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Biomedical subjects

M Lässig

Publications and source records attributed to M Lässig.

14 recordsLinked to original sources
PubMed2001

Shape of ecological networks.

We study the statistics of ecosystems with a variable number of coevolving species. The species interact in two ways: by prey-predator relationships and by direct competition with similar kinds. The interaction coefficients change slowly through successful adaptations and speciations. They are treated as quenched random variables. These interactions determine long-term topological features of the species network, which are found to agree with those of biological systems.

Animals↗
PubMed2000

Finite-temperature sequence alignment.

We develop a statistical theory of probabilistic sequence alignments derived from a 'thermodynamic' partition function at finite temperature. Such alignments are a generalization of those obtained from information-theoretic approaches. Finite-temperature statistics can be used to characterize the significance of an alignment and the reliability of its single element pairs.

Algorithms↗
PubMed1999

Optimizing Smith-Waterman alignments.

Mutual correlation between segments of DNA or protein sequences can be detected by Smith-Waterman local alignments. We present a statistical analysis of alignment of such sequences, based on a recent scaling theory. A new fidelity measure is introduced and shown to capture the significance of the local alignment, i.e., the extent to which the correlated subsequences are correctly identified. It is demonstrated how the fidelity may be optimized in the space of penalty parameters using only the alignment score data of a single sequence pair.

Algorithms↗
PubMed1998

A statistical theory of sequence alignment with gaps.

A statistical theory of local alignment algorithms with gaps is presented. Both the linear and logarithmic phases, as well as the phase transition separating the two phases, are described in a quantitative way. Markov sequences without mutual correlations are shown to have scale-invariant alignment statistics. Deviations from scale invariance indicate the presence of mutual correlations detectable by alignment algorithms. Conditions are obtained for the optimal detection of a class of mutual sequence correlations.

Algorithms↗
PubMedDate not supplied

Scaling laws and similarity detection in sequence alignment with gaps.

We study the problem of similarity detection by sequence alignment with gaps, using a recently established theoretical framework based on the morphology of alignment paths. Alignments of sequences without mutual correlations are found to have scale-invariant statistics. This is the basis for a scaling theory of alignments of correlated sequences. Using a simple Markov model of evolution, we generate sequences with well-defined mutual correlations and quantify the fidelity of an alignment in an unambiguous way. The scaling theory predicts the dependence of the fidelity on the alignment parameters and on the statistical evolution parameters characterizing the sequence correlations. Specific criteria for the optimal choice of alignment parameters emerge from this theory. The results are verified by extensive numerical simulations.

Algorithms↗