolutionary perspective.
- Asli Sahin
- , Daniel R. Weilandt
- & Vassily Hatzimanikatis
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Article
| Open AccessMolecular determinants of inhibition of UCP1-mediated respiratory uncoupling
Combining molecular dynamic simulations with in vivo functional assays, Gagelin et al. identified unique molecular features of the mitochondrial carrier uncoupling protein 1 that are crucial to its inhibition by nucleotides
- Antoine Gagelin
- , Corentin Largeau
- & Bruno Miroux
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Article
| Open AccessA blueprint for a synthetic genetic feedback optimizer
Genetic modules are sensitive to changes in their context and to environmental perturbations. Here, the authors develop a genetic optimizer based on common synthetic biology parts to ensure optimal and robust cellular performance in diverse contexts.
- Andras Gyorgy
- , Amor Menezes
- & Murat Arcak
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Article
| Open AccessReconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace
Methods to reanalyze scRNA-seq data in a spatial perspective are vital but lacking. Here, the authors develop scSpace, an integrative method that uses ST data as spatial reference to reconstruct the pseudo-space of scRNA-seq data and identify spatially variable cell subpopulations, providing insights into spatial heterogeneity from scRNA-seq data.
- Jingyang Qian
- , Jie Liao
- & Xiaohui Fan
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Article
| Open AccessComputational design and molecular dynamics simulations suggest the mode of substrate binding in ceramide synthases
Membrane proteins are involved in many critical cellular pathways. Here, authors use a combination of structural predictions, an algorithm for stabilizing membrane proteins, and molecular dynamics to reveal a putative mechanism for the action of ceramide synthases.
- Iris D. Zelnik
- , Beatriz Mestre
- & Anthony H. Futerman
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Article
| Open AccessDirect correction of haemoglobin E β-thalassaemia using base editors
The authors demonstrate efficient and direct correction of the DNA mutation causing Haemoglobin E β-thalassaemia with CRISPR Cas9 base editors. The work includes profiling of off-target effects using deep neural networks.
- Mohsin Badat
- , Ayesha Ejaz
- & James O. J. Davies
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Article
| Open AccessAccounting for assay performance when estimating the temporal dynamics in SARS-CoV-2 seroprevalence in the U.S.
SARS-CoV-2 seroprevalence surveys aim to estimate the proportion of the population that has been infected, but their accuracy depends on the characteristics of the test assay used. Here, the authors use statistical models to assess the impact of the use of different assays on estimates of seroprevalence in the United States.
- Bernardo García-Carreras
- , Matt D. T. Hitchings
- & Derek A. T. Cummings