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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2022

Using kinetic modelling to infer adaptations in Saccharomyces cerevisiae carbohydrate storage metabolism to feast famine regimes

Microbial metabolism is strongly dependent on the environmental conditions. While these can be well controlled under laboratory conditions, large-scale bioreactors are characterized by inhomogeneities and consequently dynamic conditions for the organisms. How Saccharomyces cerevisiae responds to frequent perturbations in industrial bioreactors is still not understood mechanistically. To study the adjustments to prolonged dynamic conditions, experiments under a feast/famine regime were performed and analysed using modelling approaches. Multiple types of data were integrated; including quantitative metabolomics, 13C incorporation and flux quantification. Kinetic metabolic modelling was applied to unravel the relevant intracellular metabolic response mechanisms. An existing model of yeast central carbon metabolism was extended, and different subsets of enzymatic kinetic constants were estimated. A novel parameter estimation pipeline based on combinatorial enzyme selection, supplemented by regularization, was developed to identify and predict the minimum enzyme and parameter adjustments from steady-state to feast famine conditions. This approach predicted proteomic changes in hexose transport and phosphorylation reactions, which was additionally confirmed by proteome measurements. Nevertheless, the modelling also hints to a yet unknown kinetic or regulation phenomenon. Some intracellular fluxes could not be reproduced by mechanistic rate laws, including hexose transport and intracellular trehalase activity during feast famine cycles. Author summaryKinetic metabolic models are used to understand how biological systems deal with dynamic perturbations in their environment. A well-known case of their application is the microorganism Saccharomyces cerevisiae, which was domesticated by mankind thousands of years ago, and is used to produce a wide range of products such as bread, beverages, and biofuels. When cultured in industrial-scale bioreactors, this cell factory is impacted by environmental perturbations which can challenge the bioprocess performance. The feast famine regime has been proposed as an experimental setup to downscale these industrial perturbations. Intracellularly, these perturbations impact central carbon metabolism, including carbon storage. Even though kinetic metabolic models have been developed to study the effect of extracellular perturbations, they have not explored the feast famine regime and its implications on carbon metabolism. We developed a model identification tool and used it to expand the existing models to represent carbon metabolism under feast famine regime. We used computer simulations to point at adaptations in yeast metabolism and locations in the model where our understanding is not entirely accurate. We found that combining multiple types of data, despite challenging, can be very beneficial by providing a comprehensive and realistic representation of the cell.

systems biology↗

bioRxiv2022

Energy-based Advection Modelling Using Bond Graphs

Advection, the transport of a substance by the flow of a fluid, is a key process in biological systems. The energy-based bond graph approach to modelling chemical transformation within reaction networks is extended to include transport and thus advection. The approach is illustrated using a simple model of advection via circulating flow and by a simple pharmacokinetic model of anaesthetic gas uptake. This extension provides a physically-consistent framework for linking advective flows with the fluxes associated with chemical reactions within the context of physiological systems in general and the human physiome in particular.

systems biology↗

bioRxiv2022

Statistical analysis of number of genes and chromosome lengths of different microbial species

Genome architecture concerns the organisation of genes on a chromosome, and has important implications to the fidelity in which genes are encoded on the chromosome, and how the information is read by DNA polymerase and RNA polymerase. This facet of genomics did receive attention in the early epoch of genomics, but it has received less attention in contemporary genomics as attention shifts to structural and functional genomics with the goal of annotating the function of each gene in the genome. This work sought to uncover relationships between number of genes and chromosome length in a variety of bacteria and archaea species as a preamble to understanding the prevalence and importance of repetitive sequences in the genome of prokaryotic species. Aggregate results with the ensemble of prokaryotic species profiled revealed a positive linear correlation between number of genes and chromosome length. Upon dissection into the Bacteria and Archaea domains, the linear relationship described above still stands for Bacteria but starts to break down in Archaea. This suggests that repetitive sequences are more important to Archaea species, which generally have a smaller genome (1.8 to 2.8 Mbp) and fewer genes (1500 to 2400) compared to bacterial species. In comparison, the bacterial genome is larger (4 to 5.6 Mbp), and encodes more genes (3600 to 5100). Overall, the results highlight that bacterial genome are efficiently encoded with few repetitive sequences. This, however, is not true for archaeal genome, which provides another line of evidence supporting the notion that archaea are ancestral eukaryotic cells, which like the archaea also houses large repetitive sequences. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/503871v1_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@1632506org.highwire.dtl.DTLVardef@13e91forg.highwire.dtl.DTLVardef@12e1316org.highwire.dtl.DTLVardef@1e7381e_HPS_FORMAT_FIGEXP M_FIG C_FIG Short descriptionStatistical analysis across an ensemble of 59 microbial species revealed a strong linear correlation between number of genes and chromosome length. This suggests that prokaryotic genomes are highly compact with genes, and do not carry significant amounts of repeats unlike the case in eukaryotic organisms. The result holds significant implications for our understanding of genome evolution and compaction in prokaryotic organisms, and what drove their accession as foundational species of many ecosystems. Subject areasgenomics, molecular biology, evolutionary biology, bioinformatics, systems biology,

genomics↗

bioRxiv2022

Temporally resolved kinase regulatory networks control endothelial barrier integrity

Vascular leak is a common disease complication, yet the signaling networks regulating barrier integrity are incompletely understood. We developed a novel methodology, Temporally REsolved KInase Network Generation (TREKING), which combines a 28-kinase inhibitor screen with machine learning and network reconstruction to build time-resolved, functional phosphosignaling networks. We demonstrated the utility of TREKING for identifying pathways mediating barrier integrity following thrombin stimulation with or without tumor necrosis factor (TNF) activation in brain endothelial cells. TREKING assigned distinct barrier phenotypes to mitogen-activated protein kinase (MAPK) pathways and revealed a condition-specific MAPKAPK2/MK2 switch kinase pathway with early barrier-disruptive activity in both inflammatory conditions, but late barrier-restorative activity exclusively in the absence of TNF pre-conditioning. MAPKAPK2/MK2 was activated with expected distinct kinetics under the two inflammatory conditions and late activation was linked to a MAP3K20/ZAK-MAPK14/p38-MAPKAPK2/MK2 pathway. Beyond MAPKs, TREKING predicts extensive interconnected networks that control barrier integrity and is a tool for dissecting complex temporal phosphosignaling networks across biological systems.

systems biology↗

bioRxiv2022

Binding Site-enhanced Sequence Pretraining and Out-of-cluster Meta-learning Predict Genome-Wide Chemical-Protein Interactions for Dark Proteins

Discovering chemical-protein interactions for millions of chemicals across the entire human and pathogen genomes is instrumental for chemical genomics, protein function prediction, drug discovery, and other applications. However, more than 90% of gene families remain dark, i.e., their small molecular ligands are undiscovered due to experimental limitations and human biases. Existing computational approaches typically fail when the unlabeled dark protein of interest differs from those with known ligands or structures. To address this challenge, we developed a deep learning framework PortalCG. PortalCG consists of four novel components: (i) a 3-dimensional ligand binding site enhanced sequence pre-training strategy to represent the whole universe of protein sequences in recognition of evolutionary linkage of ligand binding sites across gene families, (ii) an end-to-end pretraining-fine-tuning strategy to simulate the folding process of protein-ligand interactions and reduce the impact of inaccuracy of predicted structures on function predictions under a sequence-structure-function paradigm, (iii) a new out-of-cluster meta-learning algorithm that extracts and accumulates information learned from predicting ligands of distinct gene families (meta-data) and applies the meta-data to a dark gene family, and (iv) stress model selection that uses different gene families in the test data from those in the training and development data sets to facilitate model deployment in a real-world scenario. In extensive and rigorous benchmark experiments, PortalCG considerably outperformed state-of-the-art techniques of machine learning and protein-ligand docking when applied to dark gene families, and demonstrated its generalization power for off-target predictions and compound screenings under out-of-distribution (OOD) scenarios. Furthermore, in an external validation for the multi-target compound screening, the performance of PortalCG surpassed the human design. Our results also suggested that a differentiable sequence-structure-function deep learning framework where protein structure information serve as an intermediate layer could be superior to conventional methodology where the use of predicted protein structures for predicting protein functions from sequences. We applied PortalCG to two case studies to exemplify its potential in drug discovery: designing selective dual-antagonists of Dopamine receptors for the treatment of Opioid Use Disorder, and illuminating the undruggable human genome for targeting diseases that do not have effective and safe therapeutics. Our results suggested that PortalCG is a viable solution to the OOD problem in exploring the understudied protein functional space. Author SummaryMany complex diseases such as Alzheimers disease, mental disorders, and substance use disorders do not have effective and safe therapeutics due to the polygenic nature of diseases and the lack of thoroughly validate drug targets and their ligands. Identifying small molecule ligands for all proteins encoded in the human genome will provide new opportunity for drug discovery of currently untreatable diseases. However, the small molecule ligand of more than 90% gene families is completely unknown. Existing protein-ligand docking and machine learning methods often fail when the protein of interest is dissimilar to those with known functions or structures. We develop a new deep learning framework PortalCG for efficiently and accurately predicting ligands of understudied proteins which are out of reach of existing methods. Our method achieves unprecedented accuracy over state-of-the-arts by incorporating ligand binding site information and sequence-to-structure-to-function paradigm into a novel deep meta-learning algorithms. In a case study, the performance of PortalCG surpassed the human design. The proposed computational framework will shed new light into how chemicals modulate biological system as demonstrated by applications to drug repurposing and designing polypharmacology. It will open a new door to developing effective and safe therapeutics for currently incurable diseases. PortalCG can be extended to other scientific inquiries such as predicting protein-protein interactions and protein-nucleic acid recognition.

systems biology↗

bioRxiv2022

Flux analysis reveals specific regulatory modalities of gene expression

The level of a given protein is determined by rates of mRNA and protein synthesis and degradation. The reliable quantification of these rates is thus essential to understand how protein levels are regulated. While several studies have quantified the contribution of different steps of gene expression in regulating protein levels, these are limited by the use of equilibrium approximations when studying out-of-equilibrium biological systems. Here we introduce flux analysis to quantify the contribution of gene expression steps to regulate the dynamics of the expression level for specific proteins. We use flux analysis to analyze a published RNA-seq and proteomics dataset of mouse bone marrow-derived DCs stimulated with LPS. Our analysis revealed six regulatory modalities shared by sets of genes with clear functional signatures. We also find that protein degradation plays a stronger role than expected in the adaptation of protein levels to LPS stimulation. These findings suggest that shared regulatory strategies can lead to versatile responses at the protein level and highlight the importance of going beyond steady-state approximations to uncover the dynamic contribution of different steps of gene expression to protein levels.

systems biology↗

bioRxiv2023

Multilevel Omics-Readouts of in vitro Perturbation Studies are Determined by Memory Effects from Subculture

Mass spectrometry-based omics technologies are increasingly used to map drug effects to biological pathways by identifying significant molecular events. Significance is influenced by the effect size and the variation of each molecular parameter. While the former is largely determined by the biological system, the latter can be tuned by the experimental workflow. Here, we unequivocally show that memory effects originating from subculture of colon carcinoma cells before treating with arsenic trioxide exacerbate the variation of multiple omics levels, including eicosadomics, proteomics and phosphoproteomics, without necessarily impacting on effect size. Real-time monitoring of individual samples enables control over subculture homogeneity and improves the median variation >2-fold across omics levels. This considerably facilitated mode of action deconvolution and resulted in a bilevel perturbation network of 321 causal conjectures. Controlling memory effects from subculture revealed key signaling cascades and transcriptional regulatory events that extend the molecular understanding of arsenic trioxide in solid tumors.

systems biology↗

bioRxiv2023

Out-of-equilibrium gene expression fluctuations in presence of extrinsic noise

Cell-to-cell variability in protein concentrations is strongly affected by extrinsic noise, especially for highly expressed genes. Extrinsic noise can be due to fluctuations of several possible cellular factors connected to cell physiology and to the level of key enzymes in the expression process. However, how to identify the predominant sources of extrinsic noise in a biological system is still an open question. This work considers a general stochastic model of gene expression with extrinsic noise represented as colored fluctuations of the different model rates, and focuses on the out-of-equilibrium expression dynamics. Combining analytical calculations with stochastic simulations, we fully characterize how extrinsic noise shapes the protein variability during gene activation or inactivation, depending on the prevailing source of extrinsic variability, on its intensity and timescale. In particular, we show that qualitatively different noise profiles can be identified depending on which are the fluctuating parameters. This indicates an experimentally accessible way to pinpoint the dominant sources of extrinsic noise using time-coarse experiments. Author summaryGenetically identical cells living in the same environment may differ in their phenotypic traits. These differences originate from the inherent stochasticity in all cellular processes, starting from the basic process of gene expression. At this level, large part of the variability comes from cell-to-cell differences in the rates of the molecular reactions due to stochasticity in the level of key enzymes or in physiological parameters such as cell volume or growth rate. Which expression rates are predominantly affected by these so-called "extrinsic" fluctuations and how they impact the level of protein concentration are still open research questions. In this work, we tackle the protein fluctuation dynamics while approaching a steady state after gene activation or repression in presence of extrinsic noise. Our analytical results and simulations show the different consequences of alternative dominant sources of extrinsic noise, thus providing an experimentally-accessible way to distinguish them in specific systems.

systems biology↗

bioRxiv2023

A diffusion-driven switch specifies rhizoid precursor cells in Marchantia polymorpha

The specification of rhizoid precursor cells in the epidermis of Marchantia polymorpha gemmae has been shown to involve lateral inhibition mediated by the microRNA FRH1, which represses its activator RSL1, a rhizoidspecific transcription factor. However, how inhibition is conferred to adjacent cells and which is the mechanism underlying the emergence of rhizoid precursors remain unknown. In this paper, we use mathematical and computational modeling to show that the previously reported rhizoid patterns in WT, gain-of-function and lossof-function mutants of FRH1 and RSL1 are consistent with lateral inhibition mediated by a mobile FRH1. Our modeling results suggest that cells in Marchantia wildtype gemmae reside close to a critical state, where diffusion of FRH1 drives a switch of RSL1 expression that specifies rhizoid precursors. This process involves an initially random trigger and subsequent lateral inhibition, leading to cellular patterns consisting of small and filamentous clusters of rhizoid precursors. We confirm these predictions with new data on WT rhizoid distributions. Our findings highlight a novel mechanism of cellular pattern formation, opening new research directions for understanding cellular differentiation and tissue morphogenesis, with potential implications for a broad range of biological systems.

systems biology↗

bioRxiv2023

Particulate matter composition drives differential molecular and morphological responses in lung epithelial cells

Particulate matter (PM) is a ubiquitous component of indoor and outdoor air pollution that is epidemiologically linked to many human pulmonary diseases. PM has many emission sources, making it challenging to understand the biological effects of exposure due to the high variance in chemical composition. However, the effects of compositionally unique particulate matter mixtures on cells have not been analyzed using both biophysical and biomolecular approaches. Here, we show that in a human bronchial epithelial cell model (BEAS-2B), exposure to three chemically distinct PM mixtures drives unique cell viability patterns, transcriptional remodeling, and the emergence of distinct morphological subtypes. Specifically, PM mixtures modulate cell viability and DNA damage responses and induce the remodeling of gene expression associated with cell morphology, extracellular matrix organization and structure, and cellular motility. Profiling cellular responses showed that cell morphologies change in a PM composition-dependent manner. Lastly, we observed that particulate matter mixtures with high contents of heavy metals, such as cadmium and lead, induced larger drops in viability, increased DNA damage, and drove a redistribution among morphological subtypes. Our results demonstrate that quantitative measurement of cellular morphology provides a robust approach to gauge the effects of environmental stressors on biological systems and determine cellular susceptibilities to pollution.

systems biology↗

bioRxiv2023

Dual proteomic signature of immune cells and Yersinia pestis upon blood infection

Emerging and reemerging infectious diseases represent major public health concerns. The urgent need for infection control measures requires deep understanding of molecular pathogenesis. Global approaches to study biological systems such as mass-spectrometry based proteomics benefited from groundbreaking physical and bioinformatical technological developments over recent years. However, dual proteomic study of highly pathogenic microorganisms and their hosts in complex matrices encountered during infection remains challenging due to high protein dynamic range of samples and requirements imposed in biosafety level 3 or 4 laboratories. Here, we constructed a dual proteomic pipeline of Yersinia pestis in human blood and plasma, mirroring bacteremic phase of plague. We provide the most complete Y. pestis proteome revealing a major reshaping of important bacterial path-ways such as methionine biosynthesis and iron acquisition in human plasma. Remarkably, proteomic profiling in human blood highlights a greater Yersinia outer proteins intoxication of monocytes than neutrophils. Our study unravels global expression changes and points to a specific pathogenic signature during infection, paving the way for future exploration of proteomes in the complex context of host-pathogen interactions. Subject CategoriesMicrobiology, Virology and Host Pathogen Interaction, Proteomics

systems biology↗

bioRxiv2023

DLTKcat: deep learning based prediction of temperature dependent enzyme turnover rates

The enzyme turnover rate, kcat, quantifies enzyme kinetics by indicating the maximum efficiency of enzyme catalysis. Despite its importance, kcat values remain scarce in databases for most organisms, primarily due to the cost of experimental measurements. To predict kcat and account for its strong temperature dependence, DLTKcat was developed in this study and demonstrated superior performance (log10-scale RMSE = 0.88, R2 = 0.66) than previously published models. Through two case studies, DLTKcat showed its ability to predict the effect of protein sequence mutations and temperature changes on kcat values. Although its quantitative accuracy is not high enough yet to model the responses of cellular metabolism to temperature changes, DLTKcat has the potential to eventually become a computational tool to describe the temperature dependence of biological systems.

systems biology↗

bioRxiv2023

Delivering biochemicals with precision using bioelectronic devices enhanced with feedback control

Precision medicine tailors treatment in a way that accounts for variations in patient response. Treatment strategies can be determined based on factors such as genetic mutations, age, and diet. Another way of implementing precision medicine in a dynamic fashion is through bioelectronics equipped with real-time sensing and intelligent actuation. Bioelectronic devices such as ion pumps can be utilized to deliver therapeutic drugs. To be able to perform precision medicine, medical devices need to be able to deliver drugs with high precision. For this, closed-loop control is required to be able to change the treatment strategy as new information about the response and progression of the biological system is received. To this end, a sliding mode controller is utilized given its ability to perform satisfactory control actions when there is model uncertainty. The controller is used in an experiment with the goal of delivering a pre-determined dosage of fluoxetine throughout a period of time.

systems biology↗

bioRxiv2023

A dynamic Boolean model of molecular and cellular interactions represents psoriasis development and predicts drug candidates.

Psoriasis is a chronic skin disease affecting 2-3% of the global population. Psoriasis arises from complex interactions between keratinocytes and immune cells, leading to uncontrolled inflammation, immune hyperactivation and perturbed keratinocyte life cycle. Although the latest generation of drugs have greatly improved psoriasis management, the disease remains incurable, and the substantial variability in treatment response calls for novel approaches to comprehend the intricate mechanisms underlying disease development and to discover potential drug targets. In this study, we present a multiscale population model that captures the dynamics of cell-specific phenotypes in psoriasis, integrating discrete logical formalism and population dynamics simulations. Through simulations and network metrics, we identify potential pairwise interventions as alternative treatment options. Specifically, The model predictions suggest that targeting neutrophil activation in conjunction with either PGE2 production or STAT3 signaling shows promise comparable to IL-17 inhibition, which is currently the most used treatment option for moderate and severe cases of psoriasis. Our findings underscore the significance of considering complex intercellular interactions and intracellular signaling cascades in psoriasis, and highlight the importance of computational approaches in unraveling complex biological systems for drug target identification. Author summaryIn our study, we aimed to uncover the complex mechanisms underlying psoriasis and identify potential treatment options. By utilizing a computational model, we simulated the dynamic interactions between different cell types involved in psoriasis, such as immune cells and keratinocytes. Our model predicts that targeting neutrophil activation, combined with either PGE2 production or STAT3 signaling, may yield comparable effectiveness to the current standard treatment for moderate or severe psoriasis, namely IL-17 inhibition. Our study underscores the importance of computational modeling in unraveling the complexities of disease systems and provides a foundation for identifying new candidate treatment options in psoriasis that should be tested in the lab.

systems biology↗

bioRxiv2023

Ribosome demand links transcriptional bursts to protein expression noise

Stochastic variation in protein expression generates phenotypic heterogeneity in a cell population and has an important role in antibiotic persistence, mutation penetrance, tumor growth and therapy resistance. Studies investigating molecular origins of noise have predominantly focused on the transcription process. However, the noise generated in the transcription process is further modulated by translation. This influences the expression noise at the protein level which eventually determines the extent of phenotypic heterogeneity in a cell population. Studies across different organisms have revealed a positive association between translational efficiency and protein noise. However, the molecular basis of this association has remained unknown. In this work, through stochastic modeling of translation in single mRNA molecules and empirical measurements of protein noise, we show that ribosome demand associated with high translational efficiency in a gene drives the correlation between translational efficiency and protein noise. We also show that this correlation is present only in genes with bursty transcription. Thus, our work reveals the molecular basis of how coding sequence of genes, along with their promoters, can regulate noise. These findings have important implications for investigating protein noise and phenotypic heterogeneity across biological systems.

systems biology↗

bioRxiv2023

Mutational biases contribute to the neutral complexification of protein interaction networks following gene duplication

Biological systems can gain complexity over time. While some of these transitions are likely driven by natural selection, the extent to which they occur without providing an adaptive benefit is unknown. At the molecular level, one example is heteromeric complexes replacing homomeric ones following gene duplication. Here, we build a biophysical model and simulate the evolution of homodimers and heterodimers following gene duplication using distributions of mutational effects inferred from available protein structures. We keep the specific activity of each dimer identical, so their concentrations drift neutrally without new functions. We show that for more than 60% of tested dimer structures, the relative concentration of the heteromer increases over time due to mutational biases that favor the heterodimer. However, allowing mutational effects on synthesis rates and differences in the specific activity of homo- and heterodimers can limit or reverse the observed bias toward heterodimers. Our results show that the accumulation of more complex protein quaternary structures is likely under neutral evolution, and that natural selection would be needed to reverse this tendency.

systems biology↗

bioRxiv2023

Allometry of cell types in planarians by single cell transcriptomics

Allometry explores the relationship between an organisms body size and its various components, offering insights into ecology, physiology, metabolism, and disease. The cell is the basic unit of biological systems, and yet, the study of cell type allometry remains relatively unexplored. Single-cell RNA sequencing (scRNA-seq) provides a promising tool for investigating cell type allometry. Planarians, capable of growing and degrowing following allometric scaling rules, serve as an excellent model for such studies. We used scRNA-seq to examine cell type allometry in asexual planarians of different sizes, revealing that they consist of the same basic cell types but in varying proportions. Notably, the gut basal cells are the most responsive to changes in size, suggesting a role in energy storage. We capture the gene regulatory programs of distinct cell types in response to size. This research sheds light on the molecular and cellular aspects of cell type allometry in planarians and underscores the utility of scRNA-seq in such investigations.

systems biology↗

bioRxiv2023

Development and Analysis of Multiscale Models for Tuberculosis: From Molecules to Populations

AbstractAlthough infectious disease dynamics are often analyzed at the macro-scale, increasing numbers of drug-resistant infections highlight the importance of within-host modeling that simultaneously solves across multiple scales to effectively respond to epidemics. We review multiscale modeling approaches for complex, interconnected biological systems and discuss critical steps involved in building, analyzing, and applying such models within the discipline of model credibility. We also present our two tools: CaliPro, for calibrating multiscale models (MSMs) to datasets, and tunable resolution, for fine- and coarse-graining sub-models while retaining insights. We include as an example our work simulating infection with Mycobacterium tuberculosis to demonstrate modeling choices and how predictions are made to generate new insights and test interventions. We discuss some of the current challenges of incorporating novel datasets, rigorously training computational biologists, and increasing the reach of MSMs. We also offer several promising future research directions of incorporating within-host dynamics into applications ranging from combinatorial treatment to epidemic response.

systems biology↗