LINEAR ALGEBRA AND ITS APPLICATIONS
         Special issue on Matrices and Mathematical Biology

                           Call for papers


In the last decade the field of mathematical biology has expanded very
rapidly. Biological research furnishes both data on and insight into the
workings of biological systems. However, qualitative and quantitative
modelling and simulation are still far from allowing current knowledge to
be organized into a well-understood structure. Further, the diversity
present in mathematical biology, coupled with the absence of a single
unifying approach, has inspired the formation of entirely new scientific
disciplines such as bioinformatics.

Theoretical research activity in mathematical biology is 
naturally of an interdisciplinary character. It involves 
mathematical and statistical investigations, sometimes in 
combination with techniques originating from the computational 
sciences. In many of these approaches, linear algebra is key to 
solving the mathematical problems which arise. For instance, in 
some population models, the asymptotic rate of increase of the 
population turns out to be the spectral radius of a certain 
matrix associated with the population, while the other 
eigenvalues also yield information on the evolution of the 
population's structure. Conversely, problems in mathematical 
biology can enrich linear algebra. For example, in attempting to 
measure the influence of a single matrix entry on a simple 
eigenvalue, linear algebraists frequently employ the derivative 
of that eigenvalue with respect to the entry. However, some 
biologists have proposed the use of the elasticity, or a 
logarithmic derivative, of an eigenvalue with respect to a matrix 
entry in order to measure the effect on that eigenvalue of 
perturbing a matrix entry. Thus linear algebraists are challenged 
to deepen  and develop the understanding of the ways in which the 
effects of changes in the ecological conditions on the 
populations can be measured  through further theoretical 
investigations.

A recent book by Caswell on matrix population models makes extensive use
of linear algebraic techniques. Quoting from the introduction to that
book: "Matrix population models -- carefully constructed, correctly
analyzed, and properly interpreted - provide a theoretical basis for
population models... A goal of this book is to raise the bar of what
constitutes rigorous analysis in population models.... The work of the
population biologist is too important to settle for less." But Caswell's
call for careful mathematical construction and analysis applies to areas
beyond the subject of population models; clearly a rigorous approach would
benefit all areas of interaction between biology and mathematics.

The Special Issue of LAA dedicated to Matrices and Mathematical Biology is
intended to both foster and accelerate cross fertilization between those
working primarily in linear algebra and those working primarily in
mathematical biology. The editors hope that such an issue of LAA will be
of benefit to both fields.

This special issue will be open for all submissions containing new and
meaningful results that advance interaction between linear algebra and
mathematical biology. The editors welcome submissions in which linear
algebraic methods play an important role for novel approaches to problems
arising in mathematical biology, or in which investigations in
mathematical biology motivate new tools and problems in linear algebra.
Survey papers which discuss specific areas involving the interaction
between biology and linear algebra, particularly where such interaction
has been successful, are also very welcome.

Areas and topics of interest for the special issue include, but are not
limited to:

       metabolistic pathways
       statistical data analysis
       linear algebra problems in graph partitioning
       matrix population models
       model discrimination in biokinetics
       linear algebra problems in network analysis and synchronization
       subspace oriented eigenvalue problems
       aggregation/disaggregation or related techniques
       hidden Markov models
       epidemic models
       modelling phylogenetic trees



All papers submitted must meet the publication standards of Linear Algebra
and its Applications and will be refereed in the usual way. They should be
submitted to one of the special editors of this issue listed below by 30
November 2003.


Michael Dellnitz
Department of Mathematics and Computer Science
University of Paderborn
D-33095 Paderborn 
Germany
dellnitz@upb.de


Steve Kirkland
Department of Mathematics and Statistics
University of Regina
Regina, Saskatchewan 
Canada
S4S 0A2
kirkland@math.uregina.ca


Michael Neumann                             
Department of Mathematics
University of Connecticut                   
Storrs, Connecticut O6269-3OO9
USA
neumann@math.uconn.edu

Christof Schuette
Department of Mathematics & Computer Science         
Numerical Mathematics/Scientific Computing
Free University Berlin                 
Arnimallee 2-6                            
D-14195 Berlin                            
Germany      
schuette@math.fu-berlin.de