Biology subjects

Sterratt, D. C.

Publications and source records attributed to Sterratt, D. C..

6 recordsLinked to original sources

bioRxiv2025

One model to rule them all: unification of voltage-gated potassium channel models via deep non-linear mixed effects modelling

Ion channels are essential for signal processing and propagation in neural cells. Voltage-gated ion channels permeable to potassium (Kv) form one of the most prominent channel families. Techniques used to model the voltage-dependent gating of Kv channels date back to Hodgkin and Huxley (1952). Different Kv types can display radically different kinetic properties, requiring different mathematical models. However, the construction of Hodgkin-Huxley-like (HH-like) models is generally complex and time consuming due to the number of parameters, their tuning and having to choose functional forms to model gating. In addition to the between-Kv type heterogeneity, there can be significant within-Kv type kinetic heterogeneity between different cells with genetically identical channels. Since HH-like models do not account for such variability, extensions to it are necessary. We use scientific machine learning (SciML), the integration of machine learning methodologies with existing scientific models, and non-linear mixed effects (NLME) modelling to bypass the limitations of HH-like modelling. NLME is a modelling methodology that takes into account both within- and between-subject variability. These tools allowed us to complement the HH-like modelling and construct a unified SciML HH-like model that fits the recordings from 20 different Kv types. The unified SciML HH-like model produced closer fits to the data compared to a set of seven previous HH-like models and was able to represent the highly heterogeneous data from different cells. Our model may be the first step in producing a SciML foundation model for ion channels that would be capable of modelling the gating kinetics of any ion channel type. Author summaryIon channels are complex molecules embedded in the membranes of neurons - the cells responsible for signal propagation and processing in the brain. Ion channels can open and close in response to various types of stimuli, in particular the voltage difference across the cell membrane. Computational modelling, usage of mathematical techniques to represent a system and algorithmically solve for its dynamics, has been previously used to understand the dynamics of voltage-gated ion channels. However, computational modelling of voltage-gated ion channels requires costly and complex optimization routines to optimize their structure and parameters. We utilize two tools new to the modelling of voltage-gated ion channels - scientific machine learning and non-linear mixed effects modelling - to bypass some limitations associated with the existing methods. By using scientific machine learning and non-linear mixed effects modelling we were able to create a unified model capable of modelling the gating dynamics of 20 different ion channels. This is in stark contrast to the existing modelling approaches, where each channel requires its own model. Moreover, our unified model performed better than seven existing ion channel gating models. Therefore, the tools we used and the model we created is a significant step forward in facilitating the modelling of ion channel gating. Future work could include even more ion channels types within the scope of our unified model.

neuroscience↗

bioRxiv2024

Fitting and comparison of calcium-calmodulin kinetic schemes to a common data set using non-linear mixed effects modelling

Calmodulin is a calcium binding protein that is essential in calcium signalling in the brain. There are many computational models of calcium-calmodulin binding that capture various calmodulin features. However, existing models have generally been fit to different data sets, with some publications not reporting their training and validation performance. Moreover, there is no model comparison using a common benchmark data set as is common practice in other modeling domains. Finally, some calmodulin models have been fit as a part of a larger kinetic scheme, which may have resulted in parameters being underdetermined. We address these three limitations of previous models by fitting the published calcium-calmodulin schemes to a common calcium-calmodulin data set comprising of equlibrium data from Shifman et al. and dynamical data from Faas et al. Due to technical limitations, the amount of uncaged calcium in Faas et al. data could not be predicted with certainty. To find good parameter fits, despite this uncertainty, we used non-linear mixed effects modelling as implemented in the Pumas.jl package. The Akaike information criterion values for our reaction rates were significantly lower than for the published parameters, indicating that the published parameters are suboptimal. Moreover, there were significant differences in calmodulin activation, both between the schemes and between our reaction rate and those previously published. A kinetic scheme with independent lobes and unique, rather than identical, binding sites fit the data best. Our results support two hypotheses: (1) partially bound calmodulin is important in cellular signalling; (2) calcium binding sites within a calmodulin lobe are kinetically distinct rather than identical. We conclude that more attention should be given to validation and comparison of models of individual molecules. Author summaryLearning and memory depend on changes in synapses, the connections between nerve cells. To understand how learning and memory work, it is important to understand how the proteins involved in these changes are regulated. Computational modelling of biochemical reactions has been used to understand the regulation processes involved in learning and memory. However, computational models often rely on simplifications of biochemical reaction schemes, and it can be difficult to tell which simplifications are "the best", i.e. capture important aspects of a proteins behaviour. It is also difficult to estimate the best model parameters, such as binding reaction rates or binding strengths. Modellers often base their estimates on known experimental results, but often not in a structured way. We examine and compare a variety of computational models of calmodulin, a protein necessary for changes in synaptic strength. We apply a new approach to infer the model parameters that best fit two published datasets, and compare our parameters to previously published ones. We find that some simplified reaction schemes are more successful than others in capturing the activation pattern of calmodulin. We also find that the parameter sets found through our approach outperform previously published parameter sets in fitting the experimental data.

systems biology↗

bioRxiv2024

In Silico identification and modelling of FDA-approved drugs targeting T-type calcium channels

Studies have shown that inhibition of the Cav3.1 T-type calcium channel can prevent or suppress neurological diseases, such as epileptic seizures and diabetic neuropathy. In this study we aimed to use in silico simulations to identify a U.S. Food and Drug Administration (FDA)-approved drug that can bind to the Cav3.1 T-type calcium channel. We used the automated docking suite GOLD v5.5 with the genetic algorithm to simulate molecular docking and predict the protein-ligand binding modes, and the ChemPLP empirical scoring function to estimate the binding affinities of 2,115 FDA-approved drugs to the human Cav3.1 channel. Drugs with high binding affinity and appropriate pharmacodynamic and pharmacokinetic properties were selected for molecular mechanics Poisson-Boltzmann surface area (MMPBSA) and molecular mechanics generalised Born surface area (MMGBSA) binding free energy calculations, GROMACS molecular dynamics (MD) simulations and Monte Carlo Cell (MCell) simulations. The docking results indicated that the FDA-approved drug montelukast has a high binding affinity to Cav3.1, and data from the literature suggested that montelukast has the appropriate drug-like properties to cross the human blood-brain barrier and reach synapses in the central nervous system. MMPBSA, MMGBSA and MD simulations showed the high stability of the montelukast-Cav3.1 complex. MCell simulations indicated that the blockage of Cav3.1 by montelukast reduced the number of synaptic vesicles being released from the pre-synaptic region to the synaptic cleft, which may reduce the probability and amplitude of postsynaptic potentials. Author SummaryNew drugs for current illnesses or disorders are always necessary to enhance therapeutic outcomes or can be utilised for patients who have experienced side effects from current medicines. The Cav3.1 T-type calcium channel has been found to be linked to neurological diseases, such as epilepsy and diabetes. Consequently, identifying a drug that could bind to these receptors may yield therapeutic effects on these neurological conditions. This study utilises various computational methods to sift through 2,115 FDA-approved drugs that have been used for other diseases, not neurological ones, to determine whether some of these drugs could bind to the Cav3.1 T-type calcium channel and potentially produce therapeutic effects on neurological diseases. Our research indicates that the current asthma drug montelukast has the potential to bind to the calcium channel and is worthy of further investigation.

bioinformatics↗

bioRxiv2020

A configurable model of the synaptic proteome reveals the molecular mechanisms of disease co-morbidity.

Synapses contain highly complex proteomes which control synaptic transmission, cognition and behaviour. Genes encoding synaptic proteins are associated with neuronal disorders many of which show clinical co-morbidity. Our hypothesis is that there is mechanistic overlap that is emergent from the network properties of the molecular complex. To test this requires a detailed and comprehensive molecular network model. We integrated 57 published synaptic proteomic datasets obtained between 2000 and 2019 that describe over 7000 proteins. The complexity of the postsynaptic proteome is reaching an asymptote with a core set of ~3000 proteins, with less data on the presynaptic terminal, where each new study reveals new components in its landscape. To complete the network, we added direct protein-protein interaction data and functional metadata including disease association. The resulting amalgamated molecular interaction network model is embedded into a SQLite database. The database is highly flexible allowing the widest range of queries to derive custom network models based on meta-data including species, disease association, synaptic compartment, brain region, and method of extraction. This network model enables us to perform in-depth analyses that dissect molecular pathways of multiple diseases revealing shared and unique protein components. We can clearly identify common and unique molecular profiles for co-morbid neurological disorders such as Schizophrenia and Bipolar Disorder and even disease comorbidities which span biological systems such as the intersection of Alzheimers Disease with Hypertension.

systems biology↗

bioRxiv2019

Measuring and Modelling the Emergence of Order in the Mouse Retinocollicular Projection

In the formation of retinotopic maps both experimental and theoretical work implicate guidance molecules and patterned neuronal activity. A common view is that molecular cues define and activity cues refine mappings. Important insights have come from studies of the retinocollicular projection in transgenic mice, in which cues have been modified either in isolation or in combination. Mostly these have generated descriptions of endpoint mappings. The dynamics of map formation remain under-explored experimentally and computationally. We have quantified changes in the ordering of the mouse retinocollicular projection with age after making local collicular injections of fluorescent microspheres. Contour analysis shows that, at birth (P0), cells from over 80% of the retina converge on a given collicular locus; this percentage falls gradually to P4 then rapidly approaches adult values by P12. Paired injections reveal how the segregation of labelled cells depends both on injection site separation and relative orientation and also age. At P0, large anterior-posterior separations failed to produce segregated label: segregation improved with a similar timecourse to convergence to reach near adult-values by P12. An implementation of a combined activity-molecular model captures these segregation dynamics.\n\nThe developmental dynamics were then studied in the nAChR-{beta}2-/- mouse, which has altered patterns of retinal activity in the first postnatal week that results in a more diffuse adult retinal projection. Surprisingly, both measures of map refinement (convergence and segregation) remain largely constant and imprecise in the first postnatal week - only subsequently does the projection begin to refine. Substituting nAChR-{beta}2-/- activity patterns for wild-type patterns [5] into the model failed to capture the biology: refinement was initially faster. By reducing the relative importance of the gradients contribution to the model energy (to 15% of normal) we were able to mimic the delayed refinement observed in vivo. Our model therefore predicts the altered activity patterns may affect readout of guidance cues.

neuroscience↗

bioRxiv2018

Analysis of proteins in computational models of synaptic plasticity

The desire to explain how synaptic plasticity arises from interactions between ions, proteins and other signalling molecules has propelled the development of biophysical models of molecular pathways in hippocampal, striatal and cerebellar synapses. The experimental data underpinning such models is typically obtained from low-throughput, hypothesis-driven experiments. We used high-throughput proteomic data and bioinformatics datasets to assess the coverage of biophysical models.\n\nTo determine which molecules have been modelled, we surveyed biophysical models of synaptic plasticity, identifying which proteins are involved in each model. We were able to map 4.2% of previously reported synaptic proteins to entities in biophysical models. Linking the modelled protein list to Gene Ontology terms shows that modelled proteins are focused on functions such as calmodulin binding, cellular responses to glucagon stimulus, G-alpha signalling and DARPP-32 events.\n\nWe cross-linked the set of modelled proteins with sets of genes associated with common neurological diseases. We find some examples of disease-associated molecules that are well represented in models, such as voltage-dependent calcium channel family (CACNA1C), dopamine D1 receptor, and glutamate ionotropic NMDA type 2A and 2B receptors. Many other disease-associated genes have not been included in models of synaptic plasticity, for example catechol-O-methyltransferase (COMT) and MAO A. By incorporating pathway enrichment results, we identify LAMTOR, a gene uniquely associated with Schizophrenia, which is closely linked to the MAPK pathway found in some models.\n\nOur analysis provides a map of how molecular pathways underpinning neurological diseases relate to synaptic biophysical models that can in turn be used to explore how these molecular events might bridge scales into cellular processes and beyond. The map illustrates disease areas where biophysical models have good coverage as well as domain gaps that require significant further research.\n\nAuthor summaryThe 100 billion neurons in the human brain are connected by a billion trillion structures called synapses. Each synapse contains hundreds of different proteins. Some proteins sense the activity of the neurons connecting the synapse. Depending on what they sense, the proteins in the synapse are rearranged and new proteins are synthesised. This changes how strongly the synapse influences its target neuron, and underlies learning and memory. Scientists build computational models to reason about the complex interactions between proteins. Here we list the proteins that have been included in computational models to date. For good reasons, models do not always specify proteins precisely, so to make the list we had to translate the names used for proteins in models to gene names, which are used to identify proteins. Our translation could be used to label computational models in the future. We found that the list of modelled proteins contains only 4.2% of proteins associated with synapses, suggesting more proteins should be added to models. We used lists of genes associated with neurological diseases to suggest proteins to include in future models.

neuroscience↗