Friday, 17 February 2012

PhD Positions in Biochemistry and Biophysics at Stockholm University, Sweden

9 PhD positions at the Department of Biochemistry and Biophysics. Reference numbers SU DBB 1/12 to SU DBB 9/12, see project descriptions below. Deadline for application: February 29, 2012.

Subject description
More than 20 research groups are active at the Department of Biochemistry and Biophysics. The research projects span across a broad range of topics covering various aspects of structure and function of biological systems. A major fraction of these topics are centered on biological membranes, where many groups working
within this area are part of the Center for Biomembrane Research, which is hosted by the department. Also the Stockholm Bioinformatics Centre and the Science for Life Laboratory are closely linked to the department. The combination of the highly interdisciplinary expertise and research projects at the department is unique in Sweden and also at an international level. This expertise ranges across cell biology, biochemistry, biophysics and theory. Some specific topics that are addressed include membrane protein biogenesis, membrane protein topology & assembly, mitochondria & chloroplasts, protein structure & function, protein folding & trafficking, lipid biosynthesis & function, energy conversion, biochemical toxicology, DNA-RNA-PNA interactions, biological nitrogen fixation, viral membrane glycoproteins, protein structure & disease, bioinformatics, computational structural biology and development of theoretical tools. The experimental studies are performed on a wide range of organisms and are also combined with the use of a wide range of advanced biochemical and biophysical techniques. For more information about the department, see

To be accepted as a PhD student, credits corresponding to four years of full-time studies at the undergraduate level are required, including credits corresponding to at least two years of fulltime studies in chemistry, life sciences or physics, depending on the program. The credits should include courses at the advanced level (second cycle) corresponding to one year and of these one semester should be a degree thesis. In order to facilitate the evaluation of merits and suitability for the PhD studies the curriculum vitae (CV) should contain information about the extent and focus of the academic studies. The quantity (as part of an academic year) and the quality mark of courses in chemistry and physics are of particular interest. Please state titles of undergraduate theses and project works.

The selection among applicants will be based on the judgement of their capacity to successfully complete the PhD program. In practical terms, this means that the study merits will be the main selection criterion. The local study merits, such as passed advanced courses or project work at the department, will be given a relatively high weight. Equal opportunity aspects between men and women will be given a certain weight, as well as willingness and ability of candidates to participate in undergraduate teaching.

Terms of employment
Economic support is guaranteed during the agreed time in the individual study syllabus (study plan) for the graduate studies, totally for a maximum of 4 years. The department may request that the graduate student takes part in teaching or other departmental work in an activity additional to the graduate studies for up to 20% of full work time in years 2-4.
Stockholm University strives to be a workplace free from discrimination and gives equal opportunity for all.

More information
More details about the project can be requested from the project leaders, see below.
General information about the PhD training program may be requested from Peter Brzezinski, head of department (prefekt), telephone +046-(0)8-16 3280 or e-mail

Union representatives
Anqi Lindblom-Ahlm(SACO) and Lisbeth Häggberg (Fackförbundet ST), telephone +46-(0)8-16 2000 (switch board), and Gunnar Stenberg (SEKO), telephone +46-(0)70-316 43 41.

The application should contain a letter of intent (one to two pages that explain why you are interested in working on this project, why you are interested in studying for a PhD, what you hope to accomplish during your PhD studies, and what skills you can bring to this project), curriculum vitae, copies of degree certificates and transcripts of academic records, a list of two persons who may act as referees (with telephone numbers and e-mail addresses), and one copy of your undergraduate thesis and articles, if any.

The applicant may apply for more than one position, but in that case, a complete set of documents is required for each position.

The application should be labelled with the reference number of the project and should be received no later than February 29, 2012 by the department.
Dept. of Biochemistry and Biophysics, Stockholm University
c/o Haidi Astlind
Svante Arrhenius v. 16, SE-106 91 STOCKHOLM, SWEDEN
or by e-mail to:  (please combine all your documents into a single pdf file).

Further information on the web
Faculty of Science,
The Department of Biochemistry and Biophysics,
The handbook for postgraduate students,

Short description of the projects
Reference number SU DBB 1/12
Project title: Membrane protein bioinformatics
Project leader: Arne Elofsson,,
 Subject: biochemistry towards bioinformatics
Membrane proteins are the gateways to the cells and as such they are of great importance for the development of drugs. In addition membrane proteins are quite difficult to handle experimentally, therefore prediction methods are important to gain information about these protein. We have in recent years developed a number of state of the art prediction tools (SPOCTOPUS, SCAMPI etc) for membrane proteins. In these tools we combine different machine learning techniques (SVM, ANN, HMMs) with biological knowledge in innovative ways. In addition databases and web-tools are developed to provide users with useful tools. This project aims at the continued developed of tools to predict and understand membrane proteins.
A general interest in protein, programming and machine learning methods and a solid background in bioinformatics, physics or computer science is suitable for this position.

Reference number SU DBB 2/12
Project title: Cancer genomics and repeat proteins
Project leader: Arne Elofsson,,
Subject: biochemistry towards bioinformatics
More than 70\% of the proteins in the human proteome consist of multiple domains and about 17\% of the proteins contain repeated domains. Multi-domain proteins has played a key role in the development of complex regulatory systems and signaling required for multi-cellularity and an increased rate of creation of domain architectures has been observed in the metazoan lineage. Further, the abundance of repeat proteins in multicellular organisms has been suggested as an important source of genomic variation to compensate low generation rates. Our previous studies have shown that repeated domains are particularly common in higher eukaryotes, further strengthening the implication that they play a special role for organismal complexity. In eukaryotes, repeated domains are also frequent among protein interaction hubs. Indeed, understanding the structure, function, and evolution of repeat proteins may be important for understanding the complexity of the human genome.
Given the recent observation that copy number variations are more abundant than variations due to single nucleotide polymorphism, it is likely that genetic diseases are often affected by copy number variations. In addition, since regulation and signaling are the central tasks of repeat containing proteins, it is not surprising that many of them have been implicated as cancer genes. Consequently, better understanding of these proteins will potentially help us understand the signaling processes that determine whether a cell
develops normally or exhibits cancerous growth. However, studies of variations in repeat proteins using data from next generation sequence machines is not trivial as the correct assembly of repeated regions is often difficult to obtain.
Here, we aim to develop improved tools for improved assembly and annotation of repeated genomic regions for next generation sequence data. Although the method will primarily be developed for analysis of exons and their flanking regions, the method should be applicable on all types of repeating regions. Our primary focus will be copy number variations of whole genes, or, as is more likely in long repeat proteins, partial genes. This will provide a basis for an extension to our previous studies of protein domain evolution as well as provide technological improvements that will benefit the study of copy number variations.
The project involves programming, data analysis, benchmarking, and modeling. The successful candidate should have an M.Sc. in Bioinformatics, physics, biotechnology or a related field. Computer programming, UNIX skills, and knowledge of biological database systems are necessary merits.

Reference number SU DBB 3/12
Project title: Modeling of large biomolecular complexes
Project leader: Arne Elofsson,,
Subject: biochemistry towards bioinformatics
Proteins are the central machines of cells, and they perform their actions by interacting with each other as well as with other molecules. Large complexes involving tens or even hundreds of proteins make up the central hubs in biological interaction networks. Today, large-scale efforts in genomics, proteomics, lipidomics and metabolomics are producing complete lists of the molecules in entire cell as well as in different sub-cellular compartments. Further, interactions between molecules can be studied at different levels of detail. In small-scale studies it is possible to obtain detailed information about the interaction of a few molecules, while in large-scale studies less detailed information for a larger set of molecules can be obtained. Only for a small number of the large complexes atomistic details have been possible to obtain and in particular molecular complexes embedded in the membrane have been difficult to study experimentally.
In this project we aim to reveal detailed structural information about large biological complexes,. To obtain this goal we plan to integrate methods with information coming from different types of experimental data. A major source of information is coming from the rapid increase in genomic sequence data that is fueling our evolutionary studies of proteins and protein-complexes. However, data from large-scale studies needs to be combined with more detailed studies and integrated. One of our first focuses will be on one medically important molecular machine, the mitochondrial TOM translocase.
The project involves programming, data analysis, benchmarking, and modeling. The successful candidate should have an M.Sc. in Bioinformatics, physics, biotechnology or a related field. Computer programming, UNIX skills, and knowledge of biological database systems are necessary merits.

Reference number SU DBB 4/12
Project title: Structure-based modeling of membrane receptor-ligand interactions
Project leader: Jens Carlsson,,
Subject: biochemistry towards bioinformatics
G protein-coupled receptors (GPCRs) and ion channels are responsible for a majority of the signaling across the cell membrane and are the targets of 50% of all drugs. Breakthroughs during the last four years have led to the determination of the first atomic resolution structures of several important GPCRs and ion channels. This gives the opportunity to use simulations to understand how these receptors work and how small molecules, e.g. drugs, modulate their function. The increasing amount of structural information will also enable identification of new small-molecule modulators of membrane proteins via computational screening of large chemical libraries.
We are seeking a PhD student for projects that involve protein structure prediction, molecular dynamics simulations, and molecular docking screens. Using the recently published structures of the A2A adenosine receptor in different stages of activation, we will carry out simulations to investigate how binding of a small molecule in the extracellular binding site can activate a G-protein on the intracellular side via large conformational changes. The second part of the project involves development of methods to predict the three-dimensional structures of GPCRs and pentameric ligand-gated ion channels using homology modeling. Through computational screening of millions of molecules against our models, we will predict small molecules that bind to a receptor and test these experimentally.
The ideal background for this PhD position is a M.Sc. in bioinformatics, biotechnology, chemistry, physics or a related field.
Relevant publications
Carlsson J, Coleman RG, Setola V, Irwin JJ, Fan H, Schlessinger A, Sali A, Roth BL, and Shoichet, BK (2011) Ligand discovery from a Dopamine D3 receptor homology model and Crystal Structure. Nature Chemical Biology, published online.
Carlsson J, Yoo L, Gao GZ, Irwin JI, Shoichet BK, and Jacobson KA (2010) Structure-based discovery of A2A adenosine receptor ligands. J Med Chem 53:3748- 3755.

Reference number SU DBB 5/12
Project title: NMR studies of protein structure and function inside living cells
Project leader: Mikael Oliveberg,,
Subject: biochemistry
Disturbances of the interplay between the cellular house-keeping machinery and their client proteins stand out as the primary cause of our most common aging diseases, e.g. Alzheimer’s disease, Parkinson’s disease and amyotrophic lateral sclerosis (ALS). The unifying mechanism of these diseases is that some proteins escape the cellular control, start to aggregate and become toxic to the neurons. Yet, surprisingly little is known about how the cells maintain correct protein structure and function, and why they sometimes fail. The reason for this is mainly technical: it has posed a continuous challenge to study proteins ‘at work’ inside living cells.
Just recently, however, we have been able to obtain atomic-resolution NMR data of the ALS-provoking protein SOD1 inside human cells, and these data are of unprecedented quality and dynamic resolution. We are now extending this study to find out how SOD1 aggregates and becomes cytotoxic during ALS. The project is inter-disciplinary and involves NMR1, protein engineering/physical chemistry2, cell biology and transgenic model organisms.
  1. Inomata, K. et al. High-resolution multi-dimensional NMR spectroscopy of proteins in human cells. Nature 458, 106-109, (2009).
  2. Nordlund, A. et al. Functional features cause misfolding of the ALS-provoking enzyme SOD1. Proc Natl Acad Sci U S A 106, 9667-9672 (2009).
Reference number SU DBB 6/12
Project title: Regulation of membrane protein expression during Influenza replication
Project leader: Rob Daniels,,
Subject: biochemistry
Proteins destined for the plasma membrane are initially targeted to the endoplasmic reticulum where they are co-translationally translocated into the lumen of the endoplasmic reticulum(ER). Within the ER, a variety of cellular machinery assists in the folding and maturation of the nascent protein prior to its transport through the golgi and on to the plasma membrane. During influenza replication, its protein expression is orchestrated such that the core (early) proteins of the virus are synthesized before the components of the viral envelope. This often requires that the virus reprograms the cellular machinery to achieve the differential expression. The focus of this project is to systematically analyze how the individual components contribute to the regulation and expression of the viral membrane proteins hemagglutinin (HA) and neuraminidase (NA). The project entails analyzing the kinetics of HA and NA synthesis, trafficking, function and viral incorporation with respect to host cellular proteins and how any observed discrepancy is achieved. Our results will help refine how the expression of these viral membrane proteins is regulated which should provide mechanistic insight into the general methods of secretory protein regulation. The successful candidate should have an interest in biochemistry, molecular and cellular biology, and virology with experience in a variety of molecular and cellular techniques including: cloning, tissue culture, mammalian protein expression, pulse-chase analysis, immuno-fluorescence microscopy, and enzyme kinetics.

Reference number SU DBB 7/12
Project title: Evolutionary systems biology
Project leaders: Arne Elofsson,, and
Dr. Lukasz Huminiecki,
Subject: biochemistry towards bioinformatics
We are looking for an exceptionally talented and motivated PhD student in evolutionary systems biology to work and study under joint supervision of   Dr.
Lukasz Huminiecki and Prof. Arne Elofsson at the Department of Biochemistry and Biophysics.
You will live in Stockholm, a beautiful and international city with high standard of life and social services, the Home of the Nobel Prize, offering a vibrant scientific environment and rich opportunities for collaborations, postdoctoral fellowships, or employment in the technology sector. You will work within the newly created bioinformatics and computational biology cluster at the Science for Life Lab, a joint venture between Sweden's three top research universities: Karolinska Institutet, KTH Royal Institute of Technology, and Stockholm's University.
The project focuses on computational analysis of global patterns of gene and genome duplications in vertebrates, and their consequences for signal transduction, and microRNA (miRNA) network evolution, as well as expression pattern evolution. We will use pathway databases and freely available genomic, expression, and miRNA datasets, to infer architectural changes of the animal signal transduction and miRNA networks after small scale duplications (SSDs) and whole genome duplications (WGDs). Quantification of the patterns of nonfunctionalization, subfunctionalization, and neofunctionalization will enhance our understanding of the evolutionary forces driving duplicate retention. The identification of the shared animal developmental toolkit is one of the most significant and fundamental discoveries in biology, and we designate the toolkit to be a focus area investigated besides the global perspective.
The methodological toolkit necessary for the project will consist of phylogenetics and other molecular evolution methods, R statistical package, Bioconductor, MySQL, and Perl. Unix or Linux experience is highly desired. Candidates will have undergraduate degree background in biology, biotechnology, biophysics, molecular biology, mathematical biology, bioinformatics, mathematics, or computer science. Good command of English is required. This is not a pipeline bioinformatics project: the chosen candidate will have a natural aptitude to flexibly and creatively tackle complex and abstract problems, using a dynamic mixture of procedural programming, SQL, statistics, bioinformatics, and evolutionary theory.
2R and remodeling of vertebrate signal transduction engine. Huminiecki L, Heldin CH. BMC Biol. 2010 Dec 13;8:146.
Emergence, development and diversification of the TGF-beta signalling pathway within the animal kingdom. Huminiecki L, Goldovsky L, Freilich S, Moustakas A, Ouzounis C, Heldin CH. BMC Evol Biol. 2009 Feb 3;9:28.
Divergence of spatial gene expression profiles following species-specific gene duplications in human and mouse. Huminiecki L, Wolfe KH. Genome Res. 2004 Oct;14(10A):1870-9.

Reference number SU DBB 8/12
Project title: Functional Inference from Network Analysis
Project leader: Erik Sonnhammer,,
Subject: biochemistry towards bioinformatics
We are using machine learning and modelling techniques to infer protein function from protein sequence, protein evolution, or protein interactions. The goal is to build a map of all proteins with details on how they interact with each other and other molecules. A protein's biochemical function and its context with interaction partners are key to understanding its biological function. This map, or functional network, is explored to discover the function of previously uncharacterised proteins.
The methods include Bayesian Networks, Support Vector Machines, hidden Markov models, and own developed models. In this project, heterogeneous data sources are combined to predict functional coupling between proteins in order to build networks that model pathways and interaction cascades. These data sources include e.g. co-expression, co-evolution, protein interactions, and various sequence-based properties.
The project involves programming, data analysis, benchmarking, and modelling, as well as application of the developed methods to genes of particular interest in order to discover new protein functions. See,
The successful candidate should have an M.Sc. in bioinformatics or related field, and knowledge of molecular biology. Alternatively, an M.Sc. in molecular biology or related field and at least 1 year of practical experience in bioinformatics research. Familiarity with sequence analysis techniques is essential, as well as a high level of motivation. Computer programming, UNIX skills, and knowledge of biological database systems are necessary merits.

Reference number SU DBB 9/12
Project title: Functional Inference from Domain Architecture and Orthology
Project leader: Erik Sonnhammer,,
Subject: biochemistry towards bioinformatics
Proteins usually consist of some combination of recurring subsequences, corresponding to independently folding domains. Through mechanisms of domain shuffling, the domain composition of proteins can change, leading to situations where parts of a protein has a different evolutionary history than the whole. In order to understand the functional impact of architectural features, this project aims to chart the evolutionary history of protein domain architectures. This will be done in relation to orthology status and conservation of interaction partners. The overall aim is to establish the functional implications of different
types of domain rearrangements.
The project includes both development of new algorithms and methods, as well as applications such as tools and workbenches to enable public access for database queries. The methods include hidden Markov models, clustering methods, and own developed algorithms. The project involves programming, data analysis, benchmarking, and modelling, as well as application of the developed methods to genes of particular interest in order to discover new protein functions. See and
The successful candidate should have an M.Sc. in bioinformatics or related field, and knowledge of molecular biology. Alternatively, an M.Sc. in molecular biology or related field and at least 1 year of practical experience in bioinformatics research. Familiarity with sequence analysis techniques is essential, as well as a high level of motivation. Computer programming (e.g. Perl, Python, R), UNIX skills, and knowledge of biological database systems are necessary merits.

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