Testing Biological Hypotheses Research Articles

Integrating Bioinformatics and Computational Biology: Perspectives and Possibilities for In Silico Network Reconstruction in Molecular Systems Biology

There is a flood of molecular data about many aspects of cellular functioning. This data ranges from sequence and structural data to gene and protein regulation data, including time dependent changes in the concentration. Integration of the different datasets through computational methods is required to extract biological information that is relevant from a systems biology perspective. In this paper we discuss how different computational tools and methods can be made to work together integrating different types of data, mining these data for biological information, and assisting in pathway reconstruction and biological hypotheses generation. We review the recent body of literature where such integrative approaches are used and discuss automation of data integration and model building to generate testable biological hypotheses. We analyze issues regarding the design of such automated tools and discuss what limitations and pitfalls can be foreseen for the automation and what solutions can computer science and biologists provide to overcome them.

Model-driven analysis of experimentally determined growth phenotypes for 465 yeast gene deletion mutants under 16 different conditions

An iterative approach that integrates high-throughput measurements of yeast deletion mutants and flux balance model predictions improves understanding of both experimental and computational results.

Statistical Issues and Analyses of in vivo and in vitro Genomic Data in order to Identify Clinically Relevant Profiles

In vitro experimentation provides a convenient controlled environment for testing biological hypotheses of functional genomics in cancer induction and progression. However, it is necessary to validate resulting gene signatures from these in vitro experiments in human tumor samples (i.e. in vivo). We discuss the several methods for integrating data from these two sources paying particular attention to formulating statistical tests and corresponding null hypotheses. We propose a classification null hypothesis that can be simply modeled via permutation testing. A classification method is proposed based upon the Tissue Similarity Index of Sandberg and Ernberg (PNAS, 2005) that uses the classification null hypothesis. This method is demonstrated using the in vitro signature of Core Serum Response developed by Chang et al. (PLoS Biology, 2004).

Spatio-Temporal Analysis of Constitutive Exocytosis in Epithelial Cells

Exocytosis is an essential cellular trafficking process integral to the proper distribution and function of a plethora of molecules, including transporters, receptors, and enzymes. Moreover, incorrect protein targeting can lead to pathological conditions. Recently, the application of evanescent wave microscopy has allowed us to image the final steps of exocytosis. However, spatio-temporal analysis of fusion of constitutive vesicular traffic with the plasma membrane has not been systematically performed. Also, the spatial sites and times of vesicle fusion have not yet been analyzed together. In addition, more formal tests are required in testing biological hypotheses, rather than visual inspection combined with statistical descriptives. Ripley K-functions are used to examine the joint and marginal behavior of locations and fusion times. Semiautomatic detection and mapping of constitutive fusion sites reveals spatial and temporal clustering, but no dependency between the locations and times of fusion events. Our novel approach could be translated to other studies of membrane trafficking in health and diseases such as diabetes.

Mechanistic systems models of cell signaling networks: a case study of myocyte adrenergic regulation

Signal transduction networks coordinate a wide variety of cellular functions, including gene expression, metabolism, and cell fate processes. Understanding biological networks quantitatively is a major challenge to post-genomic biology, and mechanistic systems models will be crucial for this task. Here, we review approaches towards developing mechanistic systems models of established cell signaling networks. The ability of mechanistic system models to generate testable biological hypotheses and experimental strategies is discussed. As a case study of model development and analysis, we examined the functional roles of phospholamban, the L-type calcium channel, the ryanodine receptor, and troponin I phosphorylation upon β-adrenergic stimulation in the rat ventricular myocyte. Model analysis revealed that while protein kinase A-mediated phosphorylation of the ryanodine receptor greatly increases its calcium sensitivity, calcium autoregulation may adapt quickly by negating potential increases in contractility. Systematic combinations of in silico perturbations supported the conclusion that phospholamban phosphoregulation is the primary mechanism for increased sarcoplasmic reticulum load and calcium relaxation rate during β-adrenergic stimulation, while both phospholamban and the L-type calcium channel contribute to increased systolic calcium. Combined with detailed experimental studies, mechanistic systems models will be valuable for developing a quantitative understanding of cell signaling networks.

A stratified approach for modeling the distribution of a threatened ant species in the Swiss National Park

We present models predicting the potential distribution of a threatened ant species, Formica exsecta Nyl., in the Swiss National Park (SNP). Data to fit the models have been collected according to a random-stratified design with an equal number of replicates per stratum. The basic aim of such a sampling strategy is to allow the formal testing of biological hypotheses about those factors most likely to account for the distribution of the modeled species. The stratifying factors used in this study were: vegetation, slope angle and slope aspect, the latter two being used as surrogates of solar radiation, considered one of the basic requirements of F. exsecta. Results show that, although the basic stratifying predictors account for more than 50% of the deviance, the incorporation of additional non-spatially explicit predictors into the model, as measured in the field, allows for an increased model performance (up to nearly 75%). However, this was not corroborated by permutation tests. Implementation on a national scale was made for one model only, due to the difficulty of obtaining similar predictors on this scale. The resulting map on the national scale suggests that the species might once have had a broader distribution in Switzerland. Reasons for its particular abundance within the SNP might possibly be related to habitat fragmentation and vegetation transformation outside the SNP boundaries.

Modeling Survival and Testing Biological Hypotheses Using Marked Animals: A Unified Approach with Case Studies

The understanding of the dynamics of animal populations and of related ecological and evolutionary issues frequently depends on a direct analysis of life history parameters. For instance, examination of trade—offs between reproduction and survival usually rely on individually marked animals, for which the exact time of death is most often unknown, because marked individuals cannot be followed closely through time. Thus, the quantitative analysis of survival studies and experiments must be based on capture—recapture (or resighting) models which consider, besides the parameters of primary interest, recapture or resighting rates that are nuisance parameters. Capture—recapture models oriented to estimation of survival rates are the result of a recent change in emphasis from earlier approaches in which population size was the most important parameter, survival rates having been first introduced as nuisance parameters. This emphasis on survival rates in capture—recapture models developed rapidly in the 1980s and used as a basic structure the Cormack—Jolly—Seber survival model applied to an homogeneous group of animals, with various kinds of constraints on the model parameters. These approaches are conditional on first captures; hence they do not attempt to model the initial capture of unmarked animals as functions of population abundance in addition to survival and capture probabilities. This paper synthesizes, using a common framework, these recent developments together with new ones, with an emphasis on flexibility in modeling, model selection, and the analysis of multiple data sets. The effects on survival and capture rates of time, age, and categorical variables characterizing the individuals (e.g., sex) can be considered, as well as interactions between such effects. This "analysis of variance" philosophy emphasizes the structure of the survival and capture process rather than the technical characteristics of any particular model. The flexible array of models encompassed in this synthesis uses a common notation. As a result of the great level of flexibility and relevance achieved, the focus is changed from fitting a particular model to model building and model selection. The following procedure is recommended: (1) start from a global model compatible with the biology of the species studied and with the design of the study, and assess its fit; (2) select a more parsimonious model using Akaike's Information Criterion to limit the number of formal tests; (3) test for the most important biological questions by comparing this model with neighboring ones using likelihood ratio tests; and (4) obtain maximum likelihood estimates of model parameters with estimates of precision. Computer software is critical, as few of the models now available have parameter estimators that are in closed form. A comprehensive table of existing computer software is provided. We used RELEASE for data summary and goodness—of—fit tests and SURGE for iterative model fitting and the computation of likelihood ratio tests. Five increasingly complex examples are given to illustrate the theory. The first, using two data sets on the European Dipper (Cinclus cinclus), tests for sex—specific parameters, explores a model with time—dependent survival rates, and finally uses a priori information to model survival allowing for an environmental variable. The second uses data on two colonies of the Swift (Apus apus), and shows how interaction terms can be modeled and assessed and how survival and recapture rates sometimes partly counterbalance each other. The third shows complex variation in survival rates across sexes and age classes in the roe deer (Capreolus capreolus), with a test of density dependence in annual survival rates. The fourth is an example of experimental density manipulation using the common lizard (Lacerta vivipara). The last example attempts to examine a large and complex data set on the Greater Flamingo (Phoenicopterus ruber), where parameters are age specific, survival is a function of an environmental variable, and an age × year interaction term is important. Heterogeneity seems present in this example and cannot be adequately modeled with existing theory. The discussion presents a summary of the paradigm we recommend and details issues in model selection and design, and foreseeable future developments.

Statistical Hypothesis Testing in Biology: A Contradiction in Terms

We respond to the preceding article by Perry (J. Econ. Entomol. 79: 1149–1155) concerning the subject of hypothesis testing and meaningful presentation of statistical analyses. We agree with Perry that indiscriminate use of statistical hypothesis testing should be discouraged. We also address and refute reasons often given to support continued emphasis on hypothesis testing. We conclude that biologists should take the responsibility for evaluating whether or not the magnitude of the difference between population parameters or treatment effects is biologically important, instead of merely testing for whether such a difference exists. We recommend that authors display the estimate of the difference and the confidence limit for this difference. These statistics, along with the standard error, contain the most information: their presentation provides the clearest and most meaningful format of the statistical analyses.

Industry-university co-operation. Experiences, possibilities and problems as seen from a Swedish perspective.

The co-operation between industry and academia in Sweden has been subject to changing views in the society. The experiences of Astra Läkemedel AB are described and placed in a more general perspective. The co-operation in drug development includes the testing of biological hypotheses, drug formulation, preclinical and clinical studies, etc. An interesting way of collaboration is the position of 'adjunct professor'. Some problems and concerns relating to these kinds of co-operation are discussed. It is concluded that co-operation is very useful, provided that one is aware of possible pitfalls.

Simulation of organisms using a developmental model Part 2: The heterocyst formation problem in blue-green algae

In Part 1 of this paper ‡ , we described a general program, called CELIA, for simulating linear arrays of cells, each one changing under the same set of rules. In this Part, we discuss how the program can be applied to biological hypothesis testing, by discussing the problem of heterocyst formation in filamentous blue-green algae. We shall assume familiarity with the contents of Part 1.