Erik van Nimwegen | |
|---|---|
 Erik van Nimwegen (2011) | |
| Born | 5 November 1970 |
| Nationality | Dutch |
| Scientific career | |
| Fields | Computational Biology |
| Institutions | University of Amsterdam, Santa Fe Institute, Utrecht University, Rockefeller University, Swiss Institute of Bioinformatics, Biozentrum University of Basel |
Erik van Nimwegen (born 5 November 1970 in Amsterdam, Netherlands) is a Dutch computational biologist and Professor at the Biozentrum of the University of Basel, Switzerland.[ citation needed ]
Erik van Nimwegen studied theoretical physics at the University of Amsterdam. He performed his PhD studies at the Santa Fe Institute (SFI) in Santa Fe New Mexico, receiving his PhD from the Faculty of Biology at Utrecht University in 1999. This was followed by a year of post-doc studies at the SFI, and three years as a fellow at the Center of Studies in Physics and Biology at the Rockefeller University, New York. Since 2003 he is Professor of Computational Biology at the Biozentrum of the University of Basel, [1] and group leader at the Swiss Institute of Bioinformatics since 2004. [2]
Erik van Nimwegen’s main research topics concern genome evolution and the function and evolution of the regulatory networks by which cells control gene expression. [3] [4] He develops mathematical models for analyzing how regulatory networks evolve and function, and computational methods for the reconstruction of such networks from large biological data-sets. Van Nimwegen's work includes a general model for the evolution of robustness against mutations and the identification of a number of universal scaling laws of genome evolution. [5] Further research topics are the development of general Bayesian methods for transcription factor and miRNA binding site prediction as well as models for inferring regulatory networks from genome-wide expression and chromatin state data. [6]
Since 2010 Member of the editorial board of the journal PLoS Computational Biology . [7]
Bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biological data, in particular when the data sets are large and complex. As an interdisciplinary field of science, bioinformatics combines biology, chemistry, physics, computer science, information engineering, mathematics and statistics to analyze and interpret the biological data. Bioinformatics has been used for in silico analyses of biological queries using computational and statistical techniques.
Computational genomics refers to the use of computational and statistical analysis to decipher biology from genome sequences and related data, including both DNA and RNA sequence as well as other "post-genomic" data. These, in combination with computational and statistical approaches to understanding the function of the genes and statistical association analysis, this field is also often referred to as Computational and Statistical Genetics/genomics. As such, computational genomics may be regarded as a subset of bioinformatics and computational biology, but with a focus on using whole genomes to understand the principles of how the DNA of a species controls its biology at the molecular level and beyond. With the current abundance of massive biological datasets, computational studies have become one of the most important means to biological discovery.
Cis-regulatory elements (CREs) or Cis-regulatory modules (CRMs) are regions of non-coding DNA which regulate the transcription of neighboring genes. CREs are vital components of genetic regulatory networks, which in turn control morphogenesis, the development of anatomy, and other aspects of embryonic development, studied in evolutionary developmental biology.
David Haussler is an American bioinformatician known for his work leading the team that assembled the first human genome sequence in the race to complete the Human Genome Project and subsequently for comparative genome analysis that deepens understanding the molecular function and evolution of the genome.
Biological network inference is the process of making inferences and predictions about biological networks. By using networks to analyze patterns in biological systems, such as food-webs, we can visualize the nature and strength of interactions between species, DNA, proteins, and more.
Alexander van Oudenaarden is a Dutch biophysicist and systems biologist. He is a leading researcher in stem cell biology, specialising in single cell techniques. In 2012 he started as director of the Hubrecht Institute and was awarded two times an ERC Advanced Grant, in 2012 and in 2017. He was awarded the Spinoza Prize in 2017.
In evolutionary biology, robustness of a biological system is the persistence of a certain characteristic or trait in a system under perturbations or conditions of uncertainty. Robustness in development is known as canalization. According to the kind of perturbation involved, robustness can be classified as mutational, environmental, recombinational, or behavioral robustness etc. Robustness is achieved through the combination of many genetic and molecular mechanisms and can evolve by either direct or indirect selection. Several model systems have been developed to experimentally study robustness and its evolutionary consequences.
John Quackenbush is an American computational biologist and genome scientist. He is a professor of biostatistics and computational biology and a professor of cancer biology at the Dana–Farber Cancer Institute (DFCI), as well as the director of its Center for Cancer Computational Biology (CCCB). Quackenbush also holds an appointment as a professor of computational biology and bioinformatics in the Department of Biostatistics at the Harvard School of Public Health.
James P. Crutchfield is an American mathematician and physicist. He received his B.A. summa cum laude in Physics and Mathematics from the University of California, Santa Cruz, in 1979 and his Ph.D. in Physics there in 1983. He is currently a Professor of Physics at the University of California, Davis, where he is Director of the Complexity Sciences Center—a new research and graduate program in complex systems. Prior to this, he was Research Professor at the Santa Fe Institute for many years, where he ran the Dynamics of Learning Group and SFI's Network Dynamics Program. From 1985 to 1997, he was a Research Physicist in the Physics Department at the University of California, Berkeley. He has been a Visiting Research Professor at the Sloan Center for Theoretical Neurobiology, University of California, San Francisco; a Post-doctoral Fellow of the Miller Institute for Basic Research in Science at UCB; a UCB Physics Department IBM Post-Doctoral Fellow in Condensed Matter Physics; a Distinguished Visiting Research Professor of the Beckman Institute at the University of Illinois, Urbana-Champaign; and a Bernard Osher Fellow at the San Francisco Exploratorium.
The Biozentrum of the University of Basel specializes in basic molecular and biomedical research and teaching. Research includes the areas of cell growth and development, infection biology, neurobiology, structural biology and biophysics, and computational and systems biology. With 500 employees, the Biozentrum is the largest department at the University of Basel's Faculty of Science. It is home to 30 research groups with scientists from more than 40 nations.
Ron Shamir is an Israeli professor of computer science known for his work in graph theory and in computational biology. He holds the Raymond and Beverly Sackler Chair in Bioinformatics, and is the founder and head of the Edmond J. Safra Center for Bioinformatics at Tel Aviv University.
A neutral network is a set of genes all related by point mutations that have equivalent function or fitness. Each node represents a gene sequence and each line represents the mutation connecting two sequences. Neutral networks can be thought of as high, flat plateaus in a fitness landscape. During neutral evolution, genes can randomly move through neutral networks and traverse regions of sequence space which may have consequences for robustness and evolvability.
Mihaela Zavolan is a system biologist and Professor at the Biozentrum of the University of Basel.
Sarah Amalia Teichmann is a German scientist who is head of cellular genetics at the Wellcome Sanger Institute and a visiting research group leader at the European Bioinformatics Institute (EMBL-EBI). She serves as director of research in the Cavendish Laboratory, at the University of Cambridge and a senior research fellow at Churchill College, Cambridge.
Julian John Thurstan Gough is a Group Leader in the Laboratory of Molecular Biology (LMB) of the Medical Research Council (MRC). He was previously a professor of bioinformatics at the University of Bristol.
Barbara J. Wold is the Bren Professor of Molecular Biology, the principal investigator of the Wold Lab at the California Institute of Technology (Caltech) and the principal investigator of the Functional Genomics Resource Center at the Beckman Institute at Caltech. Wold was director of the Beckman Institute at Caltech from 2001 to 2011.
FANTOM is an international research consortium first established in 2000 as part of the RIKEN research institute in Japan. The original meeting gathered international scientists from diverse backgrounds to help annotate the function of mouse cDNA clones generated by the Hayashizaki group. Since the initial FANTOM1 effort, the consortium has released multiple projects that look to understand the mechanisms governing the regulation of mammalian genomes. Their work has generated a large collection of shared data and helped advance biochemical and bioinformatic methodologies in genomics research.
Aoife McLysaght is an Irish geneticist and a professor in the Molecular Evolution Laboratory of the Smurfit Institute of Genetics, University of Dublin in Ireland.
Metabolic gene clusters or biosynthetic gene clusters are tightly linked sets of mostly non-homologous genes participating in a common, discrete metabolic pathway. The genes are in physical vicinity to each other on the genome, and their expression is often coregulated. Metabolic gene clusters are common features of bacterial and most fungal genomes, and are less often found in other organisms. They are most widely known for producing secondary metabolites, which are the source or basis of most pharmaceutical compounds, natural toxins, and chemical communication and chemical warfare between organisms. Metabolic gene clusters are also involved in nutrient acquisition, toxin degradation, antimicrobial resistance, and vitamin biosynthesis. Given all these properties of metabolic gene clusters, they play a key role in shaping microbial ecosystems, including microbiome-host interactions. Thus several computational genomics tools have been developed to predict metabolic gene clusters.
Genetic regulatory circuits is a concept that evolved from the Operon Model discovered by François Jacob and Jacques Monod. They are functional clusters of genes that impact each other's expression through inducible transcription factors and cis-regulatory elements.
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