Name
Abstract | ||
|---|---|---|
We propose a static and a dynamic approach to model biological signaling networks, and show how each can be used to answer relevant biological questions. For this, we use the two different mathematical tools of Propositional Logic and Integer Programming. The power of discrete mathematics for handling qualitative as well as quantitative data has so far not been exploited in molecular biology, which is mostly driven by experimental research, relying on first-order or statistical models. The arising logic statements and integer programs are analyzed and can be solved with standard software. For a restricted class of problems the logic models reduce to a polynomial-time solvable satisfiability algorithm. Additionally, a more dynamic model enables enumeration of possible time resolutions in poly-logarithmic time. Computational experiments are included. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1089/cmb.2008.0163 | JOURNAL OF COMPUTATIONAL BIOLOGY |
Keywords | Field | DocType |
biological signaling networks,integer programming,modeling,monotone boolean functions,satisfiability | Integer,Computer science,Satisfiability,Theoretical computer science,Software,Integer programming,Artificial intelligence,Horn clause,Enumeration,Propositional calculus,Statistical model,Bioinformatics,Machine learning | Journal |
Volume | Issue | ISSN |
16.0 | 5 | 1066-5277 |
Citations | PageRank | References |
6 | 0.93 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Utz-Uwe Haus | 1 | 226 | 18.47 |
| Kathrin Niermann | 2 | 6 | 0.93 |
| Klaus Truemper | 3 | 84 | 10.82 |
| Robert Weismantel | 4 | 964 | 90.05 |