David Eugene Axelrod - US grants

The central theme of this project concerns the control of cell proliferation in normal and tumor cells. We intend to elucidate the molecular mechanisms that modify the transformed phenotype of mouse NIH3T3 cells containing the human rasEJ oncogene. Our strategy is to study revertant flat non-transformed cell lines derived from NIH3T3(rasEJ). We have isolated ten flat non-transformed cell lines and shown that six retain the rasEJ oncogene and produce p21ras protein. We propose to determine the molecular basis for the non-transformed phenotypes using the following approaches: (1) DNA. Non-transformed revertants will be compared to their transformed parents using Southern blot hybridization in order to detect possible differences in numbers of copies of rasEJ genes, numbers of copies of a tandemly repeated sequence 3' to ras, or methylation of ras. One non-transformed revertant shown to have an altered ras restriction enzyme map will be analyzed further to determine if it has undergone a deletion or a single base change, perhaps producing an analogue protein which acts as a competitive inhibitor of the p21ras protein. (2) Protein. Non-transformed revertants will be compared to parents using immunoprecipitation to detect possible differences in amount of p21ras protein produced, its rate of processing, or its cellular location. One revertant shown to have altered p23/p21 ratios will be further analyzed by pulse-chase experiments to confirm that it is altered in the rate of protein processing. (3) Cloning. Human DNA sequences which allow rasEJ to be re-expressed when transfected into revertants will be cloned from secondary transfectants using human Alu repetitive DNA probes and EMBL3 phage. Primary transfectants which are retransformed have already been isolated. These approaches whould allow us to elucidate molecular mechanisms that modify the transformed phenotype in cells containing the rasEJ oncogene.

biomedical equipment resource;

This project will support the Second International Conference on Mathematical Population Dynamics that will be held May 17-20, 1989 at Rutgers University. The meeting will be organized by Professor David Axelrod. The focus of this conference is the mathematical modeling of heterogeneous biological populations. An interdisciplinary forum for the exchange of ideas between biologists and mathematicians will be provided. Topics will include population of genes, cells, and tumors, the natural history of cancer, and selected topics from epidemiology. The mathematical topics relevant to these areas include branching processes, stochastic models, random walks, spatial processes, structured populations, semigroups of operators, and dynamical systems. Special efforts will be made to facilitate understanding between theoreticians and experimentalists and between established scientists and junior colleagues.

This research will develop systematic procedures which take advantage of computer-related developments and advanced combinatorial optimization techniques, to build on previously successful ad-hoc methods for optimizing feature selection in data analysis, with special attention to bioinformatics. Knowledge extraction from data represents a fundamental challenge in information technology research. A very frequent type of knowledge extraction problem is that of analyzing archives of records or observations in order to discover hidden structural relationships. Problems of this type appear in numerous areas of science, technology, medicine, management, and in countless other areas of activity. The advent of the computer and of the Internet have radically increased the role of data analysis, by allowing not only the creation of large, meaningful datasets, but also by making them accessible to researchers all over the globe.

One of the most prominent areas of applications of data analysis is in systems biology and bioinformatics. In contrast to molecular biology investigations, which typically focus on single molecules, systems biology pays attention to tens or even hundreds of thousands of biological attributes at the same time. Moreover, the number of attributes included in a dataset is predicted to increase dramatically in the very near future. The new global approach to systems biology has been enabled by new technologies that have allowed the simultaneous measurement of large numbers of attributes and the generation of large multiparameter datasets. These biological datasets represent the domain of bioinformatics. Beside the classic methods of statistics, new approaches to the analysis of data are required. Among others, these methodologies prompt the development of entirely new research areas, including e.g., machine learning, data mining, neural networks, support vector machines. In all these areas of data analysis, the knowledge of a known (finite) subset of observations is used to derive conclusions about the entire set of possible observations.

While powerful analytic tools have been developed within the framework of statistics and of newer research areas based heavily on combinatorics, logic and optimization, the size of the problems in the area of bioinformatics (as well as in some other areas), leads to major computational difficulties, and raises the challenge of developing new approaches in which systematic heuristic procedures are integrated into solution algorithms, in order to intelligently reduce the size of the problems to be solved. By using ad-hoc combinations of heuristics and of combinatorics, logic, and optimization based algorithms, this project aims to achieve spectacular reductions of problem size without significant loss in the accuracy of the resulting models.

This project will introduce new concepts for evaluating the role and the impact of features in data analysis problems. These concepts combine elements of statistics, combinatorics, information theory, the theory of Boolean functions, the theories of games and of voting. On the algorithmic side, they open possibilities for systematic heuristic approaches to the elimination of redundant features. The project will also introduce new concepts for the comparative evaluation of pairs of features, including those of similarity and domination. These concepts combine elements from statistics, the theory of partially ordered sets, and that of Boolean algebra, and can add to the arsenal of tools available for the elimination of unnecessary features. As opposed to previous studies which view the sets of attributes just as collections of individual attributes, the project proposes a study of the combined efforts of attributes. The research plan includes the development of a local optimality criterion for a set of attributes which should combine the two desired characteristics of a "support set": it should allow the construction of accurate models, and it should, at the same time, be of a computationally manageable size.

In addition, the project will introduce new, synthetic "logical" attributes which allow, on the one hand, the compression of the dataset and, on the other hand, the possibility of finding clearly understandable and practical usable "logical" discriminants, which distinguish the positive observations from the negative ones. A very significant application of these ideas is the proposed algorithmic framework for optimizing feature selection. In the field of biological research and bioinformatics, the project introduces: (i) the concept of "groups of biomarkers" (obtainable through combinatorial optimization techniques), (ii) an algorithmic hypothesis generator for biological research, and (iii) a new approach for discovering new classes of observations with highly similar characteristics. A publicly available software package will result from the work and is expected to stimulate substantial research in the computational analysis of data, and in bioinformatics.

By combining elements of statistics, combinatorial optimization, information theory, the theories of Boolean functions, games, voting, and partially ordered sets, the project is expected to attract researchers from a variety of areas to collaborative studies. The publicly available software for the analysis of bioinformatics data, as well as the hypothesis generation system proposed, will provide major tools for stimulating research in biology and bioinformatics.