The Potential Impact of High-End Capability Computing on Four Illustrative Fields of Science and Engineering (2008) / Chapter Skim
Currently Skimming:
4 The Potential Impact of HECC in Evolutionary Biology
4 The Potential Impact of HECC in Evolutionary Biology
Pages 63-88
The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.
From page 63... ...
With the rise of such fields as comparative genomics and bioinformatics, evolutionary developmental biology, and the expanded effort to build the tree of life, the discipline of biology has become increasingly dependent on the inferences, methods, and tools of evolutionary biology. These contributions from evolutionary biology have become standard in solving problems in comparative biology, the biomedical and applied sciences, agriculture and resource management, and biosecurity.
|
From page 64... ...
On this basis, the committee then assesses the potential impact of HECC on the major challenges of evolutionary biology. MAJOR CHALLENGES OF EVOLUTIONARY BIOLOGY Major Challenge 1: Understanding the History of Life The most fundamental question posed by Major Challenge 1 is this: How did life arise?
|
From page 65... ...
Computational Challenges Standard phylogenetic analysis comparing the possible evolutionary relationships between two s pecies can be done using the method of maximum parsimony, which assumes that the simplest answer is the best one, or using a model-based approach. The former entails counting character change on alternative phylogenetic trees in order to find the tree that minimizes the number of character transformations.
|
From page 66... ...
Theoretical molecular evolutionists and phylogeneticists have long simulated data sets and trees -- to understand and compare phylogenetic methods and their statistical properties, for example, and to compare models of sequence change between simulated phylogenies and those found in the real world. The scale and efficacy of these studies are inherently limited by computational capability as investigators seek to make their simulations more sophisticated and realistic.
|
From page 67... ...
Thus we are faced with the question of how to explain the continuum of responses to isolation and differentiation in genetic and developmental terms. Addressing Major Challenge 2 will also necessitate fundamental advances in understanding how changes in genetic architecture translate into changes in the development of the organism and of its p henotypic (observable)
|
From page 68... ...
Very little is known about this, and answers will require an approach calling on the expertise of numerous disciplines. A third key component of Major Challenge 2 is learning about the history of populations and species, particularly the human species.
|
From page 69... ...
Major Challenge 3: Understanding Diversification of Life Across Space and Time At a general level, it is well known that processes in the geosphere and biosphere have been tightly linked since the origin of life (NRC, 1995) , but we have only partial understanding of the linkages across different spatial and temporal scales.
|
From page 70... ...
A related part of Major Challenge 3 is how to determine the evolutionary history of microorganismal community structure and function. This is a somewhat different challenge, because new methods in comparative genomics are giving us a better understanding of microbial community organization.
|
From page 71... ...
Evolutionary biologists focused increasingly on understanding evolution at the population level and developed sophisticated genetic models to understand changes in allele frequencies, while developmental biologists focused on experimental manipulations to uncover the mechanisms of development. More recently, however, developmental biologists have taken their analysis to very deep molecular and genetic levels, and this has led to a renewed interest in understanding the interplay between evolution and development (called "evo-devo")
|
From page 72... ...
One fundamental goal of Major Challenge 4 is to understand integrated phenotypes and how they evolve. Phenotypic features -- such as morphological form, physiology, behavior, even biochemical pathways -- are often integrated into functional groups based on their interaction with the environment.
|
From page 73... ...
A second fundamental goal underlying Major Challenge 5 is to understand better the links between ecological and evolutionary processes. The conservation of biodiversity depends critically on our ability to predict the responses of populations to changes in their environment that occur on short- and mediumterm timescales.
|
From page 74... ...
found that drastic fluctuations in populations, and hence increased chances of extinction, were more likely to be found in environments that were positively correlated from year to year. Computational Challenges Increasingly, evolutionary biologists are incorporating realistic models of population and demographic changes into their modeling of population genetic processes (Whitlock and Gomulkiewicz, 2005)
|
From page 75... ...
Major Challenge 6: Understanding the Patterns and Mechanisms of Genome Evolution No tool kit has revolutionized evolutionary biology more than genomics. The foundation of largescale genome analysis is the complete sequencing of genomes, whether from single-celled bacteria or unicellular eukaryotes or from more structurally and developmentally complex animals and plants.
|
From page 76... ...
Large-scale variation in transposable elements will play an important role in explaining the 1,000-fold variation in genome size observed among living eukaryotes. As we build our knowledge of how genes arise, evolutionary biology can begin to understand how evolution is constrained by networks of interaction between genes and noncoding elements in the genome.
|
From page 77... ...
Understanding how these myriad constituents interact and influence one another and how genomes and chromosomes function and evolve as hierarchical networks is a major challenge for evolutionary biology. Many of the principles of population genetics and molecular evolution, laid down in the twentieth century, are still applicable to genome data despite the scaling up from single genes to entire genomes (Li, 1997; Lynch, 2003)
|
From page 78... ...
Major Challenge 7: Understanding the Evolutionary Dynamics of Coevolving Systems Individuals of the same species or of different species generally have either conflicting or cooperating (mutualistic) interactions.
|
From page 79... ...
. MAJOR CHALLENGES IN EVOLUTIONARY BIOLOGY THAT REQUIRE HECC Progress in most areas of evolutionary biology has been very rapid over the past several decades.
|
From page 80... ...
Moreover, advanced computing opens some new options for approaching Major Challenge 2. Evaluating demographic histories using genetic data is computationally challenging for several reasons.
|
From page 81... ...
We are still left with deciding which population model -- described by gene flows, population size changes, and so on -- best fits the set of gene trees. Many of the approaches to Major Challenge 3 could use HECC now or are moving inexorably in that direction.
|
From page 82... ...
, no doubt because of the computational challenges, but this is clearly the direction in which coupled models of ecological and evolutionary dynamics are heading. The computational methods that are essential to addressing Major Challenge 6 are largely manageable today, as explained in that section.
|
From page 83... ...
Ironically, the repeat structures created by transposable elements make the process of assembling whole genome sequences from raw data an even more complex computational problem. Right now, when we say a genome has been fully sequenced, that often applies only to its euchromatic region; the heterochromatic region, which often is rich with transposable elements, remains unassembled because computational methods are still lacking to make sense of the data.
|
From page 84... ...
Enabling evolutionary biologists to readily exploit supercomputing power would significantly change the aims and scope of many research programs. Even today we can see how progress is limited by the relative scarcity of substantial computing resources at the high end: Workstations require months to solve medium-sized problems for modeling molecular evolutionary change even though new algorithms have provided some improvements.
|
From page 85... ...
These examples illustrate the many practical advantages of such access; the incentives for using advanced computing encompass more than just the classic NP-complete nature of generating and validating phylogenetic trees. But many steps must be taken before this vision can be realized.
|
From page 86... ...
2007. Integration within the Felsenstein equation for improved Markov chain Monte Carlo methods in population genetics.
|
From page 87... ...
2005. Evolutionary Game Theory, Natural Selection, and Darwinian Dynamics.
|
Key Terms
This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More
information on Chapter Skim is available.