Introduction to Computational Biology: Maps, Sequences and Genomes.

Waterman, Michael S.

doi:10.2307/2534039

Cited by 221 publications

(309 citation statements)

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“…(21) Further details on the biophysical and computational aspects of RNA secondary structure can be found in several reviews. (7,(22)(23)(24) I will henceforth refer to the (minimum free energy) secondary structure as (mfe) shape. In the following, I will discuss various notions of phenotype that can be defined and computed at this level of structure (Fig.…”

Section: Rna Phenotypesmentioning

confidence: 99%

Modelling ‘evo‐devo’ with RNA

Fontana

2002

BioEssays

162

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SummaryThe folding of RNA sequences into secondary structures is a simple yet biophysically grounded model of a genotype-phenotype map. Its computational and mathematical analysis has uncovered a surprisingly rich statistical structure characterized by shape space covering, neutral networks and plastogenetic congruence. I review these concepts and discuss their evolutionary implications. IntroductionPhenotype refers to the physical, organizational and behavioral expression of an organism during its lifetime. Genotype refers to a heritable repository of information that instructs the production of molecules whose interactions, in conjunction with the environment, generate and maintain the phenotype. The processes linking genotype to phenotype are known as development. They intervene in the genesis of phenotypic novelty from genetic mutation. Evolutionary trajectories therefore depend on development. In turn, evolutionary processes shape development, creating a feed-back known as ''evodevo''. (1,2) The main thrust of this review is to show that some key aspects of this feedback are present even in the microcosm of RNA folding. In a narrow sense, the relation between RNA sequences and their shapes is treated as a problem in biophysics. Yet, in a wider sense, RNA folding can be regarded as a minimal model of a genotype-phenotype relation. (3) The RNA model is not a representation of organismal development. The regulatory networks of gene expression and signal transduction that coordinate the spatiotemporal unfolding of complex molecular processes in organismal development (for recent overviews see Refs. 4,5) have no concrete analogue in the RNA sequence-to-structure map. Developmental processes themselves evolve and this too is outside the scope of the rather simple notion of RNA folding considered here. Yet, the RNA folding map transparently

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Section: Rna Phenotypesmentioning

confidence: 99%

Modelling ‘evo‐devo’ with RNA

Fontana

2002

BioEssays

162

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“…For example, such alignments are used to detect homologues among sequences in genome databases, to study phylogenetic relationships, or to identify structurally or functionally important parts of the molecule in question. Consequently, establishing fast and reliable tools for sequence alignment is one of the most fundamental tasks in present day computational biology, enjoying an abundance of publications and software contributions (see [32), [8), or [48)). …”

Section: The Alignment Problemmentioning

confidence: 99%

“…Next, we define multiple alignments formally and we describe the basic principles of evaluating quantitatively the quality of a given multiple alignment (for further reference see [ll], [38), and (48]). .…”

Section: A Formal Set-up: the Weighted Sum Of Pairs Score Formentioning

confidence: 99%

Two applications of the divide&conquer principle in the molecular sciences

Brinkmann

Dress

Perrey

et al. 1997

Mathematical Programming

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“…Interval graphs are extensively used in bioinformatics, typically to model the genome physical mapping problem, which is the problem of reconstructing the relative positions of DNA fragments, called clones, out of information of their pairwise overlaps (see e.g. [21]). However, interval graphs appear also in other situations in bioinformatics, such as for gene structure prediction for example [1].…”

Section: Introductionmentioning

confidence: 99%

Optimal Linear Arrangement of Interval Graphs

Cohen

Fomin

Heggernes

et al. 2006

Lecture Notes in Computer Science

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Abstract. We study the optimal linear arrangement (OLA) problem on interval graphs. Several linear layout problems that are NP-hard on general graphs are solvable in polynomial time on interval graphs. We prove that, quite surprisingly, optimal linear arrangement of interval graphs is NP-hard. The same result holds for permutation graphs. We present a lower bound and a simple and fast 2-approximation algorithm based on any interval model of the input graph.

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Introduction to Computational Biology: Maps, Sequences and Genomes.

Cited by 221 publications

References 0 publications

Modelling ‘evo‐devo’ with RNA

Modelling ‘evo‐devo’ with RNA

Two applications of the divide&conquer principle in the molecular sciences

Optimal Linear Arrangement of Interval Graphs

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