WeiYa's Work Yard

A traveler with endless curiosity, who fell into the ocean of statistics, tries to write down his ideas and notes to save himself.

Evolutionary Systems Biology

Posted on
Tags: Evolution, Systems Biology

The note is for Chapter 1 of Soyer, Orkun S., ed. 2012 Evolutionary Systems Biology. Advances in Experimental Medicine and Biology, 751. New York: Springer.

Abstract

  1. SB -> ESB through the expansion of their (system-level approaches) explanatory and potentially predictive scope.
  2. outline the varieties of ESB
    • comparative and correlational ESB
    • network architecure ESB
    • network property ESB
    • population genetics ESB
    • standard evolutionary questions with SB methods

What is ESB?

  1. umbrella concept

From SB to ESB

  1. SB arose from the confluence of an abundance of quantified molecular data.
  2. SB’s distinctive knowledge-making characteristic: its synthesis of experimental work with mathematical modeling, on the basis of high-throughput datasets.
  3. the mathematical models implemented in SB come in a variety of forms, but involve the use of deterministic or stochastic modeling techniques to produce mechanistic, dynamic, realistic and predictive models.
  4. evolution enriches the explanatory mix and potentially the predictive capacities of SB.
  5. in order to achieve a complete understanding of systems at multiple levels and over different timescales, evolutionary understanding of those systems is needed.
  6. EB has long been seen as a historical science, in that it involves the qualitative interpretation of past events. ESB could make EB into the quantitative and predictive science for which some of its practitioners have long pined.
  7. Experimental evolution is too limited in its timescale, especially when trying to understand broad-scale phenomena such as how robustness influences evolutionary innovation.
  8. ESB can be conceived as a means by which such limitations cna be overcome, through practices that combine quantitative data, mechanistic explanations, and dynamic models of genetic systems with existing evolutionary knowledge and conceptual frameworks.

The Emergence of ESB

system-oriented biology incorporating evolutionary

  1. evolutionary models of gene networks and their system-level properties
  2. comparative analyses of the transcriptional gene regulatory networks and other gene-based processes underpinning development in numerous species.
  3. genome-scale molecular evolutionary analyses of adaptive and nonadaptive forces operating on genome structure.
  4. studies of model molecular systems in regard to dynamic evolving properties such as plasticity, modularity and evolvability.
  5. the term ESB shows that it began to be used as an institutional label and a description of a field in the mid-2000s.
  6. 2009 marks a watershed, in that it is when scholarly databases begin to accumulate an increasing number of publications in the general area of ESB.

Varieties of ESB today

  1. Orkun Soyer’s: ESB1 & ESB2
  2. Loewe’s: 3 groups
  3. Paulien: tri-fold
  4. Johannes: six groups

Comparative and Correlational ESB

  1. ESB is concerned with the evolution of relationships between genotype and phenotype
  2. Proponents of comparative ESB focus on “most basic level of analysis” by exploring the relationships between genome-wide variables such as gene expression, dispensability, and evolutionary rate.
  3. correlating such variables goes beyond what had been understood as comparative genomics because of the wider scope of comparison (especially at levels other than gene sequence) and emphasis on functional and dynamic comparison (e.g. expression rates).
  4. phylogenomic insight into large-scale molecular datasets allows not only insight into evolutionary patterns but also inferences about underlying causal structures.
  5. statistical analysis of genome-wide data can lead to major insights into gene order in prokaryote genomes, for instance, and this in turn generates new hypotheses for the evolution of mechanisms of gene expression and genome organizations across species.
  6. Phylogenomic insight into large-scale molecular datasets allows not only insight into evolutionary patterns but also inferences about underlying causal structures.

comparative and correlational ESB seeks patterns and is not primarily a model-building effort, it might be seen as not genuinely systems-biological, but new evolutionary insight into systems can be generated at the conceptual level. For example, novel understandings of the evolution of gene and genome architecture can be gained by analyses showing that expression level plays a major role in the evolution of such structural features.

coevolutionary processes, such as those involving pathogens and hosts, can be investigated by comparative ESB approaches, and in the process, may bring about the reconcepturalization of existing terms such as pathogen and virulence factor.

Network Architecture ESB

to understand network architecture from an evolutionary and functional perspective.

types:

  1. evolutionary analyses of genome-scale metabolic networks
  2. protein-protein network evolution
  3. gene regulation network evolution

network motif detection across species, and elaboration of the means by which such motifs have evolved, is an important aspect of a comparative approach to network ESB.

relationship between network architecture and system-level function

  1. effects of topological structure on network function, often use nodes connectivity
  2. effects of function on the structure of gene regulatory networks (positive causal relationship)

building up knowledge of the evolution of developmental innovations.

  1. the merger of developmental genetics and evolutionary biology

developmental regulation or other specific organismal functions, synthesize a wide range of data, models, and methods, and engineering approaches.

  1. illumination of mechanisms of network evolution
    • simulations
    • diverse modeling strategies
    • experiments on natural and synthetic systems
  2. evolutionary techniques embedded in network analyses
    • evolutionary algorithms
    • directed evolution
  3. extension of ESB analyses of network evolution
    • fall outside adaptive explanations
    • examine the complex relations between neutral and adaptive evolution

Network Property ESB

questions about the evolution of very general network properties (robustness and evolvability)

  1. network architecture
  2. connectivity
  3. redundancy
  4. history

focus: finding abstract “emergent” systems properties (aka “design principles”)

robustness may not selected directly

another property: modularity and how it evolves in biological systems

  1. networks can be simulated to
    • examine whether modularity is selected for
    • whether it is produced as a side-effect
    • whether the piecemeal evolution of modules is feasible

such findings allow the development of insight into the “design principles” of living systems, such as when modularity or even evolvability evolves, what optimality is, and how the robustness of modules evolves in dependence on different environmental conditions.

Population Genetics ESB

“black box”: the lack of an account of how genotype maps on to phenotypes

epistasis: the interaction between genes and a crucial determinant of fitness

computer simulations to examine small mutational effects in developmental regulatory networks

how population genetics could be complemented with fine-grained molecular understanding

resistance costs

  • quantitative effects of mutations and horizontal gene transfers

population genetics is synthesized with knowledge of network evolution

Standard Evolutionary Questions Answered with SB methods

An example: the ability to study gene function outside the laboratory, in relation to evolving properties such as pheotypic plasticity in changing environmental of plants, is an area where SB approaches can be integrated to enhance work that is already molecular and able to use quantitative tools.

the integration of SB into existing research questions will additionally facilitate evolutionary understanding at the molecular and phenotypic levels for non-model organisms.

evolutionary biochemistry is another field that can draw on systems-biological analyses to understand pathway interactions and their evolved properties.

Core themes in ESB

Conceptual Frameworks

standard assumptions about phenomena such as pleiotropy are being fundamentally rethought and contested due to the availability of large quantifiable datasets and the mathematical models able to take advantage of them.

from an ESB perspective, phenomena such as pleiotropy and epistasis need reconceptualizing, as “inherent, ubiquitous properties of biological networks” rather than exceptional occurrences, and their consequences have to be investigated at multiple levels ranging from the molecular to the organismal.

neutral evolution vs the network properties are affected by social evolution.

ESB, through its combination of evolutionary theory and systems-biological approaches, can change fundamentally how the evolving structure and function of living entities is understood.

Methods and Methodology

simulated and experimental evolution

Disciplinary Connections

EB & ESB

ESB and evo-devo (Evolutionary developmental biology)

Published in categories Note 
← previous    next →

See all posts →