Theory of Developmental Evolution and Complex Adaptive Systems
Developmental evolution is a recent transformation of evolutionary biology (with a deep history) that establishes evolutionary biology as a mechanistic science, one that grounds explanations of phenotypic evolution in a detailed understanding of the developmental mechanism that generate phenotypes in the first place (during ontogeny and governed to a large part by the regulatory genome).
A central concept in developmental evolution is the four dimensional regulatory genome or the gene regulatory network. Detailed understanding of these networks of interacting genes allows us to account for the causal role of the genome in generating phenotypes as well as phenotypic differences. As each phenotype is the product of an underlying gene regulatory network (acting in concert with a whole range of additional signaling processes), every phenotypic variant is therefore the product of some corresponding variation within these networks. And this is where the engineering approach meets evolutionary biology. It is now possible, by comparing different species and the structures of their gene regulatory networks, to identify the differences in these networks that have led to the acquisition of novel traits during evolution. And as it becomes possible to engineer these networks (at least to some degree), we should in principle be able to re-engineer some of these evolutionary transformations. (In those cases where we have a species representing the ancestral condition and we know enough about the gene regulatory networks that we are able to identify the changes within the network responsible for the acquisition of the new trait in the derived species.) This approach has been called Synthetic Experimental Evolution by our collaborators Eric Davidson and Doug Erwin and it is producing suggestive results that seem to confirm its logical premise. We are exploring this huge transformation of the explanatory logic of the life sciences and its theoretical implications as well as its historical and epistemological consequences.
Theory of Complex Adaptive Systems
Another theoretical focus of our work is the theory of Complex Adaptive Systems (CAS). CAS can be found across many different areas (from living and social to economical and technological systems) and are variable in form. Therefore, they are best captured by identifying the common features that describe their essential elements and convergent behaviors. CAS comprise many “agents” that exhibit independent properties and behaviors but work together to produce emergent properties that cannot be predicted by isolated understanding of these interacting agents/components. This quality of emergence is long recognized in homogeneous systems most notably in physics (e.g., turbulence, magnetism), which are relatively simple in terms of number of interacting elements (e.g., atoms/molecules). In contrast, biological and social CAS are highly heterogeneous and complex, both within and across broad scales of time and space.
As a consequence of the numbers of agents and dynamics of CAS, their behaviors play out in an interactive space that has a very high number of dimensions and degrees of freedom. Until recently, systems of such high dimensionality were not tractable to human understanding, but recent advances in computation and modeling of CAS are creating the tools to manage, mine and manipulate the vast amount of data needed to describe such high dimensionalities. All efforts to characterize and understand CAS include advanced computational sciences and modeling to capture systems-based multidimensionality and new, responsive models and constructs, for experimental analysis. Although a single prescriptive statement that embraces all categories of CAS is difficult, it is increasingly possible to think about (and model) their fundamental shared properties; complexity, robustness and adaption. One of the defining features of CAS, whether natural, technological, economical, social/cultural or biological, is that they are in large measure the product of adaptive co-evolution with their environment. For example, the merger of complexity with evolutionary theory has in recent years become increasingly recognized as a fundamental theoretical organizing model for understanding aspects of biological systems across scales and time; including systems and synthetic biology. Moreover, dysregulation of complex molecular pathways and networks is increasingly recognized as causative in a number of high prevalence diseases such as cancer, neurodegenerative diseases and obesity. A focus on evolution (history) and complexity has also transformed the social sciences and led to a much better understanding of the nature of coupled biosocial systems. Therefore, evolution and evolutionary theory are critical core elements of strategies to address myriad CAS challenges.
Our projects in the Theory of Developmental Evolution and Complex Adaptive Systems include:
This work has been supported by a National Science Foundation CAREER award 0645729.