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Biology

Biology 2581B

Jim Karagiannis

Spring

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Genetics Lecture No. 1: DNA, Molecule Of Heredity & Information
th
Wednesday January 9 , 2013
Information & DNA:
-Consider a simple game, whereby we have an unknown (x) to be a number between 1 and
100. In this way, data can only be generated by asking is the number higher or lower than x. In
the first round we find that the number is higher than 10, this yields information as the number
now has to be in between 11 and 100. In the second round, we find that the number is higher
than 0, which yields no valid information as we already know that it is greater than 10
(remember that data does not necessarily equal information). In the third round we find that the
number is higher than 5, which like round 2, yields no valid information. In round four we find
that the number is higher than 50, which means that the number now has to be in between 51
and 100. From this simple game we can adequately define information as that which reduces
uncertainty.
-Consider another scenario which includes four primary devices: Device #1, Device #2, Device
#3, and Device #4. Device # 1, which produces one letter(A), has zero uncertainty and thus
conveys no information (a message is only conveyed when there is some uncertainty present).
Device #2, which produces 2 letters (A, B), has 2 symbols of uncertainty and conveys a
message. Device #3, which produces 4 letters (A, B, C, D), has 4 symbols of uncertainty and
conveys a message. Device #4, which produces one of 2 letters (A, B) and one of 4 numbers (1,
2, 3, 4), has 8 symbols of uncertainty (combination of Devices #2 and #3) and conveys a
message. Increasing complexity allows you to encode more information in the same amount of
information.
Quantitating Information:
-We can calculate uncertainty by using the following formula: Uncertainty = log (M) whe2e M is
the # of possible symbols in a given alphabet. As an example the calculated uncertainty for
Device #3 would equal log (4)2= 2 bits. Note that a bit is a unit of information for a log base of 2,
likewise for a digit and a nat (which are for log bases of 10 and e respectively). Using the same
example, the amount of information that Device #3 could generate also depends on the length
of the sequence. Therefore the formula can be modified to: Maximum information content of any
sequence = L [log (M2] where L is the length of the sequence. The longer the sequence, the
more information it is likely to contain.
-For DNA, we know that there are four possible symbols used (A, C, G, T), which gives us an
uncertainty of log 24) = 2 bits. If we take a gene like insulin (1789 base-pairs in length), we find it
to contain 1789 [log (2)] = 3578 bits of information. Note that these calculations assume that
each symbol has an equal chance (0.25) of appearing in the sequence. Remember to use the
formula log X = log X / log b as calculators use a logarithmic base of 10 instead of 2. E.g. log 5
b a a 2
= log10 / log 10 The Reason For Using Bits:
-We use bits for quantifying the information contained in DNA because if one were to
unambiguously convert a DNA sequence into a string of 1’s and 0’s (binary code), you would
need on average 2 bits/symbol. Remember to use a 2 bits sequence though, as a 1 bit/symbol
sequence is ambiguous (represents more than one possible sequence).
The Experiments Proving DNAAs Carrying Biological Information:
-One of the first experiments to prove experimentally the capacity of a DNA sequence to store
information was Fred Griffith’s experiment on two forms of experiment on Streptococcus
pneumonia: smooth colony (wild type) and rough colony (mutant). S bacteria are virulent and
can cause lethal infections when injected into mice. Injections of R mutants by themselves do
not cause infections that kill mice. Similarly, injections of heat-killed S bacteria do not cause
lethal infections. Lethal infection does result, however, from injections of live R bacteria mixed
with heat-killed S

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