V. Anne Smith

Advances to Bayesian network inference for generating causal networks from observational biological data

MOTIVATION Network inference algorithms are powerful computational tools for identifying putative causal interactions among variables from observational data. Bayesian network inference algorithms hold particular promise in that they can capture linear, non-linear, combinatorial, stochastic and other types of relationships among variables across multiple levels of biological organization. However, challenges remain when applying these algorithms to limited quantities of experimental data collected from biological systems. Here, we use a simulation approach to make advances in our dynamic Bayesian network (DBN) inference algorithm, especially in the context of limited quantities of biological data. RESULTS We test a range of scoring metrics and search heuristics to find an effective algorithm configuration for evaluating our methodological advances. We also identify sampling intervals and levels of data discretization that allow the best recovery of the simulated networks. We develop a novel influence score for DBNs that attempts to estimate both the sign (activation or repression) and relative magnitude of interactions among variables. When faced with limited quantities of observational data, combining our influence score with moderate data interpolation reduces a significant portion of false positive interactions in the recovered networks. Together, our advances allow DBN inference algorithms to be more effective in recovering biological networks from experimentally collected data. AVAILABILITY Source code and simulated data are available upon request. SUPPLEMENTARY INFORMATION http://www.jarvislab.net/Bioinformatics/BNAdvances/

Relationship between differentially expressed mRNA and mRNA-protein correlations in a xenograft model system

Differential mRNA expression studies implicitly assume that changes in mRNA expression have biological meaning, most likely mediated by corresponding changes in protein levels. Yet studies into mRNA-protein correspondence have shown notoriously poor correlation between mRNA and protein expression levels, creating concern for inferences from only mRNA expression data. However, none of these studies have examined in particular differentially expressed mRNA. Here, we examined this question in an ovarian cancer xenograft model. We measured protein and mRNA expression for twenty-nine genes in four drug-treatment conditions and in untreated controls. We identified mRNAs differentially expressed between drug-treated xenografts and controls, then analysed mRNA-protein expression correlation across a five-point time-course within each of the four experimental conditions. We evaluated correlations between mRNAs and their protein products for mRNAs differentially expressed within an experimental condition compared to those that are not. We found that differentially expressed mRNAs correlate significantly better with their protein product than non-differentially expressed mRNAs. This result increases confidence for the use of differential mRNA expression for biological discovery in this system, as well as providing optimism for the usefulness of inferences from mRNA expression in general.

A role of her own: female cowbirds, Molothrus ater, influence the development and outcome of song learning.

Previous work has shown that captive female cowbirds, Molothrus ater, can influence the outcome of male song development by affecting retention or deletion of song elements and by stimulating improvization. Here we looked for evidence of female influence during the process of learning, as males progress from subsong to plastic song to stereotyped song. In a longitudinal study, we measured the rate and timing of vocal development in captive, juvenile male brown-headed cowbirds, M. a. artemisiae. Half the young males were housed with female cowbirds from their own population (South Dakota: SD) and half with female cowbirds from a M. a. ater population (Indiana: IN). Both populations of females prefer local songs and differ in the time of breeding, with SD females breeding 2 weeks later than IN females. The results showed significant effects of female presence on the age at which males advanced through stages of vocal development: the SD males with SD females, as opposed to SD males with IN females, developed stereotyped song earlier, reduced motor practise earlier, and produced more effective playback songs. Longitudinal observations of social interactions showed that the two groups of females reliably differed in social responses to males. Degree of social proximity of females to males in the winter predicted song maturity, rate of rehearsal and song potency. Thus, females can stimulate the progression of song learning, as well as prune song content. Copyright 2000 The Association for the Study of Animal Behaviour.

The context of social learning: association patterns in a captive flock of brown-headed cowbirds

Much work on social learning has involved behaviour transmission between pairs of individuals, but recently the need to examine the social context in which learning occurs has been recognized. Previous studies using small numbers of animals have shown social influence on the behavioural development of juvenile male brown-headed cowbirds, Molothrus ater. Here we looked at the larger social context that forms the framework for such influence in more natural settings. We allowed a captive group of over 70 cowbirds, comprising adult and juvenile males and females, to associate freely in a large complex of connected aviaries. Highly organized social assortment emerged in the group, with individuals associating with others based on similarity in age and sex. Juvenile males that associated more with adult males had higher courtship success. Juvenile males that associated more with females sang less over the year. These results indicate that the social context of social learning for juvenile males is not just random association with all other birds in the social group, but is a selective and structured pattern of interaction. Differences in navigating this social structure correlated with courtship success and vocalization, behaviour known to be affected by social learning. Studies such as this, using large groups with free assortment of individuals, are the first step towards understanding the effects of the larger social context surrounding social learning.

Revealing ecological networks using Bayesian network inference algorithms

Understanding functional relationships within ecological networks can help reveal keys to ecosystem stability or fragility. Revealing these relationships is complicated by the difficulties of isolating variables or performing experimental manipulations within a natural ecosystem, and thus inferences are often made by matching models to observational data. Such models, however, require assumptions-or detailed measurements-of parameters such as birth and death rate, encounter frequency, territorial exclusion, and predation success. Here, we evaluate the use of a Bayesian network inference algorithm, which can reveal ecological networks based upon species and habitat abundance alone. We test the algorithms performance and applicability on observational data of avian communities and habitat in the Peak District National Park, United Kingdom. The resulting networks correctly reveal known relationships among habitat types and known interspecific relationships. In addition, the networks produced novel insights into ecosystem structure and identified key species with high connectivity. Thus, Bayesian networks show potential for becoming a valuable tool in ecosystem analysis.

Hierarchical Bayesian models in ecology: Reconstructing species interaction networks from non-homogeneous species abundance data

article i nfo The relationships among organisms and their surroundings can be of immense complexity. To describe and understand an ecosystem as a tangled bank, multiple ways of interaction and their effects have to be considered, suchaspredation,competition,mutualismandfacilitation.Understandingtheresultinginteractionnetworksisa challenge in changing environments, e.g. to predict knock-on effects of invasive species and to understand how climate change impacts biodiversity. The elucidation of complex ecological systems with their interactions will benefit enormously from the development of new machine learning tools that aim to infer the structure of inter- actionnetworks from fielddata.Inthe presentstudy,wepropose a novel Bayesian regressionand multiplechan- gepoint model (BRAM) for reconstructing species interaction networks from observed species distributions. The model has been devised to allow robust inference in the presence of spatial autocorrelation and distributional heterogeneity. We have evaluated the model on simulated data that combines a trophic niche model with a stochastic population model on a 2-dimensional lattice, and we have compared the performance of our model with L1-penalized sparse regression (LASSO) and non-linear Bayesian networks with the BDe scoring scheme. In addition, we have applied our method to plant ground coverage data from the western shore of the Outer Hebrides with the objective to infer the ecological interactions.

Testing measures of animal social association by computer simulation

Summary Techniques used to measure patterns of affiliation among social animals have rarely been tested for accuracy. One reason for this lack of validation is that it is often impossible to compare sample data to the true distribution of social assortment of a group of animals. Here we test some methods of assessing social assortment by using a computer simulation of organisms whose assortment patterns were under our control. We created male and female organisms that moved in a direction that was based on a social bias parameter. As the weight of this parameter increased, organisms were more likely to move in the direction of others of their sex. We then created virtual observers to sample assortment of the organisms under different social bias conditions. Observers used three different techniques of measuring assortment. These were (1) group membership: noting all organisms that were associated in the same ‘group’, (2) nearest neighbour: noting the nearest organism to a randomly selected individual and (3) neighbourhood: noting all organisms near a selected individual. Neighbourhood was taken either by all-occurrence sampling or by focal sampling the associations of randomly selected individuals. Some techniques emerged as more sensitive than others under different conditions and biases were revealed in some measures. For example, the group membership method was biased toward finding significant assortment differences between the sexes when no difference actually existed. Nearest neighbour was insensitive to finding a difference in assortment between sexes when one existed. Focal sampling was less sensitive to finding effects than all-occurrence sampling. The computer simulation revealed properties of each technique that would have been impossible to detect in the field.

An analytical approach differentiates between individual and collective cancer invasion.

Tumour cells employ a variety of mechanisms to invade their environment and to form metastases. An important property is the ability of tumour cells to transition between individual cell invasive mode and collective mode. The switch from collective to individual cell invasion in the breast was shown recently to determine site of subsequent metastasis. Previous studies have suggested a range of invasion modes from single cells to large clusters. Here, we use a novel image analysis method to quantify and categorise invasion. We have developed a process using automated imaging for data collection, unsupervised morphological examination of breast cancer invasion using cognition network technology (CNT) to determine how many patterns of invasion can be reliably discriminated. We used Bayesian network analysis to probabilistically connect morphological variables and therefore determine that two categories of invasion are clearly distinct from one another. The Bayesian network separated individual and collective invading cell groups based on the morphological measurements, with the level of cell-cell contact the most discriminating morphological feature. Smaller invading groups were typified by smoother cellular surfaces than those invading collectively in larger groups. Interestingly, elongation was evident in all invading cell groups and was not a specific feature of single cell invasion as a surrogate of epithelial-mesenchymal transition. In conclusion, the combination of cognition network technology and Bayesian network analysis provides an insight into morphological variables associated with transition of cancer cells between invasion modes. We show that only two morphologically distinct modes of invasion exist.

Causal pattern recovery from neural spike train data using the Snap Shot Score

We present a new approach to learning directed information flow networks from multi-channel spike train data. A novel scoring function, the Snap Shot Score, is used to assess potential networks with respect to their quality of causal explanation for the data. Additionally, we suggest a generic concept of plausibility in order to assess network learning techniques under partial observability conditions. Examples demonstrate the assessment of networks with the Snap Shot Score, and neural network simulations show its performance in complex situations with partial observability. We discuss the application of the new score to real data and indicate how it can be modified to suit other neural data types.

Biology Students Building Computer Simulations Using StarLogo TNG

Abstract Confidence is an important issue for biology students in handling computational concepts. This paper describes a practical in which honours-level bioscience students simulate complex animal behaviour using StarLogo TNG, a freely-available graphical programming environment. The practical consists of two sessions, the first of which guides students through building their own computer simulation using StarLogo TNG’s graphical programming blocks. The second practical requires them to modify the simulation and carry out a simulation-based experiment. Results from pre- and post-surveys show that, after completing the practical, students have increased their confidence in answering questions requiring understanding of computer code.