Interactive modelling and prognosis of a COVID-19 hospitalized patient via multistate models

A shiny app is presented with two main goals: 1) to fit a MSM from specific data in a friendly way (programming skills are not required); 2) to predict the clinical evolution for a given patient based on the previous MSM. For illustrative purposes, we show how the app works using data from a multicohort study of more than 5,000 hospitalized adult COVID-19 patients from 8 Catalan hospitals during the first five waves of the pandemic. Different models have been fitted for the first Catalan pandemic wave, including as states the main outcomes (discharge and death) together with objective interventions during hospitalization such as non-invasive or invasive mechanical ventilation. The application and the underlying model are intended to be very useful for clinicians and to enhance the approach in modelling the course of other diseases with different stages of severity

Modelling and analysis of a genetic oscillator in E. coli

This thesis presents the modelling and analysis of an engineered genetic oscillator in E.coli. Genetic oscillators composed of transcriptional feedback loops are the central components of circadian clocks [16]. Thus understanding small genetic oscillators is key for understanding the complex regulatory networks of circadian clocks. In order to monitor clock function, a new colony based imaging assay was set up, based on luminescent transcriptional reporter constructs, that allows for automated data collection over long time spans and for the screening of clock mutants. Clock runs produced damped oscillatory behaviour after starting the clock by removal of the lac inducer IPTG or by giving a metabolic stimulus by transferring cells onto fresh agar plates. A detailed mathematical model of the clock was constructed, taking into account discrete and stochastic regulatory binding events at the promoter sites. From this model, using the theory of heterogeneous systems [69, 66], deterministic equations were derived and analysed to yield conditions for the occurrence of stable oscillations based on the system's nullclines. To facilitate the modelling, an algorithm was devised and implemented, that allows for automated construction of Markov chain models of gene activity states based on DNA binding events. In sum, the work constitutes the establishment and analysis of an integrated experimental and modelling system, which opens possibilities for further investigation in order to yield insight into the properties of genetic oscillators.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Robert Rosen and Relational System Theory: An Overview

Relational system theory is the science of organization and function. It is the study of how systems are organized which is based on their functions and the relations between their functions. The science was originally developed by Nicolas Rashevsky, and further developed by Rashevsky’s student Robert Rosen, and continues to be developed by Rosen’s student A. H. Louie amongst others. Due to its revolutionary character, it is often misunderstood, and to some, controversial. We will mainly be focusing on Rosen’s contributions to this science. The formal and conceptual setting for Rosen’s relational system theory is category theory. Rosen was the first to apply category theory to scientific problems, outside of pure mathematics, and the first to think about science from the point of view of category theory. We will provide an overview of Rosen’s theory of modeling, complexity, anticipation, and organism. We will present the foundations of this science and the philosophical motivations behind it along with conceptual clarification and historical context. The purpose of this dissertation is to present Rosen’s ideas to a wider audience

A novel simulation framework for modelling extracellular recordings in cortical tissue : implementation, validation and application to gamma oscillations in mammals

PhD ThesisThis thesis concerns the simulation of local field potentials (LFPs) from cortical network activity; network gamma oscillations in particular. Alterations in gamma oscillation measurements are observed in many brain disorders. Understanding these measurements in terms of the underlying neuronal activity is crucial for developing effective therapies. Modelling can help to unravel the details of this relationship. We first investigated a reduced compartmental neuron model for use in network simulations. We showed that reduced models containing <10 compartments could reproduce the LFP characteristics of the equivalent full-scale compartmental models to a reasonable degree of accuracy. Next, we created the Virtual Electrode Recording Tool for EXtracellular Potentials (VERTEX): a Matlab tool for simulating LFPs in large, spatially organised neuronal networks. We used VERTEX to implement a large-scale neocortical slice model exhibiting gamma frequency oscillations under bath kainate application, an experimental preparation frequently used to investigate properties of gamma oscillations. We built the model based on currently available data on neocortical anatomy. By positioning a virtual electrode grid to match Utah array placement in experiments in vitro, we could make a meaningful direct comparison between simulated and experimentally recorded LFPs. We next investigated the spatial properties of the LFP in more detail, using a smaller model of neocortical layer 2/3. We made several observations about the spatial features of the LFP that shed light on past experimental recordings: how gamma power and coherence decays away from an oscillating region, how layer thickness affects the LFP, which neurons contribute most to the LFP signal, and how the LFP power scales with frequency at different model locations. Finally, we discuss the relevance of our simulation results to experimental neuroscience. Our observations on the dominance of parvalbumin-expressing basket interneuron synapses on the LFP are of particular relevance to epilepsy and schizophrenia: changes in parvalbumin expression have been observed in both disorders. We suggest how our results could inform future experiments and aid in the interpretation of their results

A Multiple-Mechanism Developmental Model for Defining Self-Organizing Geometric Structures

This thesis introduces a model of multicellular development. The model combines elements of the chemical, cell lineage, and mechanical models of morphogenesis pioneered by Turing, Lindenmayer, and Odell, respectively. The internal state of each cell in the model is represented by a time-varying state vector that is updated by a differential equation. The differential equation is formulated as a sum of contributions from different sources, describing gene transcription, kinetics, and cell metabolism. Each term in the differential equation is multiplied by a conditional expression that models regulatory processes specific to the process described by that term. The resulting model has a broader range of fundamental mechanisms than other developmental models. Since gene transcription is included, the model can represent the genetic orchestration of a developmental process involving multiple mechanisms. We show that a computational implementation of the model represents a wide range of biologically relevant phenomena in two and three dimensions. This is illustrated by a diverse collection of simulation experiments exhibiting phenomena such as lateral inhibition, differentiation, segment formation, size regulation, and regeneration of damaged structures. We have explored several application areas with the model: Synthetic biology. We advocate the use of mathematical modeling and simulation for generating intuitions about complex biological systems, in addition to the usual application of mathematical biology to perform analysis on a simplified model. The breadth of our model makes it useful as a tool for exploring biological questions about pattern formation and morphogenesis. We show that simulated experiments to address a particular question can be done quickly and can generate useful biological intuitions. As an example, we document a simulation experiment exploring inhibition via surface chemicals. This experiment suggests that the final pattern depends strongly on the temporal sequence of events. This intuition was obtained quickly using the simulator as an aid to understanding the general behavior of the developmental system. Artificial evolution of neural networks. Neural networks can be represented using a developmental model. We investigate the use of artificial evolution to select equations and parameters that cause the model to create desired structures. We compare our approach to other work in evolutionary neural networks, and discuss the difficulties involved. Computer graphics modeling. We extend the model to allow cells to sense the presence of a 3D surface model, and then use the multicellular simulator to grow cells on the surface. This database amplification technique enables the creation of cellular textures to represent detailed geometry on a surface (e.g., scales, feathers, thorns). In the process of writing many developmental programs, we have gained some experience in the construction of self-organizing cellular structures. We identify some critical issues (size regulation and scalability), and suggest biologically-plausible strategies for addressing them