References:

1. Bolshoy, A., P. McNamara, R.E. Harrington & E.N. Trifonov. (1991). Curved DNA without A-A: experimental estimation of all 16 DNA wedge angles. Proc Natl Acad Sci U S A 88(6), 2312-2316.

2. Gabrielian, A. and Bolshoy, A. (1999) "Sequence complexity and DNA curvature." Computers & Chemistry 23, 263-274

3. Gabrielian, A. E., Landsman, D., and Bolshoy, A. "Curved DNA in promoter sequences" (1999) In Silico 1 .

4. Perez-Martin, J. & V. de Lorenzo. (1997). Clues and consequences of DNA bending in transcription. Annu Rev Microbiol 51, 593-628.

Our method was developed specifically to identify DNA-binding proteins with S-type immunoglobulin fold under exclusion of common immunoglobulins. With the the X-ray structure of the AML1 Runt domain as a starting point, we searched the PDB for related 3D structures. Next, the 3D coordinates of the domains related to Runt were aligned simultaneously using the Runt domain as a template. Amino acids that were close in space among all different protein structures were assumed to be critical for the particular fold and were used to generate a structure based multiple sequence alignment. From the multiple sequence alignment, a profile Hidden Markov Model was calculated. A sequence database was searched with the constructed profile, resulting in sequences used to generate the profile as well as new ones that were not. Only DNA-binding immunoglobulins were found. We predict that the proteins without known 3D structure are related to Runt and bind to DNA.

The Galton-Watson process (GWP) has often been used as a descriptive population model (see for example Jagers(1975)). In particular, it has been considered as a statistical model in an epidemiological context (Becker(1975,1977), Neyman and Scott(1964),...). However, if the number of infectives converges to a non degenerate variable and it is allowed the incorporation of new infected individuals from other populations, the GWP seems to be an inadequate model, being necessary to use more complex processes. The aim of this paper is to show that for such situations it can be considered, within the framework of the branching processes with immigration, the study of the limit behaviour of some infectious diseases. We present the Galton-Watson processes with immigration in an epidemiological context and taking into account some of the factors governing the spread of the diseases, we propose corrective policies. As an illustration for the theoretical study, some simulated examples are developed. The software we have used is programming language of Mathematica (v3.0).

References:

Jagers, P.(1975) Branching processes with Biological Applications, John Wiley, London.

Becker, N. G.(1975) The use of mathematical models in determining vaccination policies. Bulletin of the International Statistical Institute 46, Book 1, 478-490.

Becker, N. G.(1977) Estimation for discrete time branching processes with application to epidemics. Biometrics 33, 515-522.

Neyman, J. and Scott, E.L. (1964) Stochastic Models in Medicine and Biology. J. Gurland editor, 45-55, University of Wisconsin Press, Madison.

We consider a situation where the location of the gene for a dominant disease is known. In this case, information about additional genetic markers linked to the disease gene can be used to estimate the age (time since appearance) of the disease based on a sample of affected individuals. The behaviour of this estimate is studied using simulations.

References:

Kimmel, M., R. Chakraborty, J. P. King, M. Bamshad, W. S. Watkins and L. B. Jorde, 1998. Signatures of population expansion in microsatellite repeat data. Genetics 148: 1921-1930.

Kimmel, M., and R. Chakraborty, 1996. Measures of variation of DNA repeat loci under a general stepwise mutation model. Theoretical Population Biology 50: 345-367.

References:
M. Ohlsson, C. Peterson, M. Ringnér and R. Blancenbecler
A Novel Approach to Structure Alignment
LU TP 00-07 and SLAC-PUB-8429 (2000)

The implementation is written as a Java applet, so that students can experiment interactively using a standard webbrowser. The source code is fairly clear, so that students can investigate the finer details of the implementation.

References: The algorithms can be accessed at http://www.dina.kvl.dk/~sestoft/bsa/dinaws/bsapplet.html

The aim of our study is to present and investigate a new stochastic process for modelling population dynamics, namely Sevastyanov's age-dependent branching process with emigration. Such model appears naturally in applications to cell populations where with positive probability the cells may die before their life-cycle is completed. The results may help to give answer of such question like what one can say about the future development of the population under certain reproduction conditions of the individuals and rate of emigration, as well.

In this connection the use of branching processes to model cell populations was pioneered by Bellman and Harris, where the Bienayme-Galton-Watson branching process was generalized to a process where each individual first lives a random amount of time, independently of the others, and gives birth to a random number of offspring. Further generalization of Bellman-Harris branching process is so-called Sevastyanov's age-dependent branching process where the progeny depends on the age of the particle at the moment of giving birth to new particles of age zero.

Applying renewal technique we establish limit theorems in the case when local characteristics of the processes are finite. The limit behaviour depends essentially on the criticality of the processes and the rate of emmigration.

References:

P. Jagers (1975) Branching Processes with biological applications. John Wiley and Sons.

B. Sevastyanov (1971) Branching Processes. Nauka, Moscow.

M. Slavtchova-Bojkova, Multi-type Age-dependent Branching Processes with State-dependent Immigration, Proceeedings of the Athen's Conference on Applied Probability and Time Series Analysis, Ed. C.C. Heyde, Yu. V. Prohorov, R. Pyke, S. T. Rachev , Lecture Notes in Statistics, 114, p. 192-205, 1996, Springer Verlag, New York.

We present the method and show the results obtained from applying it to both artificial and real gene expression data from developing rat spinal cord and hippocampus. Taken together, the data from the two tissues allow us to identify the main features of the structure of the regulatory networks governing nervous system development.

References:

1. Wahde, M. and Hertz, J. "Coarse-grained reverse engineering of genetic regulatory networks", Biosystems, in press

2. www.me.chalmers.se/~mwahde/genenets.html