Top-down morphogenesis of colorectal tumors

One of the fundamental tenets of oncology is that tumors arise from stem cells. In the colon, stem cells are thought to reside at the base of crypts. In the early stages of tumorigenesis, however, dysplastic cells are routinely found at the luminal surface of the crypts whereas the cells at the bases of these same crypts appear morphologically normal. To understand this discrepancy, we evaluated the molecular characteristics of cells isolated from the bases and orifices of the same crypts in small colorectal adenomas. We found that the dysplastic cells at the tops of the crypts often exhibited genetic alterations of adenomatous polyposis coli (APC) and neoplasia-associated patterns of gene expression. In contrast, cells located at the base of these same crypts did not contain such alterations and were not clonally related to the contiguous transformed cells above them. These results imply that development of adenomatous polyps proceeds through a top-down mechanism. Genetically altered cells in the superficial portions of the mucosae spread laterally and downward to form new crypts that first connect to preexisting normal crypts and eventually replace them.

Top-down free-energy minimization on protein potential energy landscapes

The hierarchical properties of potential energy landscapes have been used to gain insight into thermodynamic and kinetic properties of protein ensembles. It also may be possible to use them to direct computational searches for thermodynamically stable macroscopic states, i.e., computational protein folding. To this end, we have developed a top-down search procedure in which conformation space is recursively dissected according to the intrinsic hierarchical structure of a landscape's effective-energy barriers. This procedure generates an inverted tree similar to the disconnectivity graphs generated by local minima-clustering methods, but it fundamentally differs in the manner in which the portion of the tree that is to be computationally explored is selected. A key ingredient is a branch-selection algorithm that takes advantage of statistically predictive properties of the landscape to guide searches down the tree branches that are most likely to lead to the physically relevant macroscopic states. Using the computational folding of a β-hairpin-forming peptide as an example, we show that such predictive properties indeed exist and can be used for structure prediction by free-energy global minimization.