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As of now, more than 60 years have passed since the first determination of protein structures through crystallography, and a significant portion of protein structures can be predicted by computers. This is due to the groundbreaking enhancement in protein structure prediction achieved through neural network training utilizing extensive sequence and structure data. However, substantial challenges persist in structure prediction due to limited data availability, with antibody structure prediction standing as one such challenge. In this paper, we propose a novel neural network architecture that effectively enables structure prediction by reflecting the inherent combinatorial nature involved in protein structure formation. The core idea of this neural network architecture is not solely to track and generate a single structure but rather to form a community of multiple structures and pursue accurate structure prediction by exchanging information among community members. Applying this concept to antibody CDR H3 loop structure prediction resulted in improved structure sampling. Such an approach could be applied in the structural and functional studies of proteins, particularly in exploring various physiological processes mediated by loops. Moreover, it holds potential in addressing various other types of combinatorial structure prediction and design problems. Author summary: In this paper, we propose a new architecture that aims to improve upon protein structure prediction algorithms like AlphaFold or RoseTTAFold by considering the combinatorial nature of protein structure formation. Such an architecture, reflecting the physical principles of nature, is expected to yield beneficial results, particularly in scenarios with limited structure and sequence information. Named ComMat, this architecture does not focus on a single structure but rather on a set of multiple structures—a community—simultaneously. In this process, combinatorial exploration of protein structure is encouraged through information exchange among community members. ComMat is an instance that integrates this idea within the structure module of AlphaFold. Applying ComMat to antibody CDR H3 loop structure prediction yielded outstanding results in structure sampling and prediction when tested on the IgFold set and compared with IgFold and AlphaFold-Multimer. It confirmed that improved structure sampling stems from effective structural exploration. The proposed concept here could potentially be used in the development of various other combinatorial protein structure prediction and protein design methods. [ABSTRACT FROM AUTHOR]

Although adaptive cancer therapy shows promise in integrating evolutionary dynamics into treatment scheduling, the stochastic nature of cancer evolution has seldom been taken into account. Various sources of random perturbations can impact the evolution of heterogeneous tumors, making performance metrics of any treatment policy random as well. In this paper, we propose an efficient method for selecting optimal adaptive treatment policies under randomly evolving tumor dynamics. The goal is to improve the cumulative "cost" of treatment, a combination of the total amount of drugs used and the total treatment time. As this cost also becomes random in any stochastic setting, we maximize the probability of reaching the treatment goals (tumor stabilization or eradication) without exceeding a pre-specified cost threshold (or a "budget"). We use a novel Stochastic Optimal Control formulation and Dynamic Programming to find such "threshold-aware" optimal treatment policies. Our approach enables an efficient algorithm to compute these policies for a range of threshold values simultaneously. Compared to treatment plans shown to be optimal in a deterministic setting, the new "threshold-aware" policies significantly improve the chances of the therapy succeeding under the budget, which is correlated with a lower general drug usage. We illustrate this method using two specific examples, but our approach is far more general and provides a new tool for optimizing adaptive therapies based on a broad range of stochastic cancer models. Author summary: Tumor heterogeneities provide an opportunity to improve therapies by leveraging complex (often competitive) interactions of different types of cancer cells. These interactions are usually stochastic due to both individual cell differences and random events affecting the patient as a whole. The new generation of cancer models strive to account for this inherent stochasticity, and adaptive treatment plans need to reflect it as well. In optimizing such treatment, the most common approach is to maximize the probability of eventually stabilizing or eradicating the tumor. In this paper, we consider a more nuanced version of success, maximizing the probability of reaching these therapy goals before the cumulative burden from the disease and treatment exceed a chosen threshold. Importantly, our method allows computing such optimal treatment plans efficiently and for a range of thresholds at once. If used on a high-fidelity personalized model, our general approach could potentially be used by clinicians to choose the most suitable threshold after a detailed discussion of a specific patient's goals (e.g., to include the trade-offs between toxicity and quality of life). [ABSTRACT FROM AUTHOR]

Astrocytes are a ubiquitous and enigmatic type of non-neuronal cell and are found in the brain of all vertebrates. While traditionally viewed as being supportive of neurons, it is increasingly recognized that astrocytes play a more direct and active role in brain function and neural computation. On account of their sensitivity to a host of physiological covariates and ability to modulate neuronal activity and connectivity on slower time scales, astrocytes may be particularly well poised to modulate the dynamics of neural circuits in functionally salient ways. In the current paper, we seek to capture these features via actionable abstractions within computational models of neuron-astrocyte interaction. Specifically, we engage how nested feedback loops of neuron-astrocyte interaction, acting over separated time-scales, may endow astrocytes with the capability to enable learning in context-dependent settings, where fluctuations in task parameters may occur much more slowly than within-task requirements. We pose a general model of neuron-synapse-astrocyte interaction and use formal analysis to characterize how astrocytic modulation may constitute a form of meta-plasticity, altering the ways in which synapses and neurons adapt as a function of time. We then embed this model in a bandit-based reinforcement learning task environment, and show how the presence of time-scale separated astrocytic modulation enables learning over multiple fluctuating contexts. Indeed, these networks learn far more reliably compared to dynamically homogeneous networks and conventional non-network-based bandit algorithms. Our results fuel the notion that neuron-astrocyte interactions in the brain benefit learning over different time-scales and the conveyance of task-relevant contextual information onto circuit dynamics. Author summary: Astrocytes, a non-neuronal cell type, constitute a significant portion of all cells in the brain, yet our understanding of their involvement in neural computation remains limited. In this paper, we use computational modeling to examine how astrocytes may interact with neurons to expand the types of activity and, ultimately, computations that neurons can produce. Two features of astrocytes make them interesting in this context. First, a single astrocyte is able to modulate dozens of neurons and synaptic connections between them. Furthermore, this modulation occurs at slower temporal scales than the neural activity itself, thus meaning that astrocytes could have long-lasting effects on neural activity and connectivity. Our computational models capture both of these features. Using these models, we show that networks with astrocytes can more readily adapt to slowly varying task parameters versus those with neurons alone. We further analyze the models to understand, in mathematical terms, why such effects may arise. Our results shed light on the potential computational role of this common, but enigmatic type of brain cell. [ABSTRACT FROM AUTHOR]

In ischaemic stroke, a large reduction in blood supply can lead to the breakdown of the blood-brain barrier and to cerebral oedema after reperfusion therapy. The resulting fluid accumulation in the brain may contribute to a significant rise in intracranial pressure (ICP) and tissue deformation. Changes in the level of ICP are essential for clinical decision-making and therapeutic strategies. However, the measurement of ICP is constrained by clinical techniques and obtaining the exact values of the ICP has proven challenging. In this study, we propose the first computational model for the simulation of cerebral oedema following acute ischaemic stroke for the investigation of ICP and midline shift (MLS) relationship. The model consists of three components for the simulation of healthy blood flow, occluded blood flow and oedema, respectively. The healthy and occluded blood flow components are utilized to obtain oedema core geometry and then imported into the oedema model for the simulation of oedema growth. The simulation results of the model are compared with clinical data from 97 traumatic brain injury patients for the validation of major model parameters. Midline shift has been widely used for the diagnosis, clinical decision-making, and prognosis of oedema patients. Therefore, we focus on quantifying the relationship between ICP and midline shift (MLS) and identify the factors that can affect the ICP-MLS relationship. Three major factors are investigated, including the brain geometry, blood-brain barrier damage severity and the types of oedema (including rare types of oedema). Meanwhile, the two major types (stress and tension/compression) of mechanical brain damage are also presented and the differences in the stress, tension, and compression between the intraparenchymal and periventricular regions are discussed. This work helps to predict ICP precisely and therefore provides improved clinical guidance for the treatment of brain oedema. Author summary: Midline shift has been widely employed in clinical settings to estimate the severity of post-stroke brain oedema. However, the midline shift is affected by many factors and its relationship with intracranial pressure has been proven to vary among patients. In this paper, we utilise a computational tool to investigate the midline shift under large brain deformation from a mechanical perspective. The Augmented Lagrange Method is for the first time employed to solve the large deformation of oedema brains with midline shifts over 5 mm. In the model validation, a reasonable agreement between clinical data and simulation results are obtained. This paper models three different factors that can affect midline shift during oedema development, including brain geometry, blood-brain barrier damage severity and type of oedema. Meanwhile, we discuss the scenarios that can lead to unexpectedly small midline shifts and present quantitative results for a better understanding of brain mechanics during oedema. [ABSTRACT FROM AUTHOR]

Quantifying fungal growth underpins our ability to effectively treat severe fungal infections. Current methods quantify fungal growth rates from time-course morphology-specific data, such as hyphal length data. However, automated large-scale collection of such data lies beyond the scope of most clinical microbiology laboratories. In this paper, we propose a mathematical model of fungal growth to estimate morphology-specific growth rates from easy-to-collect, but indirect, optical density (OD600) data of Aspergillus fumigatus growth (filamentous fungus). Our method accounts for OD600 being an indirect measure by explicitly including the relationship between the indirect OD600 measurements and the calibrating true fungal growth in the model. Therefore, the method does not require de novo generation of calibration data. Our model outperformed reference models at fitting to and predicting OD600 growth curves and overcame observed discrepancies between morphology-specific rates inferred from OD600 versus directly measured data in reference models that did not include calibration. Author summary: Quantifying fungal growth is essential for antifungal drug discovery and monitoring antifungal resistance. As fungal growth is complex, with fungal morphology (shape) dynamically changing over time, previous studies have quantified fungal growth by estimating growth rates during specific fungal morphologies (morphology-specific growth rates) or by mathematically modelling fungal growth. However, collecting time-series data that captures the morphological information required for mathematical model fitting or estimating morphology-specific growth rates is prohibitively time consuming for large-scale drug testing in most microbiology laboratories. Alternatively, fungal growth can be quickly, although indirectly, quantified by measuring the optical density (OD) of a broth culture. However, changes in OD are not always reflective of true changes in fungal growth because OD is an indirect measure. This paper proposes a method to model fungal growth and estimate a morphology-specific growth rate from indirect OD600 measurements of the major mould pathogen, Aspergillus fumigatus. We explicitly model the relationship between measured indirect OD600 data and true fungal growth (calibration). The presented work serves as the much-needed foundation for estimating and comparing morphology-specific fungal growth rates in varying antifungal drug concentrations using only OD600 data. [ABSTRACT FROM AUTHOR]

We introduce an innovative, data-driven topological data analysis (TDA) technique for estimating the state spaces of dynamically changing functional human brain networks at rest. Our method utilizes the Wasserstein distance to measure topological differences, enabling the clustering of brain networks into distinct topological states. This technique outperforms the commonly used k-means clustering in identifying brain network state spaces by effectively incorporating the temporal dynamics of the data without the need for explicit model specification. We further investigate the genetic underpinnings of these topological features using a twin study design, examining the heritability of such state changes. Our findings suggest that the topology of brain networks, particularly in their dynamic state changes, may hold significant hidden genetic information. Author summary: The paper introduces a new data-driven topological data analysis (TDA) method for studying dynamically changing human functional brain networks obtained from the resting-state functional magnetic resonance imaging (rs-fMRI). Leveraging persistent homology, a multiscale topological approach, we present a framework that incorporates the temporal dimension of brain network data. This allows for a more robust estimation of the topological features of dynamic brain networks. The method employs the Wasserstein distance to measure the topological differences between networks and demonstrates greater efficiency and performance than the commonly used k-means clustering in defining the state spaces of dynamic brain networks. Our method maintains robust performance across different scales and is especially suited for dynamic brain networks. In addition to the methodological advancement, the paper applies the proposed technique to analyze the heritability of overall brain network topology using a twin study design. The study investigates whether the dynamic pattern of brain networks is a genetically influenced trait, an area previously underexplored. By examining the state change patterns in twin brain networks, we make significant strides in understanding the genetic factors underlying dynamic brain network features. Furthermore, the paper makes its method accessible by providing MATLAB codes, contributing to reproducibility and broader application. [ABSTRACT FROM AUTHOR]

To optimize proteins for particular traits holds great promise for industrial and pharmaceutical purposes. Machine Learning is increasingly applied in this field to predict properties of proteins, thereby guiding the experimental optimization process. A natural question is: How much progress are we making with such predictions, and how important is the choice of regressor and representation? In this paper, we demonstrate that different assessment criteria for regressor performance can lead to dramatically different conclusions, depending on the choice of metric, and how one defines generalization. We highlight the fundamental issues of sample bias in typical regression scenarios and how this can lead to misleading conclusions about regressor performance. Finally, we make the case for the importance of calibrated uncertainty in this domain. Author summary: Supervised machine learning is increasingly used to predict the function and properties of proteins. The performance obtained with these methods relies on a multitude of factors including how data is represented, how observations are distributed, how training is conducted, and how performance is measured. In this paper, we systematically assess the importance of these different components in a protein regression pipeline. We discuss the benefits of using representations extracted from protein language models, the impact of the choice of regression algorithm, and the role of uncertainty. Finally, to avoid misleading performance claims, we stress the need for carefully aligning the train/test setup to reflect the setting in which the prediction algorithm will ultimately be applied. [ABSTRACT FROM AUTHOR]

One of the key problems the brain faces is inferring the state of the world from a sequence of dynamically changing stimuli, and it is not yet clear how the sensory system achieves this task. A well-established computational framework for describing perceptual processes in the brain is provided by the theory of predictive coding. Although the original proposals of predictive coding have discussed temporal prediction, later work developing this theory mostly focused on static stimuli, and key questions on neural implementation and computational properties of temporal predictive coding networks remain open. Here, we address these questions and present a formulation of the temporal predictive coding model that can be naturally implemented in recurrent networks, in which activity dynamics rely only on local inputs to the neurons, and learning only utilises local Hebbian plasticity. Additionally, we show that temporal predictive coding networks can approximate the performance of the Kalman filter in predicting behaviour of linear systems, and behave as a variant of a Kalman filter which does not track its own subjective posterior variance. Importantly, temporal predictive coding networks can achieve similar accuracy as the Kalman filter without performing complex mathematical operations, but just employing simple computations that can be implemented by biological networks. Moreover, when trained with natural dynamic inputs, we found that temporal predictive coding can produce Gabor-like, motion-sensitive receptive fields resembling those observed in real neurons in visual areas. In addition, we demonstrate how the model can be effectively generalized to nonlinear systems. Overall, models presented in this paper show how biologically plausible circuits can predict future stimuli and may guide research on understanding specific neural circuits in brain areas involved in temporal prediction. Author summary: While significant advances have been made in the neuroscience of how the brain processes static stimuli, the time dimension has often been relatively neglected. However, time is crucial since the stimuli perceived by our senses typically dynamically vary in time, and the cortex needs to make sense of these changing inputs. This paper describes a computational model of cortical networks processing temporal stimuli. This model is able to infer and track the state of the environment based on noisy inputs, and predict future sensory stimuli. By ensuring that these predictions match the incoming stimuli, the model is able to learn the structure and statistics of its temporal inputs and produces responses of neurons resembling those in the brain. The model may help in further understanding neural circuits in sensory cortical areas. [ABSTRACT FROM AUTHOR]

Using the CRISPR-Cas9 system to perform base substitutions at the target site is a typical technique for genome editing with the potential for applications in gene therapy and agricultural productivity. When the CRISPR-Cas9 system uses guide RNA to direct the Cas9 endonuclease to the target site, it may misdirect it to a potential off-target site, resulting in an unintended genome editing. Although several computational methods have been proposed to predict off-target effects, there is still room for improvement in the off-target effect prediction capability. In this paper, we present an effective approach called CRISPR-M with a new encoding scheme and a novel multi-view deep learning model to predict the sgRNA off-target effects for target sites containing indels and mismatches. CRISPR-M takes advantage of convolutional neural networks and bidirectional long short-term memory recurrent neural networks to construct a three-branch network towards multi-views. Compared with existing methods, CRISPR-M demonstrates significant performance advantages running on real-world datasets. Furthermore, experimental analysis of CRISPR-M under multiple metrics reveals its capability to extract features and validates its superiority on sgRNA off-target effect predictions. Author summary: Genome editing using the CRISPR-Cas9 system, particularly base substitutions directed by guide RNA, holds immense potential for applications in gene therapy and agricultural productivity. However, the risk of unintended off-target effects poses a challenge, as misdirection of the Cas9 endonuclease can lead to unintended genome alterations. While computational methods exist for predicting off-target effects, there remains a need for encoding methods with more representation space and deep learning models with generalization capability and the adaptability. This paper introduces CRISPR-M, an innovative approach addressing the limitations of existing methods in predicting off-target effects, especially for target sites with indels and mismatches. CRISPR-M employs a novel encoding scheme and a multi-view deep learning model, combining convolutional neural networks and bidirectional long short-term memory recurrent neural networks. The three-branch network structure enhances the prediction accuracy by considering multiple perspectives. Compared with previous representative methods, CRISPR-M exhibits remarkable performance advantages when applied to real-world datasets. The experimental evaluation of CRISPR-M, assessed by various metrics such as ROC, PRC, GC content and melting temperature, demonstrates its ability to extract meaningful features and establishes its superiority in predicting off-target effects of sgRNA. [ABSTRACT FROM AUTHOR]

From mathematical models of growth to computer simulations of pigmentation, the study of shell formation has given rise to an abundant number of models, working at various scales. Yet, attempts to combine those models have remained sparse, due to the challenge of combining categorically different approaches. In this paper, we propose a framework to streamline the process of combining the molecular and tissue scales of shell formation. We choose these levels as a proxy to link the genotype level, which is better described by molecular models, and the phenotype level, which is better described by tissue-level mechanics. We also show how to connect observations on shell populations to the approach, resulting in collections of molecular parameters that may be associated with different populations of real shell specimens. The approach is as follows: we use a Quality-Diversity algorithm, a type of black-box optimization algorithm, to explore the range of concentration profiles emerging as solutions of a molecular model, and that define growth patterns for the mechanical model. At the same time, the mechanical model is simulated over a wide range of growth patterns, resulting in a variety of spine shapes. While time-consuming, these steps only need to be performed once and then function as look-up tables. Actual pictures of shell spines can then be matched against the list of existing spine shapes, yielding a potential growth pattern which, in turn, gives us matching molecular parameters. The framework is modular, such that models can be easily swapped without changing the overall working of the method. As a demonstration of the approach, we solve specific molecular and mechanical models, adapted from available theoretical studies on molluscan shells, and apply the multiscale framework to evaluate the characteristics of spines from three distinct populations of Turbo sazae. Author summary: Connecting genotype to phenotype is a fundamental goal in developmental biology. While many studies examine this link in model organisms for which gene regulatory networks are well known, for non-model organisms, different techniques are required, and multiscale computational modeling offers a promising direction. In this paper, we develop a framework linking molecular-scale interactions to tissue-level growth and mechanics to organ-level characteristics in order to investigate spine formation in T. sazae, a species of mollusc that displays remarkable phenotypic plasticity in spine form. Our analysis uncovers a subtle but statistically significant difference in spine form between shell specimens collected from three different localities in Japan. Moreover, by tracing the difference in form through parametric differences in the multiscale framework, we provide mechanistic insight as to how environmental differences may translate to a change in form. The methodology we present may readily be extended to more detailed modeling of this system, and the conceptual framework is amenable for multiscale analysis in other systems. [ABSTRACT FROM AUTHOR]

Unlike proteins, RNAs deposited in the Protein Data Bank do not contain topological knots. Recently, admittedly, the first trefoil knot and some lasso-type conformations have been found in experimental RNA structures, but these are still exceptional cases. Meanwhile, algorithms predicting 3D RNA models have happened to form knotted structures not so rarely. Interestingly, machine learning-based predictors seem to be more prone to generate knotted RNA folds than traditional methods. A similar situation is observed for the entanglements of structural elements. In this paper, we analyze all models submitted to the CASP15 competition in the 3D RNA structure prediction category. We show what types of topological knots and structure element entanglements appear in the submitted models and highlight what methods are behind the generation of such conformations. We also study the structural aspect of susceptibility to entanglement. We suggest that predictors take care of an evaluation of RNA models to avoid publishing structures with artifacts, such as unusual entanglements, that result from hallucinations of predictive algorithms. Author summary: 3D RNA structure prediction contests such as CASP and RNA-Puzzles lack measures for topology-wise evaluation of predicted models. Thus, predictors happen to submit potentially inappropriate conformations, for example, containing entanglements that are prediction artifacts. Automated identification of entanglements in 3D RNA structures is computationally hard. Distinguishing correct from incorrectly entangled conformations is not trivial and often requires expert knowledge. We analyzed 3D RNA models submitted to CASP15 and found that all entanglements in these models are artifacts. Compared to non-ML, machine learning-based methods are more prone to generating entanglements that are not present in natural RNAs. To increase the reliability of 3D RNA structure prediction, it is necessary to reject abnormally entangled structures in the modeling stage. [ABSTRACT FROM AUTHOR]

Computable biomedical knowledge (CBK) is: "the result of an analytic and/or deliberative process about human health, or affecting human health, that is explicit, and therefore can be represented and reasned upon using logic, formal standards, and mathematical approaches." Representing biomedical knowledge in a machine-interpretable, computable form increases its ability to be discovered, accessed, understood, and deployed. Computable knowledge artifacts can greatly advance the potential for implementation, reproducibility, or extension of the knowledge by users, who may include practitioners, researchers, and learners. Enriching computable knowledge artifacts may help facilitate reuse and translation into practice. Following the examples of 10 Simple Rules papers for scientific code, software, and applications, we present 10 Simple Rules intended to make shared computable knowledge artifacts more useful and reusable. These rules are mainly for researchers and their teams who have decided that sharing their computable knowledge is important, who wish to go beyond simply describing results, algorithms, or models via traditional publication pathways, and who want to both make their research findings more accessible, and to help others use their computable knowledge. These rules are roughly organized into 3 categories: planning, engineering, and documentation. Finally, while many of the following examples are of computable knowledge in biomedical domains, these rules are generalizable to computable knowledge in any research domain. Author summary: Computable biomedical knowledge, including algorithms, models, and evidence, is already impacting—and will likely transform—health research and healthcare delivery. Representing this knowledge in formats that can be discovered and processed by computers increases its potential for use at scale. Similarly, making these algorithms, models, etc. (aka computable knowledge artifacts) understandable and reusable by others can help facilitate reuse and translation into practice. However, as with other types of scientific software or research data, creating artifacts that are usable by and useful to others requires intentionality, and often additional time and effort. We've developed over 100 computable knowledge artifacts in diverse areas of health research and healthcare and we've learned a few things along the way. We've distilled these lessons learned into 10 Simple Rules to help researchers who would like to make their computational research products available and useful to others. Our work centers on knowledge in the biomedical domain, but we believe these 10 Simple Rules are generalizable and valuable as design principles for any researcher wishing to share computational research products. [ABSTRACT FROM AUTHOR]

Periplasmic binding proteins (PBPs) are bacterial proteins commonly used as scaffolds for substrate-detecting biosensors. In these biosensors, effector proteins (for example fluorescent proteins) are inserted into a PBP such that the effector protein's output changes upon PBP-substate binding. The insertion site is often determined by comparison of PBP apo/holo crystal structures, but random insertion libraries have shown that this can miss the best sites. Here, we present a PBP biosensor design method based on residue contact analysis from molecular dynamics. This computational method identifies the best previously known insertion sites in the maltose binding PBP, and suggests further previously unknown sites. We experimentally characterise fluorescent protein insertions at these new sites, finding they too give functional biosensors. Furthermore, our method is sufficiently flexible to both suggest insertion sites compatible with a variety of effector proteins, and be applied to binding proteins beyond PBPs. Author summary: "Biosensors" are microscopic tools that can detect specific molecules of interest and are made of biological building blocks, such as proteins. Upon coming into contact with their target molecule, such as a marker of a specific disease, biosensors change shape and their properties are altered. For example, upon contact with a disease marker, a biosensor could glow brightly to work as a diagnostic, or perhaps it could produce a chemical signal that can be detected by the immune system. Understanding how biosensors change shape in response to their target is key to developing more biosensors with complex responses. In this paper, we use computer simulations of a biosensor component to understand what parameters are important for biosensor design. We use these parameters to generate new biosensors that respond to their target molecule by producing light, but put forward a case that the lesson learned are generalisable enough to inform more complex sensor outputs as well. [ABSTRACT FROM AUTHOR]

Accurate models describing the relationship between genotype and phenotype are necessary in order to understand and predict how mutations to biological sequences affect the fitness and evolution of living organisms. The apparent abundance of epistasis (genetic interactions), both between and within genes, complicates this task and how to build mechanistic models that incorporate epistatic coefficients (genetic interaction terms) is an open question. The Walsh-Hadamard transform represents a rigorous computational framework for calculating and modeling epistatic interactions at the level of individual genotypic values (known as genetical, biological or physiological epistasis), and can therefore be used to address fundamental questions related to sequence-to-function encodings. However, one of its main limitations is that it can only accommodate two alleles (amino acid or nucleotide states) per sequence position. In this paper we provide an extension of the Walsh-Hadamard transform that allows the calculation and modeling of background-averaged epistasis (also known as ensemble epistasis) in genetic landscapes with an arbitrary number of states per position (20 for amino acids, 4 for nucleotides, etc.). We also provide a recursive formula for the inverse matrix and then derive formulae to directly extract any element of either matrix without having to rely on the computationally intensive task of constructing or inverting large matrices. Finally, we demonstrate the utility of our theory by using it to model epistasis within both simulated and empirical multiallelic fitness landscapes, revealing that both pairwise and higher-order genetic interactions are enriched between physically interacting positions. Author summary: An important question in genetics is how the effects of mutations combine to alter phenotypes. Genetic interactions (epistasis) describe non-additive effects of pairs of mutations, but can also involve higher-order (three- and four-way etc.) combinations. Quantifying higher-order interactions is experimentally very challenging requiring a large number of measurements. Techniques based on deep mutational scanning (DMS) represent valuable sources of data to study epistasis. However, the best way to extract the relevant pairwise and higher-order epistatic coefficients (genetic interaction terms) from this data for the task of phenotypic prediction remains an unresolved problem. The Walsh-Hadamard transform represents a rigorous computational framework for calculating and modeling epistatic interactions at the level of individual genotypic values. Critically, this formalism currently only allows for two alleles (amino acid or nucleotide states) per sequence position, hampering applications in more biologically realistic scenarios. Here we present an extension of the Walsh-Hadamard transform that overcomes this limitation and demonstrate the utility of our theory by using it to model epistasis within both simulated and empirical multiallelic genetic landscapes. [ABSTRACT FROM AUTHOR]

An important application of CRISPR interference (CRISPRi) technology is for identifying chemical-genetic interactions (CGIs). Discovery of genes that interact with exposure to antibiotics can yield insights to drug targets and mechanisms of action or resistance. The objective is to identify CRISPRi mutants whose relative abundance is suppressed (or enriched) in the presence of a drug when the target protein is depleted, reflecting synergistic behavior. Different sgRNAs for a given target can induce a wide range of protein depletion and differential effects on growth rate. The effect of sgRNA strength can be partially predicted based on sequence features. However, the actual growth phenotype depends on the sensitivity of cells to depletion of the target protein. For essential genes, sgRNA efficiency can be empirically measured by quantifying effects on growth rate. We observe that the most efficient sgRNAs are not always optimal for detecting synergies with drugs. sgRNA efficiency interacts in a non-linear way with drug sensitivity, producing an effect where the concentration-dependence is maximized for sgRNAs of intermediate strength (and less so for sgRNAs that induce too much or too little target depletion). To capture this interaction, we propose a novel statistical method called CRISPRi-DR (for Dose-Response model) that incorporates both sgRNA efficiencies and drug concentrations in a modified dose-response equation. We use CRISPRi-DR to re-analyze data from a recent CGI experiment in Mycobacterium tuberculosis to identify genes that interact with antibiotics. This approach can be generalized to non-CGI datasets, which we show via an CRISPRi dataset for E. coli growth on different carbon sources. The performance is competitive with the best of several related analytical methods. However, for noisier datasets, some of these methods generate far more significant interactions, likely including many false positives, whereas CRISPRi-DR maintains higher precision, which we observed in both empirical and simulated data. Author summary: CRISPRi technology is revolutionizing research in various areas of the life sciences, including microbiology, affording the ability to partially deplete the expression of target proteins in a specific and controlled way. Among the applications of CRISPRi, it can be used to construct large (even genome-wide) libraries of knock-down mutants for profiling antibacterial inhibitors and identifying chemical-genetic interactions (CGIs), which can yield insights on drug targets and mechanisms of action and resistance. The data generated by these experiments (i.e., sgRNA counts from high throughput sequencing) is voluminous and subject to various sources of noise. The goal of statistical analysis of such data is to identify significant CGIs, which are genes whose depletion sensitizes cells to an inhibitor. In this paper, we show how to incorporate both sgRNA efficiency and drug concentration simultaneously in a model (CRISPRi-DR) based on an extension of the classic dose-response (Hill) equation in enzymology. This model has advantages over other analytical methods for CRISPRi, which we show using empirical and simulated data. [ABSTRACT FROM AUTHOR]

In experiments, the distributions of mRNA or protein numbers in single cells are often fitted to the random telegraph model which includes synthesis and decay of mRNA or protein, and switching of the gene between active and inactive states. While commonly used, this model does not describe how fluctuations are influenced by crucial biological mechanisms such as feedback regulation, non-exponential gene inactivation durations, and multiple gene activation pathways. Here we investigate the dynamical properties of four relatively complex gene expression models by fitting their steady-state mRNA or protein number distributions to the simple telegraph model. We show that despite the underlying complex biological mechanisms, the telegraph model with three effective parameters can accurately capture the steady-state gene product distributions, as well as the conditional distributions in the active gene state, of the complex models. Some effective parameters are reliable and can reflect realistic dynamic behaviors of the complex models, while others may deviate significantly from their real values in the complex models. The effective parameters can also be applied to characterize the capability for a complex model to exhibit multimodality. Using additional information such as single-cell data at multiple time points, we provide an effective method of distinguishing the complex models from the telegraph model. Furthermore, using measurements under varying experimental conditions, we show that fitting the mRNA or protein number distributions to the telegraph model may even reveal the underlying gene regulation mechanisms of the complex models. The effectiveness of these methods is confirmed by analysis of single-cell data for E. coli and mammalian cells. All these results are robust with respect to cooperative transcriptional regulation and extrinsic noise. In particular, we find that faster relaxation speed to the steady state results in more precise parameter inference under large extrinsic noise. Author summary: Over the past decade, significant progress has been made in the theory and experiments of single-cell stochastic gene expression dynamics. The most well studied and widely used stochastic gene expression model is the two-state telegraph model. However, the conventional telegraph model is too simple and limited in its predictive power because it lacks a description of some important biological mechanisms, such as feedback regulation, multiple gene activation steps, and multiple gene activation pathways. This raises a important question: what can we learn when fitting a complex gene expression model to a simple telegraph model? In this paper, we investigate four complex gene expression models by fitting their steady-state mRNA or protein number distributions to the telegraph model and then obtain estimates of the effective parameters. We show that while the estimated values of the parameters in the "artificial" telegraph model are not always accurate, they are still sometimes reliable and can also reveal important dynamical properties of the complex models such as the ability for a complex model to produce bimodality. Moreover, we provide an effective method of distinguishing the complex models from the telegraph model by using additional information such as gene expression data at multiple time points. Finally, we show that fitting the mRNA or protein number distributions to the telegraph model may even reveal the underlying gene regulation mechanism of a complex model by using measurements under varying experimental conditions. The effectiveness of these methods is well confirmed by analysis of single-cell gene expression data for E. coli and mammalian cells. [ABSTRACT FROM AUTHOR]

Within the islets of Langerhans, beta cells orchestrate synchronized insulin secretion, a pivotal aspect of metabolic homeostasis. Despite the inherent heterogeneity and multimodal activity of individual cells, intercellular coupling acts as a homogenizing force, enabling coordinated responses through the propagation of intercellular waves. Disruptions in this coordination are implicated in irregular insulin secretion, a hallmark of diabetes. Recently, innovative approaches, such as integrating multicellular calcium imaging with network analysis, have emerged for a quantitative assessment of the cellular activity in islets. However, different groups use distinct experimental preparations, microscopic techniques, apply different methods to process the measured signals and use various methods to derive functional connectivity patterns. This makes comparisons between findings and their integration into a bigger picture difficult and has led to disputes in functional connectivity interpretations. To address these issues, we present here a systematic analysis of how different approaches influence the network representation of islet activity. Our findings show that the choice of methods used to construct networks is not crucial, although care is needed when combining data from different islets. Conversely, the conclusions drawn from network analysis can be heavily affected by the pre-processing of the time series, the type of the oscillatory component in the signals, and by the experimental preparation. Our tutorial-like investigation aims to resolve interpretational issues, reconcile conflicting views, advance functional implications, and encourage researchers to adopt connectivity analysis. As we conclude, we outline challenges for future research, emphasizing the broader applicability of our conclusions to other tissues exhibiting complex multicellular dynamics. Author summary: Islets of Langerhans, multicellular microorgans in the pancreas, are pivotal for whole-body energy homeostasis. Hundreds of beta cells within these networks synchronize to produce insulin, a crucial hormone for metabolic control. Coordinated activity disruptions in these multicellular networks contribute to irregular insulin secretion, a hallmark of diabetes. Recognizing the significance of collective activity, network science approaches have been increasingly applied in islet research. However, variations in experimental setups, imaging techniques, signal processing, and connectivity analysis methods across different research groups pose challenges for integrating findings into a comprehensive picture. Therefore, we present here a systematic analysis of various approaches impacting results in islet activity network representation. We find that methods for constructing functional connectivity maps aren't critical, but caution is necessary when aggregating data from different islets. Network analysis conclusions are notably influenced by factors such as time series pre-processing, the oscillatory component of signals, and experimental preparation. Despite these challenges, this paper advocates for the adoption of connectivity analysis in future islet research, emphasizing that the insights gained extend beyond pancreatic islets to provide valuable contributions for understanding connectivity in other multicellular systems. [ABSTRACT FROM AUTHOR]

The heterogeneity of Hepatocellular Carcinoma (HCC) poses a barrier to effective treatment. Stratifying highly heterogeneous HCC into molecular subtypes with similar features is crucial for personalized anti-tumor therapies. Although driver genes play pivotal roles in cancer progression, their potential in HCC subtyping has been largely overlooked. This study aims to utilize driver genes to construct HCC subtype models and unravel their molecular mechanisms. Utilizing a novel computational framework, we expanded the initially identified 96 driver genes to 1192 based on mutational aspects and an additional 233 considering driver dysregulation. These genes were subsequently employed as stratification markers for further analyses. A novel multi-omics subtype classification algorithm was developed, leveraging mutation and expression data of the identified stratification genes. This algorithm successfully categorized HCC into two distinct subtypes, CLASS A and CLASS B, demonstrating significant differences in survival outcomes. Integrating multi-omics and single-cell data unveiled substantial distinctions between these subtypes regarding transcriptomics, mutations, copy number variations, and epigenomics. Moreover, our prognostic model exhibited excellent predictive performance in training and external validation cohorts. Finally, a 10-gene classification model for these subtypes identified TTK as a promising therapeutic target with robust classification capabilities. This comprehensive study provides a novel perspective on HCC stratification, offering crucial insights for a deeper understanding of its pathogenesis and the development of promising treatment strategies. Author summary: Dividing highly heterogeneous HCC into molecular subtypes with similar characteristics is crucial for personalized anti-tumor therapies. Although driver genes play pivotal roles in cancer progression, their potential in HCC subtyping has been largely overlooked. In this work, we developed a multi-omics network-based stratification algorithm that utilizes patient mutation data and requires smaller computational resources for subtype assignment. Through this algorithm, we categorized HCC into two subtypes, CLASS A and CLASS B. Using multi-omics and single-cell data, we identified differences between these subtypes in gene expression, methylation, immune infiltration, and other aspects. Beyond subtype characterization, our study established a robust clinical prediction model (https://mike-wang-bjut.shinyapps.io/DynNomapp%5fHCC%5fSutypes/) incorporating subtype information and typical clinical features, enabling precise survival predictions. Finally, we developed a high-performing machine learning classifier for our subtype. Analyzing this classification model and reviewing previous experimental papers, we identified TTK as a potential diagnostic marker and therapeutic target specific to our subtypes. In conclusion, our research offers a novel perspective on HCC stratification, which is crucial for a deeper understanding of its pathogenesis and developing promising treatment strategies. [ABSTRACT FROM AUTHOR]

This paper extends the FAIR (Findable, Accessible, Interoperable, Reusable) guidelines to provide criteria for assessing if software conforms to best practices in open source. By adding "USE" (User-Centered, Sustainable, Equitable), software development can adhere to open source best practice by incorporating user-input early on, ensuring front-end designs are accessible to all possible stakeholders, and planning long-term sustainability alongside software design. The FAIR-USE4OS guidelines will allow funders and researchers to more effectively evaluate and plan open-source software projects. There is good evidence of funders increasingly mandating that all funded research software is open source; however, even under the FAIR guidelines, this could simply mean software released on public repositories with a Zenodo DOI. By creating FAIR-USE software, best practice can be demonstrated from the very beginning of the design process and the software has the greatest chance of success by being impactful. Author summary: This research builds on the FAIR principles to ensure research software adheres to open-source development best practice, which includes community engagement and early planning for long-term sustainability. By creating guidelines ("FAIR-USE4OS") that can be followed, funders and researchers are in a stronger position to evaluate and create research software with maximal chance of success. This research is important as open-source software that is not "FAIR-USE" has a lower probability of long-term impact. These guidelines will help benefit and impact society once they are widely accepted by researchers and funders, which could happen within a relatively short time period given good evidence that funders are actively including and updating open source policies, which directly impact upon how research is conducted. [ABSTRACT FROM AUTHOR]

Biological cells replicate their genomes in a well-planned manner. The DNA replication program of an organism determines the timing at which different genomic regions are replicated, with fundamental consequences for cell homeostasis and genome stability. In a growing cell culture, genomic regions that are replicated early should be more abundant than regions that are replicated late. This abundance pattern can be experimentally measured using deep sequencing. However, a general quantitative theory linking this pattern to the replication program is still lacking. In this paper, we predict the abundance of DNA fragments in asynchronously growing cultures from any given stochastic model of the DNA replication program. As key examples, we present stochastic models of the DNA replication programs in budding yeast and Escherichia coli. In both cases, our model results are in excellent agreement with experimental data and permit to infer key information about the replication program. In particular, our method is able to infer the locations of known replication origins in budding yeast with high accuracy. These examples demonstrate that our method can provide insight into a broad range of organisms, from bacteria to eukaryotes. Author summary: Biological cells replicate their genome in a planned manner. One way of obtaining experimental information about this plan is by deep sequencing a growing culture of cells. The idea underlying these experiments is that genomic regions that are replicated earlier would be present in higher abundances than regions replicated at a later stage. In this paper, we make use of this idea to obtain precise quantitative information on replication programs from sequencing. As a main application, we infer the locations of origins of replication in budding yeast from sequencing data. Our inference is consistent with direct experimental evidences on the locations of these origins. Our method can be in principle be applied to any organism that can be cultured and sequenced, and has therefore the potential to shed light on the replication program of a broad class of organisms. [ABSTRACT FROM AUTHOR]

Multiplex panel tests identify many individual pathogens at once, using a set of component tests. In some panels the number of components can be large. If the panel is detecting causative pathogens for a single syndrome or disease then we might estimate the burden of that disease by combining the results of the panel, for example determining the prevalence of pneumococcal pneumonia as caused by many individual pneumococcal serotypes. When we are dealing with multiplex test panels with many components, test error in the individual components of a panel, even when present at very low levels, can cause significant overall error. Uncertainty in the sensitivity and specificity of the individual tests, and statistical fluctuations in the numbers of false positives and false negatives, will cause large uncertainty in the combined estimates of disease prevalence. In many cases this can be a source of significant bias. In this paper we develop a mathematical framework to characterise this issue, we determine expressions for the sensitivity and specificity of panel tests. In this we identify a counter-intuitive relationship between panel test sensitivity and disease prevalence that means panel tests become more sensitive as prevalence increases. We present novel statistical methods that adjust for bias and quantify uncertainty in prevalence estimates from panel tests, and use simulations to test these methods. As multiplex testing becomes more commonly used for screening in routine clinical practice, accumulation of test error due to the combination of large numbers of test results needs to be identified and corrected for. Author summary: During analysis of pneumococcal incidence data obtained from serotype specific multiplex urine antigen testing, we identified that despite excellent test sensitivity and specificity, the small error rate in each individual serotype test has the potential to compound and cause large uncertainty in the resulting estimates of pneumococcal prevalence, obtained by combining individual results. This limits the accuracy of estimates of the burden of disease caused by vaccine preventable pneumococcal serotypes, and in certain situations can produce marked bias. [ABSTRACT FROM AUTHOR]

Biochemical signaling pathways in living cells are often highly organized into spatially segregated volumes, membranes, scaffolds, subcellular compartments, and organelles comprising small numbers of interacting molecules. At this level of granularity stochastic behavior dominates, well-mixed continuum approximations based on concentrations break down and a particle-based approach is more accurate and more efficient. We describe and validate a new version of the open-source MCell simulation program (MCell4), which supports generalized 3D Monte Carlo modeling of diffusion and chemical reaction of discrete molecules and macromolecular complexes in solution, on surfaces representing membranes, and combinations thereof. The main improvements in MCell4 compared to the previous versions, MCell3 and MCell3-R, include a Python interface and native BioNetGen reaction language (BNGL) support. MCell4's Python interface opens up completely new possibilities for interfacing with external simulators to allow creation of sophisticated event-driven multiscale/multiphysics simulations. The native BNGL support, implemented through a new open-source library libBNG (also introduced in this paper), provides the capability to run a given BNGL model spatially resolved in MCell4 and, with appropriate simplifying assumptions, also in the BioNetGen simulation environment, greatly accelerating and simplifying model validation and comparison. [ABSTRACT FROM AUTHOR]

Neural organoids model the development of the human brain and are an indispensable tool for studying neurodevelopment. Whole-organoid lineage tracing has revealed the number of progenies arising from each initial stem cell to be highly diverse, with lineage sizes ranging from one to more than 20,000 cells. This high variability exceeds what can be explained by existing stochastic models of corticogenesis and indicates the existence of an additional source of stochasticity. To explain this variability, we introduce the SAN model which distinguishes Symmetrically diving, Asymmetrically dividing, and Non-proliferating cells. In the SAN model, the additional source of stochasticity is the survival time of a lineage's pool of symmetrically dividing cells. These survival times result from neutral competition within the sub-population of all symmetrically dividing cells. We demonstrate that our model explains the experimentally observed variability of lineage sizes and derive the quantitative relationship between survival time and lineage size. We also show that our model implies the existence of a regulatory mechanism which keeps the size of the symmetrically dividing cell population constant. Our results provide quantitative insight into the clonal composition of neural organoids and how it arises. This is relevant for many applications of neural organoids, and similar processes may occur in other developing tissues both in vitro and in vivo. Author summary: Organoids are tissue that is grown in vitro, but which replicates many aspects of a specific organ. Neural organoids grown from around 20,000 human pluripotent stem cells in particular replicate many aspects of the developing human brain, and have become an important tool for studying human neurodevelopment. Lineage-tracing allows the contribution of each ancestral stem cells to the organoid to be measured quantitatively. Surprisingly, these contributions vary between a single and more than 20,000 cells, which affects many applications of neural organoids. In this paper we study how these variations arise. We introduce the so-called SAN model, named after the symmetrically, asymmetrically, and non-dividing cells it distinguishes. This model explains the large variation in lineage sizes as being caused by neutral competition between symmetrically dividing cells, i.e. by random chance as cells divide and differentiate. Our model helps to understand the growth of neural organoids quantitatively, and provides way to account for the large difference in lineage sizes in applications of neural organoids. Neutral competition as the cause of large lineage size differences may also be relevant beyond neural organoids, and possibly in the development of tissue in vivo. [ABSTRACT FROM AUTHOR]

The time-varying effective reproduction number Rt is a widely used indicator of transmission dynamics during infectious disease outbreaks. Timely estimates of Rt can be obtained from reported cases counted by their date of symptom onset, which is generally closer to the time of infection than the date of report. Case counts by date of symptom onset are typically obtained from line list data, however these data can have missing information and are subject to right truncation. Previous methods have addressed these problems independently by first imputing missing onset dates, then adjusting truncated case counts, and finally estimating the effective reproduction number. This stepwise approach makes it difficult to propagate uncertainty and can introduce subtle biases during real-time estimation due to the continued impact of assumptions made in previous steps. In this work, we integrate imputation, truncation adjustment, and Rt estimation into a single generative Bayesian model, allowing direct joint inference of case counts and Rt from line list data with missing symptom onset dates. We then use this framework to compare the performance of nowcasting approaches with different stepwise and generative components on synthetic line list data for multiple outbreak scenarios and across different epidemic phases. We find that under reporting delays realistic for hospitalization data (50% of reports delayed by more than a week), intermediate smoothing, as is common practice in stepwise approaches, can bias nowcasts of case counts and Rt, which is avoided in a joint generative approach due to shared regularization of all model components. On incomplete line list data, a fully generative approach enables the quantification of uncertainty due to missing onset dates without the need for an initial multiple imputation step. In a real-world comparison using hospitalization line list data from the COVID-19 pandemic in Switzerland, we observe the same qualitative differences between approaches. The generative modeling components developed in this work have been integrated and further extended in the R package epinowcast, providing a flexible and interpretable tool for real-time surveillance. Author summary: During an infectious disease outbreak, public health authorities require timely indicators of transmission dynamics, such as the effective reproduction number Rt. Since reporting data are delayed and often incomplete, statistical methods must be employed to obtain real-time estimates of case numbers and Rt. Existing methods involve separate steps for imputing missing data, adjusting for reporting delays, and estimating Rt. This stepwise approach impedes uncertainty quantification and can lead to inconsistent smoothing assumptions across steps. In this paper, we propose an alternative approach based on generative Bayesian modeling which integrates all steps into a single nowcasting model that can be directly fit to observed data. Using synthetic and real-world line list data, we demonstrate that the generative approach better captures uncertainty and avoids bias from inconsistent assumptions. The model components of our approach have been integrated into the R package epinowcast for easy use in practice. [ABSTRACT FROM AUTHOR]

Recent studies have established that the circadian clock influences onset, progression and therapeutic outcomes in a number of diseases including cancer and heart diseases. Therefore, there is a need for tools to measure the functional state of the molecular circadian clock and its downstream targets in patients. Moreover, the clock is a multi-dimensional stochastic oscillator and there are few tools for analysing it as a noisy multigene dynamical system. In this paper we consider the methodology behind TimeTeller, a machine learning tool that analyses the clock as a noisy multigene dynamical system and aims to estimate circadian clock function from a single transcriptome by modelling the multi-dimensional state of the clock. We demonstrate its potential for clock systems assessment by applying it to mouse, baboon and human microarray and RNA-seq data and show how to visualise and quantify the global structure of the clock, quantitatively stratify individual transcriptomic samples by clock dysfunction and globally compare clocks across individuals, conditions and tissues thus highlighting its potential relevance for advancing circadian medicine. Author summary: The cellular circadian clock consists of an interacting set of genes that through their interactions oscillate throughout the day. This oscillator also responds to external cues so that the genes oscillate in phase with external environmental rhythms. A cell therefore uses its circadian clock to provide its genes with information about the external time. In this way it can coordinate many of the processes taking place in the cell and allocate some of these processes to specific times of the day. It is becoming increasingly clear that the quality of this timing information influences onset progression and outcome in a number of chronic diseases such as cancer. Our aim is therefore to develop a machine-learning tool that can assess how well the clock is working. We want to use this with patients and therefore, for clinical utility, it needs to work with only a single clinical sample and to produce reproducible results that can be clearly interpreted and easily compared. [ABSTRACT FROM AUTHOR]

Among existing computational algorithms for single-cell RNA-seq analysis, clustering and trajectory inference are two major types of analysis that are routinely applied. For a given dataset, clustering and trajectory inference can generate vastly different visualizations that lead to very different interpretations of the data. To address this issue, we propose multiple scores to quantify the "clusterness" and "trajectoriness" of single-cell RNA-seq data, in other words, whether the data looks like a collection of distinct clusters or a continuum of progression trajectory. The scores we introduce are based on pairwise distance distribution, persistent homology, vector magnitude, Ripley's K, and degrees of connectivity. Using simulated datasets, we demonstrate that the proposed scores are able to effectively differentiate between cluster-like data and trajectory-like data. Using real single-cell RNA-seq datasets, we demonstrate the scores can serve as indicators of whether clustering analysis or trajectory inference is a more appropriate choice for biological interpretation of the data. Author summary: Single-cell sequencing technologies have motivated development of numerous computational algorithms. Two main types of these algorithms are clustering and trajectory inference. When scientists have a scRNA-seq dataset, they usually pick one of these approaches based on what they think the data shows. If they think the data has distinct clusters of cells, they will analyze the data using clustering algorithms. If they think the data shows a continuous progression, they will use trajectory inference algorithms. However, sometimes using clustering and trajectory inference on the same data can lead to very different interpretations, where clustering algorithms produce distinct cell clusters while trajectory inference on the same data show continuous trajectories. This makes us wonder: which way of looking at the data is more appropriate? In this paper, we developed a pipeline for quantifying the "clusterness" and "trajectoriness" of scRNA-seq data, in other words, whether the data looks like a collection of distinct clusters or a continuum of progression trajectory. We think such geometric quantification is an important question that should be broadly discussed in the single-cell research community. [ABSTRACT FROM AUTHOR]

In proteomics, a crucial aspect is to identify peptide sequences. De novo sequencing methods have been widely employed to identify peptide sequences, and numerous tools have been proposed over the past two decades. Recently, deep learning approaches have been introduced for de novo sequencing. Previous methods focused on encoding tandem mass spectra and predicting peptide sequences from the first amino acid onwards. However, when predicting peptides using tandem mass spectra, the peptide sequence can be predicted not only from the first amino acid but also from the last amino acid due to the coexistence of b-ion (or a- or c-ion) and y-ion (or x- or z-ion) fragments in the tandem mass spectra. Therefore, it is essential to predict peptide sequences bidirectionally. Our approach, called NovoB, utilizes a Transformer model to predict peptide sequences bidirectionally, starting with both the first and last amino acids. In comparison to Casanovo, our method achieved an improvement of the average peptide-level accuracy rate of approximately 9.8% across all species. Author summary: Understanding the characteristics of data is very important in deep learning methods. When predicting sentences, the transformer model naturally predicts from the first word. For this reason, previous methods predicted peptide sequences from the first amino acid. However, in tandem mass spectra, it is possible to predict peptide sequences bidirectionally. This method shows better results than previous approaches because it can better encode tandem mass spectra. We have demonstrated that good results can be achieved simply by understanding the characteristics of such data and using the model appropriately. We hope that this paper will help various readers improve the performance capabilities of their models. Furthermore, given that bidirectional peptide sequence prediction is crucial in de novo peptide sequence analysis, we hope that this approach will be applied to both existing and future methods utilizing deep learning techniques. [ABSTRACT FROM AUTHOR]

Metabolic profiling (metabolomics) aims at measuring small molecules (metabolites) in complex samples like blood or urine for human health studies. While biomarker-based assessment often relies on a single molecule, metabolic profiling combines several metabolites to create a more complex and more specific fingerprint of the disease. However, in contrast to genomics, there is no unique metabolomics setup able to measure the entire metabolome. This challenge leads to tedious and resource consuming preliminary studies to be able to design the right metabolomics experiment. In that context, computer assisted metabolic profiling can be of strong added value to design metabolomics studies more quickly and efficiently. We propose a constraint-based modelling approach which predicts in silico profiles of metabolites that are more likely to be differentially abundant under a given metabolic perturbation (e.g. due to a genetic disease), using flux simulation. In genome-scale metabolic networks, the fluxes of exchange reactions, also known as the flow of metabolites through their external transport reactions, can be simulated and compared between control and disease conditions in order to calculate changes in metabolite import and export. These import/export flux differences would be expected to induce changes in circulating biofluid levels of those metabolites, which can then be interpreted as potential biomarkers or metabolites of interest. In this study, we present SAMBA (SAMpling Biomarker Analysis), an approach which simulates fluxes in exchange reactions following a metabolic perturbation using random sampling, compares the simulated flux distributions between the baseline and modulated conditions, and ranks predicted differentially exchanged metabolites as potential biomarkers for the perturbation. We show that there is a good fit between simulated metabolic exchange profiles and experimental differential metabolites detected in plasma, such as patient data from the disease database OMIM, and metabolic trait-SNP associations found in mGWAS studies. These biomarker recommendations can provide insight into the underlying mechanism or metabolic pathway perturbation lying behind observed metabolite differential abundances, and suggest new metabolites as potential avenues for further experimental analyses. Author summary: Associating diseases and other metabolic disruptions with physiological markers is key for diagnostic and personalised medicine. These markers can be metabolites—small molecules involved in every living being's metabolism, and can be measured in biofluids such as blood or urine using metabolic profiling (metabolomics). Nevertheless, this experimental metabolomics design needs to be tailor made for each disease to ensure that most relevant metabolites will be detected. The selection of metabolites to analyse for future experiments can be time-consuming and expensive. In this paper, we build upon an existing computational method for simulating metabolite changes in a human model. This provides a prediction of the change in biofluid abundance of every known metabolite involved in human metabolism in a potentially large number of metabolic situations. The newly introduced method produces a change score and a rank for each metabolite in each condition. We show the strong potential of the approach by comparing predictions with experimental results. [ABSTRACT FROM AUTHOR]

The unique expression patterns of circRNAs linked to the advancement and prognosis of cancer underscore their considerable potential as valuable biomarkers. Repurposing existing drugs for new indications can significantly reduce the cost of cancer treatment. Computational prediction of circRNA-cancer and drug-cancer relationships is crucial for precise cancer therapy. However, prior computational methods fail to analyze the interaction between circRNAs, drugs, and cancer at the systematic level. It is essential to propose a method that uncover more valuable information for achieving cancer-centered multi-association prediction. In this paper, we present a novel computational method, AutoEdge-CCP, to unveil cancer-associated circRNAs and drugs. We abstract the complex relationships between circRNAs, drugs, and cancer into a multi-source heterogeneous network. In this network, each molecule is represented by two types information, one is the intrinsic attribute information of molecular features, and the other is the link information explicitly modeled by autoGNN, which searches information from both intra-layer and inter-layer of message passing neural network. The significant performance on multi-scenario applications and case studies establishes AutoEdge-CCP as a potent and promising association prediction tool. Author summary: CircRNAs serve as crucial biomarkers and drug targets in cancer therapy. Predicting cancer-associated circRNAs and drugs contributes to uncover intricate molecular mechanisms driving tumorigenesis, thus offering novel insights into cancer diagnosis, treatment, and research. However, prevailing predictive methods often neglect the comprehensive interactions within circRNAs, drugs, and cancer, leading to an incomplete understanding of their complex interplay. In response, we introduce AutoEdge-CCP, a framework that models circRNA-cancer-drug interactions within a multi-source heterogeneous network. Each molecule combines intrinsic attribute information describing molecular features with interaction information derived through autoGNN, revealing pivotal circRNAs and drugs associated with cancer. Experimental results across multi-scenario attest to AutoEdge-CCP's superior performance compared to competing methods, particularly in predicting novel circRNAs and drugs associated with cancer. Additionally, visualization of edge embeddings and case studies provide interpretable insights into the prediction outcomes. [ABSTRACT FROM AUTHOR]

As part of a long-term research project aiming at generating a biomechanical model of a fossil human tongue from a carefully designed 3D Finite Element mesh of a living human tongue, we present a computer-based method that optimally registers 3D CT images of the head and neck of the living human into similar images of another primate. We quantitatively evaluate the method on a baboon. The method generates a geometric deformation field which is used to build up a 3D Finite Element mesh of the baboon tongue. In order to assess the method's ability to generate a realistic tongue from bony structure information alone, as would be the case for fossil humans, its performance is evaluated and compared under two conditions in which different anatomical information is available: (1) combined information from soft-tissue and bony structures; (2) information from bony structures alone. An Uncertainty Quantification method is used to evaluate the sensitivity of the transformation to two crucial parameters, namely the resolution of the transformation grid and the weight of a smoothness constraint applied to the transformation, and to determine the best possible meshes. In both conditions the baboon tongue morphology is realistically predicted, evidencing that bony structures alone provide enough relevant information to generate soft tissues. Author summary: The issue of the phylogenetic emergence of speech in humans is the focus of lively and strong debates. It questions both cognitive and physical capacities of fossil hominins to articulate speech. The ultimate goal of our research project "Origins of Speech" is the quantitative investigation of the physical aspects of the debate. We rely for that on the design biomechanical models of fossil hominins' vocal tracts and on the assessment of their capacity to articulate distinctive sounds as is required for the emergence of spoken language. Since fossil remains do not preserve soft tissue, the technical challenge is to be able to predict them, and in particular the tongue, from bony structures alone. In this paper we present our method to reach this goal, which uses medical images of the head and neck to register a reference biomechanical tongue model of a living human into a tongue model of any other primate. We evaluate it quantitatively on the prediction of a Baboon tongue, for whom we have accurate X-Ray scans of the skull and the vocal tract, by comparing the tongue model predicted from bony structures alone with the model predicted from bony and soft tissue structures and with the tongue segmented on the baboon X-Ray data. The evaluation includes a mathematical evaluation, based on uncertainty quantification methods of the sensitivity of the predictions to the variations of crucial parameters used in the optimal geometrical registration. The results are very encouraging for future application to fossil hominins. [ABSTRACT FROM AUTHOR]