Sex, viruses, and the statistical physics of evolution
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Cartoon of Evolution
2
Genotypes
Selection
Mutation & Recombination
Sex & Recombination Selection
...ATACG...
...ATGCG...
Mutation
AT
C G
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Tree of Life - billions of years
3
4 billion years
sour
ce: t
ree
of li
fe, t
olw
eb.o
rg
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Phylogenetic tree of Fruitflies - millions of years
4
~40 Millions of years image from: insects.eugenes.org/species/
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Evolution of Influenza A - few years
5
characterized by an unusually high number of amino acidchanges, reflecting the large evolutionary distance betweensections II and IV (19 amino acid changes) and sections VI andVIII (15 amino acid changes) (Table 1). In marked contrast, nosections of the HA phylogeny are merged, resulting in a tree inwhich evolutionary change is more evenly distributed across alleight trunk branches. Across the viral genome as a whole, thegreatest number of amino acid changes occurs along the maintrunk lineages of the HA tree (n = 63), followed by the NA tree(n = 55), strongly supporting the long-term action of immuneselection (antigenic drift) on these glycoproteins.The smallest number of amino acid changes occurs along
branch #4, which connects isolates from the 1950’s (section IV)with those from the 1970’s (section V). Thus, little A/H1N1
evolution is evident over the twenty-year period of the virus’sglobal disappearance [30], supporting earlier suggestions that thissubtype was most likely accidentally reintroduced into humancirculation from a laboratory environment [3,31]. Notably, ouranalysis indicates that the influenza viruses that re-emerged in the1970’s were more closely related in all gene segments to a group ofviruses sampled from the late 1940’s, in particular to isolate A/Roma/1949, supporting earlier serological and partial sequenceanalyses [16,30,32]
Multiple reassortment events within A/H1N1In general, most of the ten clades A–J fall within the same
topological section in each of the segment phylogenies. Forexample, on all eight phylogenies, clade A is positioned within
A/Canterbury/01/2001A/Canterbury/106/2004
A/Memphis/5/2003A/New York/8/2006
A/Otago/5/2005A/New York/223/2003
A/New York/291/2002A/Wellington/1/2001
A/Memphis/1/2001A/New York/239/2001
A/New Caledonia/20/1999A/New South Wales/24/1999
A/TW/4845/99A/New York/233/2000
A/Hong Kong/470/97A/TW/3355/97
A/Nanchang/13/1996A/Charlottesville/31/95A/Memphis/6/1996
A/New York/653/1996A/TW/130/96
A/New York/694/1995A/Hong Kong/427/98
A/New York/146/2000A/South Australia/44/2000
A/Texas/36/91A/Chile/1/83
A/Memphis/39/1983A/Christs Hospital/157/1982
A/Memphis/1/1984A/Tonga/14/1984A/New Zealand/7/1983
A/India/6263/1980A/Memphis/7/1980
A/Memphis/12/1986A/Memphis/3/1987
A/New York/2924-1/1986A/Taiwan/01/1986A/Texas/2922-3/1986
A/Singapore/6/1986A/Baylor/11515/82
A/Baylor/4052/1981A/Brazil/11/1978
A/Hong Kong/117/77A/Lackland/3/1978A/Memphis/10/1978A/USSR/90/1977
A/Arizona/14/1978A/Tientsin/78/1977
A/Roma/1949A/Albany/4835/1948A/Albany/4836/1950
A/Albany/12/1951A/Denver/57
A/Fort Worth/50A/Malaysia/54
A/FORT MONMOUTH/1/47A/Cam/46
A/Hickox/1940A/Weiss/43
A/AA/Huston/1945A/AA/Marton/1943
A/Iowa/1943A/Bel/42
A/Alaska/1935A/PR/8/34
A/Henry/1936A/Phila/1935A/Melbourne/35
A/WSN/1933A/Brevig Mission/1/1918
AB
CD
100 99
96
100
100
100100
E
F
G
100 100100
100
100
100 H
I
94 J
III
IIIIV
VI
VIIIIX
#1
#2
#3
#7
#8
#4
3 aa
2 aa
3 aa
1 aa
5 aa
1 aa
V
96
3 aa
#5
PB2
100
96
100
10082
98
100
100
100
100
99
100
Figure 1. Phylogenetic relationships of the PB2 gene of A/H1N1 influenza viruses (n=71) sampled globally from 1918–2006,estimated using a maximal likelihood (ML) method. All branch lengths are drawn to a scale of nucleotide substitutions per site. The tree isrooted using the oldest sequence, A/Brevig Mission/1/1918, and temporally ordered with the oldest isolates falling at the bottom left of thephylogeny and the newest isolates at the top right. Main phylogenetic sections that are separated by long trunk branches are numbered I–IX andcolored as follows: section I is red, section II is orange, section III is yellow, section IV is light green, section V is dark green, section VI is light blue,section VII is absent due to the position of clade G within section VI (would otherwise be blue), section VIII is purple, and section IX is gray. The trunkbranches separating these sections are labeled #1–#8 and highlighted in dark blue, with the bootstrap value supporting this branch highlighted inyellow and the number of amino acid changes that occur along each branch appearing above in red font. Ten main clades are labeled A–J, withsupporting bootstrap values in light blue, and are colored as follows: clade A is dark red, clade B is orange, clade C is yellow, clade D is olive green,clade E is light blue, clade F is turquoise, clade G is fuchsia, clade H is gray, clade I is pink, and clade J is white. Clades that have undergonereassortment are outlined in red (in this case Clade D).doi:10.1371/journal.ppat.1000012.g001
Evolution of H1N1 Influenza A Virus
PLoS Pathogens | www.plospathogens.org 3 2008 | Volume 4 | Issue 2 | e1000012
Influenza Aglobal epidemic
1918 2005
Nelson et al., 2008Evolution as cause of epidemics
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Human immunodeficiency virus (HIV)
6
HIV budding from an immune cell
100nm
Rapid evolution is a hallmark of HIV infections
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
HIV
7
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
HIV
7
Virus escapes the immune system by continuous evolution
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Evolution in a single patient (blood samples every 6 month)
8
Lemey et al., 2006, Shankarappa et al. 1999
AIDS Reviews 2006;8
126
Since HIV populations evolve at a rate that is several orders of magnitude faster than that of their human hosts, HIV sequences sampled longitudinally will usu-ally accumulate a significant amount of evolutionary change. Longitudinally sampled or “heterochronous”11 sequences can be obtained in one of two ways: either from a single patient over the course of infection, or from different patients over the duration of an epidemic (Fig. 1). Interestingly, phylogenies reconstructed from such sequences have distinctive features that reveal the differences in the dynamics of HIV evolution at the in-ter-host and intra-host levels12.
Within each host, the viral population is targeted by both cellular and humoral immune responses, resulting in relatively strong diversifying selection that is most noticeable in the variable regions of the envelope (env) gene. It has been demonstrated that the rate of amino acid substitution in env correlates with the rate of phe-notypic escape from neutralizing antibodies13. This im-
plies that neutralizing antibody responses cause the relative fitness of different strains within an infection to vary, thus constituting a major force that drives rapid lineage turnover. As a result, intra-host phylogenies of heterochronous env sequences exhibit an asymmetri-cal or “ladder-like” shape, with limited diversity at any one time (Fig. 1)12.
In contrast, HIV evolution at the inter-host level shows little evidence that HIV transmission is driven by a similar selective process14,15. Inter-host phylogenies of HIV sampled through time are not ladder-like and show the persistence of multiple lineages through time (Fig. 1)12. The shape of inter-host phylogenies is pri-marily determined by (selectively) neutral demographic processes12,15.
In summary, HIV lineages within a host vary in their ability to survive and infect new cells, whereas different HIV lineages among hosts show little genetic variation in their ability to infect new individuals. Some lineages
Figure 1. A: HIV-1 within-host phylogeny with branch lengths in time units: partial env gene longitudinally sampled from a single patient over 155 months (subtype B, 129 sequences, 516 bp; patient 9)34,53. B: HIV-1 population phylogeny with branch lengths in time units: full length env gene sampled during the U.S. HIV epidemic from 1981-1995 (subtype B, 39 sequences, 2396 bp)35. C: Root-to-tip divergence as a function of sampling time for the within-host phylogeny. The R2 value is indicated above the regression line. D: Root-to-tip divergence as a function of sampling time for the population phylogeny: divergence estimates for the full length and C2V5 env gene are shown with black squares and open diamonds respectively.
Time (months since seroconversion) Time (year)
Time (year)
Roo
t-to
-tip
div
erge
nce
(nuc
leot
ide
subs
titut
ions
/site
)
R2 = 0.67
R2 = 0.27
Roo
t-to
-tip
div
erge
nce
(nuc
leot
ide
subs
titut
ions
/site
)
R2 = 0.89
0 1 2 3 4 5 6 7 8-1-2 9 10 12 1311
20 40 60 800 100 140 160120
1985 1990 199519801975197019651960
1990 1992 19941988198619841982
Time (years since seroconversion)
0.200.180.160.140.120.100.080.060.04
0.14
0.12
0.10
0.08
0.06
0.04
0.02
0
A B
C D
~ 8% divergence in 10 years ==
many millions of years in Drosophila
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Evolution of HIV
9
Resistance to drugs (protease inhibitors):Drug sensitive: PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNFDrug resistant: PQITLWQRPLVTIKVGGQLTEALLDTGADDTILEDMTLPGRWKPKIVGGIGGFIKVRQYDQVPIEICGHKVISTVLIGPTPCNIIGRNLMTQIGLTLNF
The virus has to change: Escape the immune system and drug resistance
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Evolution of HIV
9
Resistance to drugs (protease inhibitors):Drug sensitive: PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNFDrug resistant: PQITLWQRPLVTIKVGGQLTEALLDTGADDTILEDMTLPGRWKPKIVGGIGGFIKVRQYDQVPIEICGHKVISTVLIGPTPCNIIGRNLMTQIGLTLNF
The virus has to change: Escape the immune system and drug resistance
High mutation rate: μ=3x10-5/generation and site
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Evolution of HIV
9
Resistance to drugs (protease inhibitors):Drug sensitive: PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNFDrug resistant: PQITLWQRPLVTIKVGGQLTEALLDTGADDTILEDMTLPGRWKPKIVGGIGGFIKVRQYDQVPIEICGHKVISTVLIGPTPCNIIGRNLMTQIGLTLNF
The virus has to change: Escape the immune system and drug resistance
High mutation rate: μ=3x10-5/generation and site
Large population:N=1010 viruses
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Evolution of HIV
9
Resistance to drugs (protease inhibitors):Drug sensitive: PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNFDrug resistant: PQITLWQRPLVTIKVGGQLTEALLDTGADDTILEDMTLPGRWKPKIVGGIGGFIKVRQYDQVPIEICGHKVISTVLIGPTPCNIIGRNLMTQIGLTLNF
The virus has to change: Escape the immune system and drug resistance
human immune cell
“Viral sex”
High mutation rate: μ=3x10-5/generation and site
Large population:N=1010 viruses
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Recombination in viruses - Influenza
10
• 11 genes on 8 segments
• 16 H (hemagglutinin) subtypes
• 9 N (neuraminidase) subtypes
• H1N1, H2N2, H3N2, H5N1 are common
100n
m
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Recombination in viruses - Influenza
10
• 11 genes on 8 segments
• 16 H (hemagglutinin) subtypes
• 9 N (neuraminidase) subtypes
• H1N1, H2N2, H3N2, H5N1 are common
100n
m
• Pandemics often follow reassortments, e.g. pandemics 1957 (H2N2) and 1968 (H3N2)
• Reassortment is frequent in waterfowl and swine, where many subtypes circulate.
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Spreading of beneficial mutations
11
Mutant individuals reproduce faster(Drug resistant strain)
Time
Popu
latio
n
Fixation probability: ~s Sweep time: ~ ln(Ns) / s
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Evolution in asexual and sexual organisms
12
E V O L U T I O N IN S E X U A L AND ASEXUAL POPUL.AT1ONS 441
LARGE POPULATION
SMALL POPULATION
FIGURE 1. Evolution i n sexua l and a s e x u a l populat ions. T h e ha tched and shaded a r e a s show the increased number of mutant individuals following the oc- currence of a favorable mutation. T h e a b s c i s s a I s time. Modified from Muller
(1932).
s i s small , and therefore that p changes very slowly s o tha t i t i s appro-
priate to replace addition by integration. T h i s l e a d s to
In the absence of recombination, p follows the logis t ic curve
Small PopulationSequential innovations:rate ~ N
Time
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Evolution in asexual and sexual organisms
12
E V O L U T I O N IN S E X U A L AND ASEXUAL POPUL.AT1ONS 441
LARGE POPULATION
SMALL POPULATION
FIGURE 1. Evolution i n sexua l and a s e x u a l populat ions. T h e ha tched and shaded a r e a s show the increased number of mutant individuals following the oc- currence of a favorable mutation. T h e a b s c i s s a I s time. Modified from Muller
(1932).
s i s small , and therefore that p changes very slowly s o tha t i t i s appro-
priate to replace addition by integration. T h i s l e a d s to
In the absence of recombination, p follows the logis t ic curve
Small PopulationSequential innovations:rate ~ N
E V O L U T I O N IN S E X U A L AND ASEXUAL POPUL.AT1ONS 441
LARGE POPULATION
SMALL POPULATION
FIGURE 1. Evolution i n sexua l and a s e x u a l populat ions. T h e ha tched and shaded a r e a s show the increased number of mutant individuals following the oc- currence of a favorable mutation. T h e a b s c i s s a I s time. Modified from Muller
(1932).
s i s small , and therefore that p changes very slowly s o tha t i t i s appro-
priate to replace addition by integration. T h i s l e a d s to
In the absence of recombination, p follows the logis t ic curve
Many concurrent mutations:rate ~ log N
Most good mutations are wasted!
Large Population
Time
Time
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Evolution in asexual and sexual organisms
12
E V O L U T I O N IN S E X U A L AND ASEXUAL POPUL.AT1ONS 441
LARGE POPULATION
SMALL POPULATION
FIGURE 1. Evolution i n sexua l and a s e x u a l populat ions. T h e ha tched and shaded a r e a s show the increased number of mutant individuals following the oc- currence of a favorable mutation. T h e a b s c i s s a I s time. Modified from Muller
(1932).
s i s small , and therefore that p changes very slowly s o tha t i t i s appro-
priate to replace addition by integration. T h i s l e a d s to
In the absence of recombination, p follows the logis t ic curve
Small PopulationSequential innovations:rate ~ N
E V O L U T I O N IN S E X U A L AND ASEXUAL POPUL.AT1ONS 441
LARGE POPULATION
SMALL POPULATION
FIGURE 1. Evolution i n sexua l and a s e x u a l populat ions. T h e ha tched and shaded a r e a s show the increased number of mutant individuals following the oc- currence of a favorable mutation. T h e a b s c i s s a I s time. Modified from Muller
(1932).
s i s small , and therefore that p changes very slowly s o tha t i t i s appro-
priate to replace addition by integration. T h i s l e a d s to
In the absence of recombination, p follows the logis t ic curve
Time
Conventional wisdom:rate ~ N
RA Fisher (1930), H Muller (1932), M Kimura and J Crow (1965)
E V O L U T I O N IN S E X U A L AND ASEXUAL POPUL.AT1ONS 441
LARGE POPULATION
SMALL POPULATION
FIGURE 1. Evolution i n sexua l and a s e x u a l populat ions. T h e ha tched and shaded a r e a s show the increased number of mutant individuals following the oc- currence of a favorable mutation. T h e a b s c i s s a I s time. Modified from Muller
(1932).
s i s small , and therefore that p changes very slowly s o tha t i t i s appro-
priate to replace addition by integration. T h i s l e a d s to
In the absence of recombination, p follows the logis t ic curve
Many concurrent mutations:rate ~ log N
Most good mutations are wasted!
Large Population
Time
Time
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Quantifying the benefits of recombination [for the virus]
13
Example: drug resistance of HIV Drug sensitive: PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNFDrug resistant: PQITLWQRPLVTIKVGGQLTEALLDTGADDTILEDMTLPGRWKPKIVGGIGGFIKVRQYDQVPIEICGHKVISTVLIGPTPCNIIGRNLMTQIGLTLNF
Hypothetical distribution of drug resistance mutations
0 1 2 3 4 5 Resistance mutations
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Quantifying the benefits of recombination [for the virus]
13
Example: drug resistance of HIV Drug sensitive: PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNFDrug resistant: PQITLWQRPLVTIKVGGQLTEALLDTGADDTILEDMTLPGRWKPKIVGGIGGFIKVRQYDQVPIEICGHKVISTVLIGPTPCNIIGRNLMTQIGLTLNF
Hypothetical distribution of drug resistance mutations • Drug resistance increases due to
selection on existing mutations
0 1 2 3 4 5 Resistance mutations
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Quantifying the benefits of recombination [for the virus]
13
Example: drug resistance of HIV Drug sensitive: PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNFDrug resistant: PQITLWQRPLVTIKVGGQLTEALLDTGADDTILEDMTLPGRWKPKIVGGIGGFIKVRQYDQVPIEICGHKVISTVLIGPTPCNIIGRNLMTQIGLTLNF
Hypothetical distribution of drug resistance mutations
• Novel mutations keep the wave going
novel mutation
• Drug resistance increases due to selection on existing mutations
0 1 2 3 4 5 Resistance mutations
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Quantifying the benefits of recombination [for the virus]
13
Example: drug resistance of HIV Drug sensitive: PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNFDrug resistant: PQITLWQRPLVTIKVGGQLTEALLDTGADDTILEDMTLPGRWKPKIVGGIGGFIKVRQYDQVPIEICGHKVISTVLIGPTPCNIIGRNLMTQIGLTLNF
Hypothetical distribution of drug resistance mutations
• Novel mutations keep the wave going
novel mutation
recombination
• Drug resistance increases due to selection on existing mutations
• Via recombination mutations can keep up with the wave and “make it”
0 1 2 3 4 5 Resistance mutations
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Surfing of beneficial mutations
14
!2 0 2 40
0.2
0.4
-4
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Surfing of beneficial mutations
14
!2 0 2 40
0.2
0.4
-4
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Evolutionary benefits of recombination
15
Conventional wisdom: rate of evolution ~ N
holds only for very frequent recombination
To slow down evolution, one has to target recombination rather than the population size!!
Realistic recombination: rate of evolution ~ r2 log N
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘0916
The more recombination, the better?
Is there a cost to recombination?
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Genetic interactions
17
So far: Fitness = # beneficial mutations
BUT:
An organism is more than the sum of its parts
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Genetic interactions
17
So far: Fitness = # beneficial mutations
BUT:
An organism is more than the sum of its parts
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Genetic interactions
17
So far: Fitness = # beneficial mutations
BUT:
An organism is more than the sum of its parts
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
The cost of recombination
18
A “recombined” soccer team is worse than the “parents”.
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
The cost of recombination
18
A “recombined” soccer team is worse than the “parents”.
Relay racing teams don’t have that problem!
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Genetic interaction and recombination
19
Different reassortments of Influenza A:
Recombination explores -- selection amplifies the best
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Genetic interaction and recombination
19
no recombination moderate recombination frequent recombination
Different reassortments of Influenza A:
Recombination explores -- selection amplifies the best
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Allele vs genotype selection
20
0 1 2 3 4 50
0.2
0.4
0.6
0.8
1
Quasi Linkage Equilibrium (QLE)Allele selection
Genotype selectionClonal Competition (CC)
Mating freq./selection
Rel.
epis
tasi
s
Recombination rate/selection
Stre
ngth
of I
nter
actio
n
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
The success of selection
21
0 0.1 0.2 0.3 0.40.2
0.3
0.4
recombination
F final
rc
Selection on the properties of the parts.
“All Stars team”
Selection on the combinations“the best clubs”
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Connecting theory and observations
22
Large data bases of HIV sequences and drug resistance allow us to study:
• The recombination rate in HIV
• Selection strength of HIV evolution without drugs
• Drug resistance mutations: team players or independent?
• Does recombination vary from patient to patient?
• Does it vary at different stages of the disease?
• With modern sequencing technology we can soon monitor viral populations at unprecedented resolution.
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Connecting theory and observations
22
Large data bases of HIV sequences and drug resistance allow us to study:
• The recombination rate in HIV
• Selection strength of HIV evolution without drugs
• Drug resistance mutations: team players or independent?
• Does recombination vary from patient to patient?
• Does it vary at different stages of the disease?
• With modern sequencing technology we can soon monitor viral populations at unprecedented resolution.
Do such observations, together with theoretical insight, explain the differences between drugs and patients?
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Connecting theory and observations
22
Large data bases of HIV sequences and drug resistance allow us to study:
• The recombination rate in HIV
• Selection strength of HIV evolution without drugs
• Drug resistance mutations: team players or independent?
• Does recombination vary from patient to patient?
• Does it vary at different stages of the disease?
• With modern sequencing technology we can soon monitor viral populations at unprecedented resolution.
Do such observations, together with theoretical insight, explain the differences between drugs and patients?
What can we learn about the evolutionary process in general?
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Sequencing Costs per Base
23
1990 1995 2000 2005 201010
!5
10!4
10!3
10!2
10!1
100
101
year
Co
st p
er
base [
$]
Data
Exponential decay: ! = 2.5 years
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Experiment & Theory
24
Darwin’s Theory
Observations:PaleontologyDiversity of species
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Experiment & Theory
24
Experiments in the Lab• Bacteria or viruses• Sequencing and Phenotyping
Darwin’s Theory
Observations:PaleontologyDiversity of species
Quantitative TheoryDynamics
Thursday, September 24, 2009
Richard Neher KITP Chalk Talk, August ‘09
Collaborators
25
Boris Shraiman, KITP
Daniel Fisher, Stanford
Thomas Leitner, LANL
Thursday, September 24, 2009