International Research Opportunities
The IGERT provides international student fellowships to 3rd, 4th and 5th year students. Each fellowship will be for 3 months travel to work with a collaborating faculty member at one of our partner institutions. The Bioinformatics Graduate Program has partnerships with the following international universities:
The lab’s main interest is the study and mathematical modeling of biological organisms on the cellular and subcellular level. Its aim is to develop new techniques and software, and incorporate new and well-established knowledge to get a better understanding of the fundamentals of life. They have a special interest in: Yeast, Bacillus subtilis and Mammalian cells.
The lab’s research focuses on molecular and cellular evolution, DNA chips and reverse engineering, and biomedical applications of nonlinear dynamics.
The lab performs statistical analysis of DNA and protein sequences. Building on methods from statistical physics (correlation functions, mutual information, entropies), statistical dependences in sequences are analyzed. There are strong indications that interesting general aspects of the evolution of genomes can be made accessible from such statistical examinations.
High density DNA-arrays (“DNA chips”) allow measurements of gene expression levels for a large number of genes simultaneously. In this way thousands of mRNA concentrations can be analyzed in parallel, potentially revealing complex gene regulatory networks. The lab assesses the data reliability (image analysis, calibration, reproducibility), identifies co-regulated genes by cluster analysis, and detects transcription factor binding sites in clusters of co-regulated genes. It is their aim to incorporate the resulting information into network models of signaling cascades such as the Ras pathway.
The group is focusing on the development of mathematical models of biological systems. We apply differential equations, convex algebra, stochastic equations, and other mathematical concepts to describe and simulate metabolic and regulatory systems. The aims of our models are to explain the mechanisms underlying molecular interaction networks and to uncover design principles which lead to increased robustness, higher flexibility or better adaptability to changing environmental factors. Applying evolutionary models to simulate the development of interaction networks under random mutations and selective pressure, we strive at understanding how characteristic structural properties of complex networks may have emerged.
The main goal of our research is the development of mathematical models enabling computer simulations of the dynamical behavior of complex molecular reaction systems operating at cellular level as, for example, metabolic pathways, proteolysis and antigen presentation, or self-organization of cellular organelles. Cell types under current investigation comprise hepatocytes, parasites (Toxoplasma gondii, Plasmodium falciparum), erythrocytes, and neurons.
The lab’s projects include simulation of long-time-dynamics of proteins by a Monte Carlo method, free energy computations of protein substrate binding, structure and dynamics of protein-water systems, normal mode analysis of protein vibrations and fluctuations, simulation of the dynamics of biological macromolecules in torsion angle space, structure and function of photosynthetic reaction centers, calculation of protonation and redox potentials of molecular groups in proteins, simulation of electron transfer in and between proteins, pharmacophore matching and indirect drug design, docking of substrates and drug design, simulation of protein-protein-association, quantum chemical computations of pKa values and redox potentials, rediction of protein structures with neural networks, and prediction of protein structures with an optimized energy function.
Our goal is to undertake bioinformatics-related research and education with emphasis on structural bioinformatics to improve our understanding of living systems.
This understanding covers research into evolution, structure, function, and interaction of proteins, and disease relationships with focus on immunology and cancer. We accept the challenge to predict the results of in vitro experiments by in silico screening and simulation.
The Rajewsky Lab uses computational and experimental methods to dissect, systems-wide, function and evolution of gene regulation in metazoans. One major focus is to understand more about gene regulation by small RNAs, in particular microRNAs. To probe general mechanisms in gene regulation of microRNAs, the lab works with cell lines. We are also investigating the function of small RNAs during very early development of C. elegans. Furthermore, the lab has established planaria as a model system within the lab. These freshwater flatworms are famous for their almost unlimited ability to regenerate any tissue via pluripotent, adult stem cells. The lab is studying the role of small RNAs in planarian regeneration.
A transcription factor tends to bind to particular DNA patterns which can be summarized by so-called Positional Weight Matrices (PWMs). After having explored the power and problems of matching PWMs to sequence in the CORG-database, we have developed an alternative biophysics-inspired approach. This “TRAP” method (for Transcription Factor Affinity Prediction) transforms the match between a sequence and a pattern into a binding probability and integrates over the region of interest, say a promoter region. The TRAP method has been validated by comparison to large-scale DNA binding experiments (ChIP-chip and ChIP seq experiments) and shown to be successful in predicting novel target genes of transcription factors. Based on a statistical normalization of the affinity scores, the most likely binding factors to a particular promoter can be inferred. Together with the group of Stefan Haas, TRAP has further been utilized to recognize transcription factor binding sites which are over-represented in co-expressed or tissue-specific groups of genes. Ongoing work applies TRAP for predicting possible effects of regulatory SNPs. It has also been applied in the context of deriving gene regulatory networks.
Yitzhak Pilpel Lab, Weizmann Institute
The lab studies how gene regulatory networks perform robustly despite environmental variations, genetic changes, and the stochastic nature of the internal cellular environment. Mechanisms studied include genomic redundancy and the design of networks that probabilistically predict environmental changes before they occur. Using a combination of genome-wide computational approaches and laboratory experiments, the lab has established dictionaries of cis-regulatory sequence motifs controlling the initiation of transcription in several organisms. In complement, micro-array experiments combined with computational analysis have been used to determine how mRNA lifetimes are encoded by 3′-UTR sequence elements. The lab also studies the control of translation by adaptation of protein coding sequences and the tRNA repertoire of an organism. In particular, by examining how codon adaptations of the same gene in different organisms may derive phenotypic differences between these organisms. In other words, how traits are encoded by evolutionary shaping, not of the functions of genes, but rather of the efficiency by which they are translated.
The lab focuses on two pattern-related problems. The first involves the recognition of regulatory targets of non-protein coding RNA elements (microRNAs). The second seeks to determine how protein primary sequence determines three-dimensional structure. The work of the lab involves adapting computer science algorithms for pattern matching and computational geometry to these problems.
The lab develops fundamental algorithmic techniques for pattern-matching problems in biosequences, including designing control antisense oligonucleotides, and locating gene clusters in multiple genomes. A current example is the weighted-matching problem which applies to a set of aligned biological sequences that are not identical but have many local similarities. The weighted sequence is a “statistical image” or profile of the set, which gives the probability of every symbol’s occurrence at every text location. The pattern is a string and the algorithm returns all locations in the set where the pattern occurs with high probability.
The lab is exploring sequence-structure similarity to find common structural patterns in RNA sequences. The lab is concentrating on developing RNA comparison algorithms that overcome current limitations in both expressibility (the type of motif that can be found) and efficiency. Current projects seek to investigate sequence and structural locality in alignments and develop new filtering techniques for fast search for functional RNA motifs in large genomic databases.
This lab studies the dynamic processes of nerve terminals and the molecular aspects of synapse functioning, plasticity, and maturation. The control of exocytosis in regulated secretory system of the parotid gland. Proteomics and genomics approaches for studying differentiation, neurotransmitter phenotype acquisition, and synapse formation in neuronal cell-lines.
DNA, RNA, and proteins are the basic molecular building blocks of life, but the living cell contains additional molecules, including water, ions, small chemical compounds, glycans, lipids, and other biochemical molecules, without which the cell would not function. Because the proteins responsible for biosynthesis, biodegradation, and transport of these additional molecules are encoded in the genome, one may assert that all cellular functions are specified by the genomic DNA sequence. In practice, however, it is not possible to infer higher-level systemic functions of the cell or the organism simply from the molecular sequence information alone. We are developing bioinformatics methods to integrate different types of data and knowledge on various aspects of the biological systems towards basic understanding of life as a molecular interaction/reaction system and also for practical applications in medical and pharmaceutical sciences.
The recent advances in biomedical research have been producing large-scale, ultra-high dimensional, ultra-heterogeneous data. Due to these post-genomic research progresses, our current mission is to create computational strategy for systems biology and medicine towards translational bioinformatics. With this mission, we have been developing computational methods for understanding life as a system and applying them to practical issues in medicine and biology.
Due to rapid progress of the genome projects, whole genome sequences of organisms ranging from bacteria to human have become available. In order to understand the meaning behind the genetic code, we have been developing algorithms and software tools for analyzing biological data. We have recently studied the following topics: prediction and comparison of protein and RNA structures, inference and control of biological networks, chemo-informatics, scale-free networks, and combinatorial algorithms for bioinformatics.
With the recent advancement of experimental techniques in molecular biology, research in modern life science is shifting to the comprehensive understanding of a biological mechanism consisting of a variety of molecules. Our focus is placed on molecular mechanisms in biological phenomena, represented by biological networks such as metabolic and signal transduction pathways. Our research objective is to develop techniques based on computer science and/or statistics to systematically understand biological entities at the cellular and organism level.