Nothing in biology makes sense
except properly quantified in the light of evolution.

Evolutionary systems biology is ultimately about rigorously quantifying the evolution of biological systems. To achieve this, EvoSysBio combines the rigor of population genetics with approaches from current systems biology to formulate testable hypotheses.

Figure 1: EvoSysBio combines the intra-individual approaches of current systems biology with the population level approaches of population genetics and ecology. From Loewe (2009).

Modelling efforts in current systems biology provide a unique opportunity to quantify how genotypes map to phenotypes. Such work can contribute much to quantifying the adaptive landscape that governs the evolution of life. Unfortunately this important concept for evolutionary biology is poorly quantified for the overwhelming majority of biological systems. Many discussions of the adaptive landscape do not go much beyond the following cartoon (for exceptions, see e.g. Dean & Thornton 2007, Lande 2008 and other refs in Loewe 2009):

Figure 2: Each adaptive landscape is defined by a 'height' (e.g. fitness) that exists for each point in a 'plane' (e.g. combinations of genes). Computing or measuring this 'height' and navigating the 'plane' is extraordinary difficult for most biological systems. If fitness correlates such as survival or fecundity increase with 'height', then selection will usually push a population towards the next locally reachable optimum.

The many levels of biological organization and interaction make it particularly difficult to quantify the adaptive landscape and both, 'plane' and 'height' routinely reflect high-dimensional spaces. To break this big problem down into more addressable ones, Loewe (2009) proposed that points that help quantify the adaptive landscape can be part of any of the following seven levels of the adaptive landscape.

1. A molecular structure in the space of genotypes (addressed by structural biology, e.g. by protein structure prediction programs)
2. A molecular function in the space of molecular structures (addressed by structural biology, comparative biology and biochemical experiments, e.g. by measuring kinetic rates or predicting them by interpolation between known rates of enzymes with known structures)
3. A computable emergent property in the space of molecular functions (addressed by current systems biology via computer simulations of biochemical networks, e.g. by computing the flux through a pathway)
4. A computable fitness correlate in the space of computable emergent properties (again adressed by computer simulations; biological expertise is used to identify combinations of emergent properties that have a direct or indirect impact on high-level fitness correlates like survival or fecundity, e.g. growth of biomass in bacteria is correlated with doubling time of bacteria and hence an interesting fitness correlate)
5. An observable fitness correlate in the space of computable fitness correlates (this is a simple 1:1 mapping if the system under investigation has been completely understood; otherwise it involves the construction of a heuristic quantitative link; e.g. the computed growth of biomass is proportional to the observed growth rate of bacteria)
6. The fitness of an individual in the space of observable fitness correlates (addressed by life-history evolution theory, e.g. balance survival and fecundity to optimize fitness)
7. The mean fitness of a population in the space of fitness values of all individuals in the population (by specifiying additional genotype or phenotype information this plane can be collapsed into genotype frequencies or phenotypic traits, producing more traditional views of the adaptive landscape, e.g. mean fitness of a population depending on the frequency of a particular allele)

These levels facilitate connecting biological data from very different disciplines to computer models that help to navigate adaptive landscapes. This opens new avenues for understanding such key topics as robustness, distributions of mutational effects, epistasis, compensatory mutations and adaptive mutations.

Current systems biology models play a pivotal role in this integrated approach, as they have the potential to connect results from molecular biology to higher order emergent properties that might again be observed in experiments. Methods of interest for this approach include the simulation of biochemical reaction networks (Gillespie 2007) and Flux Balance Analyses (Ibarra et al. 2002; Papp et al. 2004).

Numerous practical applications exist for this approach, from cancer research over investigating antibiotics resistance evolution to modelling the consequences of releasing genetically modified organisms (see Loewe 2009 for more examples).

EvoSysBio is necessarily very broad, as it relies on the inclusion of a wide array of research results from very different disciplines. It is not possible to start the integrative work of EvoSysBio without first laying the ground in more specialised disciplines. It is probably also not possible to complete the integrative work of EvoSysBio for any system without going back to the more specialised disciplines to fill in the gaps in our basic knowledge that only become apparent when working on integrated models. Thus successful EvoSysBio is ultimately inclusive, as it strongly depends on the work in many other disciplines.

For a more comprehensive overview of this approach to evosysbio, see Loewe (2009).

References:

Dean, A. M. & Thornton, J. W. (2007) Mechanistic approaches to the study of evolution: the functional synthesis. Nat. Rev. Genet. 8, 675-88. PubMed

Gillespie, D. T. (2007) Stochastic simulation of chemical kinetics. Annu Rev Phys Chem 58, 35-55. PubMed

Ibarra, R. U., Edwards, J. S., & Palsson, B. O. (2002) Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature 420, 186-9. PubMed

Lande, R. (2008) Adaptive topography of fluctuating selection in a Mendelian population. J. Evol. Biol. 21, 1096-1105. PubMed

Loewe L (2009) A framework for evolutionary systems biology. BMC Systems Biology 3:27. Journal link

Papp, B., Pal, C., & Hurst, L. D. (2004) Metabolic network analysis of the causes and evolution of enzyme dispensability in yeast. Nature 429, 661-4. PubMed