[Theory] NOW: 8/16 TTIC Colloquium: Sagi Snir, University of Haifa
Mary Marre via Theory
theory at mailman.cs.uchicago.edu
Fri Aug 16 10:56:00 CDT 2024
*When:* Friday, August 16, 2024 at* 11:00** am** CT *
*Where: *Talk will be given *live, in-person* at
TTIC, 6045 S. Kenwood Avenue
5th Floor, Room 530
*Virtually:* *via panopto: **livestream*
<https://uchicago.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=63281766-98bc-4c0c-8085-b1c8000ab3d3>
*Who: * Sagi Snir, University of Haifa
------------------------------
*Title:* Bacterial Genome Dynamics: A Random Walk Approach
*Abstract*. The dramatic decrease in time and cost for generating genetic
sequence data has opened up vast opportunities in molecular systematics,
one of which is the ability to decipher the evolutionary history of strains
of a species. Under this fine systematic resolution, the standard markers
are often too crude to provide a reliable phylogenetic signal.
Nevertheless, among prokaryotes, genome dynamics (GD) in the form of
horizontal gene transfer (HGT), the transfer of genetic material between
organisms not through lineal descent, seem to provide far richer
information by affecting both gene order and gene content. To the best of
our knowledge, no rigorous statistical modelling for GD has been suggested.
Here we provide a first statistical, two-level modeling and analysis, for
GD under a very simple operation – the Jump operation. Under this
framework, at the higher level, genome evolution is modeled as a random
walk in the genome permutation state space. At the lower level, gene
neighborhood is modeled as a birth–death–immigration process affected by
the genes jumping across the genome. Using this modeling we can infer
several innate properties for genomes evolving along a tree and
analytically relate the HGT rate and time to the expected random variables
that we define.
Besides analytical results, we will also show results on real data
conforming with existing biological knowledge.
*Bio: *Sagi Snir graduated in Computer Science from the Technion Israel,
focusing on analytical, algebraic, maximum likelihood solutions to
phylogenetics. After a postdoc in the Math and the Computer Science depts
at UC Berkeley, he returned to the University of Haifa in Israel, where he
has established the Bioinformatics program for grad students. He is now a
professor of computational evolution at the University of Haifa and the
President of the Israeli Society for Bioinformatics and Computational
Biology.
*Host: **Avrim Blum* <avrim at ttic.edu>
Mary C. Marre
Faculty Administrative Support
*Toyota Technological Institute*
*6045 S. Kenwood Avenue, Rm 517*
*Chicago, IL 60637*
*773-834-1757*
*mmarre at ttic.edu <mmarre at ttic.edu>*
On Fri, Aug 16, 2024 at 10:00 AM Mary Marre <mmarre at ttic.edu> wrote:
> *When:* Friday, August 16, 2024 at* 11:00** am** CT *
>
>
> *Where: *Talk will be given *live, in-person* at
>
> TTIC, 6045 S. Kenwood Avenue
>
> 5th Floor, Room 530
>
>
> *Virtually:* *via panopto: **livestream*
> <https://uchicago.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=63281766-98bc-4c0c-8085-b1c8000ab3d3>
>
>
>
> *Who: * Sagi Snir, University of Haifa
> ------------------------------
>
> *Title:* Bacterial Genome Dynamics: A Random Walk Approach
>
> *Abstract*. The dramatic decrease in time and cost for generating genetic
> sequence data has opened up vast opportunities in molecular systematics,
> one of which is the ability to decipher the evolutionary history of strains
> of a species. Under this fine systematic resolution, the standard markers
> are often too crude to provide a reliable phylogenetic signal.
> Nevertheless, among prokaryotes, genome dynamics (GD) in the form of
> horizontal gene transfer (HGT), the transfer of genetic material between
> organisms not through lineal descent, seem to provide far richer
> information by affecting both gene order and gene content. To the best of
> our knowledge, no rigorous statistical modelling for GD has been suggested.
>
> Here we provide a first statistical, two-level modeling and analysis, for
> GD under a very simple operation – the Jump operation. Under this
> framework, at the higher level, genome evolution is modeled as a random
> walk in the genome permutation state space. At the lower level, gene
> neighborhood is modeled as a birth–death–immigration process affected by
> the genes jumping across the genome. Using this modeling we can infer
> several innate properties for genomes evolving along a tree and
> analytically relate the HGT rate and time to the expected random variables
> that we define.
>
> Besides analytical results, we will also show results on real data
> conforming with existing biological knowledge.
>
> *Bio: *Sagi Snir graduated in Computer Science from the Technion Israel,
> focusing on analytical, algebraic, maximum likelihood solutions to
> phylogenetics. After a postdoc in the Math and the Computer Science depts
> at UC Berkeley, he returned to the University of Haifa in Israel, where he
> has established the Bioinformatics program for grad students. He is now a
> professor of computational evolution at the University of Haifa and the
> President of the Israeli Society for Bioinformatics and Computational
> Biology.
> *Host: **Avrim Blum* <avrim at ttic.edu>
>
>
>
> Mary C. Marre
> Faculty Administrative Support
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue, Rm 517*
> *Chicago, IL 60637*
> *773-834-1757*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
>
> On Thu, Aug 15, 2024 at 2:09 PM Mary Marre <mmarre at ttic.edu> wrote:
>
>> *When:* Friday, August 16, 2024 at* 11:00** am** CT *
>>
>>
>> *Where: *Talk will be given *live, in-person* at
>>
>> TTIC, 6045 S. Kenwood Avenue
>>
>> 5th Floor, Room 530
>>
>>
>> *Virtually:* *via panopto: **livestream*
>> <https://uchicago.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=63281766-98bc-4c0c-8085-b1c8000ab3d3>
>>
>>
>>
>> *Who: * Sagi Snir, University of Haifa
>> ------------------------------
>>
>> *Title:* Bacterial Genome Dynamics: A Random Walk Approach
>>
>> *Abstract*. The dramatic decrease in time and cost for generating
>> genetic sequence data has opened up vast opportunities in molecular
>> systematics, one of which is the ability to decipher the evolutionary
>> history of strains of a species. Under this fine systematic resolution, the
>> standard markers are often too crude to provide a reliable phylogenetic
>> signal. Nevertheless, among prokaryotes, genome dynamics (GD) in the form
>> of horizontal gene transfer (HGT), the transfer of genetic material between
>> organisms not through lineal descent, seem to provide far richer
>> information by affecting both gene order and gene content. To the best of
>> our knowledge, no rigorous statistical modelling for GD has been suggested.
>>
>> Here we provide a first statistical, two-level modeling and analysis, for
>> GD under a very simple operation – the Jump operation. Under this
>> framework, at the higher level, genome evolution is modeled as a random
>> walk in the genome permutation state space. At the lower level, gene
>> neighborhood is modeled as a birth–death–immigration process affected by
>> the genes jumping across the genome. Using this modeling we can infer
>> several innate properties for genomes evolving along a tree and
>> analytically relate the HGT rate and time to the expected random variables
>> that we define.
>>
>> Besides analytical results, we will also show results on real data
>> conforming with existing biological knowledge.
>>
>> *Bio: *Sagi Snir graduated in Computer Science from the Technion Israel,
>> focusing on analytical, algebraic, maximum likelihood solutions to
>> phylogenetics. After a postdoc in the Math and the Computer Science depts
>> at UC Berkeley, he returned to the University of Haifa in Israel, where he
>> has established the Bioinformatics program for grad students. He is now a
>> professor of computational evolution at the University of Haifa and the
>> President of the Israeli Society for Bioinformatics and Computational
>> Biology.
>> *Host: **Avrim Blum* <avrim at ttic.edu>
>>
>>
>>
>> Mary C. Marre
>> Faculty Administrative Support
>> *Toyota Technological Institute*
>> *6045 S. Kenwood Avenue, Rm 517*
>> *Chicago, IL 60637*
>> *773-834-1757*
>> *mmarre at ttic.edu <mmarre at ttic.edu>*
>>
>>
>> On Fri, Aug 9, 2024 at 11:42 PM Mary Marre <mmarre at ttic.edu> wrote:
>>
>>> *When:* Friday, August 16, 2024 at* 11:00** am** CT *
>>>
>>>
>>> *Where: *Talk will be given *live, in-person* at
>>>
>>> TTIC, 6045 S. Kenwood Avenue
>>>
>>> 5th Floor, Room 530
>>>
>>>
>>> *Virtually:* *via panopto: **livestream*
>>> <https://uchicago.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=63281766-98bc-4c0c-8085-b1c8000ab3d3>
>>>
>>>
>>>
>>> *Who: * Sagi Snir, University of Haifa
>>> ------------------------------
>>>
>>> *Title:* Bacterial Genome Dynamics: A Random Walk Approach
>>>
>>> *Abstract*. The dramatic decrease in time and cost for generating
>>> genetic sequence data has opened up vast opportunities in molecular
>>> systematics, one of which is the ability to decipher the evolutionary
>>> history of strains of a species. Under this fine systematic resolution, the
>>> standard markers are often too crude to provide a reliable phylogenetic
>>> signal. Nevertheless, among prokaryotes, genome dynamics (GD) in the form
>>> of horizontal gene transfer (HGT), the transfer of genetic material between
>>> organisms not through lineal descent, seem to provide far richer
>>> information by affecting both gene order and gene content. To the best of
>>> our knowledge, no rigorous statistical modelling for GD has been suggested.
>>>
>>> Here we provide a first statistical, two-level modeling and analysis,
>>> for GD under a very simple operation – the Jump operation. Under this
>>> framework, at the higher level, genome evolution is modeled as a random
>>> walk in the genome permutation state space. At the lower level, gene
>>> neighborhood is modeled as a birth–death–immigration process affected by
>>> the genes jumping across the genome. Using this modeling we can infer
>>> several innate properties for genomes evolving along a tree and
>>> analytically relate the HGT rate and time to the expected random variables
>>> that we define.
>>>
>>> Besides analytical results, we will also show results on real data
>>> conforming with existing biological knowledge.
>>>
>>> *Bio: *Sagi Snir graduated in Computer Science from the Technion
>>> Israel, focusing on analytical, algebraic, maximum likelihood solutions to
>>> phylogenetics. After a postdoc in the Math and the Computer Science depts
>>> at UC Berkeley, he returned to the University of Haifa in Israel, where he
>>> has established the Bioinformatics program for grad students. He is now a
>>> professor of computational evolution at the University of Haifa and the
>>> President of the Israeli Society for Bioinformatics and Computational
>>> Biology.
>>> *Host: **Avrim Blum* <avrim at ttic.edu>
>>>
>>>
>>>
>>> Mary C. Marre
>>> Faculty Administrative Support
>>> *Toyota Technological Institute*
>>> *6045 S. Kenwood Avenue, Rm 517*
>>> *Chicago, IL 60637*
>>> *773-834-1757*
>>> *mmarre at ttic.edu <mmarre at ttic.edu>*
>>>
>>
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