bioRxiv Science⌕ Search

SEARCH · bioRxiv Science

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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 289 records · Page 16Linked to original sources
bioRxiv2024

Reconciling conflicting selection pressures in the plant collaborative non-self recognition self-incompatibility system

Complex biological systems should often reconcile conflicting selection pressures. In systems based on molecular recognition, molecules must specifically identify certain partners while excluding others. Here we study how such selection pressures shape the evolution of the self-incompatibility system in plants. This system inhibits self-fertilization using specific molecular recognition between proteins, expressed in the plant female and male reproductive organs. We study the impact of these opposing selection pressures on the amino acid frequencies in these proteins recognition domain. We construct a theoretical framework enabling promiscuous recognition between proteins, as found empirically, and employ stochastic simulations to study its evolution. We find that selection exerts asymmetric responses of amino acid frequencies, affecting female proteins considerably, but hardly the male. Using large deviations theory, we well-approximate the simulated frequencies and find agreement with genomic data. Our work offers a general theoretical framework to study the impact of multiple selection pressures, applicable to additional biological systems.

genetics↗
bioRxiv2021

Principles for Systematic Optimization of an Orthogonal Translation System with Enhanced Biological Tolerance

Over the past twenty years, the development of orthogonal biological systems has sparked a revolution in our ability to study cellular physiology. Orthogonal translation systems (OTSs) enable site-specific incorporation of hundreds of non-standard amino acids, offering unprecedented access to the study of cellular mechanisms modulated by post-translational modifications (e.g. protein phosphorylation). Although development of phosphoserine-OTSs (pSerOTS) has been significant, little work has focused on the biology of OTS development and utilization. To better understand the impact of OTSs on host physiology, we utilize pSerOTS as a model to systematically explore the extent to which OTS components interact with Escherichia coli. Using this information, we constructed pSerOTS variants designed to enhance OTS orthogonality by minimizing interactions with host processes and decreasing stress response activation. Our expanded understanding of OTS:host interactions enables informed OTS design practices which minimize the negative impact of OTSs while improving OTS performance across a range of experimental settings.

synthetic biology↗
bioRxiv2025

A novel approach to quantify out-of-distribution uncertainty in Neural and Universal Differential Equations

Dynamical systems play a central role across the quantitative sciences, offering a powerful mathematical framework to describe, analyze, and predict the evolution of complex processes over time. In systems biology, dynamical systems provide a foundation for modeling and predicting the intricate behaviors of biological systems. Recent advances in data-driven approaches, such as Neural Ordinary Differential Equations (NODEs) and Universal Differential Equations (UDEs), have enabled the development of models that are either fully or partially data-driven. Integrating data-driven components into dynamical systems amplifies the challenge of generalization beyond training data, highlighting the need for robust methods to quantify uncertainty in out-of-distribution (OOD) scenarios--i.e., conditions not encountered during training. In this work, we investigate the reliability of uncertainty quantification (UQ) based on ensembles of models in the reconstruction of dynamical systems. We show that standard ensembles (i.e., models trained independently with different random initializations) risk producing overconfident predictions in previously unseen scenarios, as the models in the ensemble tend to exhibit similar behaviors. To address this issue, we propose a novel ensemble construction method for NODEs and UDEs that fosters diversity in the reconstructed vector field across models within specific regions of the state space, while maintaining explicit control over the fit on the training set. We first evaluate our method on numerical test cases derived from three models commonly used as benchmarks for data-driven reconstruction of dynamical systems: the Lotka-Volterra model, the damped oscillator, and the Lorenz system. We then apply the method to a biologically motivated model of cell apoptosis, considering more realistic conditions such as partial observability of the system outputs and noise in the training dataset. Overall, our results show that the proposed method improves the reliability of UQ in previously unseen scenarios compared with standard ensembles, especially where the latter exhibit overconfidence.

systems biology↗
bioRxiv2020

SBGN Bricks Ontology as a tool to describe recurring concepts in molecular networks

A comprehensible representation of a molecular network is key to communicating and understanding scientific results in systems biology. The Systems Biology Graphical Notation (SBGN) has emerged as the main standard to represent such networks graphically. It has been implemented by different software tools, and is now largely used to communicate maps in scientific publications. However, learning the standard, and using it to build large maps, can be tedious. Moreover, SBGN maps are not grounded on a formal semantic layer and therefore do not enable formal analysis. Here, we introduce a new set of patterns representing recurring concepts encountered in molecular networks, called SBGN bricks. The bricks are structured in a new ontology, the BricKs Ontology (BKO), to define clear semantics for each of the biological concepts they represent. We show the usefulness of the bricks and BKO for both the template-based construction and the semantic annotation of molecular networks. The SBGN bricks and BKO can be freely explored and downloaded at sbgnbricks.org.

systems biology↗
bioRxiv2023

A Mathematical Framework for Understanding Biological Recognition Systems

Recognition plays a crucial role in the formation of social bonds, mate selection, kin selection and survival across species. However, a unifying framework for understanding recognition systems in biology is lacking. We, therefore, propose a theoretical framework for biological recognition utilizing basic principles from category theory, information theory, dynamical systems modeling and optimization theory. We define the two types of recognition, individual recognition (IR) and class-level recognition (CR), as functors between categories of stimuli and responses. IR produces unique responses for each individual, while CR produces shared responses for multiple individuals of the same class. We identify five conditions - universality, low entropy, unfalsifiability, uniform convergence and cognitive limit - that must hold for robust IR systems, which we term "signature systems." Further, we model signature systems as attractor states with perspectives from both statistical information processing and dynamical systems. Our framework provides a basis for advancing understanding of the mechanisms underlying biological recognition and its implications for communication and behavior. Overall, the mathematical conceptualization of IR provides a basis for advancing our understanding of communication and the evolution of language.

animal behavior and cognition↗
bioRxiv2018

Molecular Process Diagram: a precise, scalable and compact visualization of rule-based models

Rule-based modeling allows representation and simulation of biological systems where molecular features (such as protein domains) and feature-specific details of molecular interactions are accounted for. The rule-based description is precise and can define very fine molecular details (e.g. how phosphorylation status of a single residue in a multi-protein complex can affect affinity of another binding site of another protein within the same complex), but makes it difficult to effectively combine the assumptions scribed within the multiple rules of a model into a diagrammatic view. Various visualization schemas have been suggested, but they are all highly rule-based centric (e.g. a visual list of unconnected rules, extended contact maps, or atom-rule graphs). None of them match the clarity of traditional reaction/pathway diagrams, where a researcher can easily visually track the transitions and modifications of chemical species occurring in the biological systems being modeled. Here we present a novel approach and software for precise, scalable and compact representation of rule-based models that we call Molecular Process Diagram. It is based on the three basic elements: interacting molecular complexes, molecular sites directly modified by a rule, and molecular sites that are not modified but contribute to a rule mechanism (e.g. a site that in a phosphorylated state changes binding affinity of another site). Multiple levels of resolution are available: pathway-like diagram of interactions among molecules, optional site-specific interactions, and additional contingencies for interactions. Inclusion of molecular sites enables unambiguous reconstruction of the rule descriptions from the visual diagram without additional supporting documentation, while still keeping a pathway-like visual appearance. The proposed approach for visualization has been implemented in the Virtual Cell (VCell) modeling and simulation framework. Our Molecular Process Diagrams extend the notion of Systems Biology Graphical Notation (SBGN) process diagrams and use SBGN-compliant conventions.\n\nSummaryKinetic models have provided significant insights into biological regulatory mechanisms even though they typically did not take into consideration the details of protein subcomponents such as binding domains and phosphorylation sites. However, these details are often required for an accurate understanding of the events that occur during cell signaling. Without such detailed understanding, intervention strategies to act on signaling pathways in pathological conditions are bound to have limited success. This need to include site-specific details into models led to the advance of rule-based modeling. While rules describe the details of interactions with unmatched precision, they often obscure the \"big picture\", i.e. a pathway-like description of the information flow through the biological system. An intuitive visual diagram is crucial for understanding the assumptions embodied into a model. Here we present a novel approach and software for precise, scalable and compact representation of rule-based models that we call Molecular Process Diagram. It allows visualizing in a pathway-like diagram of the interacting molecules, the molecular sites modified, and the molecular sites that affect the interactions. The approach is implemented in the Virtual Cell (VCell) modeling and simulation framework and suggested as an extension for the Systems Biology Graphical Notations (SBGN) standard.

systems biology↗
bioRxiv2022

A unified Method for assessing the Observability of Dynamic Complex Systems in Biology

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSProblemC_ST_ABSSystems theory applied to biology and medicine assumes that the complexity of a system can be described by quasi-generic models to predict the behavior of many other similar systems. To this end, the aim of various research works in systems theory is to develop inductive modeling (based on data-intensive analysis) or deductive modeling (based on the deduction of mechanistic principles) to discover patterns and identify plausible correlations between past and present events, or to connect different causal relationships of interacting elements at different scales and compute mathematical predictions. Mathematical principles assume that there are constant and observable universal causal principles that apply to all biological systems. Nowadays, there are no suitable tools to assess the soundness of these universal causal principles, especially considering that organisms not only respond to environmental stimuli (and inherent processes) across multiple scales but also integrate information about and within these scales. This implies an uncontrollable degree of uncertainty. MethodologyA method has been developed to detect the stability of causal processes by evaluating the information contained in the trajectories identified in a phase space. Time series patterns are analyzed using concepts from geometric information theory and persistent homology. In essence, recognizing these patterns in different time periods and evaluating their geometrically integrated information leads to the assessment of causal relationships. With this method, and together with the evaluation of persistent entropy in trajectories in relation to different individual systems, we have developed a method called{Phi} -S diagram as a complexity measure to recognize when organisms follow causal pathways leading to mechanistic responses. ResultsWe calculated the {Phi}-S diagram of a deterministic dataset available in the ICU repository to test the methods interpretability. We also calculated the {Phi}-S diagram of time series from health data available in the same repository. This includes patients physiological response to sport measured with wearables outside laboratory conditions. We confirmed the mechanistic nature of both datasets in both calculations. In addition, there is evidence that some individuals show a high degree of autonomous response and variability. Therefore, persistent individual variability may limit the ability to observe the cardiac response. In this study, we present the first demonstration of the concept of developing a more robust framework for representing complex biological systems.

systems biology↗
bioRxiv2019

Construct a molecular associations network to systematically understand intermolecular associations in Human cells

A key aim of post-genomic biomedical research is to systematically integrate and model all molecules and their interactions in living cells. Existing research usually only focusing on the associations between individual or very limited type of molecules. But the interactions between molecules shouldnt be isolated but interconnected and influenced. In this study, we revealed, constructed and analyzed a large-scale molecular association network of multiple biomolecules in human cells by modeling all associations among lncRNA, miRNA, protein, circRNA, microbe, drug, and disease, in which various associations are interconnected and any type of associations can be predicted. More specifically, we defined the molecular associations network and constructed a molecular associations dataset containing 105546 associations. Then, each node is represented by its attribute feature and network embedding learned by Structural Deep Network Embedding. Moreover, Random Forest is trained to predict any kind of associations. And we compared the features and classifiers under five-fold cross-validation. Our method achieves a remarkable performance on entire molecular associations network with an AUC of 0.9552 and an AUPR of 0.9338. To further evaluate the performance of our method, a case study for predicting lncRNA-protein interactions was executed. The experimental results demonstrate that the systematic insight for understanding the synergistic interactions between various molecules and complex diseases. It is anticipated that this work can bring beneficial inspiration and advance related systems biology and biomedical research.\n\nAuthor SummaryThe interactions between the various biomolecules in the cells should not be isolated, but interconnected and influenced. There have been many valuable studies on the interactions between two individual molecules. Based on a systematic and holistic perspective, we revealed and constructed a large-scale molecular associations network by combining various associations in human living cells, including miRNA-lncRNA association, miRNA-disease association, miRNA-protein interaction, lncRNA-disease association, protein-protein interaction, protein-disease association, drug-protein interaction, drug-disease interaction, and lncRNA-protein interaction. To model and analyze this molecular associations network, we employed the network representation learning model to learn how to represent the node. Each node in the network can be represented by network embedding and its own attribute information. Any node can be classified. And any type of the associations in this network can be predicted, which can be considered as link prediction task. Our work provides a new systematic view and conceptual framework to understand complex diseases and life activities. It is anticipated that our study can advance related biological macromolecules, systems biology and biomedical research, and bring some meaningful inspiration.

bioinformatics↗
bioRxiv2021

BioSANS: A Software Package for Symbolic and Numeric Biological Simulation

Modeling biochemical systems can provide insights into behaviors that are difficult to observe or understand. It requires software, programming, and understanding of the system to build a model and study it. Softwares exist for systems biology modeling, but most support only certain types of modeling tasks. Desirable features including ease in preparing input, symbolic or analytical computation, parameter estimation, graphical user interface, and systems biology markup language (SBML) support are not seen concurrently in one software package. In this study, we developed a python-based software that supports these features, with both deterministic and stochastic propagations. The software can be used by graphical user interface, command line, or as a python import. We also developed a semi-programmable and intuitively easy topology input method for the biochemical reactions. We tested the software with semantic and stochastic SBML test cases. Tests on symbolic solution and parameter estimation were also included. The software we developed is reliable, well performing, convenient to use, and compliant with most of the SBML tests. So far it is the only systems biology software that supports symbolic, deterministic, and stochastic modeling in one package that also features parameter estimation and SBML support. This work offers a comprehensive set of tools and allows for better availability and accessibility for studying kinetics and dynamics in biochemical systems.

bioinformatics↗
bioRxiv2024

PDBe tools for an in-depth analysis of small molecules in the Protein Data Bank

The Protein Data Bank (PDB) is the primary global repository for experimentally determined 3D structures of biological macromolecules and their complexes with ligands, proteins, and nucleic acids. PDB contains over 47,000 unique small molecules bound to the macromolecules. Despite the extensive data available, the complexity of small molecule data in the PDB necessitates specialised tools for effective analysis and visualisation. PDBe has developed a number of tools, including PDBe CCDUtils (https://github.com/PDBeurope/ccdutils) for accessing and enriching ligand data, PDBe Arpeggio (https://github.com/PDBeurope/arpeggio) for analysing interactions between ligands and macromolecules, and PDBe RelLig (https://github.com/PDBeurope/rellig) for identifying the functional roles of ligands (such as reactants, cofactors, or drug-like molecules) within protein-ligand complexes. Furthermore, the enhanced ligand annotations and data generated by these tools are presented in a comprehensive view on the novel PDBe-KB ligand pages, providing a holistic view of small molecules that enables the establishment of their biological contexts (Example page for Imatinib: https://wwwdev.ebi.ac.uk/pdbe-srv/pdbechem/chemicalCompound/show/STI). By improving the standardisation of ligand identification, adding various annotations, and offering advanced visualisation capabilities, these tools help researchers navigate the complexities of small molecules and their roles in biological systems, facilitating mechanistic understanding of biological functions. The ongoing enhancements to these resources are designed to support the scientific community in gaining valuable insights into ligands and their applications across various fields, including drug discovery, molecular biology, systems biology, structural biology, and pharmacology.

bioinformatics↗
bioRxiv2019

A systems biology-based molecular model of the protein kinase C life cycle

Protein kinase C (PKC) enzymes are a family of kinases that mediate signal transduction originating at the cell surface. Most cell membranes can contain functional PKC enzymes. Aberrations in the PKC life cycle may result cellular damage and dysfunction. For example, some cancerous cells exhibit alterations in PKC activity. Here, we use a systems biology approach to describe a molecular model of the PKC life cycle. Understanding the PKC life cycle is necessary to identify new anticancer drug targets. The PKC life cycle is composed of three key regulatory processes: maturation, activation and termination. These processes precisely control PKC enzyme levels. This model describes the fate of PKC during de novo synthesis and PKCs lipid-mediated activation cycle. We utilize a systems biology approach to show the PKC life cycle is controlled by multiple phosphorylation and dephosphorylation events. PKC processing events can be divided into two types: maturation via processing of newly synthesized enzyme and secondary messenger-dependent activation of dormant, but catalytically-competent enzyme. Newly synthesized PKC enzyme is constitutively processed through three ordered phosphorylations and stored in the cytosol as a stable, signaling-competent inactive and autoinhibited molecule. Upon extracellular stimulation, diacylglycerol (DAG) and calcium ion (Ca+2) generated at the membrane bind PKC. PKC then undergoes cytosol-to-membrane translocation and subsequent activation. Our model shows that, once activated, PKC is prone to dephosphorylation and subsequent degradation. This model also describes the role of HSP70 in stabilization and re-phosphorylation of dephosphorylated PKC, replenishing the PKC pool. Our model shows how the PKC pool responds to different intensities of extracellular stimuli? We show that blocking PHLPP dephosphorylation replenishes the PKC pool in a dose-dependent manner. This model provides a comprehensive understanding of PKC life cycle regulation.

systems biology↗
bioRxiv2018

Centrality and the shortest path approach on the human interactome

A network is one of the most convenient way to represent interactions between biological entities in systems biology. A network of molecular interactions is a graph in which the vertices are biological component and the edges correspond to the interactions between them. Many notions and approaches for network analysis came to systems biology from the theory of graphs - a field of mathematics that study graphs. We focused on the study of the shortest path approach in this work. We investigated whether this approach yields valid molecular paths. To perform this, the shortest paths in the human interactome (derived from HPRD and HIPPIE databases) were found between all relevant combinations of proteins taken from eight well-studied highly conserved signaling pathways from the KEGG database (NF-kappa B, MAPK, Jak-STAT, mTOR, ErbB, Wnt, TGF-beta and the signaling part of the apoptotic process). Canonical paths were systematically compared with the shortest counterparts and centrality of vertices and paths in subnetworks induced by the shortest paths were analyzed. We found that the sets of the shortest paths contain the canonical counterparts only for very short canonical paths (length 2-3 interactions). We also found that high centrality vertices tend to belong to canonical counterparts, to less extent this can also be said about high centrality paths.

systems biology↗
bioRxiv2021

Analysing and simulating energy-based models in biology using BondGraphTools

Like all physical systems, biological systems are constrained by the laws of physics. However, mathematical models of biochemistry frequently neglect the conservation of energy, leading to unrealistic behaviour. Energy-based models that are consistent with conservation of mass, charge and energy have the potential to aid the understanding of complex interactions between biological components, and are becoming easier to develop with recent advances in experimental measurements and databases. In this paper, we motivate the use of bond graphs (a modelling tool from engineering) for energy-based modelling and introduce, BondGraphTools, a Python library for constructing and analysing bond graph models. We use examples from biochemistry to illustrate how BondGraphTools can be used to automate model construction in systems biology while maintaining consistency with the laws of physics.

systems biology↗
bioRxiv2023

Spatial structure, chemotaxis and quorum sensing shape biomass accumulation in complex systems

Biological tissues, sediments, or engineered systems are spatially structured media with a tortuous and porous structure that host the flow of fluids. Such complex environments can influence the spatial and temporal colonization patterns of bacteria by controlling the transport of individual bacterial cells, the availability of resources, and the distribution of chemical signals for communication. Yet, due to the multi-scale structure of these complex systems, it is hard to assess how different biotic and abiotic properties work together to control the accumulation of bacterial biomass. Here, we explore how flow mediated interactions allow the gut commensal Escherichia coli to colonize a porous structure that is composed of heterogenous dead-end pores (DEPs) and connecting percolating channels, i.e. transmitting pores (TPs), mimicking the structured surface of mammalian guts. We find that in presence of flow, gradients of the quorum sensing (QS) signaling molecule autoinducer-2 (AI-2) promote E. coli chemotactic accumulation in the DEPs. In this crowded environment, the combination of growth and cell-to-cell collision favors the development of suspended bacterial aggregates. This results in hot-spots of resource consumption, which, upon resource limitation, triggers the mechanical evasion of biomass from glucose and oxygen depleted DEPs. Our findings demonstrate that microscale medium structure and complex flow coupled with bacterial quorum sensing and chemotaxis control the heterogenous accumulation of bacterial biomass in a spatially structured environment, such as villi and crypts in the gut or in tortuous pores within soil and filters.

microbiology↗
bioRxiv2018

Agronomic Linked Data (AgroLD): a Knowledge-based System to Enable Integrative Biology in Agronomy

Recent advances in high-throughput technologies have resulted in a tremendous increase in the amount of omics data produced in plant science. This increase, in conjunction with the heterogeneity and variability of the data, presents a major challenge to adopt an integrative research approach. We are facing an urgent need to effectively integrate and assimilate complementary datasets to understand the biological system as a whole. The Semantic Web offers technologies for the integration of heterogeneous data and their transformation into explicit knowledge thanks to ontologies. We have developed the Agronomic Linked Data (AgroLD - www.agrold.org), a knowledge-based system relying on Semantic Web technologies and exploiting standard domain ontologies, to integrate data about plant species of high interest for the plant science community e.g., rice, wheat, arabidopsis. We present some integration results of the project, which initially focused on genomics, proteomics and phenomics. AgroLD is now an RDF (Resource Description Format) knowledge base of 100M triples created by annotating and integrating more than 50 datasets coming from 10 data sources -such as Gramene.org and TropGeneDB- with 10 ontologies -such as the Gene Ontology and Plant Trait Ontology. Our evaluation results show users appreciate the multiple query modes which support different use cases. AgroLDs objective is to offer a domain specific knowledge platform to solve complex biological and agronomical questions related to the implication of genes/proteins in, for instances, plant disease resistance or high yield traits. We expect the resolution of these questions to facilitate the formulation of new scientific hypotheses to be validated with a knowledge-oriented approach.

bioinformatics↗
bioRxiv2018

Dynamic Flux Balance Analysis Models in SBML

Computational models in systems biology and systems medicine are typically simulated using a single formalism such as ordinary differential equations (ODE). However, more complex models require the coupling of multiple formalisms since different biological phenomena are better described by different methods. For example, metabolism in steady state is often modeled using flux-balance analysis (FBA) whereas dynamic changes of model components are better described via ODEs. The coupling of FBA and ODE modeling formalisms results in dynamic FBA models. A major challenge is how to describe such hybrid models that couple multiple formalisms in a standardized way so that they can be exchanged between tools and simulated consistently in a reproducible manner. This paper presents a scheme for encoding and implementation of dynamic FBA models in the Systems Biology Markup Language (SBML), thereby enabling the exchange of multi-framework computational models between software tools. We demonstrate the feasibility of the approach using various example models and show that different tools are able to simulate the hybrid models and agree on the results. As part of this work, two independent implementations of a multi-framework simulation method for dynamic FBA have been developed supporting such models: iBioSim and sbmiutils.

bioinformatics↗
bioRxiv2020

Decoding differential gene expression

Identifying the molecular mechanisms that control differential gene expression (DE) is a major goal of basic and disease biology. Combining the strengths of systems biology and deep learning in a model called DEcode, we are able to predict DE more accurately than traditional sequence-based methods, which do not utilize systems biology data. To determine the biological origins of this accuracy, we identify the most predictive regulators and types of regulatory interactions in DEcode, contrasting their roles across many human tissues. Diverse systems biology, ontological and disease-related assessments all point to the predominant influence of post-translational RNA-binding factors on DE. Through the combinatorial gene regulation that is captured in DEcode, it is even possible to predict relatively subtle person-to-person variation in gene expression. We demonstrate the broad applicability of these clinically-relevant predictions by predicting drivers of aging throughout the human lifespan, gene coexpression relationships on a genome-wide scale, and frequent DE in diverse conditions. Researchers can freely access DEcode to utilize genomic big data in identifying influential molecular mechanisms for any human expression data - www.differentialexpression.org.

systems biology↗
bioRxiv2024

A network toxicology approach for mechanistic modelling of nanomaterial hazard and adverse outcomes

Hazard assessment is the first step in evaluating the potential adverse effects of chemicals. Traditionally, toxicological assessment has focused on the exposure, overlooking the impact of the exposed system on the observed toxicity. However, systems toxicology emphasises how system properties significantly contribute to the observed response. Hence, systems theory states that interactions store more information than individual elements, leading to the adoption of network based models to represent complex systems in many fields of life sciences. Here, we developed a network-based approach to characterise toxicological responses in the context of a biological system, inferring biological system specific networks. We directly linked molecular alterations to the adverse outcome pathway (AOP) framework, establishing connections with toxicologically relevant phenotypic events. We applied this framework on a dataset including 31 engineered nanomaterials with different physicochemical properties in two different in vitro and one in vivo models and demonstrated how the biological system is the driving force of the observed response. This work highlights the potential of network-based methods to significantly improve our understanding of toxicological mechanisms from a systems biology perspective, guiding the hazard assessment of nanomaterials and other advanced materials.

pharmacology and toxicology↗