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Chapter I. General Introduction:
systems biology:
- systems biology can provide insight into the cell
- insights not found by examining objects in isolation
- examples using metabolic control analysis
- genome scale models provide deeper insight
- includes much of metabolism and takes into account many network effects
- make predictions based on different environments
- give examples of FBA based research
evolutionary systems biology:
- use deeper understanding of the cell to predict network effects
- examples in the literature => mainly Balazs, Csaba, and Lawrence Hurst
summary of results in thesis:
- estimating importance of objects in a system may reflect the selection pressures on them during evolution
- importance of reaction in a system => gene dosage effects and sequence evolution
- importance of metabolite => amino acid cost
Chapter II. Using FBA to estimate importance of objects in a system:
introduction:
- systems biology predicted effects
- can they be used to predict selective pressures
- estimate two measures of importance in a system
- cost of amino acid supply to the system
- shadow prices as precedent
- effect of reduction in reaction rate on the system.
- metabolic control analysis as precedent
results:
amino acid cost:
- sensitivity of amino acid demand on growth rate
- different environments => costs change depending on environment
- correlation with other amino acid costs, and atomic content
reaction flux sensitivity:
- sensitivity of the reaction flux on the growth rate
- different environments again, different reaction importance
- compare with haploinsufficiency effects
- compare with evolutionary rates
discussion:
- accuracy of these measures to in vivo conditions
- stoichiometric rather than kinetic models
- maximisation of growth rate may not be optimal strategy
- comparison with previous measures
- agreement for amino acid cost prediction
- no agreement with evolutionary rate of haploinsufficiency
- more accurate model will lead to more accurate predictions
- many complex effects still not modelled
methods:
- description of FBA
- amino acid cost estimation
- reaction sensitivity estimation
Chapter III. Role of amino acid biosynthetic cost in gene expression:
introduction:
- highlight previous work on amino acid cost in genome evolution and gene expression
- would expect cost to a factor in gene expression
- previous work use proxies for gene expression
- no work on using real data
- consider all levels of the cell hierarchy where cost may a factor
results:
- transcriptome
- proteome
- metabolome
discussion:
- weak correlation with transcript and proteome levels
- stronger correlation with metabolome levels
- similar discussion that used in manuscript
methods:
- multiple regression
- datasets used
Chapter IV. Is there any evidence in sequence evolution of optimisation of amino acid cost?
introduction:
- expect amino acid biosynthetic cost to be a force in protein evolution
- where there is not selection pressure for function, cost minimisation may become important
- expensive amino acids may be fixed, because they would be selected out quickly
results:
- compare rate of evolution of protein with its average cost
- comparing site wise evolutionary rate with cost of amino acids
- slice evolutionary rates by domain, gene expression, hydrophobicity, and predicted reaction flux
- compare across paralogs to control for structure
- intra population data if time permits
discussion:
- update this as results are produced
methods:
Chapter V. General Discussion
- Discuss the impact of the work.
- Combining systems biology to prediction selection pressures in evolution.
- Summarise the results of each paper again.
- Outline future work
Appendix. Open Notebook Science
- Short chapter about sharing research online. Why?