Inference of annealed protein fitness landscapes with AnnealDCA
by Luca Sesta, Andrea Pagnani, Jorge Fernandez-de-Cossio-Diaz, Guido Uguzzoni
The design of proteins with specific tasks is a major challenge in molecular biology with important diagnostic and therapeutic applications. High-throughput screening methods have been developed to systematically evaluate protein activity, but only a small fraction of possible protein variants can be tested using these techniques. Computational models that explore the sequence spacein-silico to identify the fittest molecules for a given function are needed to overcome this limitation. In this article, we propose AnnealDCA, a machine-learning fra...
Source: PLoS Computational Biology - February 20, 2024 Category: Biology Authors: Luca Sesta Source Type: research
Fast adaptation to rule switching using neuronal surprise
by Martin L. L. R. Barry, Wulfram Gerstner
In humans and animals, surprise is a physiological reaction to an unexpected event, but how surprise can be linked to plausible models of neuronal activity is an open problem. We propose a self-supervised spiking neural network model where a surprise signal is extracted from an increase in neural activity after an imbalance of excitation and inhibition. The surprise signal modulates synaptic plasticity via a three-factor learning rule which increases plasticity at moments of surprise. The surprise signal remains small when transitions between sensory events follow a previously le...
Source: PLoS Computational Biology - February 20, 2024 Category: Biology Authors: Martin L. L. R. Barry Source Type: research
Intra-ripple frequency accommodation in an inhibitory network model for hippocampal ripple oscillations
by Natalie Schieferstein, Tilo Schwalger, Benjamin Lindner, Richard Kempter
Hippocampal ripple oscillations have been implicated in important cognitive functions such as memory consolidation and planning. Multiple computational models have been proposed to explain the emergence of ripple oscillations, relying either on excitation or inhibition as the main pacemaker. Nevertheless, the generating mechanism of ripples remains unclear. An interesting dynamical feature of experimentally measured ripples, which may advance model selection, is intra-ripple frequency accommodation (IFA): a decay of the instantaneous ripple freque...
Source: PLoS Computational Biology - February 20, 2024 Category: Biology Authors: Natalie Schieferstein Source Type: research
Denoising diffusion probabilistic models for generation of realistic fully-annotated microscopy image datasets
In this study, we demonstrate that diffusion models can effectively generate fully-annotated microscopy image data sets through an unsupervised and intuitive approach, using rough sketches of desired structures as the starting point. The proposed pipeline helps to reduce the reliance on manual annotations when training deep learning-based segmentation approaches and enables the segmentation of diverse datasets without the need for human annotations. We demonstrate that segmentation models trained with a small set of synthetic image data reach accuracy levels comparable to those of generalist models trained with a large and...
Source: PLoS Computational Biology - February 20, 2024 Category: Biology Authors: Dennis Eschweiler Source Type: research
Connectivity Matrix Seriation via Relaxation
by Alexander Borst
Volume electron microscopy together with computer-based image analysis are yielding neural circuit diagrams of ever larger regions of the brain. These datasets are usually represented in a cell-to-cell connectivity matrix and contain important information about prevalent circuit motifs allowing to directly test various theories on the computation in that brain structure. Of particular interest are the detection of cell assemblies and the quantification of feedback, which can profoundly change circuit properties. While the ordering of cells along the rows and columns doesn ’t change the connectivity, i...
Source: PLoS Computational Biology - February 20, 2024 Category: Biology Authors: Alexander Borst Source Type: research
Enhanced perfusion following exposure to radiotherapy: A theoretical investigation
by Jakub K öry, Vedang Narain, Bernadette J. Stolz, Jakob Kaeppler, Bostjan Markelc, Ruth J. Muschel, Philip K. Maini, Joe M. Pitt-Francis, Helen M. Byrne
Tumour angiogenesis leads to the formation of blood vessels that are structurally and spatially heterogeneous. Poor blood perfusion, in conjunction with increased hypoxia and oxygen heterogeneity, impairs a tumour ’s response to radiotherapy. The optimal strategy for enhancing tumour perfusion remains unclear, preventing its regular deployment in combination therapies. In this work, we first identify vascular architectural features that correlate with enhanced perfus...
Source: PLoS Computational Biology - February 16, 2024 Category: Biology Authors: Jakub K öry Source Type: research
Linear motifs regulating protein secretion, sorting and autophagy in Leishmania parasites are diverged with respect to their host equivalents
by Andras Zeke, Toby J. Gibson, Laszlo Dobson
The pathogenic, tropicalLeishmania flagellates belong to an early-branching eukaryotic lineage (Kinetoplastida) with several unique features. Unfortunately, they are poorly understood from a molecular biology perspective, making development of mechanistically novel and selective drugs difficult. Here, we explore three functionally critical targeting short linear motif systems as well as their receptors in depth, using a combination of structural modeling, evolutionary sequence divergence and deep learning. Secretory signal peptides, endoplasmic reticulum (ER) retention motifs ...
Source: PLoS Computational Biology - February 16, 2024 Category: Biology Authors: Andras Zeke Source Type: research
Studentsourcing —Aggregating and reusing data from a practical cell biology course
by Joachim Goedhart
Practical courses mimic experimental research and may generate valuable data. Yet, data that is generated by students during a course is often lost as there is no centrally organized collection and storage of the data. The loss of data prevents its reuse. To provide access to these data, I present an approach that I call studentsourcing. It collects, aggregates, and reuses data that is generated by students in a practical course on cell biology. The course runs annually, and I have recorded the data that was generated by>100 students over 3 years. Two use cases illustrate how the data can be aggregated...
Source: PLoS Computational Biology - February 15, 2024 Category: Biology Authors: Joachim Goedhart Source Type: research
Enhancing predictive performance for spectroscopic studies in wildlife science through a multi-model approach: A case study for species classification of live amphibians
In this study, we conducted a benchmark modeling exercise to compare the performance of several machine learning algorithms in a multi-class problem utilizing a multivariate spectroscopic dataset obtained from live animals. Spectra obtained from live individuals representing eleven amphibian species were classified according to taxonomic designation. Seven modeling techniques were applied to generate prediction models, which varied significantly (p (Source: PLoS Computational Biology)
Source: PLoS Computational Biology - February 14, 2024 Category: Biology Authors: Li-Dunn Chen Source Type: research
StressME: Unified computing framework of < i > Escherichia coli < /i > metabolism, gene expression, and stress responses
by Jiao Zhao, Ke Chen, Bernhard O. Palsson, Laurence Yang
Generalist microbes have adapted to a multitude of environmental stresses through their integrated stress response system. Individual stress responses have been quantified byE.coli metabolism and expression (ME) models under thermal, oxidative and acid stress, respectively. However, the systematic quantification of cross-stress& cross-talk among these stress responses remains lacking. Here, we present StressME: the unified stress response model ofE.coli combining thermal (FoldME), oxidative (OxidizeME) and acid (AcidifyME) stress responses. StressME is the most up ...
Source: PLoS Computational Biology - February 12, 2024 Category: Biology Authors: Jiao Zhao Source Type: research
Differential allelic representation (DAR) identifies candidate eQTLs and improves transcriptome analysis
by Lachlan Baer, Karissa Barthelson, John H. Postlethwait, David L. Adelson, Stephen M. Pederson, Michael Lardelli
In comparisons between mutant and wild-type genotypes, transcriptome analysis can reveal the direct impacts of a mutation, together with the homeostatic responses of the biological system. Recent studies have highlighted that, when the effects of homozygosity for recessive mutations are studied in non-isogenic backgrounds, genes located proximal to the mutation on the same chromosome often appear over-represented among those genes identified as differentially expressed (DE). One hypothesis suggests that DE ge...
Source: PLoS Computational Biology - February 12, 2024 Category: Biology Authors: Lachlan Baer Source Type: research
< i > magpie < /i > : A power evaluation method for differential RNA methylation analysis in N6-methyladenosine sequencing
by Zhenxing Guo, Daoyu Duan, Wen Tang, Julia Zhu, William S. Bush, Liangliang Zhang, Xiaofeng Zhu, Fulai Jin, Hao Feng
Recently, novel biotechnologies to quantify RNA modifications became an increasingly popular choice for researchers who study epitranscriptome. When studying RNA methylations such as N6-methyladenosine (m6A), researchers need to make several decisions in its experimental design, especially the sample size and a proper statistical power. Due to the complexity and high-throughput nature of m6A sequencing measurements, methods for power calculation and study design are still currently unavailable. In this wo...
Source: PLoS Computational Biology - February 12, 2024 Category: Biology Authors: Zhenxing Guo Source Type: research
Generating synthetic population for simulating the spatiotemporal dynamics of epidemics
by Kemin Zhu, Kang Liu, Junli Liu, Yepeng Shi, Xuan Li, Hongyang Zou, Huibin Du, Ling Yin
Agent-based models have gained traction in exploring the intricate processes governing the spread of infectious diseases, particularly due to their proficiency in capturing nonlinear interaction dynamics. The fidelity of agent-based models in replicating real-world epidemic scenarios hinges on the accurate portrayal of both population-wide and individual-level interactions. In situations where comprehensive population data are lacking, synthetic populations serve as a vital input to agent-based models, approximating real-world demogr...
Source: PLoS Computational Biology - February 12, 2024 Category: Biology Authors: Kemin Zhu Source Type: research
Learning what matters: Synaptic plasticity with invariance to second-order input correlations
by Carlos Stein Naves de Brito, Wulfram Gerstner
Cortical populations of neurons develop sparse representations adapted to the statistics of the environment. To learn efficient population codes, synaptic plasticity mechanisms must differentiate relevant latent features from spurious input correlations, which are omnipresent in cortical networks. Here, we develop a theory for sparse coding and synaptic plasticity that is invariant to second-order correlations in the input. Going beyond classical Hebbian learning, our learning objective explains the functional form of observed excitatory plasticity mechanisms, showing how H...
Source: PLoS Computational Biology - February 12, 2024 Category: Biology Authors: Carlos Stein Naves de Brito Source Type: research
Accounting for isoform expression increases power to identify genetic regulation of gene expression
In this study, we propose and evaluate several approaches to answering this question, demonstrating that “isoform-aware” methods—those that account for the expression levels of individual isoforms—have substantially greater power to answer this question than standard “gene-level” eQTL mapping methods. We identify settings in which different approaches yield an inflated number of false disco veries or lose power. In particular, we show that calling an eGene if there is a significant association between a SNP and any isoform fails to control False Discovery Rate, even when applying standard False Discovery Rate c...
Source: PLoS Computational Biology - February 12, 2024 Category: Biology Authors: Nathan LaPierre Source Type: research
