Malgorzata E. Rowicka-Kudlicka, PhD

Assistant Professor, Department of Biochemistry & Molecular Biology,
Sealy Center for Molecular Medicine

Phone: (409) 772-1253
Email: merowick@utmb.edu
Malgorzata E. Rowicka-Kudlicka, PhD

Research Interests

A key bottleneck in today’s biology is the interpretation and integration of exponentially growing genomic data. We are interested both in developing computational methods and tools for analysis of the genomic data and in experimental testing (through collaborations) the predictions of our models. Our research focus is on the analysis of microarray data and on understanding regulation of the large-scale cellular processes, especially of the eukaryotic cell cycle and the vertebrate development.

Multilevel study of the eukaryotic cell cycle regulation. Misregulation of the cell cycle is implicated in many diseases and biological problems. Control of cell division is particularly important: uncontrollable and persistent cell divisions are observed in cancer, but rapid and controlled cell division is essential in wound healing. Eukaryotic cell division is regulated at many levels: gene transcription, protein production, localization, modification, and degradation. The precise timeline of cell-cycle gene expression has been revealed in our previous work. Now we are interested in gaining similarly precise insight into temporal orchestration of binding of cell-cycle transcription factors in vivo. We are also interested in  investigating, both computationally and experimentally, proteome dynamics during the cell cycle and studing all the layers of the cell cycle regulation together.  I am interested in evolution of the cell cycle regulation and in general principles of regulation of the large-scale cellular processes.

Identification of basic regulatory modules in gene expression data. Cellular phenotypes are often determined by multiple genetic and environmental factors. In many cases underlying basic mechanisms cannot be identified by experimental isolation of these factors (For example, observed gene expression levels are results of both a given biological process, of a cell-line (or strain) used and of growth conditions). Such environmental or experimental differences can hinder the analysis of thousands of data sets already gathered in databases. I plan to develop methods capable of computationally dissecting and identifying responses to different experimental factors and activation of transcription factor complexes. Our approach is based on Independent Component Analysis (ICA), a relatively new and powerful statistical method for revealing hidden factors that underlie sets of measurements. ICA is designed especially to analyze data that contain significantly non-Gaussian components, such as typical microarray data.

Understanding protein stability and modeling proteome dynamics. Protein regulation through selective degradation is a key cellular mechanism. Large scale experimental measurements of protein stabilities (the protein half-lives in vivo) have become available only very recently. Consequently, the protein sequence-stability relationship is not well understood. Examples of the questions we are interested in are: How is protein stability encoded in its sequence?  Is there evolutionary pressure on conserving protein stability? Global analysis of mRNA expression is widely performed, but because of differences in translation rates and stability between proteins, mRNA levels only roughly approximate the levels of the corresponding protein. Protein half-lives in vivo, either measured or predicted from the sequence,can be used, together with easily accessible expression data, to model dynamical abundances of the corresponding proteins. Even without taking into account posttranslational modifications, we expect such model to provide significantly better predictions of the dynamics of the proteome than implicitly relying on mRNA abundances as surrogates, which is a common practice today.