Life on Earth could have existed already 3.8 Ga ago, and yet, more complex, multicellular life did not evolve until over three billion years later, about 700 Ma ago. Many have searched for the reason behind this apparent delay in evolution, and the dominating theories put the blame on the hostile Precambrian environment with low oxygen levels and sulphide-rich oceans. There are, however, doubts whether this would be the full explanation, and this thesis therefore focuses on a new hypothesis; the levels of the redox sensitive element arsenic increased in the oceans as a consequence of the change in weathering patterns that followed the upcome of free oxygen in the atmosphere at about 2.4 billion years ago. Given its toxicity, this could have had negative effects upon the life of the time. To test the hypothesis, 66 samples from drill cores coming from South Africa and Gabon with ages between 2.7 and 2.05 Ga were analysed for their elemental composition, and their arsenic content were compared with carbon isotope data from the same samples. These confirmed that a rise in arsenic concentration following the upcome of free oxygen in the atmosphere and the onset of oxidative weathering of continental sulphides. Arsenic, which is commonly found in sulphide minerals, was weathered together with the sulphide and delivered into the oceans, where it in the Palaeoproterozoic increased to over 600% compared to the older Archaean levels, at least locally. Iron had the strongest control over the arsenic levels in the anoxic (ferruginous and sulphidic) oceans, probably due to its ability to remove arsenic through adsorption. During oxygenated conditions, sulphur instead had the strongest influence upon arsenic, likely because of the lack of dissolved iron. The highest arsenic levels were found in samples recognised as coming from oxygenated conditions, although this might be due to the oxygenation state of arsenic affecting its solubility. Arsenic is toxic already at low doses, especially if the necessary arsenic detoxification systems had not yet evolved. However, the lack of correlation between arsenic and changes in δ13C indicated that the increase of arsenic did not affect the primary production between 2.7 and 2.05 Ga. Thus, whether arsenic could have affected the evolution of life during the Mesoproterozoic remains to be shown.

Technology, nowadays, has given us huge computational potential, but computer sciences have major problems tapping into this pool of resources. One of the main issues is how to program and design distributed systems. Biology has solved this issue about half a billion years ago, during the Cambrian explosion: the evolution of multicellularity. The evolution of multicellularity allowed cells to differentiate and so divide different tasks to different groups of cells; this combined with evolution gives us a very good example of how massively parallel distributed computational system can function and be “programmed”. However, the evolution of multicellularity is not very well understood, and most traditional methodologies used in evolutionary theory are not apt to address and model the whole transition to multicellularity. In this thesis I develop and argue for new computational artificial life methodologies for the study of the evolution of multicellularity that are able to address the whole transition, give new insights, and complement existing methods. I argue that these methodologies should have three main characteristics: accessible across scientific disciplines, have potentiality for complex behaviour, and be easy to analyse. To design models, which possess those characteristics, I developed a model of genetic regulatory networks (GRNs) that control artificial cells, which I have used in multiple evolutionary experiments. The first experiment was designed to present some of the engineering problems of evolving multicelled systems (applied to graph-colouring), and to perfect my artificial cell model. The two subsequent experiments demonstrate the characteristics listed above: one model based on a genetic algorithm with an explicit two-level fitness function to evolve multicelled cooperative patterning, and one with freely evolving artificial cells that have evolved some multicelled cooperation as evidenced by novel measures, and has the potential to evolve multicellularity. These experiments show how artificial life models of evolution can discover and investigate new hypotheses and behaviours that traditional methods cannot.