TY  - JOUR
A1  - Frioux, Clémence
A1  - Schaub, Torsten H.
A1  - Schellhorn, Sebastian
A1  - Siegel, Anne
A1  - Wanko, Philipp
T1  - Hybrid metabolic network completion
JF  - Theory and practice of logic programming
N2  - Metabolic networks play a crucial role in biology since they capture all chemical reactions in an organism. While there are networks of high quality for many model organisms, networks for less studied organisms are often of poor quality and suffer from incompleteness. To this end, we introduced in previous work an answer set programming (ASP)-based approach to metabolic network completion. Although this qualitative approach allows for restoring moderately degraded networks, it fails to restore highly degraded ones. This is because it ignores quantitative constraints capturing reaction rates. To address this problem, we propose a hybrid approach to metabolic network completion that integrates our qualitative ASP approach with quantitative means for capturing reaction rates. We begin by formally reconciling existing stoichiometric and topological approaches to network completion in a unified formalism. With it, we develop a hybrid ASP encoding and rely upon the theory reasoning capacities of the ASP system dingo for solving the resulting logic program with linear constraints over reals. We empirically evaluate our approach by means of the metabolic network of Escherichia coli. Our analysis shows that our novel approach yields greatly superior results than obtainable from purely qualitative or quantitative approaches.
KW  - answer set programming
KW  - metabolic network
KW  - gap-filling
KW  - linear programming
KW  - hybrid solving
KW  - bioinformatics
Y1  - 2018
U6  - https://doi.org/10.1017/S1471068418000455
SN  - 1471-0684
SN  - 1475-3081
VL  - 19
IS  - 1
SP  - 83
EP  - 108
PB  - Cambridge University Press
CY  - New York
ER  - 
TY  - GEN
A1  - Halfpap, Stefan
A1  - Schlosser, Rainer
T1  - Workload-Driven Fragment Allocation for Partially Replicated Databases Using Linear Programming
T2  - 2019 IEEE 35th International Conference on Data Engineering (ICDE)
N2  - In replication schemes, replica nodes can process read-only queries on snapshots of the master node without violating transactional consistency. By analyzing the workload, we can identify query access patterns and replicate data depending to its access frequency. In this paper, we define a linear programming (LP) model to calculate the set of partial replicas with the lowest overall memory capacity while evenly balancing the query load. Furthermore, we propose a scalable decomposition heuristic to calculate solutions for larger problem sizes. While guaranteeing the same performance as state-of-the-art heuristics, our decomposition approach calculates allocations with up to 23% lower memory footprint for the TPC-H benchmark.
KW  - database replication
KW  - allocation problem
KW  - linear programming
Y1  - 2019
SN  - 978-1-5386-7474-1
SN  - 978-1-5386-7475-8
U6  - https://doi.org/10.1109/ICDE.2019.00188
SN  - 1084-4627
SN  - 2375-026X
SN  - 1063-6382
SP  - 1746
EP  - 1749
PB  - IEEE
CY  - New York
ER  - 
TY  - JOUR
A1  - Reich, Sebastian
T1  - A nonparametric ensemble transform method for bayesian inference
JF  - SIAM journal on scientific computing
N2  - Many applications, such as intermittent data assimilation, lead to a recursive application of Bayesian inference within a Monte Carlo context. Popular data assimilation algorithms include sequential Monte Carlo methods and ensemble Kalman filters (EnKFs). These methods differ in the way Bayesian inference is implemented. Sequential Monte Carlo methods rely on importance sampling combined with a resampling step, while EnKFs utilize a linear transformation of Monte Carlo samples based on the classic Kalman filter. While EnKFs have proven to be quite robust even for small ensemble sizes, they are not consistent since their derivation relies on a linear regression ansatz. In this paper, we propose another transform method, which does not rely on any a priori assumptions on the underlying prior and posterior distributions. The new method is based on solving an optimal transportation problem for discrete random variables.
KW  - Bayesian inference
KW  - Monte Carlo method
KW  - sequential data assimilation
KW  - linear programming
KW  - resampling
Y1  - 2013
U6  - https://doi.org/10.1137/130907367
SN  - 1064-8275
VL  - 35
IS  - 4
SP  - A2013
EP  - A2024
PB  - Society for Industrial and Applied Mathematics
CY  - Philadelphia
ER  -