
      
      <p>In this episode, <a href="https://tolischal.github.io/">Apostolos Chalkis</a> presents sampling steady
states of metabolic networks as an alternative to the widely used flux balance
analysis (FBA). We also discuss <a href="https://github.com/GeomScale/dingo">dingo</a>, a
Python package written by Apostolos that employs geometric random walks to
sample steady states. You can see dingo in action
<a href="https://www.tweag.io/blog/2021-07-14-metabolic-networks/">here</a>.</p>


      

      
      <p>Links:</p>

<ul>
  <li><a href="https://github.com/GeomScale/dingo">Dingo on GitHub</a></li>
  <li><a href="https://tweag.io/blog/2021-07-14-metabolic-networks/">Searching for COVID-19 treatments using metabolic networks</a></li>
  <li><a href="https://www.tweag.io/blog/tags/fellowship/">Tweag open source fellowships</a></li>
  <li>This episode was <a href="https://www.compositional.fm/dingo-metabolic-networks">originally published</a> on the <a href="https://www.compositional.fm/">Compositional</a> podcast.</li>
</ul>


      
      

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#62 Steady states of metabolic networks and Dingo with Apostolos Chalkis

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