Google Groups : Google-Labs-Google-Scholar: "Occasionally Google employees may post to the group. Any Google
employee who participates in the group will always post with the name
'Google Employee'.
The Google Scholar group encourages free and open discussion on all
aspects of Google Scholar. However, posts not on this topic are not
welcome. We reserve the right to delete the posts that we consider
inappropriate."
Friday, November 19, 2004
Sunday, November 07, 2004
Pharyngula::Symmetry breaking and genetic assimilation
Pharyngula::Symmetry breaking and genetic assimilation: "Friday, November 05, 2004
Symmetry breaking and genetic assimilation
How do evolutionary novelties arise? The conventional explanation is that the first step is the chance formation of a genetic mutation, which results in a new phenotype, which, if it is favored by selection, may be fixed in a population. No one sensible can seriously argue with this idea�it happens. I�m not going to argue with it at all.
However, there are also additional mechanisms for generating novelties, mechanisms that extend the power of evolutionary biology without contradicting our conventional understanding of it. A paper by A. Richard Palmer in Science describes the evidence for an alternative mode of evolution, genetic assimilation, that can be easily read as a radical, non-Darwinian, and even Lamarckian pattern of evolution (Sennoma at Malice Aforethought has expressed concern about this), but it is nothing of the kind; there is no hocus-pocus, no violation of the Weissmann barrier, no sudden, unexplained leaps of cause-and-effect. Comprehending it only requires a proper appreciation of the importance of environmental influences on development and an understanding that the genome does not constitute a descriptive program of the organism."
Symmetry breaking and genetic assimilation
How do evolutionary novelties arise? The conventional explanation is that the first step is the chance formation of a genetic mutation, which results in a new phenotype, which, if it is favored by selection, may be fixed in a population. No one sensible can seriously argue with this idea�it happens. I�m not going to argue with it at all.
However, there are also additional mechanisms for generating novelties, mechanisms that extend the power of evolutionary biology without contradicting our conventional understanding of it. A paper by A. Richard Palmer in Science describes the evidence for an alternative mode of evolution, genetic assimilation, that can be easily read as a radical, non-Darwinian, and even Lamarckian pattern of evolution (Sennoma at Malice Aforethought has expressed concern about this), but it is nothing of the kind; there is no hocus-pocus, no violation of the Weissmann barrier, no sudden, unexplained leaps of cause-and-effect. Comprehending it only requires a proper appreciation of the importance of environmental influences on development and an understanding that the genome does not constitute a descriptive program of the organism."
Tuesday, November 02, 2004
Harvard Medical School Division of Genetics
The definition of statistical approaches for studying genetic variation has evolved at a frightening speed. Such technologies are applied in population studies to investigate divergence and modifications over time.
The proposed research will utilize these techniques on a very refined sample to demonstrate how easily the consciousness of humans can change their own DNA. This direct application will open the door for more direct control and development of specific techniques for creating desired genetic modifications.
Brigham and Women's Division of Genetics - Sunyaev Lab: "Lab: Research Interests
We are a computational biology laboratory. We develop and apply computational methods to pursue various problems in fields of genetics, genomics and proteomics. Our main interest is to analyse the population genetic variation and the genome divergence between species with the major focus on the protein coding regions. The effect of amino acid substitutions on function and structure of proteins can be frequently understood and even predicted via comparative sequence analysis and analysis of the protein structure. We relate the above functional studies to the evolutionary process of natural selection in order to track the evolution of proteins at the molecular level. Large-scale statistical approaches are suitable to study the way new mutations, genetic drift and natural selection shape the population genetic variation and how this variation once becomes a species divergence. The results of structural and evolutionary studies can be further applied to the data on human genetic polymorphisms with the goal to understand the complex mechanisms of inheritance and most importantly the genetic basis of human multifactorial diseases.
Our future effort will be directed towards the development of methods to extract knowledge on functionality and evolution from the novel massive data on closely related genomes and population genetic variants. We are hoping to reveal epistatic interactions between allelic variants and understand their molecular basis, thus getting closer to the understanding of the interplay of genetic variants to give rise to phenotypes. We are planning to utilise the knowledge gained to study the data on genotypes of patients suffering from common complex disorders through the established collaborations with groups involved in large medical genetics research projects. "
The proposed research will utilize these techniques on a very refined sample to demonstrate how easily the consciousness of humans can change their own DNA. This direct application will open the door for more direct control and development of specific techniques for creating desired genetic modifications.
Brigham and Women's Division of Genetics - Sunyaev Lab: "Lab: Research Interests
We are a computational biology laboratory. We develop and apply computational methods to pursue various problems in fields of genetics, genomics and proteomics. Our main interest is to analyse the population genetic variation and the genome divergence between species with the major focus on the protein coding regions. The effect of amino acid substitutions on function and structure of proteins can be frequently understood and even predicted via comparative sequence analysis and analysis of the protein structure. We relate the above functional studies to the evolutionary process of natural selection in order to track the evolution of proteins at the molecular level. Large-scale statistical approaches are suitable to study the way new mutations, genetic drift and natural selection shape the population genetic variation and how this variation once becomes a species divergence. The results of structural and evolutionary studies can be further applied to the data on human genetic polymorphisms with the goal to understand the complex mechanisms of inheritance and most importantly the genetic basis of human multifactorial diseases.
Our future effort will be directed towards the development of methods to extract knowledge on functionality and evolution from the novel massive data on closely related genomes and population genetic variants. We are hoping to reveal epistatic interactions between allelic variants and understand their molecular basis, thus getting closer to the understanding of the interplay of genetic variants to give rise to phenotypes. We are planning to utilise the knowledge gained to study the data on genotypes of patients suffering from common complex disorders through the established collaborations with groups involved in large medical genetics research projects. "
Thursday, October 28, 2004
UCSB CS Colloquium
UCSB CS Colloquium: "Eric Xing
University of California Berkeley
Date: Monday April 12
Time: 3:00-4:00
Place: Engineering 1, 2114
Abstract:
I discuss two probabilistic modeling problems arising in metazoan genomic analysis: identifying motifs and cis-regulatory modules (CRMs) from transcriptional regulatory DNA sequences, and inferring haplotypes from genotypes of single nucleotide polymorphisms. Motif and CRM identification is important for understanding the gene regulatory network underlying metazoan development and functioning. I discuss a modular Bayesian model that captures rich structural characteristics of the transcriptional regulatory sequences and supports a variety of tasks such as learning motif representations, model-based motif and CRM prediction, and de novo motif detection. Haplotype inference is essential for the understanding of genetic variation within and among populations, with important applications to the genetic analysis of disease propensities and other complex traits. I discuss a Bayesian model based on a prior constructed from a Chinese restaurant process -- a nonparametric prior which provides control over the size of the unknown pool of population haplotypes, and on a likelihood that allows statistical errors in the haplotype/genotype relationship. Our models use the 'probabilistic graphical model' formalism, a formalism that exploits the conjoined talents of graph theory and probability theory to build complex models out of simpler pieces. I discuss the mathematical underpinnings for the models, how they formally incorporate biological prior knowledge about the data, and the related computational issues.
Biography:
Eric Xing received his B.S. with honors in Physics and Biology from Tsinghua University, his Ph.D. in Molecular Biology and Biochemistry "
University of California Berkeley
Date: Monday April 12
Time: 3:00-4:00
Place: Engineering 1, 2114
Abstract:
I discuss two probabilistic modeling problems arising in metazoan genomic analysis: identifying motifs and cis-regulatory modules (CRMs) from transcriptional regulatory DNA sequences, and inferring haplotypes from genotypes of single nucleotide polymorphisms. Motif and CRM identification is important for understanding the gene regulatory network underlying metazoan development and functioning. I discuss a modular Bayesian model that captures rich structural characteristics of the transcriptional regulatory sequences and supports a variety of tasks such as learning motif representations, model-based motif and CRM prediction, and de novo motif detection. Haplotype inference is essential for the understanding of genetic variation within and among populations, with important applications to the genetic analysis of disease propensities and other complex traits. I discuss a Bayesian model based on a prior constructed from a Chinese restaurant process -- a nonparametric prior which provides control over the size of the unknown pool of population haplotypes, and on a likelihood that allows statistical errors in the haplotype/genotype relationship. Our models use the 'probabilistic graphical model' formalism, a formalism that exploits the conjoined talents of graph theory and probability theory to build complex models out of simpler pieces. I discuss the mathematical underpinnings for the models, how they formally incorporate biological prior knowledge about the data, and the related computational issues.
Biography:
Eric Xing received his B.S. with honors in Physics and Biology from Tsinghua University, his Ph.D. in Molecular Biology and Biochemistry "
USC College : College Magazine : Fall 2003 : Virtually Aging
USC College : College Magazine : Fall 2003 : Virtually Aging: "Faced with an aging population and a maturing research community, investigations of aging and aging-related diseases have exploded at USC over the past 30 years. Today, by one estimate, more than 100 USC scholars, representing natural science and social science disciplines and professional schools, are studying biological, social, health, economic, policy or other aspects of aging. For a glimpse of just how extensive the USC aging research enterprise has become, and for information on aging-related research and resources, visit University Wide Aging Nexus at USC, a new web site designed by Caleb Finch, the ARCO/William F. Kieschnick Chair in the Neurobiology of Aging in the School of Gerontology and a College professor of biology, and his team. "
Friday, October 22, 2004
Innovative Funding
Article - Fishing for Answers: "When businessman and longtime philanthropist Ralph C. Wilson Jr.
decided to begin supporting biomedical research, he convened experts from six of the country's top medical institutions to suggest the most effective way he could help. Together, the institutions - including the University of Michigan - identified a problem in research funding: because so many scientists are competing for limited grant dollars, most grants go to 'safe' research projects, where success is almost certain. Researchers who come with creative ideas and new ways of thinking often have trouble finding support.
So Wilson established his Ralph C. Wilson Sr. and Ralph C. Wilson Jr. Medical Foundation with a mission of funding innovative research by the nation's top biomedical scientists.
Though they have only been in existence since 2001, Wilson Foundation grants carry enormous weight and prestige. Only six institutions are even eligible to apply, with the U-M among this select group. What's more, only the top researchers and most creative projects at those institutions pass the rigorous peer review process. At each institution, only one to three projects receive funding annually.
One of the U-M's four researchers receiving funding is Dr. Daniel Goldman, a professor of biological chemistry and senior research professor, who is 'almost bursting' with excitement over his Wilson-funded research. Dr. Daniel Goldman works with zebrafish in his lab.Dr. Goldman explores new ways of repairing damage to the central nervous system, such as from strokes or spinal cord injuries. Fish, unlike humans, can recover from similar injuries and regenerate their nervous systems. Studying a lab full of zebrafish, Dr. Goldman hopes he is on the trail of information that could revolutionize treatments for stroke, pa"
decided to begin supporting biomedical research, he convened experts from six of the country's top medical institutions to suggest the most effective way he could help. Together, the institutions - including the University of Michigan - identified a problem in research funding: because so many scientists are competing for limited grant dollars, most grants go to 'safe' research projects, where success is almost certain. Researchers who come with creative ideas and new ways of thinking often have trouble finding support.
So Wilson established his Ralph C. Wilson Sr. and Ralph C. Wilson Jr. Medical Foundation with a mission of funding innovative research by the nation's top biomedical scientists.
Though they have only been in existence since 2001, Wilson Foundation grants carry enormous weight and prestige. Only six institutions are even eligible to apply, with the U-M among this select group. What's more, only the top researchers and most creative projects at those institutions pass the rigorous peer review process. At each institution, only one to three projects receive funding annually.
One of the U-M's four researchers receiving funding is Dr. Daniel Goldman, a professor of biological chemistry and senior research professor, who is 'almost bursting' with excitement over his Wilson-funded research. Dr. Daniel Goldman works with zebrafish in his lab.Dr. Goldman explores new ways of repairing damage to the central nervous system, such as from strokes or spinal cord injuries. Fish, unlike humans, can recover from similar injuries and regenerate their nervous systems. Studying a lab full of zebrafish, Dr. Goldman hopes he is on the trail of information that could revolutionize treatments for stroke, pa"
Wednesday, October 13, 2004
Metabolic Engineering Working Group
Metabolic Engineering Working Group: "Metabolic Engineering is a new approach to understanding and using metabolic processes. As the name implies, ME is the targeted and purposeful alteration of metabolic pathways found in an organism in order to better understand and use cellular pathways for chemical transformation, energy transduction, and supramolecular assembly. Knowledge acquired from this research will benefit society in a number of ways, including the ability to modify biological pathways to produce biological substitutes for less desirable chemical processes; allowing greater agricultural production, permitting more efficient and safer energy production, and; providing better understanding of the metabolic basis for some medical conditions that could assist in the development of new cures."
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