TY  - JOUR
A1  - Grimbs, Sergio
A1  - Arnold, Anne
A1  - Koseska, Aneta
A1  - Kurths, Jürgen
A1  - Selbig, Joachim
A1  - Nikoloski, Zoran
T1  - Spatiotemporal dynamics of the Calvin cycle multistationarity and symmetry breaking instabilities
JF  - Biosystems : journal of biological and information processing sciences
N2  - The possibility of controlling the Calvin cycle has paramount implications for increasing the production of biomass. Multistationarity, as a dynamical feature of systems, is the first obvious candidate whose control could find biotechnological applications. Here we set out to resolve the debate on the multistationarity of the Calvin cycle. Unlike the existing simulation-based studies, our approach is based on a sound mathematical framework, chemical reaction network theory and algebraic geometry, which results in provable results for the investigated model of the Calvin cycle in which we embed a hierarchy of realistic kinetic laws. Our theoretical findings demonstrate that there is a possibility for multistationarity resulting from two sources, homogeneous and inhomogeneous instabilities, which partially settle the debate on multistability of the Calvin cycle. In addition, our tractable analytical treatment of the bifurcation parameters can be employed in the design of validation experiments.
KW  - Multistationarity
KW  - Calvin cycle
KW  - Algebraic geometry
KW  - Bifurcation parameters
KW  - Biomass
Y1  - 2011
U6  - https://doi.org/10.1016/j.biosystems.2010.10.015
SN  - 0303-2647
VL  - 103
IS  - 2
SP  - 212
EP  - 223
PB  - Elsevier
CY  - Oxford
ER  - 
TY  - JOUR
A1  - Nikoloski, Zoran
A1  - Grimbs, Sergio
A1  - Klie, Sebastian
A1  - Selbig, Joachim
T1  - Complexity of automated gene annotation
JF  - Biosystems : journal of biological and information processing sciences
N2  - Integration of high-throughput data with functional annotation by graph-theoretic methods has been postulated as promising way to unravel the function of unannotated genes. Here, we first review the existing graph-theoretic approaches for automated gene function annotation and classify them into two categories with respect to their relation to two instances of transductive learning on networks - with dynamic costs and with constant costs - depending on whether or not ontological relationship between functional terms is employed. The determined categories allow to characterize the computational complexity of the existing approaches and establish the relation to classical graph-theoretic problems, such as bisection and multiway cut. In addition, our results point out that the ontological form of the structured functional knowledge does not lower the complexity of the transductive learning with dynamic costs - one of the key problems in modern systems biology. The NP-hardness of automated gene annotation renders the development of heuristic or approximation algorithms a priority for additional research.
KW  - Complexity
KW  - Gene function prediction
KW  - External structural measures
KW  - Transductive learning
Y1  - 2011
U6  - https://doi.org/10.1016/j.biosystems.2010.12.003
SN  - 0303-2647
VL  - 104
IS  - 1
SP  - 1
EP  - 8
PB  - Elsevier
CY  - Oxford
ER  -