Computational Biology in the Atchley Laboratory
Developmental Quantitative Genetics (DQG) of Mammalian Growth.
Mammalian growth is a dynamic, multidimensional process involving genetic and developmental interactions at many levels of organization. Figs. A and B provide a DQG model describing some aspects of the control of this process. Fig. A describes the involvement of two genomes (fetal and maternal) arising in two generations and interacting within three environments (uterine, postnatal, and postweaning). Primary causal effects are shown as a solid line arrow while interactions are denoted by a dotted-line double arrow. Since the model involves two generations, the individual's own genotype is designated as the "progeny genotype." Modulation of development by these separate genomes and environments is responsible for the patterns of developmental change leading to the final form of the organism.
B describes the dynamic and multidimensional nature of the control of
growth along axes reflecting genetic regulation, cellular mechanics and
developmental signaling. The progression from maternal control to fetal
control to age- and tissue-specific patterns of gene expression is shown
in the genetic regulation axis. This progression occurs in the three environments
shown in Fig. A. Differential growth involving cell multiplication, programmed
cell death and cell enlargement is described on the cellular mechanics
Our research on the genetic control of mammalian growth focuses on several broad questions:
1. What is the relative contribution of uterine maternal effects on growth and development? These questions are generally approached using embryo manipulation and postnatal cross-fostering procedures followed by quantitative genetic analyses.
2. What is the genetic architecture of complex morphological and life history traits and how does the architecture change during ontogeny? These questions are explored using quantitative trait locus (QTL) mapping procedures. We are currently pursuing QTL mapping experiments on a broad front using mouse lines we constructed by selection which different in the number versus size of cells in various organs and tissues as well as a number of other biological attributes.
3. What is the biological basis for ontogenetic changes in the genetic variance - covariance structure among complex traits? In particular, what is the basis of ontogenetic changes in pleiotropy and how does this affect by age-specific patterns of selection? Do changes in covariances observed among different taxa arise from selection acting at different stages during ontogeny?
4. What is the developmental and genetic basis for morphological divergence in complex traits?
5. How are the components of complex traits integrated during ontogeny to achieve a final functioning complex trait and how does selection refine this integration?
Most of our efforts focus on the important basic Helix-Loop-Helix (bHLH) group. This structurally complex and functionally heterogeneous group is involved with neurogenesis, myogenesis, cell proliferation, tissue differentiation and many other biological processes.
of these proteins have had their 3D structure determined such as Max (shown
to the left) which is an essential component of the Myc-Max-Mad network.
We are carrying out a diverse set of experiments with bHLH proteins that range from molecular analyses of DNA binding specificity to theoretical and statistical studies of the evolution of interaction networks. Some current research projects in the lab.
1. The Myc-Max-Mad Network is a complex of interacting proteins essential for cell proliferation and differentiation. Myc is an important proto-oncogene. Using phylogenetic analyses and structural modeling, we are attempting to understand the origin of this important network, which member is the progenitor of the complex, how the various interactions might have arisen, and the structural and functional consequences of sequence evolution in the various members of this interaction network.
2. Many algorithms used in sequence alignment and the estimation of phylogenetic trees assume no significant covariation between amino acid sites. Obviously, this simplifying assumption does not reflect biological reality. Using techniques from information theory and multivariate statistics, we are attempting to estimate the extent of covariation among amino acid sites within and between functional domains. Further, we are producing methods to partition the observed covariation into that due to phylogenetic, structural and functional constraints. We then relate these covariance components to the three-dimensional structure of the proteins.
3. We are interested in determining the evolutionary boundaries of various protein families, e.g., estimating the statistical probability that a given protein belongs to a particular family or clade. We are working on multivariate statistical procedures that consider the simultaneous covariation among amino acid sites to generate a probabilistic classification of large groups of proteins. At the molecular level, we are using site-directed mutagenesis and related techniques to experimentally determine the functional limits of collections of related proteins.