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PubMed2005

Validation of qualitative models of genetic regulatory networks by model checking: analysis of the nutritional stress response in Escherichia coli.

MOTIVATION: The modeling and simulation of genetic regulatory networks have created the need for tools for model validation. The main challenges of model validation are the achievement of a match between the precision of model predictions and experimental data, as well as the efficient and reliable comparison of the predictions and observations. RESULTS: We present an approach towards the validation of models of genetic regulatory networks addressing the above challenges. It combines a method for qualitative modeling and simulation with techniques for model checking, and is supported by a new version of the computer tool Genetic Network Analyzer (GNA). The model-validation approach has been applied to the analysis of the network controlling the nutritional stress response in Escherichia coli. AVAILABILITY: GNA and the model of the stress response network are available at http://www-helix.inrialpes.fr/gna.

Bacterial Physiological Phenomena↗

PubMed2006

Genetic instability and the quasispecies model.

Genetic instability is a defining characteristic of cancers. Microsatellite instability (MIN) leads to by elevated point mutation rates, whereas chromosomal instability (CIN) refers to increased rates of losing or gaining whole chromosomes or parts of chromosomes during cell division. CIN and MIN are, in general, mutually exclusive. The quasispecies model is a very successful theoretical framework for the study of evolution at high mutation rates. It predicts the existence of an experimentally verified error catastrophe. This catastrophe occurs when the mutation rates exceed a threshold value, the error threshold, above which replicative infidelity is incompatible with cell survival. We analyse the semiconservative quasispecies model of both MIN and CIN tumors. We consider the role of post-methylation DNA repair in tumor cells and demonstrate that DNA repair is fundamental to the nature of the error catastrophe in both types of tumors. We find that CIN introduces a plateau in the maximum viable mutation rate for a repair-free model, which does not exist in the case of MIN. This provides a plausible explanation for the mutual exclusivity of CIN and MIN.

Chromosomal Instability↗

PubMed1986

Genetic programming of development: a model.

Genetic programming of the developmental processes in multicellular organisms is proposed to be so intricate and vitally important that a large set of genes is dedicated solely to this end. It is further proposed that this set can be compartmentalized into subsets on the basis of the changes in gene activities that occur during ontogenesis, and that the genes in each subset transiently control the epigenetic activities of a small group of cells. Automatic subset activation is achieved by the product of a gene in each subset that transfers activity specifically to the subset next in the developmental sequence. This device can generate a unidirectional series of activations that cascade hierarchically through development like toppling dominoes. The model provides a basis for developmental phenomena, such as pattern formation, morphogenesis, and regeneration, and it makes testable predictions at the molecular level.

Animals↗

PubMed2002

Perineuronal oligodendrocytes protect against neuronal apoptosis through the production of lipocalin-type prostaglandin D synthase in a genetic demyelinating model.

The genetic demyelinating mouse "twitcher" is a model of the human globoid cell leukodystrophy, caused by galactosylceramidase (GALC) deficiency. Demyelination in the twitcher brain is secondary to apoptotic death of oligodendrocytes (OLs). Lipocalin-type prostaglandin (PG) D synthase (L-PGDS), a protein expressed in mature OLs, was progressively upregulated in twitcher OLs; whereas expression of OL-associated proteins such as carbonic anhydrase II, myelin basic protein, and myelin-associated glycoprotein was downregulated during demyelination in twitcher brains. The upregulation of L-PGDS was more remarkable in perineuronal OLs than in interfascicular OLs. A larger number of L-PGDS-positive OLs was found in selected fiber tracts of twitcher brains where fewer apoptotic cells were detected. The distribution of L-PGDS-positive OLs was inversely related to the severity of demyelination, as assessed by accumulation of scavenger macrophages. Mice doubly deficient for L-PGDS and GALC disclosed a large number of apoptotic neurons, which were never seen in twitcher brains, in addition to an increased number of apoptotic OLs. A linear positive correlation was observed between the population of L-PGDS-positive OLs in the twitcher brain and the ratio of apoptotic nuclei in the double mutant versus those in the twitcher, suggesting a dose-dependent effect of L-PGDS against apoptosis. These lines of evidence suggest that L-PGDS is an anti-apoptotic molecule protecting neurons and OLs from apoptosis in the twitcher mouse. This is a novel example of OL-neuronal interaction.

Animals↗

PubMed1992

Genetic polymorphism of esterase in plasma of American mink (Mustela vison L.).

Plasma samples of 412 minks, including 20 families and representing 15 lines, have been investigated by isoelectric focusing for the enzyme esterase (ES). The observed variation of the enzyme may be explained as a result of one locus with at least seven codominant alleles. The segregation of six alleles assumed for the locus in 20 families supports this genetic model. Genetic divergence among the lines is observed and may be due to founder effect and/or selection.

Alleles↗

PubMed2000

Recurrence risk modelling of the genetic susceptibility to ankylosing spondylitis.

OBJECTIVES: It has long been suspected that susceptibility to ankylosing spondylitis (AS) is influenced by genes lying distant to the major histocompatibility complex. This study compares genetic models of AS to assess the most likely mode of inheritance, using recurrence risk ratios in relatives of affected subjects. METHODS: Recurrence risk ratios in different degrees of relatives were determined using published data from studies specifically designed to address the question. The methods of Risch were used to determine the expected recurrence risk ratios in different degrees of relatives, assuming equal first degree relative recurrence risk between models. Goodness of fit was determined by chi(2) comparison of the expected number of affected subjects with the observed number, given equal numbers of each type of relative studied. RESULTS: The recurrence risks in different degrees of relatives were: monozygotic (MZ) twins 63% (17/27), first degree relatives 8.2% (441/5390), second degree relatives 1.0% (8/834), and third degree relatives 0. 7% (7/997). Parent-child recurrence risk (7.9%, 37/466) was not significantly different from the sibling recurrence risk (8.2%, 404/4924), excluding a significant dominance genetic component to susceptibility. Poor fitting models included single gene, genetic heterogeneity, additive, two locus multiplicative, and one locus and residual polygenes (chi(2) >32 (two degrees of freedom), p<10(-6) for all models). The best fitting model studied was a five locus model with multiplicative interaction between loci (chi(2)=1.4 (two degrees of freedom), p=0.5). Oligogenic multiplicative models were the best fitting over a range of population prevalences and first degree recurrence risk rates. CONCLUSIONS: This study suggests that of the genetic models tested, the most likely model operating in AS is an oligogenic model with predominantly multiplicative interaction between loci.

Female↗

PubMed1992

Threshold model in the genetics of age-dependent disease in twins: I. General principles as applied to Alzheimer disease.

In the context of the etiology and pathogenesis of Alzheimer disease (AD), we discuss assumptions under which categorical data on the phenotypes of twin pairs may be used to estimate standardized characteristics (correlation and the critical threshold) for an age-dependent multinomial process. Important topics include Erlangian, gamma, and Poisson processes, tetrachoric and trichoric functions, and degrees of freedom and how they relate to estimation from both an abstract and a practical standpoint. Under plausible assumptions about the age-dependence of a heritable trait, it is possible to generate sufficient degrees of freedom to test the genetic model and to explore age-dependence and the impact of environmental factors that may influence it systematically. Though general in scope, the model is focused on data from twin pairs. The statistical strategy is briefly outlined, but its properties are not examined in detail.

Aging↗

PubMed2004

Translating therapies for Huntington's disease from genetic animal models to clinical trials.

Genetic animal models of inherited neurological diseases provide an opportunity to test potential treatments and explore their promise for translation to humans experiencing these diseases. Therapeutic trials conducted in mouse models of Huntington's disease have identified a growing number of potential therapies that are candidates for clinical trials. Although it is very exciting to have these candidates, there has been increasing concern about the feasibility and desirability of taking all of the compounds that may work in mice and testing them in patients with HD. There is a need to begin to prioritize leads emerging from transgenic mouse studies; however, it is difficult to compare results between compounds and laboratories, and there are also many additional factors that can affect translation to humans. Among the important issues are what constitutes an informative genetic model, what principals should be followed in designing and conducting experiments using genetic animal models, how can results from different laboratories and in different models be compared, what body of evidence is desirable to fully inform clinical decision making, and what factors contribute to the equipoise in determining whether preclinical information about a therapy makes clinical study warranted. In the context of Huntington's disease, we will review the current state of genetic models and their successes in putting forward therapeutic leads, provide a guide to assessing studies in mouse models, and discuss some of the salient issues related to translation from mice to humans.

Animals↗

PubMed1997

Reinforcement of genetic coherence: a single-locus model.

Genetic coherence and genetic separation are the outcome of evolutionary mechanisms which maintain genetic variation within populations through recombination on the one hand, and which divide this variation via speciation between reproductively (recombinatorically) more or less isolated populations on the other. While mechanisms of speciation have received considerable attention in biology, their counterpart, mechanisms of genetic coherence, are addressed only implicitly, if at all. Usually, genetic coherence is intuitively associated with the forces maintaining genetic polymorphisms and thus potential for flexible adaptational reaction of populations. However, so far no models seem to exist which explain the evolution of genetic coherence as the natural counterpart of genetic separation or speciation. In this paper a single-locus model is analyzed, in which a mutant allele is introduced into a resident stable diallelic polymorphism, and where this allele is equivalent to one of the resident alleles in all respects with the exception of mating relations. The conditions for replacement of the resident allele by its selectively equivalent mutant are obtained with reference to the associated mating relations. It turned out that for heterozygote advantage the mutant replaces the selectively equivalent resident allele if it increases the mating preferences for carriers of other alleles. The evolution of lower such preferences requires heterozygote inferiority, which confirms the Wallace effect of speciation (by reinforcement). It is argued that this observation suggests that non-selective constituents of the mating system form the section of the genetic system that is responsible for moderating the genetic load implied by adapting selection while simultaneously securing the adaptational potential embodied in the resident allelic variation. Mating systems thus serve the preservation of adaptability.

Adaptation, Physiological↗

PubMed2004

Genetic network models and statistical properties of gene expression data in knock-out experiments.

It is shown here how gene knock-out experiments can be simulated in Random Boolean Networks (RBN), which are well-known simplified models of genetic networks. The results of the simulations are presented and compared with those of actual experiments in S. cerevisiae. RBN with two incoming links per node have been considered, and the Boolean functions have been chosen at random among the set of so-called canalizing functions. Genes are knocked-out (i.e. silenced) one at a time, and the variations in the expression levels of the other genes, with respect to the unperturbed case, are considered. Two important variables are defined: (i) avalanches, which measure the size of the perturbation generated by knocking out a single gene, and (ii) susceptibilities, which measure how often the expression of a given gene is modified in these experiments. A remarkable observation is that the distributions of avalanches and susceptibilities are very robust, i.e. they are very similar in different random networks; this should be contrasted with the distribution of other variables that show a high variance in RBN. Moreover, the distribution of avalanches and susceptibilities of the RBN models are close to those observed in actual experiments performed with S. cerevisiae, where the changes in gene expression levels have been recorded with DNA microarrays. These findings suggest that these distributions might be "generic" properties, common to a wide range of genetic models and real genetic networks. The importance of such generic properties is discussed.

Computational Biology↗

PubMed1994

Genetic deafness--progress with mouse models.

Genetic deafness is relatively common, accounting for about half of the 1 in 1000 children born with a significant hearing impairment. Heterogeneity is a particular problem when searching for genes for deafness in humans, and the mouse may prove to be a valuable model not only for investigating the nature of the deafness once the gene is known, but also for finding the gene by positional cloning. Several genes causing syndromic deafness have been identified in humans, but the largest group in the population have autosomal recessive deafness, and identification of homologous genes in the mouse may be the only route to these genes. Progress with positional cloning of the shaker-1 mouse mutation is described.

Animals↗

PubMed2006

Genetic mouse models of schizophrenia: from hypothesis-based to susceptibility gene-based models.

Translation of human genetic mutations into genetic mouse models is an important strategy to study the pathogenesis of schizophrenia, identify potential drug targets, and test new drugs for new antipsychotic treatments. Although it is impossible to recapitulate the full spectrum of schizophrenia symptoms in animal models, hypothesis-driven genetic mouse models have been successful in reproducing several schizophrenia-like behaviors and uncovering the roles of specific genes in dopamine and glutamine neurotransmission systems in mediating schizophrenia-like behaviors. Recent discoveries of susceptibility genes for schizophrenia and recognition of cognitive dysfunction as a core feature of schizophrenia and a phenotype of susceptibility for schizophrenia offer opportunities to develop newer genetic mouse models based on susceptibility. This new generation of genetic mouse models could shed light on the etiology of schizophrenia and lead us to new hypotheses, novel diagnostic tools, and more effective therapy.

Animals↗

PubMed2002

Genetic "code": representations and dynamical models of genetic components and networks.

Dynamical modeling of biological systems is becoming increasingly widespread as people attempt to grasp biological phenomena in their full complexity and make sense of an accelerating stream of experimental data. We review a number of recent modeling studies that focus on systems specifically involving gene expression and regulation. These systems include bacterial metabolic operons and phase-variable piliation, bacteriophages T7 and lambda, and interacting networks of eukaryotic developmental genes. A wide range of conceptual and mathematical representations of genetic components and phenomena appears in these works. We discuss these representations in depth and give an overview of the tools currently available for creating and exploring dynamical models. We argue that for modeling to realize its full potential as a mainstream biological research technique the tools must become more general and flexible, and formal, standardized representations of biological knowledge and data must be developed.

Animals↗

PubMed2004

Acinetobacter sp. ADP1: an ideal model organism for genetic analysis and genome engineering.

Acinetobacter sp. strain ADP1 is a naturally transformable gram-negative bacterium with simple culture requirements, a prototrophic metabolism and a compact genome of 3.7 Mb which has recently been sequenced. Wild-type ADP1 can be genetically manipulated by the direct addition of linear DNA constructs to log-phase cultures. This makes it an ideal organism for the automation of complex strain construction. Here, we demonstrate the flexibility and versatility of ADP1 as a genetic model through the construction of a broad variety of mutants. These include marked and unmarked insertions and deletions, complementary replacements, chromosomal expression tags and complex combinations thereof. In the process of these constructions, we demonstrate that ADP1 can effectively express a wide variety of foreign genes including antibiotic resistance cassettes, essential metabolic genes, negatively selectable catabolic genes and even intact operons from highly divergent bacteria. All of the described mutations were achieved by the same process of splicing PCR, direct transformation of growing cultures and plating on selective media. The simplicity of these tools make genetic analysis and engineering with Acinetobacter ADP1 accessible to laboratories with minimal microbial genetics expertise and very little equipment. They are also compatible with complete automation of genetic analysis and engineering protocols.

Acinetobacter↗

PubMedDate not supplied

Integrated genetic epidemiology of infectious diseases: the Chagas model.

Genetic typing of pathogenic agents and of vectors has known impressive developments in the last 10 years, thanks to the progresses of molecular biology, and to the contribution of the concepts of evolutionary genetics. Moreover, we know more and more on the genetic susceptibility of man to infectious diseases. I propose here to settle a new, synthetic field of research, which I call 'integrated genetic epidemiology of infectious diseases' (IGEID). I aim at evaluating, by an evolutionary genetic approach, the respective impact, on the transmission and pathogenicity of infectious diseases, of the host's, the pathogen's and the vector's genetic diversity, and their possible interactions (co-evolution phenomena). Chagas' disease constitutes a fine model to develop the IGEID methodology, by both field and experimental studies.

Animals↗

PubMed2000

Comparison of models for genetic evaluation of scrotal circumference in crossbred bulls.

The aim of this study was to evaluate the effect of including concomitant body weight and(or) a random dam effect in genetic evaluation models on variance component estimates and standard error of prediction for scrotal circumference (SC) at 6, 8, 10, and 12 mo. Variance components and average standard errors of prediction were compared under models differing in either the number of related traits (M11 [SC], M12 [SC and BW]) or an uncorrelated random dam effect (M21 [SC], M22 [SC and BW]) using records on 1,547 bull calves. In a single-trait model (M11), estimates of direct heritabilities (h2a) for SC were .45, .49, .57, and .66 at 6, 8, 10, and 12 mo, respectively. In a two-trait model (M12), h2a were similar to those in M11 model. In M21, h2a for SC were .37, .42, .54, and .65, whereas the proportions of phenotypic variance due to dams (d2) were .12, .11, .04, and .02 at 6, 8, 10, and 12 mo, respectively. Similarly, in M22, h2a for SC were .36, .44, .56, and .65 and d2 were .13, .10, .02, and .02. Standard errors of prediction for SC EBV from M22 were reduced by 2.86, 1.21, 3.02, and 1.99% relative to M21 and by 6.45, 2.70, 2.72, and 1.21% relative to M11 at 6, 8, 10, and 12 mo, respectively. Standard errors of prediction for SC EBV from M12 were reduced by .06, .73, 1.56, and .87% relative to M11 at 6, 8, 10, and 12 mo, respectively. The importance of the dam effect decreased with age for both SC and BW. These results demonstrate that a two-trait (SC and BW) animal model would result in more accurate evaluations of yearling SC EBV in beef cattle than a single-trait model.

Animals↗

PubMed1999

Preventive aspects of genetic morbidity: experiences of the Canadian model.

Genetic disorders are emerging as a major health problem in industrialized countries, especially with greater control of environmental diseases. Of total admissions to major paediatric hospitals, 50% are due to genetic disorders, and at least 10% of all adult admissions to hospitals involve major genetic contributions. Several programmes aimed at preventing or ameliorating these genetic disorders are being implemented. Identifying people at risk of genetic disease has helped decrease the burden of such diseases on families and society. Early recognition also leads to greater success of treatment and improves outcome and prognosis. In Arab populations, genetic disorders are still not perceived as a major health problem, even though they are widely prevalent. Applying similar programmes of early detection, maternal screening, neonatal screening, carrier testing and susceptibility testing will significantly help reduce the impact of these disorders in our populations.

Algorithms↗

PubMed2004

Quantifying the percent increase in minimum sample size for SNP genotyping errors in genetic model-based association studies.

Kang et al. [Genet Epidemiol 2004;26:132-141] addressed the question of which genotype misclassification errors are most costly, in terms of minimum percentage increase in sample size necessary (%MSSN) to maintain constant asymptotic power and significance level, when performing case/control studies of genetic association in a genetic model-free setting. They answered the question for single nucleotide polymorphisms (SNPs) using the 2 x 3 chi2 test of independence. We address the same question here for a genetic model-based framework. The genetic model parameters considered are: disease model (dominant, recessive), genotypic relative risk, SNP (marker) and disease allele frequency, and linkage disequilibrium. %MSSN coefficients of each of the six possible error rates are determined by expanding the non-centrality parameter of the asymptotic distribution of the 2 x 3 chi2 test under a specified alternative hypothesis to approximate %MSSN using a linear Taylor series in the error rates. In this work we assume errors misclassifying one homozygote as another homozygote are 0, since these errors are thought to rarely occur in practice. Our findings are that there are settings of the genetic model parameters that lead to large total %MSSN for both dominant and recessive models. As SNP minor allele approaches 0, total %MSSN increases without bound, independent of other genetic model parameters. In general, %MSSN is a complex function of the genetic model parameters. Use of SNPs with small minor allele frequency requires careful attention to frequency of genotyping errors to insure that power specifications are met. Software to perform these calculations for study design is available, and an example of its use to study a disease is given.

Alleles↗