pwc-archive/papers-with-abstracts huggingface dataset:
Multiscale Modeling Primer: Focus on Chromatin and Epigenetics
Authors: Achal Mahajan, Erik J. Navarro, William Poole, Carlos F Lopez
Date: 2025-04-04 00:00:00

Essential life processes take place across multiple space and time scales in living organisms but understanding their mechanistic interactions remains an ongoing challenge. Advanced multiscale modeling techniques are providing new opportunities and insights into these complex processes. In cells, meters of chromatin are folded into a nucleus with a diameter on the order of microns. The three-dimensional chromatin structure coupled with biochemical processes that turn genes on or off, specify a given cell type through a complicated set of interactions collectively referred to as epigenetics. Important epigenetic processes include the differential accessibility of genomic loci to transcription factors and chemical modifications to DNA and DNA-binding molecules such as histones. The dynamics of these epigenetic processes span timescales from milliseconds to years. How do chemical modifications consisting of a handful of atoms cooperate to modulate genome folding at the scale of the nucleus and impact organism outcomes? In this review, we highlight the inherently multiscale nature of chromatin organization, with a focus on computational modeling to bridge the gaps in our understanding of biochemical processes across scales. We review relevant chromatin biology, including major types of epigenetic modifications as well as the higher order chromatin structures to present a multiscale view of chromatin. We also review relevant computational methods to simulate chromatin structure, function, and dynamics, as well as experimental techniques that inform and validate said models. Finally, we argue that multiscale modeling provides a path forward towards understanding emergent behavior in this inherently multiscale system.
Artificial Intelligence and Deep Learning Algorithms for Epigenetic Sequence Analysis: A Review for Epigeneticists and AI Experts
Authors: Muhammad Tahir, Mahboobeh Norouzi, Shehroz S. Khan, James R. Davie, Soichiro Yamanaka, Ahmed Ashraf
Date: 2025-04-01 00:00:00

Epigenetics encompasses mechanisms that can alter the expression of genes without changing the underlying genetic sequence. The epigenetic regulation of gene expression is initiated and sustained by several mechanisms such as DNA methylation, histone modifications, chromatin conformation, and non-coding RNA. The changes in gene regulation and expression can manifest in the form of various diseases and disorders such as cancer and congenital deformities. Over the last few decades, high throughput experimental approaches have been used to identify and understand epigenetic changes, but these laboratory experimental approaches and biochemical processes are time-consuming and expensive. To overcome these challenges, machine learning and artificial intelligence (AI) approaches have been extensively used for mapping epigenetic modifications to their phenotypic manifestations. In this paper we provide a narrative review of published research on AI models trained on epigenomic data to address a variety of problems such as prediction of disease markers, gene expression, enhancer promoter interaction, and chromatin states. The purpose of this review is twofold as it is addressed to both AI experts and epigeneticists. For AI researchers, we provided a taxonomy of epigenetics research problems that can benefit from an AI-based approach. For epigeneticists, given each of the above problems we provide a list of candidate AI solutions in the literature. We have also identified several gaps in the literature, research challenges, and recommendations to address these challenges.
Emergent kinetics of in vitro transcription from interactions of T7 RNA polymerase and DNA
Authors: Nathan M. Stover, Marieke De Bock, Julie Chen, Jacob Rosenfeld, Maria del Carme Pons Royo, Allan S. Myerson, Richard D. Braatz
Date: 2025-04-09 00:00:00

The in vitro transcription reaction (IVT) is of growing importance for the manufacture of RNA vaccines and therapeutics. While the kinetics of the microscopic steps of this reaction (promoter binding, initiation, and elongation) are well studied, the rate law of overall RNA synthesis that emerges from this system is unclear. In this work, we show that a model that incorporates both initiation and elongation steps is essential for describing trends in IVT kinetics in conditions relevant to RNA manufacturing. In contrast to previous reports, we find that the IVT reaction can be either initiation- or elongation-limited depending on solution conditions. This initiation-elongation model is also essential for describing the effect of salts, which disrupt polymerase-promoter binding, on transcription rates. Polymerase-polymerase interactions during elongation are incorporated into our modeling framework and found to have nonzero but unidentifiable effects on macroscopic transcription rates. Finally, we develop an extension of our modeling approach to quantitatively describe and experimentally evaluate RNA- and DNA-templated mechanisms for the formation of double-stranded RNA (dsRNA) impurities. We show experimental results that indicate that an RNA-templated mechanism is not appropriate for describing macroscopic dsRNA formation in the context of RNA manufacturing.
Characterisation of conserved and reacting moieties in chemical reaction networks
Authors: Hadjar Rahou, Hulda S. Haraldsdottir, Filippo Martinelli, Ines Thiele, Ronan M. T. Fleming
Date: 2025-04-07 00:00:00

A detailed understanding of biochemical networks at the molecular level is essential for studying complex cellular processes. In this paper, we provide a comprehensive description of biochemical networks by considering individual atoms and chemical bonds. To address combinatorial complexity, we introduce a well-established approach to group similar types of information within biochemical networks. A conserved moiety is a set of atoms whose association is invariant across all reactions in a network. A reacting moiety is a set of bonds that are either broken, formed, or undergo a change in bond order in at least one reaction in the network. By mathematically identifying these moieties, we establish the biological significance of conserved and reacting moieties according to the mathematical properties of the stoichiometric matrix. We also present a novel decomposition of the stoichiometric matrix based on conserved moieties. This approach bridges the gap between graph theory, linear algebra, and biological interpretation, thus opening up new horizons in the study of chemical reaction networks.
Bidirectional Hierarchical Protein Multi-Modal Representation Learning
Authors: Xuefeng Liu, Songhao Jiang, Chih-chan Tien, Jinbo Xu, Rick Stevens
Date: 2025-04-07 00:00:00
Tasks: Prediction, Representation Learning

Protein representation learning is critical for numerous biological tasks. Recently, large transformer-based protein language models (pLMs) pretrained on large scale protein sequences have demonstrated significant success in sequence-based tasks. However, pLMs lack structural information. Conversely, graph neural networks (GNNs) designed to leverage 3D structural information have shown promising generalization in protein-related prediction tasks, but their effectiveness is often constrained by the scarcity of labeled structural data. Recognizing that sequence and structural representations are complementary perspectives of the same protein entity, we propose a multimodal bidirectional hierarchical fusion framework to effectively merge these modalities. Our framework employs attention and gating mechanisms to enable effective interaction between pLMs-generated sequential representations and GNN-extracted structural features, improving information exchange and enhancement across layers of the neural network. Based on the framework, we further introduce local Bi-Hierarchical Fusion with gating and global Bi-Hierarchical Fusion with multihead self-attention approaches. Through extensive experiments on a diverse set of protein-related tasks, our method demonstrates consistent improvements over strong baselines and existing fusion techniques in a variety of protein representation learning benchmarks, including react (enzyme/EC classification), model quality assessment (MQA), protein-ligand binding affinity prediction (LBA), protein-protein binding site prediction (PPBS), and B cell epitopes prediction (BCEs). Our method establishes a new state-of-the-art for multimodal protein representation learning, emphasizing the efficacy of BIHIERARCHICAL FUSION in bridging sequence and structural modalities.
Deterministic and Stochastic Models in Enzyme Kinetics
Authors: Jiaji Qu, Malini Rajbhandari
Date: 2025-03-22 00:00:00

Enzyme kinetics has historically been described by deterministic models, with the Michaelis-Menten (MM) equation serving as a paradigm. However, recent experimental and theoretical advances have made it clear that stochastic fluctuations, particularly at low copy numbers or single-enzyme levels, can profoundly impact reaction outcomes. In this paper, we present a comprehensive view of enzyme kinetics from both deterministic and stochastic perspectives. We begin by deriving the classical Michaelis-Menten equation under the quasi-steady-state assumption (QSSA) and discuss its validity. We then formulate the corresponding stochastic model via the chemical master equation (CME) and illustrate how the Gillespie algorithm can simulate single-molecule events and briefly use Kampen's system-size expansion to justify our simulation methods. Through extended computational analyses -- including variance calculations, phase-plane exploration, and parameter sensitivity -- we highlight how deterministic and stochastic predictions coincide in certain limits but can diverge in small systems. We further incorporate case studies from single-enzyme turnover experiments and cellular contexts to showcase the real-world implications of noise. Taken together, our results underscore the necessity of a multifaceted modeling strategy, whereby one can switch between deterministic methods and stochastic realism to gain a fuller understanding of enzyme kinetics at different scales.
Go Figure: Transparency in neuroscience images preserves context and clarifies interpretation
Authors: Paul A. Taylor, Himanshu Aggarwal, Peter Bandettini, Marco Barilari, Molly Bright, Cesar Caballero-Gaudes, Vince Calhoun, Mallar Chakravarty, Gabriel Devenyi, Jennifer Evans, Eduardo Garza-Villarreal, Jalil Rasgado-Toledo, Remi Gau, Daniel Glen, Rainer Goebel, Javier Gonzalez-Castillo, Omer Faruk Gulban, Yaroslav Halchenko, Daniel Handwerker, Taylor Hanayik, Peter Lauren, David Leopold, Jason Lerch, Christian Mathys, Paul McCarthy, Anke McLeod, Amanda Mejia, Stefano Moia, Thomas Nichols, Cyril Pernet, Luiz Pessoa, Bettina Pfleiderer, Justin Rajendra, Laura Reyes, Richard Reynolds, Vinai Roopchansingh, Chris Rorden, Brian Russ, Benedikt Sundermann, Bertrand Thirion, Salvatore Torrisi, Gang Chen
Date: 2025-04-10 00:00:00

Visualizations are vital for communicating scientific results. Historically, neuroimaging figures have only depicted regions that surpass a given statistical threshold. This practice substantially biases interpretation of the results and subsequent meta-analyses, particularly towards non-reproducibility. Here we advocate for a "transparent thresholding" approach that not only highlights statistically significant regions but also includes subthreshold locations, which provide key experimental context. This balances the dual needs of distilling modeling results and enabling informed interpretations for modern neuroimaging. We present four examples that demonstrate the many benefits of transparent thresholding, including: removing ambiguity, decreasing hypersensitivity to non-physiological features, catching potential artifacts, improving cross-study comparisons, reducing non-reproducibility biases, and clarifying interpretations. We also demonstrate the many software packages that implement transparent thresholding, several of which were added or streamlined recently as part of this work. A point-counterpoint discussion addresses issues with thresholding raised in real conversations with researchers in the field. We hope that by showing how transparent thresholding can drastically improve the interpretation (and reproducibility) of neuroimaging findings, more researchers will adopt this method.
An introduction to memory competitions, records and techniques
Authors: Bastian Wiederhold
Date: 2025-04-09 00:00:00
Tasks: Memorization

This article provides an overview of memory competitions, analyzes differences between disciplines and explains current state-of-the-art techniques. Performances have increased dramatically over the past three decades. Nowadays, information processing reaches up to 42 bit/s in short disciplines with most of the time spent on reading, suggesting that mental associations are formed even faster. Records show a remarkable concordance across all time scales: the processing speed depends on memorization time as a power law.
Multihead self-attention in cortico-thalamic circuits
Authors: Arno Granier, Walter Senn
Date: 2025-04-08 00:00:00

Both biological cortico-thalamic networks and artificial transformer networks use canonical computations to perform a wide range of cognitive tasks. In this work, we propose that the structure of cortico-thalamic circuits is well suited to realize a computation analogous to multihead self-attention, the main algorithmic innovation of transformers. We start with the concept of a cortical unit module or microcolumn, and propose that superficial and deep pyramidal cells carry distinct computational roles. Specifically, superficial pyramidal cells encode an attention mask applied onto deep pyramidal cells to compute attention-modulated values. We show how to wire such microcolumns into a circuit equivalent to a single head of self-attention. We then suggest the parallel between one head of attention and a cortical area. On this basis, we show how to wire cortico-thalamic circuits to perform multihead self-attention. Along these constructions, we refer back to existing experimental data, and find noticeable correspondence. Finally, as a first step towards a mechanistic theory of synaptic learning in this framework, we formally derive gradient-based updates for the parameters of a multihead linear self-attention block and propose steps towards their implementation by local synaptic plasticity.
Resting State Functional Connectivity Patterns Associate with Alcohol Use Disorder Characteristics: Insights from the Triple Network Model
Authors: Daniel Guerrero, Mario Dzemidzic, Mahdi Moghaddam, Mintao Liu, Andrea Avena-Koenigsberger, Jaroslaw Harezlak, David A. Kareken, Martin H. Plawecki, Melissa A. Cyders, Joaquín Goñi
Date: 2025-04-08 00:00:00
Tasks: Functional Connectivity

Prolonged alcohol use results in neuroadaptations that mark more severe and treatment-resistant alcohol use. The goal of this study was to identify functional connectivity brain patterns underlying Alcohol Use Disorder (AUD)-related characteristics in fifty-five adults (31 female) who endorsed heavy alcohol use. We hypothesized that resting-state functional connectivity (rsFC) of the Salience (SN), Frontoparietal (FPN), and Default Mode (DMN) networks would reflect self reported recent and lifetime alcohol use, laboratory-based alcohol seeking, urgency, and sociodemographic characteristics related to AUD. To test our hypothesis, we combined the triple network model (TNM) of psychopathology with a multivariate data-driven approach, regularized partial least squares (rPLS), to unfold concurrent functional connectivity (FC) patterns and their association with AUD characteristics. We observed three concurrent associations of interest: i) drinking and age-related cross communication between the SN and both the FPN and DMN; ii) family history density of AUD and urgency anticorrelations between the SN and FPN; and iii) alcohol seeking and sex-associated SN and DMN interactions. These findings demonstrate the utility of combining theory- and data-driven approaches to uncover associations between resting-state functional substrates and AUD-related characteristics that could aid in the identification, development, and testing of novel treatment targets across preclinical and clinical models.
Riemannian Geometry for the classification of brain states with intracortical brain-computer interfaces
Authors: Arnau Marin-Llobet, Arnau Manasanch, Sergio Sanchez-Manso, Lluc Tresserras, Xinhe Zhang, Yining Hua, Hao Zhao, Melody Torao-Angosto, Maria V Sanchez-Vives, Leonardo Dalla Porta
Date: 2025-04-07 00:00:00
Tasks: Benchmarking, Brain Computer Interface, Brain Decoding

This study investigates the application of Riemannian geometry-based methods for brain decoding using invasive electrophysiological recordings. Although previously employed in non-invasive, the utility of Riemannian geometry for invasive datasets, which are typically smaller and scarcer, remains less explored. Here, we propose a Minimum Distance to Mean (MDM) classifier using a Riemannian geometry approach based on covariance matrices extracted from intracortical Local Field Potential (LFP) recordings across various regions during different brain state dynamics. For benchmarking, we evaluated the performance of our approach against Convolutional Neural Networks (CNNs) and Euclidean MDM classifiers. Our results indicate that the Riemannian geometry-based classification not only achieves a superior mean F1 macro-averaged score across different channel configurations but also requires up to two orders of magnitude less computational training time. Additionally, the geometric framework reveals distinct spatial contributions of brain regions across varying brain states, suggesting a state-dependent organization that traditional time series-based methods often fail to capture. Our findings align with previous studies supporting the efficacy of geometry-based methods and extending their application to invasive brain recordings, highlighting their potential for broader clinical use, such as brain computer interface applications.
The many roads to dementia: a systems view of Alzheimer's disease
Authors: Irina Kareva
Date: 2025-04-07 00:00:00

Alzheimer's disease is not the outcome of a single cause but the convergence of many. This review reframes dementia as a systemic failure, where amyloid plaques and tau tangles are not root causes but late-stage byproducts of the underlying metabolic collapse. We begin by tracing the historical merger of early- and late-onset Alzheimer's into a single disease category, a conceptual error that may have misdirected decades of research. We then synthesize evidence pointing to metabolic dysfunction - especially mitochondrial damage - as a more likely initiating event. Through this lens, we examine diverse contributing factors including type 2 diabetes, hyperglycemia-induced oxidative stress, infections and neuroinflammation. Finally, we assess current treatment limitations and argue that prevention, grounded in early metabolic and vascular interventions, holds the most promise for altering the course of this complex disease.
Modeling the Dynamics of Attentional Gamma Oscillations During the Encoding Process of Noise-Mixed Speech Signals
Authors: Duoyu Feng, Jiajia Li, Ying Wu
Date: 2025-04-06 00:00:00
Tasks: EEG, Rhythm

The brain's bottom-up loop for processing speech influx involves both the selective attention and the encoding of specific speech information. Previous human studies have found that such attention can be represented by the cortical gamma-rhythm oscillations. However, the underlying mechanisms remain unclear. To address this issue, this paper proposes a neural network model that incorporates speech signal input, the cochlea, the thalamus, and a balanced excitatory-inhibitory cortical neural network, with the aim of connecting real speech signals to brain cortical responses. Using this model, we explored neural oscillation patterns in response to mixed speech stimuli and background noise. The findings revealed that the peak of gamma oscillation decreased as the frequency of the pure-tone stimuli diminished. This suggests a strong correlation and coding role of gamma oscillation peaks in auditory attention. Similar results were confirmed by analyzing the rhythmic oscillations of EEG data in response to pure-tone signals. Further results indicated that dynamic gamma oscillations are involved in the encoding capacity of continuous speech input. The coding entropy of the dynamic series was found to be proportional to the complexity of the content. This suggests that gamma oscillations play multiple roles, not only in sustaining the bottom-up attentional state but also in potentially conveying specific information from external speech inputs. Finally, we found that enhancing the excitatory-inhibitory balance level properly could improve auditory attention. This finding provides a potential endogenous explanation for the dynamic switching process of brain attention in processing auditory signals.
A Roadmap for Improving Data Reliability and Sharing in Crosslinking Mass Spectrometry
Authors: Juri Rappsilber, James Bruce, Colin Combe, Stephen Fried, Albert J R Heck, Claudio Iacobucci, Alexander Leitner, Karl Mechtler, Petr Novak, Francis O'Reilly, David C. Schriemer, Andrea Sinz, Florian Stengel, Andrea Graziadei, Konstantinos Thalassinos
Date: 2025-04-09 00:00:00
Tasks: Benchmarking

Crosslinking Mass Spectrometry (MS) can uncover protein-protein interactions and provide structural information on proteins in their native cellular environments. Despite its promise, the field remains hampered by inconsistent data formats, variable approaches to error control, and insufficient interoperability with global data repositories. Recent advances, especially in false discovery rate (FDR) models and pipeline benchmarking, show that Crosslinking MS data can reach a reliability that matches the demand of integrative structural biology. To drive meaningful progress, however, the community must agree on error estimation, open data formats, and streamlined repository submissions. This perspective highlights these challenges, clarifies remaining barriers, and frames practical next steps. Successful field harmonisation will enhance the acceptance of Crosslinking MS in the broader biological community and is critical for the dependability of the data, no matter where it is produced.
Addressing ecological challenges from a quantum computing perspective
Authors: Maxime Clenet, Maxime Dion, F. Guillaume Blanchet
Date: 2025-04-04 00:00:00

With increased access to data and the advent of computers, the use of statistical tools and numerical simulations is becoming commonplace for ecologists. These approaches help improve our understanding of ecological phenomena and their underlying mechanisms in increasingly complex environments. However, the development of mathematical and computational tools has made it possible to study high-dimensional problems up to a certain limit. To overcome this issue, quantum computers could be used to study ecological problems on a larger scale by creating new bridges between fields that at first glance appear to be quite different. We introduce the basic concepts needed to understand quantum computers, give an overview of their applications, and discuss their challenges and future opportunities in ecology. Quantum computers will have a significant impact on ecology by improving the power of statistical tools, solve intractable problems in networks, and help understand the dynamics of large systems of interacting species. This innovative computational perspective could redefine our understanding of species interactions, improve predictive modeling of distributions, and optimize conservation strategies, thereby advancing the field of ecology into a new era of discovery and insight.