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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2018

Intrinsic limitations in mainstream methods of identifying network motifs in biology

Network motifs are connectivity structures that occur with significantly higher frequency than chance, and are thought to play important roles in complex biological networks, for example in gene regulation, interactomes, and metabolomes. Network motifs may also become pivotal in the rational design and engineering of complex biological systems underpinning the field of synthetic biology. Distinguishing true motifs from arbitrary substructures, however, remains a challenge. Here we demonstrate both theoretically and empirically that implicit assumptions present in mainstream methods for motif identification do not necessarily hold, with the ramification that motif studies using these mainstream methods are less able to effectively differentiate between spurious results and events of true statistical significance than is often presented. We show that these difficulties cannot be overcome without revising the methods of statistical analysis used to identify motifs. The implications of these findings are therefore far-reaching across diverse areas of biology.

systems biology↗

bioRxiv2023

From Years to Hours: Accelerating Model Refinement

Network-based dynamic modeling is useful for studying the responses of complex biomolecular systems to environmental changes and internal perturbations. In modeling signal transduction and other regulatory networks, it is common to integrate evidence from perturbation (e.g. gene knockout) - observation pairs, where the perturbed and observed variables may be distant in the network. For a model to capture these non-local effects, its construction, validation, and refinement necessarily involve trial and error, constrained by domain knowledge. We propose and implement a genetic algorithm-based workflow to streamline model refinement. This workflow applies to any biological system for which an interaction network and enough perturbation experiments exist. We implement our workflow for Boolean networks, which are a popular and successful tool for modeling biological systems. The algorithm we introduce adjusts the functions of the model to enhance agreement with a corpus of curated experimental results and leverages existing mechanistic knowledge to automatically limit the search space to biologically plausible models. To account for the interdependence of experimental results, we develop a hierarchical scoring technique for assessing model performance. Our implementation is available as the open-source Python library boolmore. We demonstrate boolmores effectiveness in a published plant signaling model that exemplifies the challenges of manual model construction and refinement. This model describes how plant stomata close in response to the drought hormone abscisic acid. After several hours of automatic refinement on a personal computer, the fittest models recapture and surpass the accuracy gain achieved over two years of manual revision. The refined models yield new, testable predictions, such as explanations for the role of reactive oxygen species in drought response. By automating the laborious task of model validation and refinement, this workflow is a step towards fast, fully automated, and reliable model construction.

systems biology↗

bioRxiv2016

Experimental evolution of Escherichia coli harboring an ancient translation protein

The ability to design synthetic genes and engineer biological systems at the genome scale opens new means by which to characterize phenotypic states and the responses of biological systems to perturbations. One emerging method involves inserting artificial genes into bacterial genomes, and examining how the genome and its new genes adapt to each other. Here we report the development and implementation of a modified approach to this method, in which phylogenetically inferred genes are inserted into a microbial genome, and laboratory evolution is then used to examine the adaptive potential of the resulting hybrid genome. Specifically, we engineered an approximately 700-million-year old inferred ancestral variant of tufB, an essential gene encoding Elongation Factor Tu, and inserted it in a modern Escherichia coli genome in place of the native tufB gene. While the ancient homolog was not lethal to the cell, it did cause a two-fold decrease in organismal fitness, mainly due to reduced protein dosage. We subsequently evolved replicate hybrid bacterial populations for 2,000 generations in the laboratory, and examined the adaptive response via fitness assays, whole-genome sequencing, proteomics and biochemical assays. Hybrid lineages exhibit a general adaptive strategy in which the fitness cost of the ancient gene was ameliorated in part by up-regulation of protein production. We expect that this ancient-modern recombinant method may pave the way for the synthesis of organisms that exhibit ancient phenotypes, and that laboratory evolution of these organisms may prove useful in elucidating insights into historical adaptive processes.

Evolutionary Biology↗

bioRxiv2020

LipidLynxX: lipid annotations converter for large scale lipidomics and epilipidomics datasets

Modern high throughput lipidomics provides large-scale datasets reporting hundreds of lipid molecular species. However, cross-laboratory comparison, meta-analysis, and systems biology integration of in-house generated and published datasets remain challenging due to a high diversity of used lipid annotation systems, different levels of reported structural information, and shortage in links to data integration resources. To support lipidomics data integration and interoperability of experimental lipidomics with data integration tools, we developed LipidLynxX serving as a hub facilitating data flow from high-throughput lipidomics analysis to systems biology data integration. LipidLynxX provides the possibility to convert, cross-match, and link various lipid annotations to the tools supporting lipid ontology, pathway, and network analysis aiming systems-wide integration and functional annotation of lipidome dynamics in health and disease. LipidLynxX is a flexible, customizable open-access tool freely available for download at https://github.com/SysMedOs/LipidLynxX.

bioinformatics↗

bioRxiv2024

Automated Collective Variable Discovery for MFSD2A transporter from molecular dynamics simulations

Biomolecules often exhibit complex free energy landscapes in which long-lived metastable states are separated by large energy barriers. Overcoming these barriers to robustly sample transitions between the metastable states with classical molecular dynamics (MD) simulations presents a challenge. To circumvent this issue, collective variable (CV)-based enhanced sampling MD approaches are often employed. Traditional CV selection relies on intuition and prior knowledge of the system. This approach introduces bias, which can lead to incomplete mechanistic insights. Thus, automated CV detection is desired to gain a deeper understanding of the system/process. Analysis of MD data with various machine learning algorithms, such as Principal Component Analysis (PCA), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA)-based approaches have been implemented for automated CV detection. However, their performance has not been systematically evaluated on structurally and mechanistically complex biological systems. Here, we applied these methods to MD simulations of the MFSD2A (Major Facilitator Superfamily Domain 2A) lysolipid transporter in multiple functionally relevant metastable states with the goal of identifying optimal CVs that would structurally discriminate these states. Specific emphasis was on the automated detection and interpretive power of LDA-based CVs. We found that LDA methods, which included a novel gradient descent-based multiclass harmonic variant, termed GDHLDA, we developed here, outperform PCA in class separation, exhibiting remarkable consistency in extracting CVs critical for distinguishing metastable states. Furthermore, the identified CVs included features previously associated with conformational transitions in MFSD2A. Specifically, conformational shifts in transmembrane helix 7 and in residue Y294 on this helix emerged as critical features discriminating the metastable states in MFSD2A. This highlights the effectiveness of LDA-based approaches in automatically extracting from MD trajectories CVs of functional relevance that can be used to drive biased MD simulations to efficiently sample conformational transitions in the molecular system. STATEMENT OF SIGNIFICANCETo elucidate the biological mechanisms of pertinent biomolecules, it is crucial to understand their complex free energy landscapes. Such landscapes are often constructed from molecular dynamics (MD) simulations using collective variable (CV)-guided enhanced sampling methods. Identifying proper CVs for this task is critical but can be challenging with traditional intuition-based approaches. Here we propose an automated protocol for CV discovery which is based on linear discriminant analysis (LDA) for dimensionality reduction of MD data. By applying the protocol to MD simulations of the MFSD2A lysolipid transporter, a structurally and mechanistically complex biological system, we show that LDA-based methods efficiently detect system-specific CVs that accurately classify different metastable states of MFSD2A and are highly interpretable in a detailed structural context.

biophysics↗

bioRxiv2023

Time-deterministic cryo-optical microscopy

We developed a technique to rapidly freeze biological systems during observation under an optical microscope, which allows for detailed optical observation at a precise time in the biological dynamics at high spatial resolution and quantifiability under low-temperature conditions. Our study further found that ion distributions and molecular conformations can be fixed in space and time, demonstrated by visualization of calcium waves rapid-frozen at an instant using fluorescent ion indicators. These results show that time-deterministic cryo-optical microscopy can suspend cellular dynamics while maintaining molecular and ionic states, allowing us to view cellular dynamics in a way that has not been possible before. One-Sentence SummaryRapid freezing of dynamic biological systems during optical observation presents time information to the fixed samples

biophysics↗

bioRxiv2019

ScalaFlux: a scalable approach to quantify fluxes in metabolic subnetworks

13C-metabolic flux analysis (13C-MFA) allows metabolic fluxes to be quantified in living organisms and is a major tool in biotechnology and systems biology. Current 13C-MFA approaches model label propagation starting from the extracellular 13C-labeled nutrient(s), which limits their applicability to the analysis of pathways close to this metabolic entry point. Here, we propose a new approach to quantify fluxes through any metabolic subnetwork of interest by modeling label propagation directly from the metabolic precursor(s) of this subnetwork. The flux calculations are thus purely based on information from within the subnetwork of interest, and no additional knowledge about the surrounding network (such as atom transitions in upstream reactions or the labeling of the extracellular nutrient) is required. This approach, termed ScalaFlux for SCALAble metabolic FLUX analysis, can be scaled up from individual reactions to pathways to sets of pathways. ScalaFlux has several benefits compared with current 13C-MFA approaches: greater network coverage, lower data requirements, independence from cell physiology, robustness to gaps in data and network information, better computational efficiency, applicability to rich media, and enhanced flux identifiability. We validated ScalaFlux using a theoretical network and simulated data. We also used the approach to quantify fluxes through the prenyl pyrophosphate pathway of Saccharomyces cerevisiae mutants engineered to produce phytoene, using a dataset for which fluxes could not be calculated using existing approaches. A broad range of metabolic systems can be targeted with minimal cost and effort, making ScalaFlux a valuable tool for the analysis of metabolic fluxes.\n\nAuthor SummaryMetabolism is a fundamental biochemical process that enables all organisms to operate and grow by converting nutrients into energy and building blocks. Metabolic flux analysis allows the quantification of metabolic fluxes in vivo, i.e. the actual rates of biochemical conversions in biological systems, and is increasingly used to probe metabolic activity in biology, biotechnology and medicine. Isotope labeling experiments coupled with mathematical models of large metabolic networks are the most commonly used approaches to quantify fluxes within cells. However, many biological questions only require flux information from a subset of reactions, not the full network. Here, we propose a new approach with three main advantages over existing methods: better scalability (fluxes can be measured through a single reaction, a metabolic pathway or a set of pathways of interest), better robustness to missing data and information gaps, and lower requirements in terms of measurements and computational resources. We validate our method both theoretically and experimentally. ScalaFlux can be used for high-throughput flux measurements in virtually any metabolic system and paves the way to the analysis of dynamic fluxome rearrangements.

systems biology↗

bioRxiv2019

Genome-wide Analysis In Response to N and C Identifies New Regulators for root AtNRT2 Transporters

In Arabidopsis thaliana, the High-Affinity Transport System (HATS) for root NO3- uptake depends mainly on four NRT2 transporters, namely NRT2.1, NRT2.2, NRT2.4 and NRT2.5. The HATS is the target of many regulations to coordinate nitrogen (N) acquisition with the N status of the plant and with carbon (C) assimilation through photosynthesis. At the molecular level, C and N signaling pathways have been shown to control gene expression of the NRT2 transporters. Although several regulators of these transporters have been identified in response to either N or C signals, the response of NRT2 genes expression to the interaction of these signals has never been specifically investigated and the underlying molecular mechanisms remain largely unknown. To address this question we used an original systems biology approach to model a regulatory gene network targeting NRT2.1, NRT2.2, NRT2.4 and NRT2.5 in response to N/C signals. Our systems analysis of the data highlighted the potential role of three putative transcription factors, TGA3, MYC1 and bHLH093. Functional analysis of mutants combined with yeast one hybrid experiments confirmed that all 3 transcription factors are regulators of NRT2.4 or NRT2.5 in response to N or C signals.\n\nOne sentence summaryIdentification of three transcription factors involved in the regulation of NRT2s transporters using a systems biology approach and NRT2.1 as target gene in response to combinations of N/C treatments

plant biology↗

bioRxiv2017

Quantitative Modeling of Integrase Dynamics Using a Novel Python Toolbox for Parameter Inference in Synthetic Biology

In systems and synthetic biology, it is common to build chemical reaction network (CRN) models of biochemical circuits and networks. Although automation and other high-throughput techniques have led to an abundance of data enabling data-driven quantitative modeling and parameter estimation, the intense amount of simulation needed for these methods still frequently results in a computational bottleneck. Here we present bioscrape (Bio-circuit Stochastic Single-cell Reaction Analysis and Parameter Estimation) - a Python package for fast and flexible modeling and simulation of highly customizable chemical reaction networks. Specifically, bioscrape supports deterministic and stochastic simulations, which can incorporate delay, cell growth, and cell division. All functionalities - reaction models, simulation algorithms, cell growth models, partioning models, and Bayesian inference - are implemented as interfaces in an easily extensible and modular object-oriented framework. Models can be constructed via Systems Biology Markup Language (SBML) or specified programmatically via a Python API. Simulation run times obtained with the package are comparable to those obtained using C code - this is particularly advantageous for computationally expensive applications such as Bayesian inference or simulation of cell lineages. We first show the packages simulation capabilities on a variety of example simulations of stochastic gene expression. We then further demonstrate the package by using it to do parameter inference on a model of integrase enzyme-mediated DNA recombination dynamics with experimental data. The bioscrape package is publicly available online (https://github.com/biocircuits/bioscrape) along with more detailed documentation and examples.

synthetic biology↗

bioRxiv2019

STATegra: a comprehensive multi-omics dataset of B-cell differentiation in mouse

Multi-omics approaches use a diversity of high-throughput technologies to profile the different molecular layers of living cells. Ideally, the integration of this information should result in comprehensive systems models of cellular physiology and regulation. However, most multi-omics projects still include a limited number of molecular assays and there have been very few multi-omic studies that evaluate dynamic processes such as cellular growth, development and adaptation. Hence, we lack formal analysis methods and comprehensive multi-omics datasets that can be leveraged to develop true multi-layered models for dynamic cellular systems. Here we present the STATegra multi-omics dataset that combines measurements from up to 10 different omics technologies applied to the same biological system, namely the well-studied mouse pre-B-cell differentiation. STATegra includes high-throughput measurements of chromatin structure, gene expression, proteomics and metabolomics, and it is complemented with single-cell data. To our knowledge, the STATegra collection is the most diverse multi-omics dataset describing a dynamic biological system.

bioinformatics↗

bioRxiv2024

Sequence-based coevolutionary prediction of species-specific interactomes

Evolutionarily conserved protein-protein interactions (PPIs) reveal fundamental biological processes. However, predicting protein function solely from these interactions provides an incomplete picture of biological systems. Computational methods for predicting PPIs often struggle due to limited functional annotations in databases, making it difficult to fully understand unique biological systems.. This study introduces ContextMirror2.0 (CM2.0), a coevolution-based method designed to address these limitations. Coevolutionary approaches, unlike supervised machine learning methods, do not rely on labelled datasets, proving valuable in addressing the scarcity of data for species-specific PPIs. While around 40% of CM2.0s top-1000 predicted PPIs for E. coli, S. enterica, and S. aureus are present in experimental PPI databases, a comparative analysis of predicted functional communities revealed highly conserved interaction patterns and species-specific interactions. CM2.0 leverages coevolutionary information to explore protein interaction dynamics and understand the functional consequences of species-specific variations. This information can inform further studies on the emergence of novel functions, adaptation to specific environments, and the development of targeted therapies.

bioinformatics↗

bioRxiv2022

Pytri: A multi-weight detection system for biological entities

The enumeration of biological entities is a critical part of experimental assays and usually requires large lengths of time. The standard method is to count the entities by hand or with OpenCV-based software, which can lead to inaccurate results. Here, we propose an online platform for biologists consisting of a system with multiple trained machine learning weights to detect various biological entities such as yeast colonies, bacterial colonies, and melanoma clusters. The Pytri model achieved a median relative error rate of 7.56% for bacterial and yeast colonies on Petri dishes, 6.58% for colonies on 96-well plates and 10.28% for melanoma cluster microscopy images. We showcase the application of state-of-the-art deep learning tools in bacterial entity detection, achieving significantly higher accuracy than traditional methods when compared to our base standard manual count.

bioinformatics↗

bioRxiv2018

The shape of density dependence and the relationship between population growth, intraspecific competition and equilibrium population density

The logistic growth model is one of the most frequently used formalizations of density dependence affecting population growth, persistence and evolution. Ecological and evolutionary theory and applications to understand population change over time often include this model. However, the assumptions and limitations of this popular model are often not well appreciated. Here, we briefly review past use of the logistic growth model and highlight limitations by deriving population growth models from underlying consumer-resource dynamics. We show that the logistic equation likely is not applicable to many biological systems. Rather, density-regulation functions are usually non-linear and may exhibit convex or both concave and convex curvatures depending on the biology of resources and consumers. In simple cases, the dynamics can be fully described by the continuous-time Beverton-Holt model. More complex consumer dynamics show similarities to a Maynard Smith-Slatkin model. Importantly, we show how population-level parameters, such as intrinsic rates of increase and equilibrium population densities are not independent, as often assumed. Rather, they are functions of the same underlying parameters. The commonly assumed positive relationship between equilibrium population density and competitive ability is typically invalid. As a solution, we propose simple and general relationships between intrinsic rates of increase and equilibrium population densities that capture the essence of different consumer-resource systems. Relating population level models to underlying mechanisms allows us to discuss applications to evolutionary outcomes and how these models depend on environmental conditions, like temperature via metabolic scaling. Finally, we use time-series from microbial food chains to fit population growth models and validate theoretical predictions. Our results show that density-regulation functions need to be chosen carefully as their shapes will depend on the study systems biology. Importantly, we provide a mechanistic understanding of relationships between model parameters, which has implications for theory and for formulating biologically sound and empirically testable predictions.

ecology↗

bioRxiv2022

In vitro and in silico analysis proving DPP4 inhibition and diabetes associated gene network modulation by a polyherbal formulation - Nisakathakadi Kashaya.

Frontiers of disease biology started recognizing the importance of systems and network medicine approach for managing chronic disease like diabetes. Dipeptidyl-peptidase IV (DPP4) inhibitors are one such class of anti-diabetic drugs recognized for their systemic biological actions. Polyherbal preparations like Ayurveda formulations are ideal for identifying novel DPP4 inhibitors having greater efficacy and safety profile. Additionally, expanding the research on the multitargeted mode of action of these polyherbal formulations can render novel insights into the complex biology of disease manifestations. The current study aims at identifying DPP4 inhibitory potential of a clinically established Ayurveda anti-diabetic formulation Nisakathakadi Kashaya (NK) using in vitro and in silico methods as well as the modulation of diabetes associated gene network by NK. a. Standard enzyme inhibition assay was used to study the DPP4 inhibitory potential of NK, followed by bioinformatics and computational biology tools for identifying the potential bioactives and their molecular interactions involved in DPP4 inhibition. STITCH, CHEMBL and BindingDB databases were used for target mapping and depicting the multi-targeted network pharmacology interaction of NK and the formulation. EnrichR was used to depict a sub-network of diabetes proteins and their relationship with diabetes associated comorbidities. NK demonstrated a dose dependent DPP4 inhibition with an IC50 of 2.06 g GAE/mL. Molecular docking identified three compounds namely Terchebin, Locaracemoside B and 1,2,4,6 Tetra o Galloyl Beta D Glucose showing stable interactions with DPP4 similar to the standard drug Vildagliptin. The network pharmacology analysis of NK identified a number of targets like TNF, TGF{beta}1, SOD1, SOD2, AKT1, DPP4 and GLP1R in its protein-protein interaction network which are vital to diabetic progression and complications. The present work demonstrated that the polyherbal formulation NK has DPP4 inhibition potential and modulates a large number of diabetes related proteins and pathways. The approach adopted in the current study by combining in vitro and in silico methods allowed us to understand the mechanism of DPP4 inhibition by the formulation and also the possible pharmacological networking through which the formulation exert its systemic effect in diabetes management.

systems biology↗

bioRxiv2019

Dynamic clamp constructed phase diagram of the Hodgkin-Huxley action potential model

Excitability - a threshold governed transient in transmembrane voltage - is a fundamental physiological process that controls the function of the heart, endocrine, muscles and neuronal tissues. The 1950s Hodgkin and Huxley explicit formulation provides a mathematical framework for understanding excitability, as the consequence of the properties of voltage-gated sodium and potassium channels. The Hodgkin-Huxley model is more sensitive to parametric variations of protein densities and kinetics than biological systems whose excitability is apparently more robust. It is generally assumed that the models sensitivity reflects missing functional relations between its parameters or other components present in biological systems. Here we experimentally construct excitable membranes using the dynamic clamp and voltage-gated potassium ionic channels (Kv1.3) expressed in Xenopus oocytes. We take advantage of a theoretically derived phase diagram, where the phenomenon of excitability is reduced to two dimensions defined as combinations of the Hodgkin-Huxley model parameters. This biological-computational hybrid enabled us to explore functional relations in the parameter space, experimentally validate the phase diagram of the Hodgkin-Huxley model, and demonstrate activity-dependence and hysteretic dynamics due to the impacts of slow inactivation kinetics. The experimental results presented here provide new in-sights into the gap between technology-guided high-dimensional descriptions, and a lower, physiological dimensionality, within which biological function is embedded.

neuroscience↗

bioRxiv2022

SSB toolkit: from molecular structure to subcellular signaling pathways.

We present, here, an open-source systems biology toolkit to simulate mathematical models of the signal-transduction pathways of G-protein coupled receptors (GPCRs). By merging structural macromolecular data with systems biology simulations, we developed a framework to simulate the signal-transduction kinetics induced by ligand-GPCR interactions, as well as the consequent change of concentration of signaling molecular species, as a function of time and ligand concentration. Therefore, this tool brings to the light the possibility to investigate the subcellular effects of ligand binding upon receptor activation, deepening the understanding of the relationship between the molecular level of ligand-target interactions and higher-level cellular and physiologic or pathological response mechanisms.

bioinformatics↗

bioRxiv2017

High sensitivity quantitative proteomics using accumulated ion monitoring and automated multidimensional nano-flow chromatography

Quantitative proteomics using high-resolution and accuracy mass spectrometry promises to transform our understanding of biological systems and disease. Recent development of parallel reaction monitoring (PRM) using hybrid instruments substantially improved the specificity of targeted mass spectrometry. Combined with high-efficiency ion trapping, this approach also provided significant improvements in sensitivity. Here, we investigated the effects of ion isolation and accumulation on the sensitivity and quantitative accuracy of targeted proteomics using the recently developed hybrid quadrupole-Orbitrap-linear ion trap mass spectrometer. We leveraged ultra-high efficiency nano-electrospray ionization under optimized conditions to achieve yoctomolar sensitivity with more than seven orders of linear quantitative accuracy. To enable sensitive and specific targeted mass spectrometry, we implemented an automated, scalable two-dimensional (2D) ion exchange-reversed phase nano-scale chromatography system. We found that 2D chromatography improved the sensitivity and accuracy of both PRM and an intact precursor scanning mass spectrometry method, termed accumulated ion monitoring (AIM), by more than 100-fold. Combined with automated 2D nano-scale chromatography, AIM achieved sub-attomolar limits of detection of endogenous proteins in complex biological proteomes. This allowed quantitation of absolute abundance of the human transcription factor MEF2C at approximately 100 molecules/cell, and determination of its phosphorylation stoichiometry from as little as 1 g of extracts isolated from 10,000 human cells. The combination of automated multidimensional nano-scale chromatography and targeted mass spectrometry should enable ultra-sensitive high-accuracy quantitative proteomics of complex biological systems and diseases.

molecular biology↗

bioRxiv2024

Metabolic profiling using benchtop NMR identifies the metabolomic signature of persistent CAR-T cells

Analytical technologies for engineered biological systems hold great promise in addressing various challenges in modern pharmaceuticals and biomedical therapies. These endeavors often follow a design-build-test-learn approach, utilizing biological data from genetic circuits, signal pathways, metabolites, and proteins to optimize biological systems. Deciphering large and intricate datasets can prove to be a formidable task. Principal component analysis (PCA) tool is an invaluable method for reducing dataset complexity and enhancing interpretability while minimizing information loss, simultaneously. PCA tool achieves this by creating new, uncorrelated variables that capture the maximum variance in the data. Herein chimeric antigen receptors (CARs) T cells metabolomic study presents a slightly inverse problem, where PCA is applied to model sparse extracellular metabolites data from CAR-T cells, resulting in a two-component model. Using an benchtop NMR spectrometer only six metabolites could be annotated, nevertheless, the PCA model could identify differences in the metabolites of CAR-T cells based on the design of CARs, specifically the combinations of the intracellular domain (ICDs). Its noteworthy that the behavior and fate of CAR-T cells are distinctly influenced by the type of ICDs used upon antigen recognition.

cell biology↗