1,521 research outputs found
QSSPN: dynamic simulation of molecular interaction networks describing gene regulation, signalling and whole-cell metabolism in human cells
Motivation: Dynamic simulation of genome-scale molecular interaction networks will enable the mechanistic prediction of genotype–phenotype relationships. Despite advances in quantitative biology, full parameterization of whole-cell models is not yet possible. Simulation methods capable of using available qualitative data are required to develop dynamic whole-cell models through an iterative process of modelling and experimental validation. Results: We formulate quasi-steady state Petri nets (QSSPN), a novel method integrating Petri nets and constraint-based analysis to predict the feasibility of qualitative dynamic behaviours in qualitative models of gene regulation, signalling and whole-cell metabolism. We present the first dynamic simulations including regulatory mechanisms and a genome-scale metabolic network in human cell, using bile acid homeostasis in human hepatocytes as a case study. QSSPN simulations reproduce experimentally determined qualitative dynamic behaviours and permit mechanistic analysis of genotype–phenotype relationships. Availability and implementation: The model and simulation software implemented in Cþþ are available in supplementary material and at http://sysbio3.fhms.surrey.ac.uk/qsspn/. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin
Data-driven modelling of biological multi-scale processes
Biological processes involve a variety of spatial and temporal scales. A
holistic understanding of many biological processes therefore requires
multi-scale models which capture the relevant properties on all these scales.
In this manuscript we review mathematical modelling approaches used to describe
the individual spatial scales and how they are integrated into holistic models.
We discuss the relation between spatial and temporal scales and the implication
of that on multi-scale modelling. Based upon this overview over
state-of-the-art modelling approaches, we formulate key challenges in
mathematical and computational modelling of biological multi-scale and
multi-physics processes. In particular, we considered the availability of
analysis tools for multi-scale models and model-based multi-scale data
integration. We provide a compact review of methods for model-based data
integration and model-based hypothesis testing. Furthermore, novel approaches
and recent trends are discussed, including computation time reduction using
reduced order and surrogate models, which contribute to the solution of
inference problems. We conclude the manuscript by providing a few ideas for the
development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and
Multiscale Dynamics (American Scientific Publishers
Computational Techniques for the Structural and Dynamic Analysis of Biological Networks
The analysis of biological systems involves the study of networks from different omics such as genomics, transcriptomics, metabolomics and proteomics. In general, the computational techniques used in the analysis of biological networks can be divided into those that perform (i) structural analysis, (ii) dynamic analysis of structural prop- erties and (iii) dynamic simulation. Structural analysis is related to the study of the topology or stoichiometry of the biological network such as important nodes of the net- work, network motifs and the analysis of the flux distribution within the network. Dy- namic analysis of structural properties, generally, takes advantage from the availability of interaction and expression datasets in order to analyze the structural properties of a biological network in different conditions or time points. Dynamic simulation is useful to study those changes of the biological system in time that cannot be derived from a structural analysis because it is required to have additional information on the dynamics of the system. This thesis addresses each of these topics proposing three computational techniques useful to study different types of biological networks in which the structural and dynamic analysis is crucial to answer to specific biological questions. In particu- lar, the thesis proposes computational techniques for the analysis of the network motifs of a biological network through the design of heuristics useful to efficiently solve the subgraph isomorphism problem, the construction of a new analysis workflow able to integrate interaction and expression datasets to extract information about the chromo- somal connectivity of miRNA-mRNA interaction networks and, finally, the design of a methodology that applies techniques coming from the Electronic Design Automation (EDA) field that allows the dynamic simulation of biochemical interaction networks and the parameter estimation
Executable cancer models: successes and challenges
Making decisions on how best to treat cancer patients requires the integration of different data sets, including genomic profiles, tumour histopathology, radiological images, proteomic analysis and more. This wealth of biological information calls for novel strategies to integrate such information in a meaningful, predictive and experimentally verifiable way. In this Perspective we explain how executable computational models meet this need. Such models provide a means for comprehensive data integration, can be experimentally validated, are readily interpreted both biologically and clinically, and have the potential to predict effective therapies for different cancer types and subtypes. We explain what executable models are and how they can be used to represent the dynamic biological behaviours inherent in cancer, and demonstrate how such models, when coupled with automated reasoning, facilitate our understanding of the mechanisms by which oncogenic signalling pathways regulate tumours. We explore how executable models have impacted the field of cancer research and argue that extending them to represent a tumour in a specific patient (that is, an avatar) will pave the way for improved personalized treatments and precision medicine. Finally, we highlight some of the ongoing challenges in developing executable models and stress that effective cross-disciplinary efforts are key to forward progress in the field
Setting the basis of best practices and standards for curation and annotation of logical models in biology
International audienceThe fast accumulation of biological data calls for their integration, analysis and exploitation through more systematic approaches. The generation of novel, relevant hypotheses from this enormous quantity of data remains challenging. Logical models have long been used to answer a variety of questions regarding the dynamical behaviours of regulatory networks. As the number of published logical models increases, there is a pressing need for systematic model annotation, referencing and curation in community-supported and standardised formats. This article summarises the key topics and future directions of a meeting entitled ‘Annotation and curation of computational models in biology’, organised as part of the 2019 [BC]2 conference. The purpose of the meeting was to develop and drive forward a plan towards the standardised annotation of logical models, review and connect various ongoing projects of experts from different communities involved in the modelling and annotation of molecular biological entities, interactions, pathways and models. This article defines a roadmap towards the annotation and curation of logical models, including milestones for best practices and minimum standard requirements
Modelling of signal transduction in yeast – sensitivity and model analysis
Experimental research has revealed components and mechanisms of cellular stress sensing and adaptation. In addition, mathematical modelling has proven to foster the understanding of some basic principles of signal transduction and signal processing as well as of sensitivity and robustness of information perception and cellular response. Here we review some modelling principles, results and open questions exemplified for a model organism, the yeast Saccharomyces cerevisiae
Reverse engineering of drug induced DNA damage response signalling pathway reveals dual outcomes of ATM kinase inhibition
The DNA Damage Response (DDR) pathway represents a signalling mechanism that is activated in eukaryotic cells following DNA damage and comprises of proteins involved in DNA damage detection, DNA repair, cell cycle arrest and apoptosis. This pathway consists of an intricate network of signalling interactions driving the cellular ability to recognise DNA damage and recruit specialised proteins to take decisions between DNA repair or apoptosis. ATM and ATR are central components of the DDR pathway. The activities of these kinases are vital in DNA damage induced phosphorylational induction of DDR substrates. Here, firstly we have experimentally determined DDR signalling network surrounding the ATM/ATR pathway induced following double stranded DNA damage by monitoring and quantifying time dependent inductions of their phosphorylated forms and their key substrates. We next involved an automated inference of unsupervised predictive models of time series data to generate in silico (molecular) interaction maps. We characterized the complex signalling network through system analysis and gradual utilisation of small time series measurements of key substrates through a novel network inference algorithm. Furthermore, we demonstrate an application of an assumption-free reverse engineering of the intricate signalling network of the activated ATM/ATR pathway. We next studied the consequences of such drug induced inductions as well as of time dependent ATM kinase inhibition on cell survival through further biological experiments. Intermediate and temporal modelling outcomes revealed the distinct signaling profile associated with ATM kinase activity and inhibition and explained the underlying signalling mechanism for dual ATM functionality in cytotoxic and cytoprotective pathways
Applications of Boolean modelling to study and stratify dynamics of a complex disease
Interpretation of omics data is needed to form meaningful hypotheses about
disease mechanisms. Pathway databases give an overview of disease-related processes, while mathematical models give qualitative and quantitative insights into
their complexity. Similarly to pathway databases, mathematical models are stored
and shared on dedicated platforms. Moreover, community-driven initiatives such
as disease maps encode disease-specific mechanisms in both computable and
diagrammatic form using dedicated tools for diagram biocuration and visualisation. To investigate the dynamic properties of complex disease mechanisms,
computationally readable content can be used as a scaffold for building dynamic
models in an automated fashion. The dynamic properties of a disease are extremely complex. Therefore, more research is required to better understand the
complexity of molecular mechanisms, which may advance personalized medicine
in the future.
In this study, Parkinson’s disease (PD) is analyzed as an example of a complex
disorder. PD is associated with complex genetic, environmental causes and
comorbidities that need to be analysed in a systematic way to better understand
the progression of different disease subtypes. Studying PD as a multifactorial
disease requires deconvoluting the multiple and overlapping changes to identify
the driving neurodegenerative mechanisms. Integrated systems analysis and
modelling can enable us to study different aspects of a disease such as progression,
diagnosis, and response to therapeutics. Therefore, more research is required to
better understand the complexity of molecular mechanisms, which may advance
personalized medicine in the future. Modelling such complex processes depends
on the scope and it may vary depending on the nature of the process (e.g. signalling
vs metabolic). Experimental design and the resulting data also influence model
structure and analysis. Boolean modelling is proposed to analyse the complexity
of PD mechanisms. Boolean models (BMs) are qualitative rather than quantitative
and do not require detailed kinetic information such as Petri nets or Ordinary
Differential equations (ODEs). Boolean modelling represents a logical formalism
where available variables have binary values of one (ON) or zero (OFF), making it
a plausible approach in cases where quantitative details and kinetic parameters
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are not available. Boolean modelling is well validated in clinical and translational
medicine research.
In this project, the PD map was translated into BMs in an automated fashion
using different methods. Therefore, the complexity of disease pathways can be
analysed by simulating the effect of genomic burden on omics data. In order to
make sure that BMs accurately represent the biological system, validation was
performed by simulating models at different scales of complexity. The behaviour
of the models was compared with expected behavior based on validated biological
knowledge. The TCA cycle was used as an example of a well-studied simple
network. Different scales of complex signalling networks were used including the
Wnt-PI3k/AKT pathway, and T-cell differentiation models. As a result, matched
and mismatched behaviours were identified, allowing the models to be modified
to better represent disease mechanisms. The BMs were stratified by integrating
omics data from multiple disease cohorts. The miRNA datasets from the Parkinson’s Progression Markers Initiative study (PPMI) were analysed. PPMI provides
an important resource for the investigation of potential biomarkers and therapeutic targets for PD. Such stratification allowed studying disease heterogeneity and
specific responses to molecular perturbations. The results can support research
hypotheses, diagnose a condition, and maximize the benefit of a treatment. Furthermore, the challenges and limitations associated with Boolean modelling in
general were discussed, as well as those specific to the current study.
Based on the results, there are different ways to improve Boolean modelling
applications. Modellers can perform exploratory investigations, gathering the
associated information about the model from literature and data resources. The
missing details can be inferred by integrating omics data, which identifies missing
components and optimises model accuracy. Accurate and computable models
improve the efficiency of simulations and the resulting analysis of their controllability. In parallel, the maintenance of model repositories and the sharing of
models in easily interoperable formats are also important
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