pwc-archive/papers-with-abstracts huggingface dataset:
A kinetic derivation of spatial distributed models for tumor-immune system interactions
Authors: Martina Conte, Romina Travaglini
Date: 2024-10-22 00:00:00

We propose a mathematical kinetic framework to investigate interactions between tumor cells and the immune system, focusing on the spatial dynamics of tumor progression and immune responses. We develop two kinetic models: one describes a conservative scenario where immune cells switch between active and passive states without proliferation, while the other incorporates immune cell proliferation and apoptosis. By considering specific assumptions about the microscopic processes, we derive macroscopic systems featuring linear diffusion, nonlinear cross-diffusion, and nonlinear self-diffusion. Our analysis provides insights into equilibrium configurations and stability, revealing clear correspondences among the macroscopic models derived from the same kinetic framework. Using dynamical systems theory, we examine the stability of equilibrium states and conduct numerical simulations to validate our findings. These results highlight the significance of spatial interactions in tumor-immune dynamics, paving the way for a structured exploration of therapeutic strategies and further investigations into immune responses in various pathological contexts.
TopoQA: a topological deep learning-based approach for protein complex structure interface quality assessment
Authors: Bingqing Han, YiPeng Zhang, Longlong Li, Xinqi Gong, Kelin Xia
Date: 2024-10-23 00:00:00
Tasks: Model Selection, Representation Learning, Topological Data Analysis

Even with the significant advances of AlphaFold-Multimer (AF-Multimer) and AlphaFold3 (AF3) in protein complex structure prediction, their accuracy is still not comparable with monomer structure prediction. Efficient quality assessment (QA) or estimation of model accuracy (EMA) models that can evaluate the quality of the predicted protein-complexes without knowing their native structures, are of key importance for protein structure generation and model selection. In this paper, we leverage persistent homology (PH) to capture the atomic-level topological information around residues and design a topological deep learning-based QA method, TopoQA, to assess the accuracy of protein complex interfaces. We integrate PH from topological data analysis into graph neural networks (GNNs) to characterize complex higher-order structures that GNNs might overlook, enhancing the learning of the relationship between the topological structure of complex interfaces and quality scores. Our TopoQA model is extensively validated based on the two most-widely used datasets, DBM55-AF2 and HAF2, along with our newly constructed ABAG-AF3 dataset to facilitate comparisons with AF3. For all three datasets, TopoQA outperforms AF-Multimer-based AF2Rank and shows an advantage over AF3 in nearly half of the targets. In particular, in the DBM55-AF2 dataset, a ranking loss of 73.6% lower than AF-Multimer-based AF2Rank is obtained. Further, other than AF-Multimer and AF3, we have also extensively compared with nearly-all the state-of-the-art models (as far as we know), it has been found that our TopoQA can achieve the highest Top 10 Hit-rate on the DBM55-AF2 dataset and the lowest ranking loss on the HAF2 dataset. Ablation experiments show that our topological features significantly improve the model performance. At the same time, our method also provides a new paradigm for protein structure representation learning.
Optimizing First-Line Therapeutics in Non-Small Cell Lung Cancer: Insights from Joint Modeling and Large-Scale Data Analysis
Authors: Benjamin K. Schneider, Sebastien Benzekry, Jonathan P. Mochel
Date: 2024-10-22 00:00:00
Tasks: Scheduling

Non-small cell lung cancer (NSCLC) is often intrinsically resistant to several first- and second-line therapeutics and can rapidly acquire further resistance after a patient begins receiving treatment. Treatment outcomes are therefore significantly impacted by the optimization of therapeutic scheduling. Previous preclinical research has suggested scheduling bevacizumab in sequence with combination antiproliferatives could significantly improve clinical outcomes. Mathematical modeling is a well-suited tool for investigating this proposed scheduling modification. To address this critical need, individual patient tumor data from 11 clinical trials in NSCLC has been collated and used to develop a semi-mechanistic model of NSCLC growth and response to the various therapeutics represented in those trials. Precise estimates of clinical parameters fundamental to cancer modeling have been produced - such as the rate of acquired resistance to various pharmaceuticals, the relationship between drug concentration and cancer cell death, as well as the fine temporal dynamics of vascular remodeling in response to bevacizumab. In a reserved portion of the dataset, this model was used to predict the efficacy of individual treatment time courses with a mean error rate of 59.7% after a single tumor measurement and 11.7% after three successive tumor measurements. A delay of 9.6 hours between pemetrexed-cisplatin and bevacizumab administration is predicted to optimize the benefit of sequential administration. At this gap, approximately 93.5% of simulated patients benefited from a gap in sequential administration compared with concomitant administration. Of those simulated patients, the mean improvement in tumor reduction was 20.7%. This result suggests that scheduling a modest gap between the administration of bevacizumab and its partner antiproliferatives could meaningfully improve patient outcomes in NSCLC.
MeMDLM: De Novo Membrane Protein Design with Masked Discrete Diffusion Protein Language Models
Authors: Shrey Goel, Vishrut Thoutam, Edgar Mariano Marroquin, Aaron Gokaslan, Arash Firouzbakht, Sophia Vincoff, Volodymyr Kuleshov, Huong T. Kratochvil, Pranam Chatterjee
Date: 2024-10-22 00:00:00
Tasks: Language Modeling, Language Modelling, Protein Design

Masked Diffusion Language Models (MDLMs) have recently emerged as a strong class of generative models, paralleling state-of-the-art (SOTA) autoregressive (AR) performance across natural language modeling domains. While there have been advances in AR as well as both latent and discrete diffusion-based approaches for protein sequence design, masked diffusion language modeling with protein language models (pLMs) is unexplored. In this work, we introduce MeMDLM, an MDLM tailored for membrane protein design, harnessing the SOTA pLM ESM-2 to de novo generate realistic membrane proteins for downstream experimental applications. Our evaluations demonstrate that MeMDLM-generated proteins exceed AR-based methods by generating sequences with greater transmembrane (TM) character. We further apply our design framework to scaffold soluble and TM motifs in sequences, demonstrating that MeMDLM-reconstructed sequences achieve greater biological similarity to their original counterparts compared to SOTA inpainting methods. Finally, we show that MeMDLM captures physicochemical membrane protein properties with similar fidelity as SOTA pLMs, paving the way for experimental applications. In total, our pipeline motivates future exploration of MDLM-based pLMs for protein design.
MOFFlow: Flow Matching for Structure Prediction of Metal-Organic Frameworks
Authors: Nayoung Kim, Seongsu Kim, Minsu Kim, Jinkyoo Park, Sungsoo Ahn
Date: 2024-10-07 00:00:00

Metal-organic frameworks (MOFs) are a class of crystalline materials with promising applications in many areas such as carbon capture and drug delivery. In this work, we introduce MOFFlow, the first deep generative model tailored for MOF structure prediction. Existing approaches, including ab initio calculations and even deep generative models, struggle with the complexity of MOF structures due to the large number of atoms in the unit cells. To address this limitation, we propose a novel Riemannian flow matching framework that reduces the dimensionality of the problem by treating the metal nodes and organic linkers as rigid bodies, capitalizing on the inherent modularity of MOFs. By operating in the $SE(3)$ space, MOFFlow effectively captures the roto-translational dynamics of these rigid components in a scalable way. Our experiment demonstrates that MOFFlow accurately predicts MOF structures containing several hundred atoms, significantly outperforming conventional methods and state-of-the-art machine learning baselines while being much faster.
Computational design of target-specific linear peptide binders with TransformerBeta
Authors: Haowen Zhao, Francesco A. Aprile, Barbara Bravi
Date: 2024-10-07 00:00:00
Tasks: Protein Design

The computational prediction and design of peptide binders targeting specific linear epitopes is crucial in biological and biomedical research, yet it remains challenging due to their highly dynamic nature and the scarcity of experimentally solved binding data. To address this problem, we built an unprecedentedly large-scale library of peptide pairs within stable secondary structures (beta sheets), leveraging newly available AlphaFold predicted structures. We then developed a machine learning method based on the Transformer architecture for the design of specific linear binders, in analogy to a language translation task. Our method, TransformerBeta, accurately predicts specific beta strand interactions and samples sequences with beta sheet-like molecular properties, while capturing interpretable physico-chemical interaction patterns. As such, it can propose specific candidate binders targeting linear epitope for experimental validation to inform protein design.
Rawsamble: Overlapping and Assembling Raw Nanopore Signals using a Hash-based Seeding Mechanism
Authors: Can Firtina, Maximilian Mordig, Harun Mustafa, Sayan Goswami, Nika Mansouri Ghiasi, Stefano Mercogliano, Furkan Eris, Joël Lindegger, Andre Kahles, Onur Mutlu
Date: 2024-10-23 00:00:00
Tasks: CPU

Raw nanopore signal analysis is a common approach in genomics to provide fast and resource-efficient analysis without translating the signals to bases (i.e., without basecalling). However, existing solutions cannot interpret raw signals directly if a reference genome is unknown due to a lack of accurate mechanisms to handle increased noise in pairwise raw signal comparison. Our goal is to enable the direct analysis of raw signals without a reference genome. To this end, we propose Rawsamble, the first mechanism that can 1) identify regions of similarity between all raw signal pairs, known as all-vs-all overlapping, using a hash-based search mechanism and 2) use these to construct genomes from scratch, called de novo assembly. Our extensive evaluations across multiple genomes of varying sizes show that Rawsamble provides a significant speedup (on average by 16.36x and up to 41.59x) and reduces peak memory usage (on average by 11.73x and up to by 41.99x) compared to a conventional genome assembly pipeline using the state-of-the-art tools for basecalling (Dorado's fastest mode) and overlapping (minimap2) on a CPU. We find that 36.57% of overlapping pairs generated by Rawsamble are identical to those generated by minimap2. Using the overlaps from Rawsamble, we construct the first de novo assemblies directly from raw signals without basecalling. We show that we can construct contiguous assembly segments (unitigs) up to 2.7 million bases in length (half the genome length of E. coli). We identify previously unexplored directions that can be enabled by finding overlaps and constructing de novo assemblies. Rawsamble is available at https://github.com/CMU-SAFARI/RawHash. We also provide the scripts to fully reproduce our results on our GitHub page.
Proteome-wide prediction of mode of inheritance and molecular mechanism underlying genetic diseases using structural interactomics
Authors: Ali Saadat, Jacques Fellay
Date: 2024-10-23 00:00:00

Genetic diseases can be classified according to their modes of inheritance and their underlying molecular mechanisms. Autosomal dominant disorders often result from DNA variants that cause loss-of-function, gain-of-function, or dominant-negative effects, while autosomal recessive diseases are primarily linked to loss-of-function variants. In this study, we introduce a graph-of-graphs approach that leverages protein-protein interaction networks and high-resolution protein structures to predict the mode of inheritance of diseases caused by variants in autosomal genes, and to classify dominant-associated proteins based on their functional effect. Our approach integrates graph neural networks, structural interactomics and topological network features to provide proteome-wide predictions, thus offering a scalable method for understanding genetic disease mechanisms.
Hierarchical Classification for Predicting Metastasis Using Elastic-Net Regularization on Gene Expression Data
Authors: Benjamin Osafo Agyare, Alec Chu, Blessing Oloyede
Date: 2024-10-22 00:00:00

Metastasis is a leading cause of cancer-related mortality and remains challenging to detect during early stages. Accurate identification of cancers likely to metastasize can improve treatment strategies and patient outcomes. This study leverages publicly available gene expression profiles from primary cancers, with and without distal metastasis, to build predictive models. We utilize elastic net regularization within a hierarchical classification framework to predict both the tissue of origin and the metastasis status of primary tumors. Our elastic net-based hierarchical classification achieved a tissue-of-origin prediction accuracy of 97%, and a metastasis prediction accuracy of 90%. Notably, mitochondrial gene expression exhibited significant negative correlations with metastasis, providing potential biological insights into the underlying mechanisms of cancer progression.
Bacterial Pathogenicity Regulation by RNA-binding Antiterminators
Authors: Diane Soussan, Ali Tahrioui, R R de la Haba, Adrien Forge, Sylvie Chevalier, Olivier Lesouhaitier, Cécile Muller
Date: 2024-10-22 00:00:00

Antiterminators are essential components of bacterial transcriptional regulation, allowing the control of gene expression in response to fluctuating environmental conditions. RNA-binding antiterminators are particularly important regulatory proteins that play a significant role in preventing transcription termination by binding to specific RNA sequences. These RNA-binding antiterminators have been extensively studied for their roles in regulating various metabolic pathways. However, their role in modulating the physiology of pathogens requires further investigations. This review focuses on these RNA-binding proteins in both Gram-positive and Gram-negative bacteria, particularly on their structures, mechanism of action, and target genes. Additionally, the involvement of the antitermination mechanisms in bacterial pathogenicity will be discussed. This knowledge is crucial for understanding the regulatory mechanisms that govern bacterial pathogenicity, opening up exciting prospects for future research, and potentially new alternative strategies to fight against infectious diseases.
Fitting, Evaluating, and Comparing Cognitive Architecture Models Using Likelihood: A Primer With Examples in ACT-R
Authors: Andrea Stocco, Konstantinos Mitsopoulos, Yuxue C. Yang, Holly S. Hake, Theodros Haile, Bridget Leonard, Kevin Gluck
Date: 2024-10-23 00:00:00

Cognitive architectures are influential, integrated computational frameworks for modeling cognitive processes. Due to a variety of factors, however, researchers using cognitive architectures to explain and predict human performance rarely employ model validation, comparison, and selection techniques based on likelihood. This paper provides a primer on how to implement maximum likelihood techniques and its derivatives to fit and compare models at the individual and group level, using models implemented in the ACT-R cognitive architecture as examples. The paper covers the most common ways in which likelihood measures can be applied, under different scenarios, for models of different complexity, and provides further technical references for the interested reader. An accompanying notebook in Python provides the code to implement all of the suggestions.
Neural association between musical features and shared emotional perception while movie-watching: fMRI study
Authors: Leonardo Muller-Rodriguez, Owen O'Daly
Date: 2024-10-22 00:00:00
Tasks: Diagnostic, STS

The growing use of naturalistic stimuli, such as feature films, brings research on emotions closer to ecologically valid settings within brain scanners, such as functional magnetic resonance imaging (fMRI). Music is another cultural artifact known to evoke emotions, and film soundtracks are often designed to enhance the emotional impact of the narrative. However, the neural basis of shared emotional perception during movie-watching, and its relationship to the soundtrack, remains unclear. In this study, participants watched Forrest Gump in a 3T fMRI scanner. The reported shared arousal and valence (positive and negative) was correlated with the spectrogram and tempo information extracted from the soundtrack. For neuroimaging, four regions of interest (ROIs) related to audiovisual input and narrative content were examined: the superior temporal sulcus (STS), amygdala, inferior frontal gyrus, and precuneus. Results: Strong correlations between spectral and tempo, and emotional features (Bonferroni corr). BOLD responses associated with shared positive and negative valence and high-arousal moments were compared to film events with low emotional agreement. Significant activations were found in the STS and precuneus (pFWE < 0.05 peak level). No significant responses were observed in the amygdala or inferior frontal gyrus, but whole-brain analysis (pFWE < 0.05 cluster extent) revealed frontal activity linked to negative valence, alongside expected occipito-temporal lobe responses to the audiovisual stimuli. The STS and precuneus were particularly active during shared emotional perception of valenced and high-arousal events, with the precuneus encoding valence from auditory information. Naturalistic stimuli show promise as diagnostic and therapeutic tools for emotion regulation deficits although challenges may arise when crafting an experimental control.
Topological and Graph Theoretical Analysis of Dynamic Functional Connectivity for Autism Spectrum Disorder
Authors: Yuzhe Chen, Dayu Qin, Ercan Engin Kuruoglu
Date: 2024-10-22 00:00:00
Tasks: Functional Connectivity, Topological Data Analysis

Autism Spectrum Disorder (ASD) is a prevalent neurological disorder. However, the multi-faceted symptoms and large individual differences among ASD patients are hindering the diagnosis process, which largely relies on subject descriptions and lacks quantitative biomarkers. To remediate such problems, this paper explores the use of graph theory and topological data analysis (TDA) to study brain activity in ASD patients and normal controls. We employ the Mapper algorithm in TDA and the distance correlation graphical model (DCGM) in graph theory to create brain state networks, then innovatively adopt complex network metrics in Graph signal processing (GSP) and physical quantities to analyze brain activities over time. Our findings reveal statistical differences in network characteristics between ASD and control groups. Compared to normal subjects, brain state networks of ASD patients tend to have decreased modularity, higher von Neumann entropy, increased Betti-0 numbers, and decreased Betti-1 numbers. These findings attest to the biological traits of ASD, suggesting less organized and more variable brain dynamics. These findings offer potential biomarkers for ASD diagnosis and deepen our understanding of its neural correlations.
Cortical Dynamics of Neural-Connectivity Fields
Authors: Gerald K. Cooray, Vernon Cooray, Karl J. Friston
Date: 2024-10-22 00:00:00

Macroscopic studies of cortical tissue reveal a prevalence of oscillatory activity, that reflect a fine tuning of neural interactions. This research extends neural field theories by incorporating generalized oscillatory dynamics into previous work on conservative or semi-conservative neural field dynamics. Prior studies have largely assumed isotropic connections among neural units; however, this study demonstrates that a broad range of anisotropic and fluctuating connections can still sustain oscillations. Using Lagrangian field methods, we examine different types of connectivity, their dynamics, and potential interactions with neural fields. From this theoretical foundation, we derive a framework that incorporates Hebbian and non-Hebbian learning, i.e., plasticity, into the study of neural fields via the concept of a connectivity field.
How host mobility patterns shape antigenic escape during viral-immune co-evolution
Authors: Natalie Blot, Caelan Brooks, Daniel W. Swartz, Eslam Abdelaleem, Martin Garic, Andrea Iglesias-Ramas, Michael Pasek, Thierry Mora, Aleksandra M. Walczak
Date: 2024-10-22 00:00:00

Viruses like influenza have long coevolved with host immune systems, gradually shaping the evolutionary trajectory of these pathogens. Host immune systems develop immunity against circulating strains, which in turn avoid extinction by exploiting antigenic escape mutations that render new strains immune from existing antibodies in the host population. Infected hosts are also mobile, which can spread the virus to regions without developed host immunity, offering additional reservoirs for viral growth. While the effects of migration on long term stability have been investigated, we know little about how antigenic escape coupled with migration changes the survival and spread of emerging viruses. By considering the two processes on equal footing, we show that on short timescales an intermediate host mobility rate increases the survival probability of the virus through antigenic escape. We show that more strongly connected migratory networks decrease the survival probability of the virus. Using data from high traffic airports we argue that current human migration rates are beneficial for viral survival.
An Exploration of Modeling Approaches for Capturing Seasonal Transmission in Stochastic Epidemic Models
Authors: Mahmudul Bari Hridoy
Date: 2024-10-22 00:00:00

Seasonal variations in the incidence of infectious diseases are a well-established phenomenon, driven by factors such as climate changes, social behaviors, and ecological interactions that influence host susceptibility and transmission rates. While seasonality plays a significant role in shaping epidemiological dynamics, it is often overlooked in both empirical and theoretical studies. Incorporating seasonal parameters into mathematical models of infectious diseases is crucial for accurately capturing disease dynamics, enhancing the predictive power of these models, and developing successful control strategies. This paper highlights key modeling approaches for incorporating seasonality into disease transmission, including sinusoidal functions, periodic piecewise linear functions, Fourier series expansions, Gaussian functions, and data-driven methods, accompanied by real-world examples. Additionally, a stochastic Susceptible-Infected-Recovered (SIR) model with seasonal transmission is demonstrated through numerical simulations. Important outcome measures, such as the basic and instantaneous reproduction numbers and the probability of a disease outbreak using branching process approximation of the Markov chain, are also presented to illustrate the impact of seasonality on disease dynamics.