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Results for “systems biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2017

Elucidating interplay of speed and accuracy in biological error correction

One of the most fascinating features of biological systems is the ability to sustain high accuracy of all major cellular processes despite the stochastic nature of underlying chemical processes. It is widely believed that such low errors are the result of the error correcting mechanism known as kinetic proofreading. However, it is usually argued that enhancing the accuracy should result in slowing down the process leading to so-called speed-accuracy trade-off. We developed a discrete-state stochastic framework that allowed us to investigate the mechanisms of the proofreading using the method of first-passage processes. With this framework, we simultaneously analyzed speed and accuracy of the two fundamental biological processes, DNA replication and tRNA selection during the translation. The results indicate that speed-accuracy trade-off is not always observed. However, when the trade-off is present, the biological systems tend to optimize the speed rather than the accuracy of the processes, as long as the error level is tolerable. Additional constraints due to the energetic cost of proofreading also play a role in the error correcting process. Our theoretical findings provide a new microscopic picture of how complex biological processes are able to function so fast with a high accuracy.

biophysics↗

bioRxiv2025

Applications of Wedderburn's Theorem in Modelling Non-Commutative Biological and Evolutionary Systems

A wide range of biological and evolutionary processes is determined not merely by the occurrence of specific events, but by the exact order in which those events unfold. Gene regulation, developmental pathways, metabolic cascades and genetic evolution often display non-commutative behaviour, in which reversing the sequence of events results in functionally distinct outcomes. Conventional modelling approaches often fail to account for such directionality and sequence dependence, thereby limiting their capacity to capture the complexity of regulatory logic. We represent ordered sequences of biological operations--such as transcription factor binding or mutational trajectories--as elements of a non-commutative algebra designed to encode the functional logic of systems where the order of events is critically determinant. Subsequently, we apply Wedderburns Theorem to decompose the algebra into a direct sum of matrix modules, each capturing an irreducible and functionally distinct component of the underlying sequence-dependent system. We provide examples from gene expression regulation and evolutionary dynamics, focusing on scenarios where trait development is determined by the specific order of underlying molecular or mutational events. Our results demonstrate that the algebraic framework effectively maps intricate biological processes onto smaller, linear components, facilitating clearer interpretation and analysis. Simplifying non-commutative biological systems into interpretable submodules, Wedderburn-style decomposition may clarify gene regulatory logic, capture behavioural outcomes, reduce computational burden and uncover pathway redundancies and structural symmetries. Overall, by unifying diverse biological processes within a coherent algebraic structure, our method may improve the tractability of complex, order-dependent systems.

evolutionary biology↗

bioRxiv2016

Interacting networks of resistance, virulence and core machinery genes identified by genome-wide epistasis analysis

Recent advances in the scale and diversity of population genomic datasets for bacteria now provide the potential for genome-wide patterns of co-evolution to be studied at the resolution of individual bases. The major human pathogen Streptococcus pneumoniae represents the first bacterial organism for which densely enough sampled population data became available for such an analysis. Here we describe a new statistical method, genomeDCA, which uses recent advances in computational structural biology to identify the polymorphic loci under the strongest co-evolutionary pressures. Genome data from over three thousand pneumococcal isolates identified 5,199 putative epistatic interactions between 1,936 sites. Over three-quarters of the links were between sites within the pbp2x, pbp1a and pbp2b genes, the sequences of which are critical in determining non-susceptibility to beta-lactam antibiotics. A network-based analysis found these genes were also coupled to that encoding dihydrofolate reductase, changes to which underlie trimethoprim resistance. Distinct from these resistance genes, a large network component of 384 protein coding sequences encompassed many genes critical in basic cellular functions, while another distinct component included genes associated with virulence. These results have the potential both to identify previously unsuspected protein-protein interactions, as well as genes making independent contributions to the same phenotype. This approach greatly enhances the future potential of epistasis analysis for systems biology, and can complement genome-wide association studies as a means of formulating hypotheses for experimental work.\n\nAuthor SummaryEpistatic interactions between polymorphisms in DNA are recognized as important drivers of evolution in numerous organisms. Study of epistasis in bacteria has been hampered by the lack of both densely sampled population genomic data, suitable statistical models and powerful inference algorithms for extremely high-dimensional parameter spaces. We introduce the first model-based method for genome-wide epistasis analysis and use the largest available bacterial population genome data set on Streptococcus pneumoniae (the pneumococcus) to demonstrate its potential for biological discovery. Our approach reveals interacting networks of resistance, virulence and core machinery genes in the pneumococcus, which highlights putative candidates for novel drug targets. Our method significantly enhances the future potential of epistasis analysis for systems biology, and can complement genome-wide association studies as a means of formulating hypotheses for experimental work.

Genetics↗

bioRxiv2024

EpiCHAOS: a metric to quantify epigenomic heterogeneity in single-cell data

Epigenetic heterogeneity is a fundamental property of biological systems, and is recognized as a potential driver of tumor plasticity and therapy resistance. Single-cell epigenomics technologies have been widely employed to study epigenetic variation between - but not within - cellular clusters. We introduce epiCHAOS: a quantitative metric of cell-to-cell heterogeneity, applicable to any single-cell epigenomics data type. After validation in synthetic datasets, we applied epiCHAOS to investigate global and region-specific patterns of epigenetic heterogeneity across diverse biological systems. EpiCHAOS provides an excellent approximation of stemness and plasticity in development and malignancy, making it a valuable addition to single-cell cancer epigenomics analyses.

bioinformatics↗

bioRxiv2020

Functional genomic analyses highlights a shift in Gpr17-regulated cellular processes in oligodendrocyte progenitor cells (OPC) and underlying myelin dysregulation in the aged forebrain

Brain aging is characterised by a decline in neuronal function and associated cognitive deficits. There is increasing evidence that myelin disruption is an important factor that contributes to the age-related loss of brain plasticity and repair responses. In the brain, myelin is produced by oligodendrocytes, which are generated throughout life by oligodendrocyte progenitor cells (OPCs). Currently, a leading hypothesis points to aging as a major reason for the ultimate breakdown of remyelination in Multiple Sclerosis (MS). However, an incomplete understanding of the cellular and molecular processes underlying brain aging hinders the development of regenerative strategies. Here, our combined systems biology and neurobiological approach demonstrates that oligodendroglial and myelin genes are amongst the most altered in the aging mouse cortex. This was underscored by the identification of causal links between signaling pathways and their downstream transcriptional networks that define oligodendroglial disruption in aging. The results highlighted that the G-protein coupled receptor GPR17 is central to the disruption of OPC in aging and this was confirmed by genetic fate mapping and cellular analyses. Finally, we used systems biology strategies to identify therapeutic agents that rejuvenate OPC and restore myelination in age-related neuropathological contexts.

neuroscience↗

bioRxiv2021

FRAME-tags: genetically encoded fluorescent markers for multiplexed barcoding and time-resolved tracking of live cells

Cellular barcodes offer critical tools for tracking cellular identity in biological systems. Although genetically encoded fluorescent barcodes are ideal for real-time tracking, their scalability is constrained by the broad, overlapping emission spectra characteristic of fluorescent proteins (FPs). Here, we describe a palette of genetically encoded fluorescent barcodes called FRAME-tags, which break this scalability barrier by encoding barcode identity as unique FP expression ratios. FRAME-tags use -1 programmed ribosomal frameshifting RNA motifs to precisely control the translational output of multiple FPs from a single mRNA, leading to extremely narrow and resolvable ratios of the corresponding cellular fluorescence distributions. With this platform, we constructed 20 resolvable FRAME-tags in yeast using just two FPs, and further demonstrated that 100 or more distinguishable FRAME-tags could be made by the addition of a third FP. We used FRAME-tags to map the dynamic fitness landscape of yeast co-cultures, and to characterize the expression pattern of 20 yeast promoters in multiplex across diverse conditions. FRAME-tags offer a valuable new tool for cellular barcoding that enables time-resolved characterization of complex biological systems using widely available fluorescence detection techniques and a minimal number of spectral channels.

synthetic biology↗

bioRxiv2013

Exploring community structure in biological networks with random graphs

BackgroundCommunity structure is ubiquitous in biological networks. There has been an increased interest in unraveling the community structure of biological systems as it may provide important insights into a systems functional components and the impact of local structures on dynamics at a global scale. Choosing an appropriate community detection algorithm to identify the community structure in an empirical network can be difficult, however, as the many algorithms available are based on a variety of cost functions and are difficult to validate. Even when community structure is identified in an empirical system, disentangling the effect of community structure from other network properties such as clustering coefficient and assortativity can be a challenge.\n\nResultsHere, we develop a generative model to produce undirected, simple, connected graphs with a specified degrees and pattern of communities, while maintaining a graph structure that is as random as possible. Additionally, we demonstrate two important applications of our model: (a) to generate networks that can be used to benchmark existing and new algorithms for detecting communities in biological networks; and (b) to generate null models to serve as random controls when investigating the impact of complex network features beyond the byproduct of degree and modularity in empirical biological networks.\n\nConclusionOur model allows for the systematic study of the presence of community structure and its impact on network function and dynamics. This process is a crucial step in unraveling the functional consequences of the structural properties of biological systems and uncovering the mechanisms that drive these systems.

Bioinformatics↗

bioRxiv2023

Single-cell bisulfite-free 5mC and 5hmC sequencing with high sensitivity and scalability

Existing single cell bisulfite-based DNA methylation analysis is limited by low DNA recovery, and the measurement of 5hmC at single-base resolution remains challenging. Here we present a bisulfite-free single-cell whole-genome 5mC and 5hmC profiling technique, named Cabernet, which can characterize 5mC and 5hmC at single-base resolution with high genomic coverage. Cabernet utilizes Tn5 transposome for DNA fragmentation, which enables the discrimination between different alleles for measuring hemi-methylation status. Using Cabernet, we revealed the 5mC, hemi-5mC and 5hmC dynamics during early mouse embryo development, uncovering genomic regions exclusively governed by active or passive demethylation. We show that hemi-methylation status can be used to distinguish between pre- and post-replication cells, enabling more efficient cell grouping when integrated with 5mC profile. The property of Tn5 naturally enables Cabernet to achieve high-throughput single-cell methylome profiling, where we probed mouse cortical neurons and embryonic day 7.5 (E7.5) embryos, and constructed the library for thousands of single cells at high efficiency, demonstrating its potential for analyzing complex tissues at substantially low cost. Together, we present a new way of high-throughput methylome and hydroxymethylome detection at single-cell resolution, enabling efficient analysis of epigenetic status of biological systems with complicated nature such as neurons and cancer cells. Significance StatementMost of current methylation profiling techniques rely on bisulfite treatment, which suffers low DNA recovery. The technique proposed in this study, named Cabernet, can be used to measure 5mC and 5hmC at single-base resolution with high genomic coverage. By using Tn5 transposome, hemi-methylation status can be measured and high-throughput methylome profiling can be achieved. Together, it provides an efficient way to analyze the epigenetic landscape of complicated biological systems.

genomics↗

bioRxiv2026

Adaptive evolution of transcriptome and transcriptomic plasticity in stable and fluctuating environments

Adaptation to new environments often involves multiple genes and can induce substantial changes in biological systems, but the overall consequences of the genetic response to specific environmental pressures remain poorly understood. Here, we investigate the large-scale effects of adaptation to new temperature regimes on the whole transcriptome from experimental evolution carried on the fungus Zymoseptoria tritici. Two distinct strains from the wild were grown in constant cool (17{degrees}C), constant warm (23{degrees}C), and fluctuating temperature environments, for about 250 clonal generations. The expression of > 10,000 genes was estimated by RNA-seq before and after evolution at several temperatures. We observed massive convergent change in both gene expression and gene expression plasticity. Fluctuating environments did not favor plastic gene expression in general, although fluctuating populations did evolve towards the optimal reaction norms. Some of our observations were at odds with common expectations; adaptive mutations were highly pleiotropic, and adaptation to stable temperature conditions did not match gene expression plasticity. We thus showed that the evolution of complex biological systems follow some general patterns which challenge gene-centered or quantitative genetics predictions.

evolutionary biology↗

bioRxiv2024

Quantitative omnigenic model discovers interpretable genome-wide associations

As their statistical power grows, genome-wide association studies (GWAS) have identified an increasing number of loci underlying quantitative traits of interest. These loci are scattered throughout the genome and are individually responsible only for small fractions of the total heritable trait variance. The recently proposed omnigenic model provides a conceptual framework to explain these observations by postulating that numerous distant loci contribute to each complex trait via effect propagation through intracellular regulatory networks. We formalize this conceptual framework by proposing the "quantitative omnigenic model" (QOM), a statistical model that combines prior knowledge of the regulatory network topology with genomic data. By applying our model to gene expression traits in yeast, we demonstrate that QOM achieves similar gene expression prediction performance to traditional GWAS with hundreds of times less parameters, while simultaneously extracting candidate causal and quantitative chains of effect propagation through the regulatory network for every individual gene. We estimate the fraction of heritable trait variance in cis- and in trans-, break the latter down by effect propagation order, assess the trans- variance not attributable to transcriptional regulation, and show that QOM correctly accounts for the low-dimensional structure of gene expression covariance. We furthermore demonstrate the relevance of QOM for systems biology, by employing it as a statistical test for the quality of regulatory network reconstructions, and linking it to the propagation of non-transcriptional (including environmental) effects. Significance statementGenetic variation leads to differences in traits implicated in health and disease. Identifying genetic variants associated with these traits is spearheaded by "genome-wide association studies" (GWAS) - statistically rigorous procedures whose power has grown with the number of genotyped samples. Nevertheless, GWAS have a substantial shortcoming: they are ill-equipped to detect the causal basis and reveal the complex systemic mechanisms of polygenic traits. Even a single genetic change can propagate throughout the entire genetic regulatory network causing a myriad of spurious detections, thereby significantly limiting GWAS usefulness. To this end, we propose a novel statistical approach that incorporates known regulatory network information with the potential to boost interpretability of state-of-the-art genomic analyses while simultaneously extracting systems biology insights.

genomics↗

bioRxiv2023

The amino acid composition of a protein influences its expression

The quantity of each protein in a cell only is only partially correlated with its gene transcription rate. Independent influences on protein synthesis levels include mRNA sequence motifs, amino acyl-tRNA synthesis levels, elongation factor action, and protein susceptibility to degradation. Here we report two novel forms of interaction between the amino acid composition of a protein and its expression level. In animals, the differing origins of amino acids define a nutritional classification system and indicate their potential for scarcity - essential amino acids (EAA) are solely obtained from dietary supply, non-essential amino acids (NEAA) from biosynthetic supply, and conditionally essential amino acids (CEAA) from both. Accessing public proteomic datasets, we demonstrate that CEAA sequence composition is inversely correlated with expression - a rule of supply that is further magnified by rapid cellular proliferation. Similarly, proteins with the most extreme compositions of EAA are reduced in abundance. Homeostatic responses to malnutrition may result from the reductions in expression of extreme composition proteins participating in biological systems such as taste and food-seeking behaviour, oxidative phosphorylation, and chemokine function. The rule can also influence general human phenotypes and disease susceptibility: stature proteins are enriched in CEAAs, and a curated dataset of over 700 cancer proteins is significantly under-represented in EAAs. A second rule, whereby individual amino acids influence protein expression is also described. This rule is shared across all kingdoms of life and rooted in the immutable structural and encoding parameters of each amino acid. Species-specific environmental survival pathways are shown to be enriched in proteins with amino acid compositions favouring higher expression according to this rule. These two rules of protein expression regulation promise new insights into systems biology, evolutionary studies, experimental research design, and public health intervention.

biochemistry↗

bioRxiv2024

Pre-training artificial neural networks with spontaneous retinal activity improves motion prediction in natural scenes

The ability to process visual stimuli rich with motion represents an essential skill for animal survival and is largely already present at the onset of vision. Although the exact mechanisms underlying its maturation remain elusive, spontaneous activity patterns in the retina, known as retinal waves, have been shown to contribute to this developmental process. Retinal waves exhibit complex spatio-temporal statistics and contribute to the establishment of circuit connectivity and function in the visual system, including the formation of retinotopic maps and the refinement of receptive fields in downstream areas such as the thalamus and visual cortex. Recent work in mice has shown that retinal waves have statistical features matching those of natural visual stimuli, such as optic flow, suggesting that they could prime the visual system for motion processing upon vision onset. Motivated by these findings, we examined whether artificial neural network (ANN) models trained on natural movies show improved performance if pre-trained with retinal waves. We employed the spatio-temporally complex task of next-frame prediction, in which the ANN was trained to predict the next frame based on preceding input frames of a movie. We found that pre-training ANNs with retinal waves enhances the processing of real-world visual stimuli and accelerates learning. Strikingly, when we merely replaced the initial training epochs on naturalistic stimuli with retinal waves, keeping the total training time the same, we still found that an ANN trained on retinal waves temporarily outperforms one trained solely on natural movies. Similar to observations made in biological systems, we also found that pre-training with spontaneous activity refines the receptive field of ANN neurons. Overall, our work sheds light on the functional role of spatio-temporally patterned spontaneous activity in the processing of motion in natural scenes, suggesting it acts as a training signal to prepare the developing visual system for adult visual processing. Author summaryBefore the onset of vision, the retina generates its own spontaneous activity, referred to as retinal waves. This activity is crucial for establishing neural connections and, hence, ensuring the proper functionality of the visual system. Recent research has shown that retinal waves exhibit statistical properties similar to those of natural visual stimuli, such as the optic flow of objects in the environment during forward motion. We investigate whether retinal waves can prepare the visual system for motion processing by pre-training artificial neural network (ANN) models with retinal waves. We tested the ANNs on next-frame prediction tasks, where the model predicts the next frame of a video based on previous frames. Our results showed that ANNs pre-trained with retinal waves exhibit faster learning on movies featuring naturalistic stimuli. Additionally, pre-training with retinal waves refined the receptive fields of ANN neurons, similar to processes seen in biological systems. Our work highlights the importance of spatio-temporally patterned spontaneous activity in preparing the visual system for motion processing in natural scenes.

neuroscience↗

bioRxiv2026

Scattering-enabled epi-quantitative phase imaging reveals subcellular detail in organoids and deep mouse brains

Imaging subcellular structures deep within thick, turbid biological tissues remains fundamentally limited by light scattering, which distorts optical wavefronts and degrades contrast, resolution, and sensitivity. These limitations hinder quantitative interrogation of complex biological systems where resolving dynamic microenvironments at subcellular resolution is critical. Here, we introduce scattering-enabled epi-quantitative phase imaging (SEEQPI), a label-free method that leverages tissue scattering and provides subcellular spatial resolution, nanometer-scale spatiotemporal phase sensitivity, and millimeter-scale imaging depth in murine brains. SEEQPI is enabled by common-path phase-shifting confocal epi-interferometry with near-infrared illumination and the scattering-enabled phase reconstruction algorithm. SEEQPI requires low illumination power, minimizing tissue damage while enabling high-speed imaging of biological dynamics. We demonstrate simultaneous, colocalized imaging of subcellular structures with SEEQPI, third-harmonic generation, and three-photon fluorescence microscopy in liver cancer spheroids and in vivo mouse brains. SEEQPI enables quantitative, longitudinal studies of dry mass dynamics in intact, living biological systems.

bioengineering↗

bioRxiv2024

Quencher-free fluorescence monitoring of G-Quadruplex folding

Guanine-rich sequences exhibit a high degree of polymorphism and can form single-stranded, Watson-Crick duplex, and four-stranded G-quadruplex structures. These sequences have found a wide range of uses in synthetic biology applications, arising in part from their structural plasticity. High-throughput, low-cost tools for monitoring the folding and unfolding transitions of G-rich sequences would provide an enabling technology for accelerating prototyping of synthetic biological systems and for accelerating design-build-test cycles. Here, we show that unfolding transitions of a range of G-quadruplex-forming DNA sequences can be monitored in a FRET-like format using DNA sequences that possess only a single dye label, with no quencher. These quencher-free assays can be performed at low cost, with both cost and lead times ca. 1 order of magnitude lower than FRET-labeled strands. Thus, quencher-free secondary structure monitoring promises to be a valuable tool for testing and development of synthetic biology systems employing G-quadruplexes.

biophysics↗

bioRxiv2019

A curated knowledgebase enabling a network perspective on endocrine disrupting chemicals and their biological systems-level perturbations

Human well-being can be affected by exposure to several chemicals in the environment. One such group is endocrine disrupting chemicals (EDCs) that can perturb the hormonal homeostasis leading to adverse health effects. In this work, we have developed a detailed workflow to identify EDCs with supporting evidence of endocrine disruption in published experiments in humans or rodents. Thereafter, this workflow was used to manually evaluate more than 16000 published research articles and identify 686 potential EDCs with published evidence in humans or rodents. Importantly, we have compiled the observed adverse effects or endocrine-specific perturbations along with the dosage information for the potential EDCs from their supporting published experiments. Subsequently, the potential EDCs were classified based on the type of supporting evidence, their environmental source and their chemical properties. Additional compiled information for potential EDCs include their chemical structure, physicochemical properties, predicted ADMET properties and target genes. In order to enable future research based on this compiled information on potential EDCs, we have built an online knowledgebase, Database of Endocrine Disrupting Chemicals and their Toxicity profiles (DEDuCT), accessible at: https://cb.imsc.res.in/deduct/. After building this comprehensive resource, we employed a network biology approach to study the chemical space of EDCs and its potential link to the biological space of target genes of EDCs. Specifically, we have constructed two networks of EDCs using our resource based on similarity of chemical structures or target genes. Ensuing analysis of these two networks revealed that EDCs can differ both in their chemical structure and set of target genes. Though our detailed results highlight potential challenges in developing predictive models for EDCs, the compiled information in our resource will undoubtedly enable future research in the field, especially, those focussed towards mechanistic understanding of the systems-level perturbations caused by EDCs.

bioinformatics↗

bioRxiv2024

Revealing cancer driver genes through integrative transcriptomic and epigenomic analyses with Moonlight

Cancer involves dynamic changes caused by (epi)genetic alterations such as mutations or abnormal DNA methylation patterns which occur in cancer driver genes. These driver genes are divided into oncogenes and tumor suppressors depending on their function and mechanism of action. Discovering driver genes in different cancer (sub)types is important not only for increasing current understanding of carcinogenesis but also from prognostic and therapeutic perspectives. We have previously developed a framework called Moonlight which uses a systems biology multi-omics approach for prediction of driver genes. Here, we present an important development in Moonlight2 by incorporating a DNA methylation layer which provides epigenetic evidence for deregulated expression profiles of driver genes. To this end, we present a novel functionality called Gene Methylation Analysis (GMA) which investigates abnormal DNA methylation patterns to predict driver genes. This is achieved by integrating the tool EpiMix which is designed to detect such aberrant DNA methylation patterns in a cohort of patients and further couples these patterns with gene expression changes. To showcase GMA, we applied it to three cancer (sub)types (basal-like breast cancer, lung adenocarcinoma, and thyroid carcinoma) where we discovered 33, 190, and 263 epigenetically driven genes, respectively. A subset of these driver genes had prognostic effects with expression levels significantly affecting survival of the patients. Moreover, a subset of the driver genes demonstrated therapeutic potential as drug targets. This study provides a framework for exploring the driving forces behind cancer and provides novel insights into the landscape of three cancer sub(types) by integrating gene expression and methylation data. Moonlight2R is available on GitHub (https://github.com/ELELAB/Moonlight2R) and BioCondcutor (https://bioconduc-tor.org/packages/release/bioc/html/Moonlight2R.html). The associated case studies presented here are available on GitHub (https://github.com/ELELAB/Moon-light2_GMA_case_studies) and OSF (https://osf.io/j4n8q/). Author summaryCancer is a complex disease and a main cause of mortality worldwide. This heterogeneous disease arises due to accumulation of changes which occur in driver genes that drive cancer progression when they are altered. These driver genes are commonly divided into oncogenes, which promote cancer, and tumor suppressors, which prevent it. A major goal of cancer research is identifying these driver genes, crucial for increasing our current understanding of cancer biology and for developing novel treatment approaches. A large number of cancer driver genes have already been identified. However, the underlying mechanisms for the alterations in these genes is challenging to predict given their context-dependent behavior and the complexity of cancer. Such explanations are the focus of this study with the aim of providing evidence of why certain genes do not function normally in cancer. Within this context, we present new functionalities to our previously developed cancer driver predictive framework, Moonlight. These new functionalities integrate multiple data types to predict oncogenes and tumor suppressors in a systems-biology-oriented manner that is freely available as a R package for the community.

bioinformatics↗

bioRxiv2026

BARTsc identifies key transcriptional regulators from single-cell omics data

Inference of transcriptional regulatory mechanisms from single-cell (sc) omics data, such as scRNA-seq, scATAC-seq, and scMultiome, remains an important problem in single-cell biology and functional genomics. Most existing methods for predicting functional transcriptional regulators (TRs) from single-cell data rely on co-expression between regulator and target genes and/or sequence motif enrichment, holding inherent limitations. Here, we present BARTsc, a computational method that accurately predicts functional TRs from clustered single-cell omics data by leveraging a large collection of public ChIP-seq profiles. BARTsc implements a novel framework to infer a cis-regulatory profile from differential genomic features from either unimodal (RNA or ATAC) or bimodal (scMultiome) single-cell profiling data and identify TRs whose binding profiles most associate with the cis-regulatory profile. BARTsc can quantify TR activity across cell clusters and predict key regulators for each cell cluster. We demonstrate that BARTsc can successfully identify active TRs in each cell type and cell-type-defining key regulators across diverse biological systems, including mouse cortex, human peripheral blood mononuclear cells (PBMCs), and human pancreatic ductal adenocarcinoma (PDAC). Using a generative-AI-assisted, literature-supported collection of cell-type key regulators as benchmarks, we show that BARTsc consistently outperforms existing state-of-the-art methods. We apply BARTsc to identify critical regulators in PDAC, including NEFLA, a novel PDAC key regulator, and validate its function in pancreatic tumor proliferation by experiments. As a robust and versatile computational method, BARTsc provides deeper insights into cell-type-specific regulatory programs, facilitating the discovery of key regulators across diverse biological systems.

bioinformatics↗

bioRxiv2018

Survival of the simplest: the cost of complexity in microbial evolution

The evolution of microbial and viral organisms often generates clonal interference, a mode of competition between genetic clades within a population. In this paper, we show that interference strongly constrains the genetic and phenotypic complexity of evolving systems. Our analysis uses biophysically grounded evolutionary models for an organisms quantitative molecular phenotypes, such as fold stability and enzymatic activity of genes. We find a generic mode of asexual evolution called phenotypic interference with strong implications for systems biology: it couples the stability and function of individual genes to the populations global speed of evolution. This mode occurs over a wide range of evolutionary parameters appropriate for microbial populations. It generates selection against genome complexity, because the fitness cost of mutations increases faster than linearly with the number of genes. Recombination can generate a distinct mode of sexual evolution that eliminates the superlinear cost. We show that positive selection can drive a transition from asexual to facultative sexual evolution, providing a specific, biophysically grounded scenario for the evolution of sex. In a broader context, our analysis suggests that the systems biology of microbial organisms is strongly intertwined with their mode of evolution.

evolutionary biology↗