Open course ware

PROFESSOR: OK, well let's move on then, and just talk about the amino acids. Amino acids side chains. And you won't have to memorize these structures. We will give you a chart if you have a problem. On the other hand, you need to get very familiar with them, so they're old friends even if you can't quite remember how many methylene are in a chain, or something like that. And you will find that they fall into certain categories. And I'm just going to try and give you examples of the major categories. There are negatively charged side chains. An example would be amino acids known as aspartate, or Asp, in which the side chain which corresponds to the R1 or to the R2 over there, has a methylene group, and then a carboxyl group. But at pH sevenish, which is the pH that you find inside a cell, that carboxyl group would be deprotonated so it would have a negative charge. The other negatively charged amino acid is glutamate, which also, as you'll see has a...

SPEAKER: I'd like to move now to a special announcement and bring back President Susan Hockfield, who will describe the next chapter, I would say, in the unfolding of the OpenCourseWare movement. SUSAN HOCKFIELD: Yes. [CHUCKLES] You don't have to sit here. You may move back to-- [LAUGHTER] Thank you all. That was a great panel. Thank you. Thank you, audience participants also. This is open sourcing of information. You all participate. So I get the great fun of today, which is announcing the next chapter in OpenCourseWare. Now as you have probably gathered of the [INAUDIBLE] today, if you all weren't there at the very beginning, that OpenCourseWare was designed really for university and college faculty and their students. And the idea was to share what we do on this campus with what happens on other campuses. And it has been successful, I would say, probably beyond almost anyone's wildest, wildest dreams, depending on how you count visits to the OpenCourseW...

We now move to a new topic-- conditioning. Every probabilistic concept or probabilistic fact has a conditional counterpart. As we have seen before, we can start with a probabilistic model and some initial probabilities. But then if we are told that the certain event has occurred, we can revise our model and consider conditional probabilities that take into account the available information. But as a consequence, the probabilities associated with any given random variable will also have to be revised. So a PMF will have to be changed to a conditional PMF. Let us see what is involved. Consider a random variable X with some given PMF, whose values, of course, sum to 1, as must be true for any valid PMF. We are then told that a certain event, A, has occurred. In that case, the event that X is equal to-- little x-- will now have a conditional probability of this form. We will use this notation here to denote the conditional probability that the random variable takes the value ...

HAZEL SIVE: Let's move on with Biochemistry III. And we have, as you recall, being exploring the fascinating and complicated set of macromolecules inside a cell-- inside the cell that we're using, in a way, analogous to a factory that is a production line for synthesis of various components that are required for function of the cell, for its replication, and for perpetuation of the organism. Today I want to talk to you about some parameters that surround the chemical reactions of biochemistry. We'll talk briefly about thermodynamics and energy considerations. And then I want to introduce you to a huge and pivotal class of proteins, the enzymes. And then we'll talk very briefly about the currency of energy in the cell. Let's start with thermodynamics, which we will define as the rules underlying energy usage in chemical reactions. Rules underlying energy use. And really, it's the first law of thermodynamics that we're interested in. We're in...