Predicting Cell Cycle Regulated Genes by Causal Interactions

The fundamental difference between classic and modern biology is that technological innovations allow to generate high-throughput data to get insights into molecular interactions on a genomic scale. These high-throughput data can be used to infer gene networks, e. g., the transcriptional regulatory or signaling network, representing a blue print of the current dynamical state of the cellular system. However, gene networks do not provide direct answers to biological questions, instead, they need to be analyzed to reveal functional information of molecular working mechanisms. In this paper we propose a new approach to analyze the transcriptional regulatory network of yeast to predict cell cycle regulated genes. The novelty of our approach is that, in contrast to all other approaches aiming to predict cell cycle regulated genes, we do not use time series data but base our analysis on the prior information of causal interactions among genes. The major purpose of the present paper is to predict cell cycle regulated genes in S. cerevisiae. Our analysis is based on the transcriptional regulatory network, representing causal interactions between genes, and a list of known periodic genes. No further data are used. Our approach utilizes the causal membership of genes and the hierarchical organization of the transcriptional regulatory network leading to two groups of periodic genes with a well defined direction of information flow. We predict genes as periodic if they appear on unique shortest paths connecting two periodic genes from different hierarchy levels. Our results demonstrate that a classical problem as the prediction of cell cycle regulated genes can be seen in a new light if the concept of a causal membership of a gene is applied consequently. This also shows that there is a wealth of information buried in the transcriptional regulatory network whose unraveling may require more elaborate concepts than it might seem at first.

Help or hindrance? Supplier involvement and causal ambiguity within inter-organisational NPD teams

Suppliers are increasingly involved in buyer firms’ interorganizational new product development (NPD) teams. Yet the transfer of knowledge within this context may be subject to varying degrees of causal ambiguity, potentially limiting the effect of supplier involvement on performance. We develop a theoretical model exploring the effect of supplier involvement practices on the level of causal ambiguity within interorganizational NPD teams, and the subsequent impact on competitor imitation, new product advantage, and project performance. Our model also serves as a test of the paradox that causal ambiguity both inhibits imitation by competitors, but also adversely affects organisational outcomes. Results from an empirical study of 119 R&D intensive manufacturing firms in the United Kingdom largely support these hypotheses. Results from structural equation modeling show that supplier involvement orientation and long-term commitment lower causal ambiguity within interorganizational NPD teams. In turn, this lower causal ambiguity generates a new product advantage and increases project performance for the buyer firm, but has no significant effect on competitor imitation. Instead, competitor imitation is delayed by the extent to which the firm develops a new product advantage within the market. These results shed light on the causal ambiguity paradox showing that lower causal ambiguity during interorganizational new product development increases both product and project performance, but without reducing barriers to imitation. Product development managers are encouraged to utilize supplier involvement practices to minimise ambiguity in the NPD project, and to target their supplier involvement efforts on solving causally ambiguous technological problems to sustain a competitive advantage.

Inferring the conservative causal core of gene regulatory networks

Background
Inferring gene regulatory networks from large-scale expression data is an important problem that received much attention in recent years. These networks have the potential to gain insights into causal molecular interactions of biological processes. Hence, from a methodological point of view, reliable estimation methods based on observational data are needed to approach this problem practically.

Results
In this paper, we introduce a novel gene regulatory network inference (GRNI) algorithm, called C3NET. We compare C3NET with four well known methods, ARACNE, CLR, MRNET and RN, conducting in-depth numerical ensemble simulations and demonstrate also for biological expression data from E. coli that C3NET performs consistently better than the best known GRNI methods in the literature. In addition, it has also a low computational complexity. Since C3NET is based on estimates of mutual information values in conjunction with a maximization step, our numerical investigations demonstrate that our inference algorithm exploits causal structural information in the data efficiently.

Conclusions
For systems biology to succeed in the long run, it is of crucial importance to establish methods that extract large-scale gene networks from high-throughput data that reflect the underlying causal interactions among genes or gene products. Our method can contribute to this endeavor by demonstrating that an inference algorithm with a neat design permits not only a more intuitive and possibly biological interpretation of its working mechanism but can also result in superior results.

The causal attributions of nursing students towards adolescent survivors of brain injury

Background: The hidden nature of brain injury means that it is often difficult for people to understand the sometimes challenging behaviors that individuals exhibit. The misattribution of these behaviors may lead to a lack of consideration and public censure if the individual is seen as simply misbehaving.

Objective: The aim of this study was to explore the impact of visual cues indicating the presence or absence of brain injury on prejudice, desire for social interaction, and causal attributions of nursing and computing science students.

Method: An independent-groups design was employed in this research, which recruited 190 first-year nursing students and 194 first-year computing science students from a major university in Belfast, UK. A short passage describing an adolescent’s behavior after a brain injury, together with one of three images portraying a young adolescent with a scar, a head dressing, or neither of these, was given to participants. They were then asked to answer questions relating to prejudice, social interaction, locus of control, and causal attributions. The attributional statements suggested that the character’s behavior could be the result of brain injury or adolescence.

Results: Analysis of variance demonstrated a statistically significant difference between the student groups, where nursing students (M = 45.17, SD = 4.69) desired more social interaction with the fictional adolescent than their computer science peers (M = 38.64, SD = 7.69). Further, analysis of variance showed a main effect of image on the attributional statement that described adolescence as a suitable explanation for the character’s lack of self-confidence.

Discussion: Attributions of brain injury were influenced by the presence of a visible but potentially specious indicator of injury. This suggests that survivors of brain injury who do not display any outward indicator may receive less care and face expectations to behave in a manner consistent with the norms of society. If their injury does not allow them to meet with these expectations, they may face public censure and discrimination.

Are causal and intervention judgments inextricably linked? A developmental study.

The application of the formal framework of causal Bayesian Networks to children's causal learning provides the motivation to examine the link between judgments about the causal structure of a system, and the ability to make inferences about interventions on components of the system. Three experiments examined whether children are able to make correct inferences about interventions on different causal structures. The first two experiments examined whether children's causal structure and intervention judgments were consistent with one another. In Experiment 1, children aged between 4 and 8years made causal structure judgments on a three-component causal system followed by counterfactual intervention judgments. In Experiment 2, children's causal structure judgments were followed by intervention judgments phrased as future hypotheticals. In Experiment 3, we explicitly told children what the correct causal structure was and asked them to make intervention judgments. The results of the three experiments suggest that the representations that support causal structure judgments do not easily support simple judgments about interventions in children. We discuss our findings in light of strong interventionist claims that the two types of judgments should be closely linked. © 2011 Cognitive Science Society, Inc.


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Additivity Pretraining and cue competition effects: Developmental evidence for a reasoning account of causal learning

The effect of additivity pretraining on blocking has been taken as evidence for a reasoning account of human and animal causal learning. If inferential reasoning underpins this effect, then developmental differences in the magnitude of this effect in children would be expected. Experiment 1 examined cue competition effects in children's (4- to 5-year-olds and 6- to 7-year-olds) causal learning using a new paradigm analogous to the food allergy task used in studies of human adult causal learning. Blocking was stronger in the older than the younger children, and additivity pretraining only affected blocking in the older group. Unovershadowing was not affected by age or by pretraining. In experiment 2, levels of blocking were found to be correlated with the ability to answer questions that required children to reason about additivity. Our results support an inferential reasoning explanation of cue competition effects. (c) 2012 APA, all rights reserved.

Causal Models of Clinically Significant Behaviors in Angelman, Cornelia de Lange, Prader-Willi and Smith-Magenis Syndromes

The operant learning theory account of behaviors of clinical significance in people with intellectual disability (ID) has dominated the field for nearly 50 years. However, in the last two decades, there has been a substantial increase in published research that describes the behavioral phenotypes of genetic disorders and shows that behaviors such as self-injury and aggression are more common in some syndromes than might be expected given group characteristics. These cross-syndrome differences in prevalence warrant explanation, not least because this observation challenges an exclusively operant learning theory account. To explore this possible conflict between theoretical account and empirical observation, we describe the genetic cause and physical, social, cognitive and behavioral phenotypes of four disorders associated with ID (Angleman, Cornelia de Lange, Prader-Willi and Smith-Magenis syndromes) and focus on the behaviors of clinical significance in each syndrome. For each syndrome we then describe a model of the interactions between physical characteristics, cognitive and motivational endophenotypes and environmental factors (including operant reinforcement) to account for the resultant behavioral phenotype. In each syndrome it is possible to identify pathways from gene to physical phenotype to cognitive or motivational endophenotype to behavior to environment and back to behavior. We identify the implications of these models for responsive and early intervention and the challenges for research in this area. We identify a pressing need for meaningful dialog between different disciplines to construct better informed models that can incorporate all relevant and robust empirical evidence.

Encouraging children to think counterfactually enhances blocking in a causal learning task

According to a higher order reasoning account, inferential reasoning processes underpin the widely observed cue competition effect of blocking in causal learning. The inference required for blocking has been described as modus tollens (if p then q, not q therefore not p). Young children are known to have difficulties with this type of inference, but research with adults suggests that this inference is easier if participants think counterfactually. In this study, 100 children (51 five-year-olds and 49 six- to seven-year-olds) were assigned to two types of pretraining groups. The counterfactual group observed demonstrations of cues paired with outcomes and answered questions about what the outcome would have been if the causal status of cues had been different, whereas the factual group answered factual questions about the same demonstrations. Children then completed a causal learning task. Counterfactual pretraining enhanced levels of blocking as well as modus tollens reasoning but only for the younger children. These findings provide new evidence for an important role for inferential reasoning in causal learning.

Blocking in children's causal learning depends on working memory and reasoning abilities.

A sample of 99 children completed a causal learning task that was an analogue of the food allergy paradigm used with adults. The cue competition effects of blocking and unovershadowing were assessed under forward and backward presentation conditions. Children also answered questions probing their ability to make the inference posited to be necessary for blocking by a reasoning account of cue competition. For the first time, children's working memory and general verbal ability were also measured alongside their causal learning. The magnitude of blocking and unovershadowing effects increased with age. However, analyses showed that the best predictor of both blocking and unovershadowing effects was children's performance on the reasoning questions. The magnitude of the blocking effect was also predicted by children's working memory abilities. These findings provide new evidence that cue competition effects such as blocking are underpinned by effortful reasoning processes. 

When does information about causal structure improve statistical reasoning?

Base rate neglect on the mammography problem can be overcome by explicitly presenting a causal basis for the typically vague false-positive statistic. One account of this causal facilitation effect is that people make probabilistic judgements over intuitive causal models parameterized with the evidence in the problem. Poorly defined or difficult-to-map evidence interferes with this process, leading to errors in statistical reasoning. To assess whether the construction of parameterized causal representations is an intuitive or deliberative process, in Experiment 1 we combined a secondary load paradigm with manipulations of the presence or absence of an alternative cause in typical statistical reasoning problems. We found limited effects of a secondary load, no evidence that information about an alternative cause improves statistical reasoning, but some evidence that it reduces base rate neglect errors. In Experiments 2 and 3 where we did not impose a load, we observed causal facilitation effects. The amount of Bayesian responding in the causal conditions was impervious to the presence of a load (Experiment 1) and to the precise statistical information that was presented (Experiment 3). However, we found less Bayesian responding in the causal condition than previously reported. We conclude with a discussion of the implications of our findings and the suggestion that there may be population effects in the accuracy of statistical reasoning.