Parameter estimation with bio-inspired meta-heuristic optimization: modeling the dynamics of endocytosis

<p>Abstract</p> <p>Background</p> <p>We address the task of parameter estimation in models of the dynamics of biological systems based on ordinary differential equations (ODEs) from measured data, where the models are typically non-linear and have many parameters, the measurements are imperfect due to noise, and the studied system can often be only partially observed. A representative task is to estimate the parameters in a model of the dynamics of endocytosis, i.e., endosome maturation, reflected in a cut-out switch transition between the Rab5 and Rab7 domain protein concentrations, from experimental measurements of these concentrations. The general parameter estimation task and the specific instance considered here are challenging optimization problems, calling for the use of advanced meta-heuristic optimization methods, such as evolutionary or swarm-based methods.</p> <p>Results</p> <p>We apply three global-search meta-heuristic algorithms for numerical optimization, i.e., differential ant-stigmergy algorithm (DASA), particle-swarm optimization (PSO), and differential evolution (DE), as well as a local-search derivative-based algorithm 717 (A717) to the task of estimating parameters in ODEs. We evaluate their performance on the considered representative task along a number of metrics, including the quality of reconstructing the system output and the complete dynamics, as well as the speed of convergence, both on real-experimental data and on artificial pseudo-experimental data with varying amounts of noise. We compare the four optimization methods under a range of observation scenarios, where data of different completeness and accuracy of interpretation are given as input.</p> <p>Conclusions</p> <p>Overall, the global meta-heuristic methods (DASA, PSO, and DE) clearly and significantly outperform the local derivative-based method (A717). Among the three meta-heuristics, differential evolution (DE) performs best in terms of the objective function, i.e., reconstructing the output, and in terms of convergence. These results hold for both real and artificial data, for all observability scenarios considered, and for all amounts of noise added to the artificial data. In sum, the meta-heuristic methods considered are suitable for estimating the parameters in the ODE model of the dynamics of endocytosis under a range of conditions: With the model and conditions being representative of parameter estimation tasks in ODE models of biochemical systems, our results clearly highlight the promise of bio-inspired meta-heuristic methods for parameter estimation in dynamic system models within system biology.</p

Increase in colonic PRopionate as a method of prEVENTing weight gain in adults aged 20–40 years (iPREVENT): a multi-centre, double-blind, randomised, parallel-group study to investigate the efficacy of inulin-propionate ester versus inulin (control) in the prevention of weight gain over 12 months

Introduction: Overweight and obesity affects over 70% of the UK population and is a major risk factor for the development of co-morbidities, including type 2 diabetes and cardiovascular disease. There now exists a considerable evidence base for the management of obesity. However, this is not the case for the prevention of obesity. Preventing weight gain in periods of life where there is an elevated risk of fat mass expansion could be beneficial to preventing associated diseases in later life. This protocol investigates the impact of novel food ingredient inulin propionate ester (IPE) in the prevention of weight gain. This trial aims to investigate the primary hypothesis that IPE has a superior effect on preventing body weight gain, compared with inulin, in young (&lt;40 years old) adults over 12 months, whilst also investigating several complementary mechanisms that may explain the prevention of weight gain and improved long-term energy balance from consuming IPE. Methods: In this multi-centre, double-blind, randomised, parallel-group study, eligible participants will be randomly assigned to consume 10g IPE or 10g inulin (control) daily for 12 months. Study visits will be conducted at baseline, two-month, six-month and 12-month time points. The primary outcome is weight gain from baseline to 12 months. Secondary outcomes will examine changes in metabolic and cardiovascular health biomarkers, body composition and appetite. A mechanistic sub-group will explore causal mechanisms around energy balance, body composition, appetite regulation and the gut microbiota. Based on the power calculation, the sample size required is 270 participants or 135 per study group. Ethics and dissemination: The trial protocol and participant-facing documents have been reviewed and approved, by the London Hampstead Ethics Committee (REC Reference 19/LO/0095, 29th January 2019). Upon completion, the trial results will be published in peer-reviewed journals and presented at scientific conferences. Trial registration number: ISRCTN16299902, 1st March 2018