Modelling capture efficiency of single-cell RNA-sequencing data improves inference of transcriptome-wide burst kinetics

Motivation: Gene expression is characterized by stochastic bursts of transcription that occur at brief and random periods of promoter activity. The kinetics of gene expression burstiness differs across the genome and is dependent on the promoter sequence, among other factors. Single-cell RNA sequencing (scRNA-seq) has made it possible to quantify the cell-to-cell variability in transcription at a global genome-wide level. However, scRNA-seq data are prone to technical variability, including low and variable capture efficiency of transcripts from individual cells. // Results: Here, we propose a novel mathematical theory for the observed variability in scRNA-seq data. Our method captures burst kinetics and variability in both the cell size and capture efficiency, which allows us to propose several likelihood-based and simulation-based methods for the inference of burst kinetics from scRNA-seq data. Using both synthetic and real data, we show that the simulation-based methods provide an accurate, robust and flexible tool for inferring burst kinetics from scRNA-seq data. In particular, in a supervised manner, a simulation-based inference method based on neural networks proves to be accurate and useful when applied to both allele and nonallele-specific scRNA-seq data. // Availability and implementation: The code for Neural Network and Approximate Bayesian Computation inference is available at https://github.com/WT215/nnRNA and https://github.com/WT215/Julia_ABC, respectively

Phenotypic Robustness of Epidermal Stem Cell Number in <i>C. elegans</i> Is Modulated by the Activity of the Conserved N-acetyltransferase <i>nath-10</i>/NAT10

Individual cells and organisms experience perturbations from internal and external sources, yet manage to buffer these to produce consistent phenotypes, a property known as robustness. While phenotypic robustness has often been examined in unicellular organisms, it has not been sufficiently studied in multicellular animals. Here, we investigate phenotypic robustness in Caenorhabditis elegans seam cells. Seam cells are stem cell-like epithelial cells along the lateral edges of the animal, which go through asymmetric and symmetric divisions contributing cells to the hypodermis and neurons, while replenishing the stem cell reservoir. The terminal number of seam cells is almost invariant in the wild-type population, allowing the investigation of how developmental precision is achieved. We report here that a loss-of-function mutation in the highly conserved N-acetyltransferase nath-10/NAT10 increases seam cell number variance in the isogenic population. RNA-seq analysis revealed increased levels of mRNA transcript variability in nath-10 mutant populations, which may have an impact on the phenotypic variability observed. Furthermore, we found disruption of Wnt signaling upon perturbing nath-10 function, as evidenced by changes in POP-1/TCF nuclear distribution and ectopic activation of its GATA transcription factor target egl-18. These results highlight that NATH-10/NAT-10 can influence phenotypic variability partly through modulation of the Wnt signaling pathway

Colored extrinsic fluctuations and stochastic gene expression

Stochasticity is both exploited and controlled by cells. Although the intrinsic stochasticity inherent in biochemistry is relatively well understood, cellular variation, or ‘noise', is predominantly generated by interactions of the system of interest with other stochastic systems in the cell or its environment. Such extrinsic fluctuations are nonspecific, affecting many system components, and have a substantial lifetime, comparable to the cell cycle (they are ‘colored'). Here, we extend the standard stochastic simulation algorithm to include extrinsic fluctuations. We show that these fluctuations affect mean protein numbers and intrinsic noise, can speed up typical network response times, and can explain trends in high-throughput measurements of variation. If extrinsic fluctuations in two components of the network are correlated, they may combine constructively (amplifying each other) or destructively (attenuating each other). Consequently, we predict that incoherent feedforward loops attenuate stochasticity, while coherent feedforwards amplify it. Our results demonstrate that both the timescales of extrinsic fluctuations and their nonspecificity substantially affect the function and performance of biochemical networks

Growth-rate-dependent and nutrient-specific gene expression resource allocation in fission yeast

Cellular resources are limited and their relative allocation to gene expression programmes determines physiological states and global properties such as the growth rate. Here, we determined the importance of the growth rate in explaining relative changes in protein and mRNA levels in the simple eukaryote Schizosaccharomyces pombe grown on non-limiting nitrogen sources. Although expression of half of fission yeast genes was significantly correlated with the growth rate, this came alongside wide-spread nutrient-specific regulation. Proteome and transcriptome often showed coordinated regulation but with notable exceptions, such as metabolic enzymes. Genes positively correlated with growth rate participated in every level of protein production apart from RNA polymerase II-dependent transcription. Negatively correlated genes belonged mainly to the environmental stress response programme. Critically, metabolic enzymes, which represent ∼55-70% of the proteome by mass, showed mostly condition-specific regulation. In summary, we provide a rich account of resource allocation to gene expression in a simple eukaryote, advancing our basic understanding of the interplay between growth-rate-dependent and nutrient-specific gene expression

Data-driven spatio-temporal modelling of glioblastoma

Mathematical oncology provides unique and invaluable insights into tumour growth on both the microscopic and macroscopic levels. This review presents state-of-the-art modelling techniques and focuses on their role in understanding glioblastoma, a malignant form of brain cancer. For each approach, we summarise the scope, drawbacks, and assets. We highlight the potential clinical applications of each modelling technique and discuss the connections between the mathematical models and the molecular and imaging data used to inform them. By doing so, we aim to prime cancer researchers with current and emerging computational tools for understanding tumour progression. Finally, by providing an in-depth picture of the different modelling techniques, we also aim to assist researchers who seek to build and develop their own models and the associated inference frameworks.Comment: 30 pages, 3 figures, 3 table

Modelling the Influence of Foot-and-Mouth Disease Vaccine Antigen Stability and Dose on the Bovine Immune Response

Foot and mouth disease virus causes a livestock disease of significant global socio-economic importance. Advances in its control and eradication depend critically on improvements in vaccine efficacy, which can be best achieved by better understanding the complex within-host immunodynamic response to inoculation. We present a detailed and empirically parametrised dynamical mathematical model of the hypothesised immune response in cattle, and explore its behaviour with reference to a variety of experimental observations relating to foot and mouth immunology. The model system is able to qualitatively account for the observed responses during in-vivo experiments, and we use it to gain insight into the incompletely understood effect of single and repeat inoculations of differing dosage using vaccine formulations of different structural stability

Scalable rule-based modelling of allosteric proteins and biochemical networks

Much of the complexity of biochemical networks comes from the information-processing abilities of allosteric proteins, be they receptors, ion-channels, signalling molecules or transcription factors. An allosteric protein can be uniquely regulated by each combination of input molecules that it binds. This "regulatory complexity" causes a combinatorial increase in the number of parameters required to fit experimental data as the number of protein interactions increases. It therefore challenges the creation, updating, and re-use of biochemical models. Here, we propose a rule-based modelling framework that exploits the intrinsic modularity of protein structure to address regulatory complexity. Rather than treating proteins as "black boxes", we model their hierarchical structure and, as conformational changes, internal dynamics. By modelling the regulation of allosteric proteins through these conformational changes, we often decrease the number of parameters required to fit data, and so reduce over-fitting and improve the predictive power of a model. Our method is thermodynamically grounded, imposes detailed balance, and also includes molecular cross-talk and the background activity of enzymes. We use our Allosteric Network Compiler to examine how allostery can facilitate macromolecular assembly and how competitive ligands can change the observed cooperativity of an allosteric protein. We also develop a parsimonious model of G protein-coupled receptors that explains functional selectivity and can predict the rank order of potency of agonists acting through a receptor. Our methodology should provide a basis for scalable, modular and executable modelling of biochemical networks in systems and synthetic biology

Role of active zone geometry in neurotransmitter release: A Monte Carlo study of presynaptic calcium diffusion and calcium sensor interactions

Calcium triggered neurotransmitter release is distinguished from other types of cellular exocytosis by its fast on and off kinetics and its precise spatial localization. To address the importance of the precise geometrical relationship among the vesicles, the Ca channels, and the proteins of the release machinery on neurotransmitter release, I developed a Monte Carlo simulation of Ca diffusion and reaction with nanometer spatial and nanosecond temporal resolution. I first examined the simple case of a single vesicle and its associated Ca channel. I found that the vesicle acts as a diffusion barrier, altering the shape of the Ca microdomain around the vesicle. Therefore, Ca-sensor(s) for release would be exposed to markedly different [Ca], varying by up to 13-fold, depending on their position around the vesicle. Using simple models of Ca triggered release, I showed that, due to the brevity of the Ca influx, the binding kinetics of the Ca-sensor rather than its equilibrium affinity determines receptor occupancy. Also, redundant copies of the binding sites could account for apparent differences in Ca-sensor affinities between synapses. I further showed that the random positioning of the Ca-sensor molecules around the vesicle can result in the emergence of two distinct populations of vesicles with low and high release probability. To study the case of multiple channels and vesicles, I constructed a geometrically realistic model of the active zone in frog neuromuscular junction. By tracking all Ca ions as they enter though particular channels and diffuse in the terminal, I showed that on average only 2-6 open Ca channels are required to produce experimentally observed channel cooperativity in this preparation. I then systematically compared the functional consequences of low and high number of open channels per action potential in this active zone. Modeling suggested that, while a low number of open channels reduced the energy needed to restore [Ca] after influx and decreased the delay of release, it resulted in high trial-to-trial variability of the number of vesicles released, which was not consistent with the experimental observation. I proposed a short-term lateral inhibition of fusion by each vesicle released to resolve this paradox

When we need particle-based reaction-diffusion simulations and when we don’t

Non UBCUnreviewedAuthor affiliation: Imperial College LondonFacult