Andrzej Kudlicki, Ph.D.

Employment Opportunities

Affiliations: Department of Biochemistry & Molecular Biology,Scientist, Sealy Center for Molecular Medicine

Email: askudlic@utmb.edu

Phone: (409) 747-6860

 

 

 

 

 

 

Andrzej Kudlicki, Ph.D.

Assistant Professor

Research Interests

Our main research interest is in various aspects of transcriptional and epigenetic regulation in development and disease. In a range of biological systems, we aim to discover how and why transcription factor binding sites are selected and how chromatin modifications are directed to specific sites, depending on the time, cell type and environmental factors.

* The logic of segmental identity
In human and other vertebrates, the numbers of metameric segments in each region of the spine (cervical, thoracic, lumbar, sacral, coccygeal), as well as the total number of vertebrae are highly conserved within a species, and often even between species, for example almost all mammals have exactly seven cervical vertebrae. We are developing and testing a new theoretical model explaining the primordial mechanism controlling segmental identity and the role of colinearity of Hox genes.

* Modulation of transcription factor activity.
We have developed a formalism for inferring the three-gene regulator-modulator-target interactions in transcriptional networks. The method was successfully applied to uncovering the conditional activation of targets of the RelA/NfkB transcription factor.

* Transcriptional dysregulation in trinucleotide repeat disorders
Trinucleotide repeat disorders (including Huntington's Disease, and autosomal dominant Spinocerebellar ataxias) are lethal diseases with an unexplained primary mechanism. Neither of the two primary hypotheses (toxic protein or toxic mRNA) fully explains the characteristic properties of these diseases. We are developing an alternative model, attributing the disease to transcriptional dysregulation caused by the CAG expansion mimicking a regulatory element in an ectopic position.

* Spatiotemporal Organization of Somitogenesis.
Generation of somites in a vertebrate embryo is dependent on waves of gene expression which exhibits periodicity in the spatiotemporal domain. We have developed a novel approach, based on a maximum a posteriori deconvolution principle, to reconstruct the spatiotemporal expression pattern and infer causal interactions involved in this process. We are now studying the evolutionary conservation of the somite clock in order to identify the most basic of its components.

Timing of cell-cycle regulated gene transcription.
We develop computational methods of analysis of timecourse gene expression data, based on MAP optimization and the Maximum Entropy principle. We have designed and implemented an algorithm which deconvolves the measured culture-average profiles and allows to recover the single cell expression profile for each cell-cycle regulated gene. Peaks of transcripts regulated by the yeast cell cycle were recovered with a precision an order of magnitude better than the resolution of the source data. We have identified a previously undescribed, pre-replicative (G1/P) wave of transcription of cell-cycle genes. Our results have provided new insight into the assembly and dynamics of molecular complexes involved in the mitotic cell division (e.g. MCM, ORC), as well as allowed us to discover transcriptional regulation of genes previously thought to be constitutively expressed, as Cdc28/Cdk1, the master cell cycle regulator.

Inferring causation in protein networks from non-Gaussian probability distributions
Reconstructing protein networks is important for selecting candidate biomarkers and targets for drugs. The task is facilitated if the directionality (or causality) of interactions is known. We are working on inferring causal interactions in protein networks without the need for experimental interventions, by identifying asymmetric features in joint distributions of expression levels of pairs of genes, collected in a large number of conditions. We select and calibrate various statistical measures of asymmetry, using known interactions in yeast and human protein networks as training sets.

Online Tools for Analysis of Time Course Gene Expression Profiles
SCEPTRANS is a comprehensive on-line tool for analysis of microarrays from cell-division and metabolic cycles in the budding yeast. We are expanding this project into a general repository of periodic expression profiles, including data from different processes, such as circadian rhythms, sleep phases and organism development, supplemented with relations based on ontologies, evolutionary homology, regulatory motifs and profile clustering.

Other contributions

I am also interested in the numerical and statistical methods of model building and phasing in X-ray crystallography.

Links to additional online tools and datasets are available from our lab web server at http://moment.utmb.edu/