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Biology subjects

Crampin, E.

Publications and source records attributed to Crampin, E..

9 recordsLinked to original sources

Energy dependence of signalling dynamics and robustness in bacterial two component systems

One of the best known ways bacteria cells understand and respond to the environment are through Two-Component Systems (TCS). These signalling systems are highly diverse in function and can detect a range of physical stimuli including molecular concentrations and temperature, with a range of responses including chemotaxis and anaerobic energy production. TCS exhibit a range of different molecular structures and energy costs, and multiple types co-exist in the same cell. TCSs that incur relatively high energy cost are abundant in biology, despite strong evolutionary pressure to efficiently spend energy. We are motivated to discern what benefits, if any, the more energetically expensive variants had for a cell. We seek to answer this question by modelling energy flow through two variants of TCS. This was accomplished using bond graphs, a physics-based modelling framework that accurately models energy transfer through different physical domains. Our analysis demonstrates that energy availability can affect a cells signal sensitivity, noise filtering effectiveness, and the stimulus level where cell response is maximal. We also found that these properties are determined not by the molecular parameters themselves, but the reaction rate parameters that govern the reaction systems as a whole. This suggests possible connections between the molecular structure and evolutionary purpose of any two-component system. This opens the door to new synthetic circuit design in systems biology, and we propose new hypotheses about this link between structure and purpose that could be experimentally verified. Author summaryTwo-component systems are the main way many bacteria sense and respond to their environment. They exist in such well-studied bacteria as E. coli where they have been shown to detect a range of stimuli including nutrients, temperature, acidity, and pressure. Two-component systems are ubiquitous in bacteria yet have a deceptively simple structure. Knowing how they operate and the purpose of variations in signalling structure is helpful to our understanding of cellular biology and the design of synthetic biological circuits. Critical unanswered questions remain about the energy usage and functional benefits of these systems. We sought to improve our understanding of two-component systems by applying a physics-based modelling framework. We found that tracking energy flow through the cell reveals new energy-dependent behaviour in signalling sensitivity, noise filtering, and maximal cell response. We also found that these properties are not strictly dependent on the molecular properties themselves, but from the configuration of the reaction system as a whole.

biophysics↗

SBML to bond graphs: from conversion to composition

The Systems Biology Markup Language (SBML) is a popular software-independent XML-based format for describing models of biological phenomena. The BioModels Database is the largest online repository of SBML models. Several tools and platforms are available to support the reuse and composition of SBML models. However, these tools do not explicitly assess whether models are physically plausibile or thermodynamically consistent. This often leads to ill-posed models that are physically impossible, impeding the development of realistic complex models in biology. Here, we present a framework that can automatically convert SBML models into bond graphs, which imposes energy conservation laws on these models. The new bond graph models are easily mergeable, resulting in physically plausible coupled models. We illustrate this by automatically converting and coupling a model of pyruvate distribution to a model of the pentose phosphate pathway. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/493355v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@16f5998org.highwire.dtl.DTLVardef@1630beborg.highwire.dtl.DTLVardef@1a37309org.highwire.dtl.DTLVardef@1d582df_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIA framework to convert suitable SBML models of biochemical networks into bond graphs is developed. C_LIO_LIThe framework is applied here to two interconnecting models of metabolism pathways. C_LIO_LIWe automatically integrate the generated bond graph modules. C_LIO_LIWe qualitatively illustrate the functionality of the composed model. C_LI

bioengineering↗

A semantics, energy-based approach to automate biomodel composition

Hierarchical modelling is essential to achieving complex, large-scale models. However, not all modelling schemes support hierarchical composition, and correctly mapping points of connection between models requires comprehensive knowledge of each models components and assumptions. To address these challenges in integrating biosimulation models, we propose an approach to automatically and confidently compose biosimulation models. The approach uses bond graphs to combine aspects of physical and thermodynamics-based modelling with biological semantics. We improved on existing approaches by using semantic annotations to automate the recognition of common components. The approach is illustrated by coupling a model of the Ras-MAPK cascade to a model of the upstream activation of EGFR. Through this methodology, we aim to assist researchers and modellers in readily having access to more comprehensive biological systems models. Author summaryDetailed, multi-scale computational models bridging from biomolecular processes to entire organs and bodies have the potential to revolutionise medicine by enabling personalised treatments. One of the key challenges to achieving these models is connecting together the vast number of isolated biosimulation models into a coherent whole. Using recent advances in both modelling techniques and biological standards in the scientific community, we developed an approach to integrate and compose models in a physics-based environment. This provides significant advantages, including the automation of model composition and post-model-composition adjustments. We anticipate that our approach will enable the faster development of realistic and accurate models to understand complex biological systems.

bioengineering↗

High temporal resolution RNA-seq time course data reveals mammalian lncRNA activationmirrors neighbouring protein-coding genes

BackgroundThe advent of next-generation sequencing revealed extensive transcription beyond protein-coding genes, identifying tens of thousands of long non-coding RNAs (lncRNAs). Selected functional examples raised the possibility that lncRNAs, as a class, may maintain broad regulatory roles. Compellingly, lncRNA expression is strongly linked with adjacent protein-coding gene expression, suggesting a potential cis-regulatory function. Evidence for these regulatory roles may be obtained through careful examination of the precise timing of lncRNA expression relative to adjacent protein-coding genes. ResultsWhere causal cis-regulatory relationships exist, lncRNA activation is expected to precede changes in adjacent target gene expression. Using an RNA-seq time course of uniquely high temporal resolution, we profiled the expression dynamics of several thousand lncRNAs and protein-coding genes in synchronized, transitioning human cells. Our findings reveal lncRNAs are expressed synchronously with adjacent protein-coding genes. Analysis of lipopolysaccharide-activated mouse dendritic cells revealed the same temporal relationship observed in transitioning human cells. ConclusionOur findings suggest broad-scale cis-regulatory roles for lncRNAs are not common. The strong association between lncRNAs and adjacent genes may instead indicate an origin as transcriptional by-products from active protein-coding gene promoters and enhancers.

bioinformatics↗

Modular assembly of dynamic models in systems biology

It is widely acknowledged that the construction of large-scale dynamic models in systems biology requires complex modelling problems to be broken up into more manageable pieces. To this end, both modelling and software frameworks are required to enable modular modelling. While there has been consistent progress in the development of software tools to enhance model reusability, there has been a relative lack of consideration for how underlying biophysical principles can be applied to this space. Bond graphs combine the aspects of both modularity and physics-based modelling. In this paper, we argue that bond graphs are compatible with recent developments in modularity and abstraction in systems biology, and are thus a desirable framework for constructing large-scale models. We use two examples to illustrate the utility of bond graphs in this context: a model of a mitogen-activated protein kinase (MAPK) cascade to illustrate the reusability of modules and a model of glycolysis to illustrate the ability to modify the model granularity. Author summaryThe biochemistry within a cell is complex, being composed of numerous biomolecules and reactions. In order to develop fully detailed mathematical models of cells, smaller submodels need to be constructed and connected together. Software and standards can assist in this endeavour, but challenges remain in ensuring submodels are both consistent with each other and consistent with the fundamental conservation laws of physics. In this paper, we propose a new approach using bond graphs from engineering. In this approach, connections between models are defined using physical conservation laws. We show that this approach is compatible with current software approaches in the field, and can therefore be readily used to incorporate physical consistency into existing model integration methodologies. We illustrate the utility of this approach in streamlining the development of models for a signalling network (the MAPK cascade) and a metabolic network (the glycolysis pathway). The advantage of this approach is that models can be developed in a scalable manner while also ensuring consistency with the laws of physics, enhancing the range of data available to train models. This approach can be used to quickly construct detailed and accurate models of cells, facilitating future advances in biotechnology and personalised medicine.

systems biology↗

Modular Dynamic Biomolecular Modelling: The Unification of Stoichiometry, Thermodynamics, Kinetics and Data.

Renewed interest in dynamic simulation models of biomolecular systems has arisen from advances in genome-wide measurement and applications of such models in biotechnology and synthetic biology. In particular, genome-scale models of cellular metabolism beyond the steady state are required in order to represent transient and dynamic regulatory properties of the system. Development of such whole-cell models requires new modelling approaches. Here we propose the energy-based bond graph methodology, which integrates stoichiometric models with thermo-dynamic principles and kinetic modelling. We demonstrate how the bond graph approach intrinsically enforces thermodynamic constraints, provides a modular approach to modelling, and gives a basis for estimation of model parameters leading to dynamic models of biomolecular systems. The approach is illustrated using a well-established stoichiometric model of Escherichia coli (E. coli) and published experimental data.

systems biology↗

Detecting RyR clusters with CaCLEAN: influence of spatial distribution and structural heterogeneity

In cardiomyocytes, the coordinated release of calcium ions from intracellular stores through ryanodine receptor (RyR) clusters is key to the generation of a calcium transient and induction of contraction. Recently, a deconvolution algorithm from radio astronomy was adapted and applied to analysis of calcium fluorescence imaging of cardiomyocytes. The algorithm, CaCLEAN, showed potential in revealing both the spatial locations of RyR clusters and their functional response in living cells. However, whether the RyR clusters identified by CaCLEAN analysis of the imaging data were true or false positives remained unvalidated in the absence of ground truth values. In this work, a structurally realistic finite element model was developed to simulate reaction-diffusion of calcium emanating from RyR clusters during the rising phase (first 30 ms) of the calcium transient. The effect of two sets of factors were examined with the model: (1) the number and spacing of simulated RyR clusters and (2) the effect of mitochondria acting as barriers to diffusion. Confocal fluorescence microscopy images were simulated from the model results and analysed using CaCLEAN. The performance of CaCLEAN was found to be sensitive to cluster spacing and distance from the imaging plane. In a case with sparsely-packed clusters, detection recall and precision were 0.82 for clusters up to 610 nm from the imaging plane; in a densely-packed cluster case, recall and precision were 0.69 for clusters up to 280 nm from the imaging plane. Users interested in applying CaCLEAN to their data should therefore consider the likely density of cluster distributions and the trade-off between precision and recall when determining the maximum relevant depth of RyR clusters in their application.

bioengineering↗

Longitudinal single cell fate in hematopoiesis in vivo using cellular barcoding and DiSNE movie visualization

Identifying the progeny of many single progenitor cells simultaneously can be achieved by tagging progenitors with unique heritable DNA barcodes, and allows inferences of lineage relationships, including longitudinally. While this approach has shed new light on single cell fate heterogeneity, data interpretation remains a major challenge. In this study, we applied our developmental interpolated t-Distributed Stochastic Neighbor Embedding (DiSNE) movie approach to visualize the clonal dynamics of hematopoietic reconstitution in primates and identify novel developmental patterns, namely a potential cluster of hematopoietic progenitors with early T cell and later granulocyte production.\n\nKey pointsO_LIComplex single cell haematopoietic fate heterogeneity can be visualized and assessed with tSNE pie maps\nC_LIO_LIDiSNE movie visualization of in vivo haematopoiesis allows \"play back\" of the waves of haematopoiesis\nC_LIO_LIIdentification of novel hematopoietic progenitors with early T cell and later granulocyte production\nC_LI

developmental biology↗

Assessment of clonal kinetics reveals multiple trajectories of dendritic cell development

A thorough understanding of cellular development is incumbent on assessing the complexities of fate and kinetics of individual clones within a population. Here, we develop a system for robust periodical assessment of lineage outputs of thousands of transient clones and establishment of bona fide cellular trajectories. We appraise the development of dendritic cells (DCs) from barcode-labeled hematopoietic stem and progenitor cells (HSPCs) by serially measuring barcode signatures, and visualize this multidimensional data using novel developmental interpolated t-distributed stochastic neighborhood embedding (Di-SNE) time-lapse movies. We identify multiple cellular trajectories of DC development that are characterized by distinct fate bias and expansion kinetics, and determine that these are intrinsically programmed. We demonstrate that conventional DC and plasmacytoid DC trajectories are largely separated already at the HSPC stage. This framework allows systematic evaluation of clonal dynamics and can be applied to other steady-state or perturbed developmental systems.

systems biology↗