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bioRxiv2019

Translational readthrough goes unseen by natural selection.

Occasionally during protein synthesis, the ribosome bypasses the stop codon and continues translation to the next stop codon in frame. This error is called translational readthrough (TR). Earlier research suggest that TR is a relatively common error, in several taxa, yet the evolutionary relevance of this translational error is still unclear. By analysing ribosome profiling data, we have conducted species comparisons between yeasts to infer conservation of TR between orthologs. Moreover, we infer the evolutionary rate of error prone and canonically translated proteins to deduct differential selective pressure. We find that about 40% of error prone proteins in Schizosaccharomyces pombe do not have any orthologs in Saccharomyces cerevisiae, but that 60% of error prone proteins in S. pombe are undergoing canonical translation in S. cerevisiae. Error prone proteins tend to have a higher GC-content in the 3-UTR, unlike their canonically translated ortholog. We do not find the same trends for GC-content of the CDS. We discuss the role of 3-UTR and GC-content regarding translational readthrough. Moreover, we find that there is neither selective pressure against or for TR. We suggest that TR is a near-neutral error that goes unseen by natural selection. We speculate that TR yield neutral protein isoforms that are not being purged. We suggest that isoforms, yielded by TR, increase proteomic diversity in the cell, which is readily available upon sudden environmental shifts and which therefore may become adaptive. Author SummaryThere is an evolutionary balance act between adaptation and selection against change. Any system needs to be able to adapt facing novel environmental conditions. Simultaneously, biological systems are under selection to maintain fitness and thus undergo selection against mutations. Phenotypic mutations - translational errors during protein synthesis - have been suggested to play a role in protein evolvability by enabling quick assessment of viable phenotypes and thus enable quick adaptation. Here we test this hypothesis, by inferring evolutionary rate of proteins prone to a specific case of phenotypic mutations: translational readthrough (TR). By making use of publicly available data of yeasts, we find that TR goes unseen by natural selection and appear as a neutral event. We suggest that TR goes unseen by selection and occurs as "permissive wallflowers", which may become relevant and yield adaptive benefits. This work highlights that stochastic processes are not necessarily under stringent selection but may prevail. In conclusion, we suggest that TR is a neutral non-adaptive process that can yield adaptive benefits.

evolutionary biology↗

bioRxiv2022

The SARS-CoV-2 Spike S1 Protein Induces Global Proteomic Changes in ATII-Like Rat L2 Cells that are Attenuated by Hyaluronan

The COVID-19 pandemic continues to impose a major impact on global health and economy since its identification in early 2020, causing significant morbidity and mortality worldwide. Caused by the SARS-CoV-2 virus, along with a growing number of variants that have been characterized to date, COVID-19 has led to 571,198,904 confirmed cases, and 6,387,863 deaths worldwide (as of July 15th, 2022). Despite tremendous advances in our understanding of COVID19 pathogenesis, the precise mechanism by which SARS-CoV2 causes epithelial injury is incompletely understood. In this current study, robust application of global-discovery proteomics applications combined with systems biology analysis identified highly significant induced changes by the Spike S1 protein of SARS-CoV-2 in an ATII-like Rat L2 cells that include three significant network hubs: E2F1, CREB1/ RelA, and ROCK2/ RhoA. Separately, we found that pre-treatment with High Molecular Weight Hyaluronan (HMW-HA), greatly attenuated the S1 effects. Immuno-targeted studies carried out on E2F1 and Rock2/ RhoA induction and kinase-mediated activation, in addition to cell cycle measurements, validated these observations. Taken as a whole, our discovery proteomics and systems analysis workflow, combined with standard immuno-targeted and cell cycle measurements revealed profound and novel biological changes that contribute to our current understanding of both Spike S1 and Hyaluronan biology. This data shows that the Spike S1 protein may contribute to epithelial injury induced by SARS-CoV-2. In addition, our work supports the potential benefit of HMW-HA in ameliorating SARS CoV2 induced cell injury.

systems biology↗

bioRxiv2018

CyanoGate: A Golden Gate modular cloning suite for engineering cyanobacteria based on the plant MoClo syntax

Recent advances in synthetic biology research have been underpinned by an exponential increase in available genomic information and a proliferation of advanced DNA assembly tools. The adoption of plasmid vector assembly standards and parts libraries has greatly enhanced the reproducibility of research and exchange of parts between different labs and biological systems. However, a standardised Modular Cloning (MoClo) system is not yet available for cyanobacteria, which lag behind other prokaryotes in synthetic biology despite their huge potential in biotechnological applications. By building on the assembly library and syntax of the Plant Golden Gate MoClo kit, we have developed a versatile system called CyanoGate that unites cyanobacteria with plant and algal systems. We have generated a suite of parts and acceptor vectors for making i) marked/unmarked knock-outs or integrations using an integrative acceptor vector, and ii) transient multigene expression and repression systems using known and novel replicative vectors. We have tested and compared the CyanoGate system in the established model cyanobacterium Synechocystis sp. PCC 6803 and the more recently described fast-growing strain Synechococcus elongatus UTEX 2973. The system is publicly available and can be readily expanded to accommodate other standardised MoClo parts.

synthetic biology↗

bioRxiv2020

A Non-Negative Tensor Factorization Approach to Deconvolute Microenvironment in Breast Cancer

An in-depth understanding of epithelial breast cell response to growth-promoting ligands is required to elucidate how signals from the microenvironment affect cell-intrinsic regulatory networks and their resultant cellular phenotypes, such as cell growth, progression, and differentiation. Understanding the cellular response to these signals is particularly important in understanding the mechanisms of breast cancer initiation and progression. There is increasing evidence that aberrant epigenetic marks are present in cells of the breast tumor microenvironment and are known to affect these cellular processes. However, the mechanisms by which epigenetic microenvironment signals influence these cellular phenotypes are complex and currently not well established. To deconvolute the complexity of the epigenetic microenvironment signals in breast cancer, we developed a novel tensor-based correlation method: HOCMO (Higher-Order Correlation Model), applying to proteomics time series data to reveal the four-way regulatory dynamics among signaling proteins, histones, and growth-promoting ligands across multiple time points in the breast epithelial cells. HOCMO reveals two functional modules and the onset of specific protein-histone signatures in response to growth ligands contributing to distinct cellular phenotypes indicative of breast cancer initiation and progression. We evaluate robustness of our tensor model against baseline method TensorLy and achieved slight improvement in terms of reconstruction error and execution time. HOCMO is a data independent self-supervised learning method with superior interpretability that can capture the strength of complex interactions such as inter- and intra-pathway cellular signaling networks in any diseases or biological systems.

cancer biology↗

bioRxiv2023

Evolution of apoptotic signalling pathways among metazoans: insights from lophotrochozoans

Apoptosis is the main form of regulated cell death in metazoans. Apoptotic pathways are well characterised in nematode, fly and mammals, leading to a vision of the conservation of apoptotic pathways in metazoans. However, we recently showed that intrinsic apoptosis is in fact divergent among metazoans. In addition, extrinsic apoptosis is poorly studied in non-mammalian animals, making its evolution unclear. Consequently, our understanding of apoptotic signalling pathways evolution is a black-box which must be illuminated by extending research to new biological systems. Lophotrochozoans are a major clade of metazoans which, despite their considerable biological diversity and key phylogenetic position as sister group of ecdysozoans (i.e. fly, nematode), are poorly explored, especially regarding apoptosis mechanisms. Traditionally each apoptotic signalling pathway was considered to rely on a specific initiator Caspase, associated with an activator. To shed light on apoptosis evolution in animals, we explored the evolutionary history of initiator Caspases, Caspase activators and the BCL-2 family (which control mitochondrial apoptotic pathway) in lophotrochozoans using phylogenetic analysis and protein interaction predictions. We discovered a diversification of initiator Caspases in molluscs, annelids and brachiopods, and the loss of key extrinsic apoptosis components in platyhelminths, along with the emergence of a clade specific Caspase with an ankyrin pro-domain. Taken together, our data show a specific history of apoptotic actors evolution in lophotrochozoans, further demonstrating the appearance of distinct apoptotic signalling pathways during metazoan evolution. Significance statementApoptosis, a form of programmed cell death, has been long studied in model organisms such as fly, mouse, and in humans. The restricted focus on these models has led to an overall view that the evolution of genes involved in apoptosis is highly conserved across all animals. The advent of next generation sequencing has led to a boom in the omics data available across the tree of life. Thanks to this, we explored the evolution of key genes involved in apoptosis in the clade Lophotrochozoa (i.e. molluscs, annelids, flatworms, brachiopods), one of the three large clades that make up bilaterian animals. We found a complex evolutionary history of apoptosis genes, with multiple losses, gains, divergences and redundancies, highlighting the value of exploring gene evolution and apoptotic mechanisms in Lophotrochozoans.

evolutionary biology↗

bioRxiv2025

Electro-composting: an emerging technology

The present study focuses on electrical stimulation for composting. Using the PSALSAR method, a systematic review resulted in 24 relevant articles. For each system, the review describes key materials, composter design, operating conditions, temperature evolution, compost maturity, microbial community, and outcome. The examined studies fall into four main systems: electric field-assisted aerobic composting (EAAC), electrolytic oxygen aerobic composting (EOAC), microbial fuel cells (MFC), and thermoelectric generators (TEG). Apart from the main systems highlighted above, there is a rare case, which remains hardly studied. This includes bioelectrochemically assisted anaerobic composting (AnCBE, III). EAAC and EOAC systems biologically balance microbial activity and organic matter decomposition, whereas MFC and TEG systems have dual functioning due to energy generated alongside waste degradation. These innovative systems significantly improve composting efficiency by speeding up organic matter breakdown, increasing oxygen supply, and facilitating energy recovery. Together, they overcome the drawbacks of conventional composting systems and promote more environmentally friendly waste management solutions. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=115 SRC="FIGDIR/small/633320v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@108fa94org.highwire.dtl.DTLVardef@7b1b7dorg.highwire.dtl.DTLVardef@49d6dforg.highwire.dtl.DTLVardef@1b9fad5_HPS_FORMAT_FIGEXP M_FIG C_FIG Research HighlightsO_LIThe four most frequently investigated electro-composting systems are EAAC, EOAC, MFC, and TEG. C_LIO_LIMFCs make it easier to turn organic waste into electricity, which makes decomposition more environmentally friendly. C_LIO_LIThermal energy generated during decomposition is utilized by TEGs to produce electricity. C_LIO_LIThe research on AnCBE is highly limited. C_LI

bioengineering↗

bioRxiv2025

X-intNMF: A Cross- and Intra-Omics Regularized NMF Framework for Multi-Omics Integration

MotivationThe rapid accumulation of multi-omics data presents a valuable opportunity to advance our understanding of complex diseases and biological systems, driving the development of integrative computational methods. However, the complexity of biological processes--spanning multiple molecular layers and involving intricate regulatory interactions--requires models that can capture both intra- and cross-omics relationships. Most existing integration methods primarily focus on sample-level similarities or intra-omics feature interactions, often neglecting the interactions across different omics layers. This limitation can result in the loss of critical biological information and suboptimal performance. To address this gap, we propose X-intNMF, a network-regularized non-negative matrix factorization (NMF) model that explicitly incorporates both cross-omics and intra-omics feature interaction networks during multi-omics integration. By modeling these multi-layered relationships, X-intNMF enhances the representation of biological interactions and improves integration quality and prediction accuracy. ResultsFor evaluation, we applied X-intNMF to predict breast cancer phenotypes and classify clinical outcomes in lung and ovarian cancers using mRNA expression, microRNA expression, and DNA methylation data from TCGA. The results show that X-intNMF consistently outperforms state-of-the-art methods. Ablation studies confirm that incorporating both cross-omics and intra-omics interactions contributes significantly to the models improved performance. Additionally, survival analysis demonstrates that the integrated multi-omics representation offers strong prognostic value for both overall survival and disease-free status. These findings highlight X-intNMFs ability to effectively model multi-layered molecular interactions while maintaining interpretability, robustness, and scalability within the NMF framework. Availability and implementationSource code is available at: https://github.com/compbiolabucf/X-intNMF

bioinformatics↗

bioRxiv2026

Identifying Convergent Therapeutic Targets and Pathways for Post-Traumatic Stress Disorder, Schizophrenia And Bipolar Disorder via In Silico Approaches

ObjectiveThe objective of this study is to provide a concise overview of the various molecular problems and possible treatment targets that have been linked and associated with the onset of certain psychiatric diseases. MethodsObtaining the data from NCBI, we applied GREIN to analyze our datasets. The protein-protein interaction, gene regulatory network, protein-drug-chemical, gene ontology, and pathway network were constructed using STRING, Funrich and DAVID libraries. In order to display our suggested network, we utilized Cytoscape and R studio, verifying our hub gene using roc analysis. ResultsWe discovered a number of strong candidate hub proteins in significant pathways, namely out of 32 (HLA-DRA, HLA-A, HLA-B, HLA-DOB and BRD2) common genes. We also identified a number of TFs (FOXC1, NFYA, RELA, GATA2, FOXL1, SRF and NFIC); miRNA (hsa-mir-129-2-3p, hsa-mir-148b-3p, hsa-mir-196a-5p, hsa-mir-26a-5p, hsa-mir-27a-3p, hsa-mir-23b-3p, hsa-mir-500a-3p, hsa-mir-423-5p, hsa-miR-142-5p, and hsa-miR-671-5p) and chemicals (Estradiol, Antirheumatic Agents, Valproic Acid, Selenium, Vitamin E, ICG 001, Ifosfamide, Tetrachlorodibenzodioxin, arsenic trioxide, entinostat, sodium arsenite and Hydralazine) may control DEGs in transcription as well as post-transcriptional expression levels. ConclusionIn summary, our computational methods have identified distinct potential biomarkers that demonstrate the impact of PTSD, Schizophrenia, and BD on autoimmune inflammation and infectious diseases. Additionally, we have identified pathways and gene regulators through which these psychiatric disorders may affect biological processes. Graphical AbstractThe graphical abstract demonstrates the thorough strategy of combining systems biology and computational technologies to identify significant markers and pathways in blood tissues impacted by post-traumatic stress disorder, Schizophrenia, and Bipolar disorder. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=152 SRC="FIGDIR/small/708243v1_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@1cd13bforg.highwire.dtl.DTLVardef@cb6392org.highwire.dtl.DTLVardef@f634cforg.highwire.dtl.DTLVardef@532bd_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2026

A reusable neural approach to recombination mapping for model and non-model species

Pedigree and crossing experiments can measure crossovers directly and provide the gold standard for recombination mapping, but their cost restricts fine-scale recombination mapping to only a few species. Patterns of linkage disequilibrium (LD) provide an alternative statistical approach for inferring variation in recombination along the genome. LD, however, is confounded by many evolutionary factors, such as demographic changes, life-history traits, and genomic structural variation. We present fastrho, a state-space neural-network estimator trained across a range of simulation-based priors. In simulated bottleneck and expansion scenarios, the fixed checkpoint recovered local map shape without target-specific retraining; comparisons with pyrho used lookup tables constructed under the simulation-generating history. We further evaluated generalizability across multiple species and, to account for additional confounders not represented in the initial training data, designed specialized models for inference in selfing plants, structured Arabis populations, and large- malaria-vector populations. A major biological application of the mosquito model was the construction of a five-arm recombination atlas spanning 13 Ag3 populations, providing a detailed view of recombination-rate variation across the dataset. Recombination maps inferred from Ag3 pedigrees provided independent, coarse-scale support for this atlas. Finally, analyses of resistance loci and redpoll bird supergenes demonstrate how selection and structural variation influence LD. Throughout our study, we use experimental maps for independent validation. Together, our results establish fastrho as a flexible framework for robust recombination mapping across diverse biological systems.

evolutionary biology↗

bioRxiv2026

Exploring the influence of mutation rates with stochastic perturbations on genome evolution

We present a stochastic version of the classical Luria-Delbruck mutation rate model using Ito calculus. Our model is used to explore implications of population growth with randomly varying mutation rates. By comparing the It&ocirc version of the Luria-Delbruck mutation rate model to the deterministic we find that stochastic effects can have profound implications for genome evolution. In particular, as the growth rate decreases below the population's carrying capacity, or another given threshold, we show that stochastic effects can dominate the evolution of a population in a feedback-based manner. Moreover, we find that the influence of stochastic effects increase as the population reaches its carrying capacity if not accounted for. Our model also suggests strategies for a population to overcome increasing mutational load and evolve 'robustness' to genetic drift. We discuss our methodology in light of the SARS-CoV-2 virus and implications for its molecular evolution. Finally, we conclude that Ito calculus incorporates stochastic effects in mathematical models in a way that is very natural to biological systems.

evolutionary biology↗

bioRxiv2023

Engineered Reversible Inhibition of SpyCatcher Reactivity Enables Rapid Generation of Bispecific Antibodies

The precise regulation of protein function is essential in biological systems, and achieving such control is a fundamental objective in the fields of chemical biology and protein engineering. Here, we describe a straightforward method to engineer functional control into the isopeptide bond-forming SpyTag/SpyCatcher protein ligation system. First, we performed a cysteine scan of SpyCatcher, exchanging each amino acid in the structured region against cysteine. Except for the two known reactive and catalytic residues, none of these mutations abolished reactivity. In a second screening step, we modified the cysteines with disulfide bond-forming small molecules and screened for reactivity again. Here we found 8 positions that, when modified, strongly inhibited reactivity. This inhibition could be reversed by treatment with reducing agents. We call such a reversibly inhibitable SpyCatcher "SpyLock". We then used "BiLock", a fusion of SpyLock and wildtype SpyCatcher, in combination with SpyTagged antibody fragments to generate bispecific antibodies. A first antibody was reacted with the regular SpyCatcher moiety, followed by unlocking of the SpyLock through reduction and its reaction with a second antibody. This method to generate bispecific antibodies requires only a single antibody format and is readily scalable, facilitating the screening of a large number of antibody combinations. We demonstrate the utility of this approach to screen anti-PD-1/anti-PD-L1 bispecific antibodies using a cellular reporter assay.

biochemistry↗

bioRxiv2019

Genetic Algorithms for model refinement and rule discovery in a high-dimensional agent-based model of inflammation

IntroductionAgent-based modeling frequently used modeling method for multi-scale mechanistic modeling. However, the same properties that make agent-based models (ABMs) well suited to representing biological systems also present significant challenges with respect to their construction and calibration, particularly with respect to the selection of potential mechanistic rules and the large number of free parameters often present in these models. We have proposed that various machine learning approaches (such as genetic algorithms (GAs)) can be used to more effectively and efficiently deal with rule selection and parameter space characterization; the current work applies GAs to the challenge of calibrating a complex ABM to a specific data set, while preserving biological heterogeneity. MethodsThis project uses a GA to augment the rule-set for a previously validated ABM of acute systemic inflammation, the Innate Immune Response ABM (IIRABM) to clinical time series data of systemic cytokine levels from a population of burn patients. The genome for the GA is a vector generated from the IIRABMs Model Rule Matrix (MRM), which is a matrix representation of not only the constants/parameters associated with the IIRABMs cytokine interaction rules, but also the existence of rules themselves. Capturing heterogeneity is accomplished by a fitness function that incorporates the sample value range ("error bars") of the clinical data. ResultsThe GA-enabled parameter space exploration resulted in a set of putative MRM rules and associated parameterizations which closely match the cytokine time course data used to design the fitness function. The number of non-zero elements in the MRM increases significantly as the model parameterizations evolve towards a fitness function minimum, transitioning from a sparse to a dense matrix. This results in a model structure that more closely resembles (at a superficial level) the structure of data generated by a standard differential gene expression experimental study. ConclusionWe present an HPC-enabled evolutionary computing approach to calibrate a complex ABM to clinical data while preserving biological heterogeneity. The integration of machine learning, HPC, and multi-scale mechanistic modeling provides a pathway forward to effectively represent the heterogeneity of clinical populations and their data. Author SummaryIn this work, we utilize genetic algorithms (GA) to operate on the internal rule set of a computational of the human immune response to injury, the Innate Immune Response Agent-Based Model (IIRABM), such that it is iteratively refined to generate cytokine time series that closely match what is seen in a clinical cohort of burn patients. At the termination of the GA, there exists an ensemble of candidate model rule-sets/parameterizations which are validated by the experimental data;

systems biology↗

bioRxiv2018

Design Guidelines For Sequestration Feedback Networks

Integral control is commonly used in mechanical and electrical systems to ensure perfect adaptation. A proposed design of integral control for synthetic biological systems employs the sequestration of two biochemical controller species. The unbound amount of controller species captures the integral of the error between the current and the desired state of the system. However, implementing integral control inside bacterial cells using sequestration feedback has been challenging due to the controller molecules being degraded and diluted. Furthermore, integral control can only be achieved under stability conditions that not all sequestration feedback networks fulfill. In this work, we give guidelines for ensuring stability and good performance (small steady-state error) in sequestration feedback networks. Our guidelines provide simple tuning options to obtain a flexible and practical biological implementation of sequestration feedback control. Using tools and metrics from control theory, we pave the path for the systematic design of synthetic biological circuits.

synthetic biology↗

bioRxiv2023

SR2: Sparse Representation Learning for Scalable Single-cell RNA Sequencing Data Analysis

Single-cell RNA-sequencing (scRNA-seq) technology has been widely used to measure the transcriptome of cells in complex and heterogeneous systems. Integrative analysis of multiple scRNA-seq data can transform our understanding of various aspects of biology at the single-cell level. Many computational methods are proposed for data integration. However, few methods for scRNA-seq data integration explicitly model variation from heterogeneous biological conditions for interpretation. Modeling the variation helps understand the effect of biological conditions on complex biological systems. Our study proposes SR2 to capture gene expression patterns from heterogeneous biological conditions and discover cell identity simultaneously. Therefore, it can uncover the effect of biological conditions on the gene expression of cells and simultaneously achieve state-of-the-performance in cell identity discovery in our comprehensive comparison. Notably, SR2 is extended to model the effects of biological conditions on gene expression for cell populations, thus uncovering the effect of biological conditions on gene expression for cell populations and identifying putative condition-associated cell populations. To improve its scalability, we incorporate a batch-fitting strategy to ensure it is scalable to scRNA-seq data with arbitrary sample sizes. Moreover, the broad applicability of SR2 in biomedical studies has been demonstrated via applications. The complete package of SR2 is available at https://github.com/kai0511/SR2.

bioinformatics↗

bioRxiv2021

Plant-based biosensors for detecting CRISPR-mediated genome engineering

CRISPR/Cas has recently emerged as the most reliable system for genome engineering in various species. However, concerns about risks associated with CRISPR/Cas9 technology are increasing on potential unintended DNA changes that might accidentally arise from CRISPR gene editing. Developing a system that can detect and report the presence of active CRIPSR/Cas tools in biological systems is therefore very necessary. Here, we developed the real-time detection systems that can spontaneously indicate CRISPR-Cas tools for genome editing and gene regulation including CRISPR/Cas9 nuclease, base editing, prime editing and CRISPRa in plants. Using the fluorescence-based molecular biosensors, we demonstrated that the activities of CRISPR/Cas9 nuclease, base editing, prime editing and CRIPSRa can be effectively detected in transient expression via protoplast transformation and leaf infiltration (in Arabidopsis, poplar, and tobacco) and stable transformation in Arabidopsis.

plant biology↗

bioRxiv2018

The Epistasis Boundary: Linear vs. Nonlinear Genotype-Phenotype Relationships

Nonlinearity in the genotype-phenotype relationship can take the form of dominance, epistasis, or gene-environment interaction. The importance of nonlinearity, especially epistasis, in real complex traits is controversial. Network models in systems biology are typically highly nonlinear, yet the predictive power of linear quantitative genetic models is rarely improved by addition of nonlinear terms, and association studies detect few strong gene-gene interactions. We find that complex traits satisfying certain conditions can be well represented by a linear genetic model on an appropriate scale despite underlying biological complexity. Recent mathematical results in separability theory determine these conditions, which correspond to three biological criteria (Directional Consistency, Environmental Compensability, and Pathway Redundancy) together making up an Epistatic Boundary between systems suitable and unsuitable for linear modeling. For nonlinear traits, we introduce a classification of types of nonlinearity from a systems perspective, and use this to illustrate how upstream controlling genes, potentially more important to explaining biological function, can be intrinsically harder to detect by GWAS than their downstream controlled counterparts.

genomics↗

bioRxiv2019

A computational framework for a Lyapunov-enabled analysis of biochemical reaction networks

Complex molecular biological processes such as transcription and translation, signal transduction, post-translational modification cascades, and metabolic pathways can be described in principle by biochemical reactions that explicitly take into account the sophisticated network of chemical interactions regulating cell life. The ability to deduce the possible qualitative behaviors of such networks from a set of reactions is a central objective and an ongoing challenge in the field of systems biology. Unfortunately, the construction of complete mathematical models is often hindered by a pervasive problem: despite the wealth of qualitative graphical knowledge about network interactions, the form of the governing nonlinearities and/or the values of kinetic constants are hard to uncover experimentally. The kinetics can also change with environmental variations. This work addresses the following question: given a set of reactions and without assuming a particular form for the kinetics, what can we say about the asymptotic behavior of the network? Specifically, it introduces a class of networks that are "structurally (mono) attractive" meaning that they are incapable of exhibiting multiple steady states, oscillation, or chaos by virtue of their reaction graphs. These networks are characterized by the existence of a universal energy-like function called a Robust Lyapunov function (RLF). To find such functions, a finite set of rank-one linear systems is introduced, which form the extremals of a linear convex cone. The problem is then reduced to that of finding a common Lyapunov function for this set of extremals. Based on this characterization, a computational package, Lyapunov-Enabled Analysis of Reaction Networks (LEARN), is provided that constructs such functions or rules out their existence. An extensive study of biochemical networks demonstrates that LEARN offers a new unified framework. Basic motifs, three-body binding, and genetic networks are studied first. The work then focuses on cellular signalling networks including various post-translational modification cascades, phosphotransfer and phosphorelay networks, T-cell kinetic proofreading, and ERK signalling. The Ribosome Flow Model is also studied. Author summaryA theoretical and computational framework is developed for the identification of biochemical networks that are "structurally attractive". This means that they only allow global point attractors and they cannot exhibit any other asymptotic behavior such as multi-stability, oscillations, or chaos for any choice of the kinetics. They are characterized by the existence of energy-like functions. A computational package is made available for usage by a wider community. Many relevant networks in molecular biology satisfy the assumptions, and some are analyzed for the first time.

systems biology↗

bioRxiv2022

The Graphical R2D2 Estimator for the Precision Matrices

Biological networks are important for the analysis of human diseases, which summarize the regulatory interactions and other relationships between different molecules. Understanding and constructing networks for molecules, such as DNA, RNA and proteins, can help elucidate the mechanisms of complex biological systems. The Gaussian Graphical Models (GGMs) are popular tools for the estimation of biological networks. Nonetheless, reconstructing GGMs from high-dimensional datasets is still challenging. Current methods cannot handle the sparsity and high-dimensionality issues arising from datasets very well. Here we developed a new GGM, called the GR2D2 (Graphical R2-induced Dirichlet Decomposition) model, based on the R2D2 priors for linear models. Besides, we provided a data-augmented block Gibbs sampler algorithm. The R code is available at https://github.com/RavenGan/GR2D2. The GR2D2 estimator shows superior performance in estimating the precision matrices compared to existing techniques in various simulation settings. When the true precision matrix is sparse and of high dimension, the GR2D2 provides the estimates with smallest information divergence from the underlying truth. We also compare the GR2D2 estimator to the graphical horseshoe estimator in five cancer RNA-seq gene expression datasets grouped by three cancer types. Our results show that GR2D2 successfully identifies common cancer pathways and cancer-specific pathways for each dataset.

bioinformatics↗