The functional properties of living organisms have a complexity exceeding the human capacity for analysis. A basic conviction in computational biology is that it should be possible to develop computational tools allowing us to considerably increase our understanding of such functional properties.
On the one hand, this conviction is supported by a phenomenal recent progress in some computational and mathematical methods. On the other hand, this belief rests on the rapid increase in collaboration between specialists of both biology and computer science. As a result, numerous works have exhibited applications of formal techniques to biological problems. Moreover, computational and mathematical methods are often mentioned as being indispensable for increasing our understanding of living organisms. Nonetheless, through sheer complexity, many important biological problems are at present intractable, and it is not clear whether we will ever be able to solve such problems. Consequently, by virtue of the inherent complexity of biological systems, many of these formal methods have only been applicable to toy examples.
Formal techniques applied for solving biological problems have mainly been used in areas other than biology. For instance, model checking is at present mostly used in the verification of digital circuits. A straightforward attempt at applying existing methods to biological problems faces immediate difficulties. A reason is that many biological problems are only similar, but not identical, to problems where such methods have been successfully applied. Other biological problems are new altogether. For example, the computation of the set of stationary states is of prime interest in the analysis of Boolean gene networks, but is not normally so in the design of digital circuits. Adapting existing formal methods for solving biological problems is therefore a major challenge.
Extending current formal methods to biological problems should translate to progress in biology, as our knowledge of living organisms should increase by our ability to answer more questions. It is natural to expect, however, benefits in the other direction as well: new problems stemming from biological needs would advance theoretical formalisms. Hence, establishing a close connection between formal tools and biological problems is doubly important.
Subjects welcomed include, but are not restricted to:
- abstract interpretation
- algorithms for computational biology
- answer set programming
- constraint-based programming
- genome alignment
- logical modeling of cellular networks
- model checking
- next-generation sequencing
- Petri nets
- phylogeny reconstruction
- process algebras
- rule-based modeling
- satisfiability modulo theories
- sequence analysis and alignment
- stochastic simulation
- symbolic methods (BDDs and SAT solvers)
- systems biology
The functional properties of living organisms have a complexity exceeding the human capacity for analysis. A basic conviction in computational biology is that it should be possible to develop computational tools allowing us to considerably increase our understanding of such functional properties.
On the one hand, this conviction is supported by a phenomenal recent progress in some computational and mathematical methods. On the other hand, this belief rests on the rapid increase in collaboration between specialists of both biology and computer science. As a result, numerous works have exhibited applications of formal techniques to biological problems. Moreover, computational and mathematical methods are often mentioned as being indispensable for increasing our understanding of living organisms. Nonetheless, through sheer complexity, many important biological problems are at present intractable, and it is not clear whether we will ever be able to solve such problems. Consequently, by virtue of the inherent complexity of biological systems, many of these formal methods have only been applicable to toy examples.
Formal techniques applied for solving biological problems have mainly been used in areas other than biology. For instance, model checking is at present mostly used in the verification of digital circuits. A straightforward attempt at applying existing methods to biological problems faces immediate difficulties. A reason is that many biological problems are only similar, but not identical, to problems where such methods have been successfully applied. Other biological problems are new altogether. For example, the computation of the set of stationary states is of prime interest in the analysis of Boolean gene networks, but is not normally so in the design of digital circuits. Adapting existing formal methods for solving biological problems is therefore a major challenge.
Extending current formal methods to biological problems should translate to progress in biology, as our knowledge of living organisms should increase by our ability to answer more questions. It is natural to expect, however, benefits in the other direction as well: new problems stemming from biological needs would advance theoretical formalisms. Hence, establishing a close connection between formal tools and biological problems is doubly important.
Subjects welcomed include, but are not restricted to:
- abstract interpretation
- algorithms for computational biology
- answer set programming
- constraint-based programming
- genome alignment
- logical modeling of cellular networks
- model checking
- next-generation sequencing
- Petri nets
- phylogeny reconstruction
- process algebras
- rule-based modeling
- satisfiability modulo theories
- sequence analysis and alignment
- stochastic simulation
- symbolic methods (BDDs and SAT solvers)
- systems biology
