We might agree that the question about the causes of evolution has been answered within the classical Darwinian framework. Evolution is fueled by heritable variation and propelled by natural selection, to the extent that selection can discriminate among variants. Yet, there is a sense that answering the question about the causes of evolution is different from answering the question about what causes the outcomes of evolution. The latter is about why living systems are the way they are, not why they won a competition. Mutations are random, but their phenotypic consequences are not, and so we need to know how mutations map into phenotypes. It seems that only by understanding the possible can we come to recognize the extent to which evolution is shaped by accidents or (inclusively) by "laws" that can be understood in terms of some mechanistic theory of organization. Questions like these are as old as evolutionary theory itself, but for a while nothing much could be done about them. Biology lacked a mechanistic picture of molecular organization that could have provided a few toeholds. The reductionist triumphs of biophysics, biochemistry, and molecular biology have now put us in a position to revisit these questions more forcefully.
Much of my past work was motivated in one way or another by issues related to the concept of organization, the origin of novelty, and evolvability. At some point, however, I felt that the abstract framework I was pursuing was not relevant enough and the more biophysically grounded RNA model, while useful, had exhausted itself, at least in my hands. I wanted a setting more central to molecular organization (or systems biology for want of a better word) and more respectful of empirical givens. The processes underlying cellular decision making, such as signal transduction, seemed a good place to start. Yet, one soon realizes that capturing, analyzing, and understanding these processes demands new tools. Parts of our lab are therefore engaged in collaborations with computer scientists aimed at building computational reasoning instruments and applying them to complex real-world settings like molecular signaling. It is important to grasp that these problems are not the exclusive domain of physics or chemistry; they also are the conceptual province of those areas of computer science that are concerned with the formalization of distributed systems of "asynchronously communicating" (think reacting) "agents" (think molecules).
When taking a long view, these efforts appear as necessary prolegomena for the next appointment with evolvability. What kind of “evolvable language” has evolved within a medium of macromolecules that control, destroy and produce one another? Has this chemical language an intelligible abstract content or is it inextricably buried in its material substrate? What "programming" discipline does it entail?
Here is a thought that you may be able to take further. There seems to be a connection between plasticity (roughly, the environmentally induced change of phenotype at constant genotype) and variability (roughly, the mutational change of phenotype at constant environment). This has been shown to be the case for models of polymer structure (such as RNA), and predicts the possibility of a Baldwin effect at the molecular level. A connection of this kind has also been hypothesized for more complex phenotypic levels and processes, such as development. It may ultimately correspond to a general insight from the theory of dynamical systems, that those directions an attractor (a stable dynamical configuration) moves most in when parameters are varied (analogous to “genetic variability”) correlate with the directions that are softest with respect to perturbations of the dynamical variables (analogous to “plasticity”).