Scientific Objectives (SO) and Tasks (ST)

ST 0 Review literature and develop common framework across fields.R, J, A, P, C

SO 1 Develop the formalism for representations of genetic operators by transition kernels.
ST 1.1 Define and implement functions to measure fitness and information distances in the experimental systems. A, C
ST 1.2 Develop kernels for mutation operators. R
ST 1.3 Develop kernels for recombination operators. R
ST 1.4 Attempt to develop kernels for selection operators and combining them with other operators. R

SO 2 Develop the theory of the dynamics of information and fitness in evolutionary systems.
ST 2.1 Develop the initial theory as outlined in this document. R
ST 2.2 Derive closed-form or approximate parametrisation of kernels for different information distances. R

SO 3 Evaluate the theory by testing its predictions experimentally in vitro, in silico and in vivo.
ST 3.1 Review the design of experiments in vitro, in silico and in vivo. J
ST 3.2 Update the theory in light of empirical results and their analysis. R

SO 4 Analyse the information and fitness dynamics observed in the experimental systems using methods of information geometry, topology, optimisation and the semi-group of operators theory.
ST 4.1 Analyse vector spaces with quasi-pseudometric topologies that may occur in information dynamics. R
ST 4.2 Analyse the parametrisation expressions from ST 2.2 in light of the semi-group of operators theory. R
ST 4.3 Analyse the theory from the category theoretic viewpoint (i.e. morphisms, commutative diagrams). R
ST 4.4 Investigate the relation between the theory and generalisations of the second law of thermodynamics. R

Technological Objectives (TO) and Tasks (TT)

TO 1 Identify the trajectory for fitness improvement in the in vitro, in silico and in vivo systems with different parameters and operators chosen to test theory, with and without environmental change.
TT 1.1 Establish in vitro assays for multiple ligands binding to micro-arrays. RA
TT 1.2 Establish microbial evolution assays. RA
TT 1.3 Establish and test the system for mutation rate variation in vivo. RA
TT 1.4 Design and carry out experiments to identify the trajectory for fitness improvement in the in vitro and in vivo systems with the mutation operators alone, with and without environmental change, with and without theory-based control of rate of mutation. RA
TT 1.5 As in TT 1.4, but in the in silico systems. A, P
TT 1.6 As in TT 1.4, but for recombination. C, RA
TT 1.7 As in TT 1.5, but for recombination. A, P
TT 1.8 As in TT 1.4, but for selection. C, RA
TT 1.9 As in TT 1.5, but for selection. A, P

TO 2 Analyse these experimental results statistically in light of the predictions from the theory.
TT 2 Analyse the experimental results from TT 1.5--1.9 statistically in light of the predictions from the theory. J (+R)

TO 3 Identify the differences between the experimental systems that give rise to variations in the results.
TT 3.1 Identify the differences between the experimental systems that give rise to variations in the results from TT 1.4 & 1.5 (mutation), primarily by modifying the in silico systems. A, P, C, RA (J, R)
TT 3.2 As in TT 3.1, but for the results from TT 1.6 & 1.7 (recombination). A, P, C, RA (J, R)
TT 3.3 As in TT 3.1, but for the results from TT 1.8 & 1.9 (selection). A, P, C, RA (J, R)

TO 4 Identify the genetic basis for the growth-rate improvements in the in vivo system.
TT 4.1 Obtain DNA sequence for in strains across the in vivo evolution. RA
TT 4.2 Identify mutations in deep sequence data for in vivo system. RA
TT 4.3 Map mutations back to original evolving populations in in vivo system. RA
TT 4.4 Identify fitness effects of specific mutation in in vivo system. RA