https://gilgi.org/enContents © 2020 <a href="mailto:site@gilgi.org">gilgi</a> Sat, 23 May 2020 20:33:04 GMTNikola (getnikola.com)http://blogs.law.harvard.edu/tech/rsshttps://gilgi.org/blog/biochemical-kinetics-steady-state-combinations/gilgi<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> </div><div class="inner_cell"> <div class="text_cell_render border-box-sizing rendered_html"> <p><img src="https://gilgi.org/images/blog/biochemical-kinetics-steady-state-combinations.png" alt=""></p> <p>In <a href="https://gilgi.org/blog/biochemical-kinetics-reaction-velocities">yesterday's post</a>, we did some simple fitting of a <a href="https://en.wikipedia.org/wiki/Enzyme_kinetics#Michaelis%E2%80%93Menten_kinetics">Michaelis-Menten enzyme kinetics model</a> for a single step of an isolated reaction. What happens when we have multiple reactions with multiple species involved occuring at the same time? Is it possible to infer something about the kinetic parameters of such a system by only looking at the steady-state concentrations of the species in the system under different experimental conditions? In this post, we'll apply some differential equations and simple optimization in Python to try to find out.</p> <p><a href="https://colab.research.google.com/github/gilgi/gilgi.github.com/blob/src/posts/biochemical_kinetics_steady_state_combinations.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></p> <p><a href="https://gilgi.org/blog/biochemical-kinetics-steady-state-combinations/">Read more…</a> (8 min remaining to read)</p></div></div></div>biochemistyenzyme kineticsnotebookoptimizationPythonhttps://gilgi.org/blog/biochemical-kinetics-steady-state-combinations/Fri, 12 Jan 2018 05:00:00 GMThttps://gilgi.org/blog/biochemical-kinetics-reaction-velocities/gilgi<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> </div><div class="inner_cell"> <div class="text_cell_render border-box-sizing rendered_html"> <p><img src="https://gilgi.org/images/blog/biochemical-kinetics-reaction-velocities.png" alt=""></p> <p>Biochem students will likely remember the mathematical beauty of <a href="https://en.wikipedia.org/wiki/Enzyme_kinetics#Michaelis%E2%80%93Menten_kinetics">enzyme kinetics models like the Michaelis-Menten model</a>. In this short post, we'll take a look at how we can fit this kind of model to experimental data in Python using some staightforward optimization.</p> <p><a href="https://colab.research.google.com/github/gilgi/gilgi.github.com/blob/src/posts/biochemical_kinetics_reaction_velocities.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></p> <p><a href="https://gilgi.org/blog/biochemical-kinetics-reaction-velocities/">Read more…</a> (2 min remaining to read)</p></div></div></div>biochemistyenzyme kineticsnotebookoptimizationPythonhttps://gilgi.org/blog/biochemical-kinetics-reaction-velocities/Thu, 11 Jan 2018 05:00:00 GMT