Journal article Open Access

From Infection to Immunity: Understanding the Response to SARS-CoV2 Through In-Silico Modeling

Filippo Castiglione; Debashrito Deb; Anurag P. Srivastava; Pietro Liò; Arcangelo Liso


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    <subfield code="a">in-silico modeling</subfield>
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    <subfield code="u">Department of Biochemistry, School of Applied Sciences, REVA University, Bangalore, India</subfield>
    <subfield code="a">Debashrito Deb</subfield>
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    <subfield code="u">Department of Life Sciences, Garden City University, Bangalore, India</subfield>
    <subfield code="a">Anurag P. Srivastava</subfield>
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    <subfield code="u">Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom</subfield>
    <subfield code="a">Pietro Liò</subfield>
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    <subfield code="u">Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy</subfield>
    <subfield code="a">Arcangelo Liso</subfield>
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    <subfield code="u">Institute for Applied Computing (IAC), National Research Council of Italy (CNR)</subfield>
    <subfield code="a">Filippo Castiglione</subfield>
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    <subfield code="a">From Infection to Immunity: Understanding the Response to SARS-CoV2 Through In-Silico Modeling</subfield>
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    <subfield code="a">&lt;p&gt;&lt;strong&gt;Background:&lt;/strong&gt;&amp;nbsp;Immune system conditions of the patient is a key factor in COVID-19 infection survival. A growing number of studies have focused on immunological determinants to develop better biomarkers for therapies.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Aim:&lt;/strong&gt;&amp;nbsp;Studies of the insurgence of immunity is at the core of both SARS-CoV-2 vaccine development and therapies. This paper attempts to describe the insurgence (and the span) of immunity in COVID-19 at the population level by developing an in-silico model. We simulate the immune response to SARS-CoV-2 and analyze the impact of infecting viral load, affinity to the ACE2 receptor, and age in an artificially infected population on the course of the disease.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Methods:&lt;/strong&gt;&amp;nbsp;We use a stochastic agent-based immune simulation platform to construct a virtual cohort of infected individuals with age-dependent varying degrees of immune competence. We use a parameter set to reproduce known inter-patient variability and general epidemiological statistics.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Results:&lt;/strong&gt;&amp;nbsp;By assuming the viremia at day 30 of the infection to be the proxy for lethality, we reproduce in-silico several clinical observations and identify critical factors in the statistical evolution of the infection. In particular, we evidence the importance of the humoral response over the cytotoxic response and find that the antibody titers measured after day 25 from the infection are a prognostic factor for determining the clinical outcome of the infection. Our modeling framework uses COVID-19 infection to demonstrate the actionable effectiveness of modeling the immune response at individual and population levels. The model developed can explain and interpret observed patterns of infection and makes verifiable temporal predictions. Within the limitations imposed by the simulated environment, this work proposes quantitatively that the great variability observed in the patient outcomes in real life can be the mere result of subtle variability in the infecting viral load and immune competence in the population. In this work, we exemplify how computational modeling of immune response provides an important view to discuss hypothesis and design new experiments, in particular paving the way to further investigations about the duration of vaccine-elicited immunity especially in the view of the blundering effect of immunosenescence.&lt;/p&gt;</subfield>
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    <subfield code="a">10.3389/fimmu.2021.646972</subfield>
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