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Vaccines01:21

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Vaccines are among the most effective tools in preventive medicine, designed to prepare the immune system to recognize and combat infectious agents. By introducing antigens—substances that the immune system identifies as foreign—vaccines stimulate an adaptive immune response that leads to immunological memory. This immunological memory enables the body to mount a faster and more effective response upon future exposures to the actual pathogen.Vaccines can be categorized based on the...
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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
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Vaccine Production01:23

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Vaccine production involves a sequence of upstream and downstream processes to generate a safe and effective immunological product. It begins with cultivating microorganisms, such as viruses or bacteria, to obtain antigenic material. For viral vaccines, mammalian host cells are grown in bioreactors and subsequently infected with the target virus. The virus replicates within the host cells, which are lysed to release viral particles. This lysate is then clarified through filtration or...
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Use of an Influenza Antigen Microarray to Measure the Breadth of Serum Antibodies Across Virus Subtypes
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Evolutionary non-linear modelling for selecting vaccines against antigenically variable viruses.

Tameera Rahman1, Mana Mahapatra1, Emma Laing1

  • 1Department of Computing, University of Surrey, Guildford GU2 7XH, UK, The Pirbright Institute, Pirbright GU24 0NF, UK and Department of Microbial and Cellular Sciences, University of Surrey, Guildford GU2 7XH, UK.

Bioinformatics (Oxford, England)
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Developing computational models to predict virus variants is crucial for effective vaccine design. This study uses regression models and evolutionary algorithms to predict antigenic similarity in foot-and-mouth disease and influenza viruses, improving broad-spectrum vaccine development.

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Area of Science:

  • Computational virology and vaccinology
  • Bioinformatics and computational biology
  • Machine learning in infectious disease research

Background:

  • Traditional vaccine selection methods are costly and time-consuming, often failing against novel viral strains.
  • Predicting antigenic variant strains computationally can reduce resource expenditure on serological testing.
  • Developing in silico methods for predicting vaccine efficacy against diverse viral strains remains a significant challenge.

Purpose of the Study:

  • To investigate the efficacy of linear and non-linear regression models for predicting antigenic similarity in viral strains.
  • To optimize computational models using evolutionary algorithms (EA) for enhanced prediction accuracy.
  • To evaluate different scoring methods for amino acid substitutions in predicting viral antigenic drift.

Main Methods:

  • Application of linear and non-linear regression models to predict antigenic similarity in foot-and-mouth disease virus (FMDV) and influenza strains.
  • Optimization of non-linear model parameters using an evolutionary algorithm (EA).
  • Evaluation of two scoring methods for amino acid substitutions, focusing on biochemical properties and antigenic regions.

Main Results:

  • Non-linear regression models combined with biochemical property scoring achieved the best prediction results across FMDV (SAT2, serotype A) and influenza datasets.
  • Models focusing on substitutions within antigenic areas outperformed those considering entire viral capsid proteins.
  • EA-optimized non-linear models demonstrated superior performance compared to least-squares methods, indicating the non-linear influence of amino acid substitutions.

Conclusions:

  • Computational models, particularly EA-optimized non-linear regression, can effectively predict antigenic similarity in viral strains like FMDV and influenza.
  • Biochemical properties of amino acid substitutions are critical factors in predicting antigenic variation.
  • The developed models show promise for facilitating the creation of broad-spectrum vaccines and achieving high agreement rates on new datasets.