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Williams B, Paterson J, Rawsthorne-Manning HJ, Jeffrey PA, Gillard JJ, Lythe G, Laws TR, López-García M. Quantifying in vitro B. anthracis growth and PA production and decay: a mathematical modelling approach. NPJ Syst Biol Appl 2024; 10:33. [PMID: 38553532 PMCID: PMC10980772 DOI: 10.1038/s41540-024-00357-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 03/05/2024] [Indexed: 04/02/2024] Open
Abstract
Protective antigen (PA) is a protein produced by Bacillus anthracis. It forms part of the anthrax toxin and is a key immunogen in US and UK anthrax vaccines. In this study, we have conducted experiments to quantify PA in the supernatants of cultures of B. anthracis Sterne strain, which is the strain used in the manufacture of the UK anthrax vaccine. Then, for the first time, we quantify PA production and degradation via mathematical modelling and Bayesian statistical techniques, making use of this new experimental data as well as two other independent published data sets. We propose a single mathematical model, in terms of delay differential equations (DDEs), which can explain the in vitro dynamics of all three data sets. Since we did not heat activate the B. anthracis spores prior to inoculation, germination occurred much slower in our experiments, allowing us to calibrate two additional parameters with respect to the other data sets. Our model is able to distinguish between natural PA decay and that triggered by bacteria via proteases. There is promising consistency between the different independent data sets for most of the parameter estimates. The quantitative characterisation of B. anthracis PA production and degradation obtained here will contribute towards the ambition to include a realistic description of toxin dynamics, the host immune response, and anti-toxin treatments in future mechanistic models of anthrax infection.
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Affiliation(s)
- Bevelynn Williams
- Department of Applied Mathematics, School of Mathematics, University of Leeds, Leeds, UK
| | - Jamie Paterson
- Department of Applied Mathematics, School of Mathematics, University of Leeds, Leeds, UK
| | | | - Polly-Anne Jeffrey
- Department of Applied Mathematics, School of Mathematics, University of Leeds, Leeds, UK
| | - Joseph J Gillard
- CBR Division, Defence Science and Technology Laboratory, Salisbury, UK
| | - Grant Lythe
- Department of Applied Mathematics, School of Mathematics, University of Leeds, Leeds, UK
| | - Thomas R Laws
- CBR Division, Defence Science and Technology Laboratory, Salisbury, UK
| | - Martín López-García
- Department of Applied Mathematics, School of Mathematics, University of Leeds, Leeds, UK.
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2
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Iglesias CF, Ristovski M, Bolic M, Cuperlovic-Culf M. rAAV Manufacturing: The Challenges of Soft Sensing during Upstream Processing. Bioengineering (Basel) 2023; 10:bioengineering10020229. [PMID: 36829723 PMCID: PMC9951952 DOI: 10.3390/bioengineering10020229] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 01/31/2023] [Accepted: 02/02/2023] [Indexed: 02/11/2023] Open
Abstract
Recombinant adeno-associated virus (rAAV) is the most effective viral vector technology for directly translating the genomic revolution into medicinal therapies. However, the manufacturing of rAAV viral vectors remains challenging in the upstream processing with low rAAV yield in large-scale production and high cost, limiting the generalization of rAAV-based treatments. This situation can be improved by real-time monitoring of critical process parameters (CPP) that affect critical quality attributes (CQA). To achieve this aim, soft sensing combined with predictive modeling is an important strategy that can be used for optimizing the upstream process of rAAV production by monitoring critical process variables in real time. However, the development of soft sensors for rAAV production as a fast and low-cost monitoring approach is not an easy task. This review article describes four challenges and critically discusses the possible solutions that can enable the application of soft sensors for rAAV production monitoring. The challenges from a data scientist's perspective are (i) a predictor variable (soft-sensor inputs) set without AAV viral titer, (ii) multi-step forecasting, (iii) multiple process phases, and (iv) soft-sensor development composed of the mechanistic model.
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Affiliation(s)
| | - Milica Ristovski
- Faculty of Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada
- Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Miodrag Bolic
- Faculty of Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | - Miroslava Cuperlovic-Culf
- Digital Technologies Research Center, National Research Council, Ottawa, ON K1A 0R6, Canada
- Department of Biochemistry, Microbiology, and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
- Correspondence:
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3
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Zanca A, Osborne JM, Zaloumis SG, Weller CD, Flegg JA. How quickly does a wound heal? Bayesian calibration of a mathematical model of venous leg ulcer healing. MATHEMATICAL MEDICINE AND BIOLOGY : A JOURNAL OF THE IMA 2022; 39:313-331. [PMID: 35698448 DOI: 10.1093/imammb/dqac007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Revised: 03/27/2022] [Accepted: 05/14/2022] [Indexed: 01/01/2023]
Abstract
Chronic wounds, such as venous leg ulcers, are difficult to treat and can reduce the quality of life for patients. Clinical trials have been conducted to identify the most effective venous leg ulcer treatments and the clinical factors that may indicate whether a wound will successfully heal. More recently, mathematical modelling has been used to gain insight into biological factors that may affect treatment success but are difficult to measure clinically, such as the rate of oxygen flow into wounded tissue. In this work, we calibrate an existing mathematical model using a Bayesian approach with clinical data for individual patients to explore which clinical factors may impact the rate of wound healing for individuals. Although the model describes group-level behaviour well, it is not able to capture individual-level responses in all cases. From the individual-level analysis, we propose distributions for coefficients of clinical factors in a linear regression model, but ultimately find that it is difficult to draw conclusions about which factors lead to faster wound healing based on the existing model and data. This work highlights the challenges of using Bayesian methods to calibrate partial differential equation models to individual patient clinical data. However, the methods used in this work may be modified and extended to calibrate spatiotemporal mathematical models to multiple data sets, such as clinical trials with several patients, to extract additional information from the model and answer outstanding biological questions.
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Affiliation(s)
- Adriana Zanca
- School of Mathematics and Statistics, University of Melbourne, Parkville, 3010, Victoria, Australia
| | - James M Osborne
- School of Mathematics and Statistics, University of Melbourne, Parkville, 3010, Victoria, Australia
| | - Sophie G Zaloumis
- School of Population and Global Health, University of Melbourne, Parkville, 3010, Victoria, Australia
| | - Carolina D Weller
- School of Nursing and Midwifery, Monash University, Clayton, 3800, Victoria, Australia
| | - Jennifer A Flegg
- School of Mathematics and Statistics, University of Melbourne, Parkville, 3010, Victoria, Australia
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4
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McCain JSP, Tagliabue A, Susko E, Achterberg EP, Allen AE, Bertrand EM. Cellular costs underpin micronutrient limitation in phytoplankton. SCIENCE ADVANCES 2021; 7:7/32/eabg6501. [PMID: 34362734 PMCID: PMC8346223 DOI: 10.1126/sciadv.abg6501] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 06/22/2021] [Indexed: 05/08/2023]
Abstract
Micronutrients control phytoplankton growth in the ocean, influencing carbon export and fisheries. It is currently unclear how micronutrient scarcity affects cellular processes and how interdependence across micronutrients arises. We show that proximate causes of micronutrient growth limitation and interdependence are governed by cumulative cellular costs of acquiring and using micronutrients. Using a mechanistic proteomic allocation model of a polar diatom focused on iron and manganese, we demonstrate how cellular processes fundamentally underpin micronutrient limitation, and how they interact and compensate for each other to shape cellular elemental stoichiometry and resource interdependence. We coupled our model with metaproteomic and environmental data, yielding an approach for estimating biogeochemical metrics, including taxon-specific growth rates. Our results show that cumulative cellular costs govern how environmental conditions modify phytoplankton growth.
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Affiliation(s)
- J Scott P McCain
- Department of Biology, Dalhousie University, Halifax, Nova Scotia, Canada.
- Centre for Comparative Genomics and Evolutionary Bioinformatics, Dalhousie University, Halifax, Nova Scotia, Canada
| | | | - Edward Susko
- Centre for Comparative Genomics and Evolutionary Bioinformatics, Dalhousie University, Halifax, Nova Scotia, Canada
- Department of Mathematics and Statistics, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Eric P Achterberg
- GEOMAR Helmholtz Center for Ocean Research Kiel, Wischhofstrasse 1-3, 24148 Kiel, Germany
| | - Andrew E Allen
- Microbial and Environmental Genomics, J. Craig Venter Institute, La Jolla, CA 92037, USA
- Integrative Oceanography Division, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA 92037, USA
| | - Erin M Bertrand
- Department of Biology, Dalhousie University, Halifax, Nova Scotia, Canada.
- Centre for Comparative Genomics and Evolutionary Bioinformatics, Dalhousie University, Halifax, Nova Scotia, Canada
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Chen Y, Cheng J, Gupta A, Huang H, Xu S. Numerical method for parameter inference of systems of nonlinear ordinary differential equations with partial observations. ROYAL SOCIETY OPEN SCIENCE 2021; 8:210171. [PMID: 34350015 PMCID: PMC8316824 DOI: 10.1098/rsos.210171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 07/06/2021] [Indexed: 06/13/2023]
Abstract
Parameter inference of dynamical systems is a challenging task faced by many researchers and practitioners across various fields. In many applications, it is common that only limited variables are observable. In this paper, we propose a method for parameter inference of a system of nonlinear coupled ordinary differential equations with partial observations. Our method combines fast Gaussian process-based gradient matching and deterministic optimization algorithms. By using initial values obtained by Bayesian steps with low sampling numbers, our deterministic optimization algorithm is both accurate, robust and efficient with partial observations and large noise.
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Affiliation(s)
- Yu Chen
- School of Mathematics, Shanghai University of Finance and Economics, Shanghai, People’s Republic of China
- Centre for Quantitative Analysis and Modeling (CQAM), The Fields Institute for Research in Mathematical Sciences, 222 College Street, Toronto, Ontario, Canada
| | - Jin Cheng
- School of Mathematics, Shanghai University of Finance and Economics, Shanghai, People’s Republic of China
- School of Mathematical Sciences, Fudan University, Shanghai 200433, People’s Republic of China
| | - Arvind Gupta
- Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Huaxiong Huang
- Centre for Quantitative Analysis and Modeling (CQAM), The Fields Institute for Research in Mathematical Sciences, 222 College Street, Toronto, Ontario, Canada
- Computer Science, University of Toronto, Toronto, Ontario, Canada
- Joint Mathematical Research Centre of Beijing Normal University and BNU-HKBU United International College, Zhuhai, People’s Republic of China
- Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada
| | - Shixin Xu
- Duke Kunshan University, 8 Duke Ave, Kunshan, Jiangsu, People’s Republic of China
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Xing WW, Cheng M, Cheng K, Zhang W, Wang P. InfPolyn, a Nonparametric Bayesian Characterization for Composition-Dependent Interdiffusion Coefficients. MATERIALS 2021; 14:ma14133635. [PMID: 34209855 PMCID: PMC8269731 DOI: 10.3390/ma14133635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 06/18/2021] [Accepted: 06/23/2021] [Indexed: 11/16/2022]
Abstract
Composition-dependent interdiffusion coefficients are key parameters in many physical processes. However, finding such coefficients for a system with few components is challenging due to the underdetermination of the governing diffusion equations, the lack of data in practice, and the unknown parametric form of the interdiffusion coefficients. In this work, we propose InfPolyn, Infinite Polynomial, a novel statistical framework to characterize the component-dependent interdiffusion coefficients. Our model is a generalization of the commonly used polynomial fitting method with extended model capacity and flexibility and it is combined with the numerical inversion-based Boltzmann-Matano method for the interdiffusion coefficient estimations. We assess InfPolyn on ternary and quaternary systems with predefined polynomial, exponential, and sinusoidal interdiffusion coefficients. The experiments show that InfPolyn outperforms the competitors, the SOTA numerical inversion-based Boltzmann-Matano methods, with a large margin in terms of relative error (10× more accurate). Its performance is also consistent and stable, whereas the number of samples required remains small.
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Affiliation(s)
- Wei W. Xing
- School of Integrated Circuit Science and Engineering, Beihang University, Beijing 100191, China;
| | - Ming Cheng
- Department of Civil and Environmental Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801-2352, USA;
| | - Kaiming Cheng
- Shandong Key Laboratory for High Strength Lightweight Metallic Materials, Advanced Materials Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
- Correspondence: (K.C.); (P.W.)
| | - Wei Zhang
- Sino-French Engineer School, Beihang University, Beijing 100191, China;
| | - Peng Wang
- School of Integrated Circuit Science and Engineering, Beihang University, Beijing 100191, China;
- Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing 100191, China
- Correspondence: (K.C.); (P.W.)
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7
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Xiao Y, Thomas L, Chaplain MAJ. Calibrating models of cancer invasion: parameter estimation using approximate Bayesian computation and gradient matching. ROYAL SOCIETY OPEN SCIENCE 2021; 8:202237. [PMID: 34150312 PMCID: PMC8206694 DOI: 10.1098/rsos.202237] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 06/01/2021] [Indexed: 06/12/2023]
Abstract
We present two different methods to estimate parameters within a partial differential equation model of cancer invasion. The model describes the spatio-temporal evolution of three variables-tumour cell density, extracellular matrix density and matrix degrading enzyme concentration-in a one-dimensional tissue domain. The first method is a likelihood-free approach associated with approximate Bayesian computation; the second is a two-stage gradient matching method based on smoothing the data with a generalized additive model (GAM) and matching gradients from the GAM to those from the model. Both methods performed well on simulated data. To increase realism, additionally we tested the gradient matching scheme with simulated measurement error and found that the ability to estimate some model parameters deteriorated rapidly as measurement error increased.
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Affiliation(s)
- Yunchen Xiao
- School of Mathematics and Statistics, University of St Andrews, St Andrews, KY16 9SS UK
| | - Len Thomas
- School of Mathematics and Statistics, University of St Andrews, St Andrews, KY16 9SS UK
| | - Mark A. J. Chaplain
- School of Mathematics and Statistics, University of St Andrews, St Andrews, KY16 9SS UK
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