Conference paper Open Access

Quantification of loading effects in interconnections of stochastic reaction networks

Gupta, Ankit; Dürrenberger, Patrik; Khammash, Mustafa


DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="URL">https://zenodo.org/record/4836216</identifier>
  <creators>
    <creator>
      <creatorName>Gupta, Ankit</creatorName>
      <givenName>Ankit</givenName>
      <familyName>Gupta</familyName>
      <affiliation>Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland</affiliation>
    </creator>
    <creator>
      <creatorName>Dürrenberger, Patrik</creatorName>
      <givenName>Patrik</givenName>
      <familyName>Dürrenberger</familyName>
      <affiliation>Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland</affiliation>
    </creator>
    <creator>
      <creatorName>Khammash, Mustafa</creatorName>
      <givenName>Mustafa</givenName>
      <familyName>Khammash</familyName>
      <affiliation>Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Quantification of loading effects in interconnections of stochastic reaction networks</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <dates>
    <date dateType="Issued">2019-08-15</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/4836216</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.3929/ethz-b-000363981</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/cosy-bio</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Abstract&lt;/p&gt;

&lt;p&gt;Modular design of networks in synthetic biology is highly desirable but difficult to achieve due to loading effects that change the properties of upstream modules upon connection with downstream networks. Precise quantification of these loading effects would allow us to predict the behavior of large interconnected networks more accurately, and enable us to systematically identify insulator circuits that can help in achieving modularity. Most of the existing results on this topic apply only in the deterministic setting and hence they do not account for the stochastic nature of biomolecular interactions. In this work we propose a novel sensitivity-based metric for quantifying loading effects in the stochastic setting. We discuss how this metric can be efficiently computed for stochastic reaction dynamics and demonstrate its usefulness in rational design of insulator circuits.&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;</description>
    <description descriptionType="Other">This is the preprint of the conference paper published in "2019 18th European Control Conference (ECC)"</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/766840/">766840</awardNumber>
      <awardTitle>Control Engineering of Biological Systems for Reliable Synthetic Biology Applications</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
2
3
views
downloads
Views 2
Downloads 3
Data volume 2.8 MB
Unique views 2
Unique downloads 3

Share

Cite as