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	<title>Physics 434, 2012: Lecture 10 - Revision history</title>
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	<updated>2026-05-17T09:39:45Z</updated>
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		<title>Ilya: 1 revision imported</title>
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		<updated>2018-07-04T16:28:42Z</updated>

		<summary type="html">&lt;p&gt;1 revision imported&lt;/p&gt;
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		<author><name>Ilya</name></author>
		
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		<id>https://nemenmanlab.org/~ilya/index.php?title=Physics_434,_2012:_Lecture_10&amp;diff=436&amp;oldid=prev</id>
		<title>nemenman&gt;Ilya at 01:51, 4 October 2012</title>
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		<updated>2012-10-04T01:51:39Z</updated>

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&lt;p&gt;&lt;b&gt;New page&lt;/b&gt;&lt;/p&gt;&lt;div&gt;{{PHYS434-2012}}&lt;br /&gt;
&lt;br /&gt;
We are wrapping up all the loose ends for the probability/random walks section of the class. We will return to some of the related questions in the later sections, of course.&lt;br /&gt;
During this lecture, we also started the new block on information theory, [[Physics 434, 2012: Lectures 10-11]].&lt;br /&gt;
&lt;br /&gt;
===Main Lecture===&lt;br /&gt;
*Langevin equation. If a chemical species is produced in a reaction &amp;lt;math&amp;gt;F&amp;lt;/math&amp;gt; and degraded in a reaction &amp;lt;math&amp;gt;G&amp;lt;/math&amp;gt;, and all of reactions are independent, then the mean number of produced particles per time &amp;lt;math&amp;gt;t&amp;lt;/math&amp;gt; is &amp;lt;math&amp;gt;(F-G)\Delta t&amp;lt;/math&amp;gt; and the variance is &amp;lt;math&amp;gt;(F-G)\Delta t&amp;lt;/math&amp;gt;. If the number of production and degradation events is large, &amp;lt;math&amp;gt;F,G\gg 1&amp;lt;/math&amp;gt;, then these terms can be approximated as Gaussians. We can, therefore, write &amp;lt;math&amp;gt;x(t+\Delta t)=x(t)+\Delta x=x(t) + (F-G)\Delta t+\sqrt{(F+G)\Delta t}\eta&amp;lt;/math&amp;gt;, where &amp;lt;math&amp;gt;\eta\sim N(0,1)&amp;lt;/math&amp;gt;.&lt;br /&gt;
**Notice that the noise term scales as &amp;lt;math&amp;gt;\sqrt{\Delta t}&amp;lt;/math&amp;gt; and is larger than the deterministic term for small &amp;lt;math&amp;gt;\Delta t&amp;lt;/math&amp;gt;&lt;br /&gt;
**We can transform this into a (stochastic) differential equation by taking a limit &amp;lt;math&amp;gt;\Delta t\to0&amp;lt;/math&amp;gt;, &amp;lt;math&amp;gt;\frac{dx}{dt}=F-G+\sqrt{F+G}\eta&amp;lt;/math&amp;gt;, where &amp;lt;math&amp;gt;\langle \eta\rangle =0&amp;lt;/math&amp;gt;, and &amp;lt;math&amp;gt;\langle \eta(t)\eta(t')\rangle = \delta(t-t')&amp;lt;/math&amp;gt;. Such &amp;lt;math&amp;gt;\eta&amp;lt;/math&amp;gt; is called a Wiener process, after [http://en.wikipedia.org/wiki/Norbert_Wiener Norbert Wiener], or white noise -- we will understand why later.&lt;br /&gt;
*As the last item in this block of the class, we have talked about gradient sensing in another organism, ''D. discoideum''. It's a large bug, and so many of the constraints that ''E. coli'' had do not apply. We have introduced the Local Excitation -- Global Inhibition (LEGI) (Levchenko and Iglesias, 2002), which allows an organism to do a spatial, rather than a temporal  comparison of chemical concentrations. The question then is: does what we have discussed in this &amp;quot;probability&amp;quot; block of the class apply? We discussed that a comparison is made locally, by a small volume (about a size of a molecular complex), and hence the arrival of molecules is very stochastic. Even for a large organism, the noise may be quite important!&lt;/div&gt;</summary>
		<author><name>nemenman&gt;Ilya</name></author>
		
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