Catálogo de publicaciones - libros

Mathematical interpretation and integration of experimental data for the goal of biological theory development has had little, if no, impact on previous progress in life sciences compared with the sophistication of experimental approaches themselves. The genesis of recent spectacular breakthroughs in molecular biology that led to the discovery of the enzymatic function of several nonmetabolic enzymes illustrates that this relationship is beginning to change.

The development of high-throughput technologies, for example of complete genome sequencing, leads to large amounts of quantified data on biological systems without direct link to biological function that require formalized and complex mathematical approaches for their interpretation. The research success in life sciences depends increasingly on the ability of researchers in experimental and theoretical biology to jointly focus on important questions. Currendy, theoretical methods have best chances to contribute to new biological insight independently of experiments in the area of genome text interpretation and especially for gene function prediction. Experimental studies can help progress in the development of theoretical methods by providing verified, sufficiently large and variable sequence datasets for the exploration of sequence-function relationships.

Completely sequenced genomes and other types of genomics data provide us with new information to predict protein function. While classical, homology-based function prediction provides information about a proteins’ molecular function (what does the protein do at a molecular scale?), the analysis of the sequence in the context of its genome or in other types of genomics data provides information about its functional context (what are the proteins’ interaction partners, and in which biological process does it play a role?) Genomic context data are however inherently noisy. Only by combining different types of genomic(s) context data (vertical comparative genomics) or by combining the same type of genomics data from different species (horizontal comparative genomics) do they become sufficiently reliable to be used for protein function prediction. Homology-based function prediction and context-based function prediction provide complementary information about a protein’s function and can becombined to make predictions that are specific enough for experimental testing. Here we discuss the genomic coverage and reliability of combining genomics data for protein function prediction and survey predictions that have actually led to experimental confirmation. Using a number of examples we illustrate how combining the information from various types of genomics data can lead to specific protein function predictions. These include the prediction that the Ribonuclease L inhibitor (RLI) is involved in the maturation of ribosomal RNA.

Cytochrome P450 is a focus of attention as it comprises one of the largest superfamilies of enzyme proteins. Metabolization of many drugs is affected by cytochrome P450. It is an attractive drug target, e.g., cytochrome P450s of are promising targets in the fight against tuberculosis. The structure provides new insights for investigation of structure/mechanism of cytochrome P450, and for rational design of inhibitor molecules. We will illustrate how biocomputing and bioinformatical techniques reveal details, functions and further secrets of this exciting molecule. Molecular modelling along with site-directed mutagenesis of P450 2B1 elucidated the molecular determinants of substrate specifity. Regioselectivity of progesterone hydroxylation by cytochrome P450 2B1 was reengineered based on the X-ray structure of cytochrome 2C5. Docking approaches rationalized the regioselectivity of the reengineered cytochrome P450 2B1. Furthermore, by methods of molecular dynamic simulations, routes were identified by which substrates may enter into and products exit from the active site of cytochrome P450.

The analysis of uncharacterized biomolecular sequences obtained as a result of genetic screens, expression profile studies, etc. is a standard task in a life science research environment. The understanding of protein function is typically the main difficulty. This chapter intends to give practical advise to students and researchers that have only introductory knowledge in the field of protein sequence analysis.

Applicable theoretical approaches range from (1) textual analyses, interpretation in terms of patterns of physical properties of amino acid side chains and (2) the extrapolation of empirically established relationships between local sequence motifs with known structural and functional properties to the collection of sequence segment families with sequence distance metrics and protein function derivation with annotation transfer (concept of homologous families). Here, the impact of different techniques for the biological interpretation of targets is discussed from the practitioner s point of view and illustrated with examples from recent research reports. Although sequence similarity searching techniques are the most powerful instruments for the analysis of high-complexity regions, other techniques can supply important additional evaluations including the assessment of applicability of the sequence homology concept for the given target segment.

One of the emerging technologies in computational biology is text-mining which includes natural language processing. This technology enables extraction of parts of relevant biological knowledge from a large volume of scientific documents in an automated fashion. We present several systems which cover different facets of text-mining biological information with applications in transcription control, metabolic pathways, and bacterial cross-species comparison. We demonstrate how this technology can efficiently support biologists and medical scientists to infer function of biological entities and save them a lot of time, paving way for more focused and detailed follow-up research.

The first step in understanding the molecular biology of an inherited disease is to identify which gene or genes are carrying variants. This process starts with locating the mutations in a chromosomal band, as narrow as possible, and follows with the manual analysis of all the genes mapping in this region. Usually this is not an easy task, but it can be facilitated by complementary computational approaches that evaluate all genes in a region of interest. We present here a method that combines literature mining, gene annotations, and sequence homology searches to prioritize candidate genes involved in a given genetic disorder. The method progresses in two steps. Firstly, we compute associations of molecular and phenotypic features as taken from MEDLINE. Secondly, for a disease with a given phenotype and linked to a chromosomal region, sequence homology based searches are carried on the chromosomal region to identify potential candidates that are scored using the precomputed associations. The scoring of associations between biological concepts using links across databases can be extended to other databases in Molecular Biology and to nondisease phenotypes.

The development of DNA microarray technology has made it possible to monitor the mRNA abundance of all genes simultaneously (the transcriptome) for a variety of cellular conditions. In addition, microarray-based genomewide measurements of promoter occupancy (the occupome) are now available for an increasing number of transcription factors. With this data and the complete genome sequence of many important organisms, it is becoming possible to quantitatively model the molecular computation performed at each promoter, which has as input the nuclear concentration of the active form of various regulatory proteins (the regulome) and as output a transcription rate, which in turn determines mRNA abundance. In this chapter, we describe how our group has used multivariate linear regression methods to: (i) discover cis-regulatory elements in upstream regulatory regions in an unbiased manner; (ii) infer a regulatory activity profile across conditions for each transcription factor; and (iii) determine whether the mRNA expression level of a gene whose promoter is occupied by a particular transcription factor is truly regulated by that factor, through integrated modeling of expression and promoter occupancy data. Together, these results show model-based analysis of functional genomics data to be a versatile conceptual and practical framework for the elucidation of regulatory circuitry, and a powerful alternative to the currently prevalent clustering-based methods.

Since the 1960s, the mathematical modelling of intracellular systems, such as metabolic pathways, signal transduction cascades and transport processes, is an ever-increasing field of research. The results of most modelling studies in this field are in good qualitative or even quantitative agreement with experimental results. However, a widely held view among many experimentalists is that modelling and simulation only reproduce what has been known before from experiment. A true justification of theoretical biology would arise if theoreticians could predict something unknown, which would later be found experimentally. Theoretical physics has achieved this justification by making many right predictions, for example, on the existence of positrons. Here, we review three cases where experimental groups that were independent of the theoreticians who had made the predictions confirmed theoretical predictions on features of intracellular biological systems later. The three cases concern the optimal time course of gene expression in metabolic pathways, the operation of a metabolic route involving part of the tricarboxylic acid cycle and glyoxylate shunt, and the decoding of calcium oscillations by calcium-dependent protein kinases.

Anew theory of early molecular evolution is described, proceeding from original speculations to specific predictions and their confirmations. This classical cycle is then repeated generating the earliest picture of evolving Life. First, a consensus temporal order (“chronology”) of appearance of amino acids and their respective codons on evolutionary scene is reconstructed on the basis of 60 different criteria, resulting in the order: G, A, D, V, P, S, E, L, T, R, I, Q, N, K, H, C, F, Y, M, W. It reveals two fundamental features: the amino acids synthesized in experiments imitating primordial conditions appeared first, while the amino acids associated with codon capture events came last. The reconstruction of codon chronology then follows based on the above consensus temporal order, supplemented by the stability and complementarity rules first suggested by M. Eigen and P. Schuster, and on earlier established processivity rule. The derived genealogy of all 64 codons suggests several important predictions that are confirmed: Gradual decay of glycine content in protein evolution; traces of the most ancient 6-residue long gly-rich and ala-rich minigenes in extant sequences; and manifestations of a fundamental binary code of protein sequences.

A recent series of publications demonstrated that identification of genomic regions subjected to positive selection (hitchhiking mapping) is possible and could be applied in an ecological context. This review focuses on the use of microsatellite markers in genome scans for the identification of beneficial mutations. The pitfalls and potential of the lnR6 test statistic are discussed as well as different approaches for the identification of the molecular change(s) underlying an observed selective sweep.