Epithelial-mesenchymal transition (EMT) plays an essential role in embryonic development, wound healing, and tumor progression. Partial EMT states have been linked to metastatic dissemination and drug resistance. Several interconnected feedback loops at the RNA and protein levels control the transition between different cellular states. Using a combination of mathematical modeling and experimental analyses in the TGFβ-responsive breast epithelial MCF10A cell model, we identify a central role for the tumor suppressor protein Deleted in Liver Cancer 1 (DLC1) during EMT. By extending a previous model of EMT comprising key transcription factors and microRNAs, our work shows that DLC1 acts as a positive regulator of TGFβ-driven EMT, mainly by promoting SNAIL1 expression. Our model predictions indicate that DLC1 loss impairs EMT progression. Experimental analyses confirm this prediction and reveal the acquisition of a partial EMT phenotype in DLC1-depleted cells. Furthermore, our model results indicate a possible EMT reversion to partial or epithelial states upon DLC1 loss in MCF10A cells induced toward mesenchymal phenotypes. The increased EMT plasticity of cells lacking DLC1 may explain its importance as a tumor suppressor.

From simulating galaxy formation to viral transmission in a pandemic, scientific models play a pivotal role in developing scientific theories and supporting government policy decisions that affect us all. Given these critical applications, a poor modelling assumption or bug could have far-reaching consequences. However, scientific models possess several properties that make them notoriously difficult to test, including a complex input space, long execution times, and non-determinism, rendering existing testing techniques impractical. In fields such as epidemiology, where researchers seek answers to challenging causal questions, a statistical methodology known as Causal inference has addressed similar problems, enabling the inference of causal conclusions from noisy, biased, and sparse data instead of costly experiments. This article introduces the causal testing framework: a framework that uses causal inference techniques to establish causal effects from existing data, enabling users to conduct software testing activities concerning the effect of a change, such as metamorphic testing, a posteriori . We present three case studies covering real-world scientific models, demonstrating how the causal testing framework can infer metamorphic test outcomes from reused, confounded test data to provide an efficient solution for testing scientific modelling software.

Metamorphic testing provides a means by which to generate succinct test oracles that can apply to large input spaces. For this it depends on the formulation of metamorphic relations, which generally require extensive domain expertise and human input. To address this problem, we present a model-based testing approach that can automatically generate metamorphic relations and associated tests. Our approach is motivated by the observation that metamorphic testing is a fundamentally causal task. We show how it is possible to leverage lightweight graph-based modelling techniques from the field of causal inference to specify causal properties of the system-under-test. Through a series of controlled experiments, we find that the proposed approach is robust to misspecification and can test evasive causal relationships (i.e. those that are difficult to exercise and observe) when combined with an appropriate test generation strategy. We also apply the approach to two case studies from the Defects4J framework with known bugs that affect causal behaviour. The results of these case studies suggest that the approach is not only useful for catching bugs affecting causal structure, but also alerting the user to inaccuracies in the specification.

It is widely hoped that variation in the human genome will provide a means of predicting risk of a variety of complex, chronic diseases. A major stumbling block to the successful identification of association between human DNA polymorphisms (SNPs) and variability in risk of complex diseases is the enormous number of SNPs in the human genome (4,9). The large number of SNPs results in unacceptably high costs for exhaustive genotyping, and so there is a broad effort to determine ways to select SNPs so as to maximize the informativeness of a subset.In this paper we contrast two methods for reducing the complexity of SNP variation: haplotype tagging, i.e. typing a subset of SNPs to identify segments of the genome that appear to be nearly unrecombined (haplotype blocks), and a new block-free model that we develop in this report. We present a statistic for comparing haplotype blocks and show that while the concept of haplotype blocks is reasonably robust there is substantial variability among block partitions. We develop a measure for selecting an informative subset of SNPs in a block free model. We show that the general version of this problem is NP-hard and give efficient algorithms for two important special cases of this problem.

Most of the information being collected on DNA variation among people does not identify which of the two parents transmitted which of the two copies of each gene. Even worse, the parent of origin is often scrambled for each single nucleotide polymorphism (SNP) that is identified, so that each gene may be represented by hundreds of pairs of SNP vectors or haplotypes. Collection of a population sample of this kind of genotype data, however, does contain information about these haplotypes, and inference of the haplotype pairs from this kind of data is referred to has haplotype phase inference. The problem has a rich geometric representation, and has spawned a wealth of algorithms that span graph theoretic to Bayesian approaches. Good solutions to this problem are strongly motivated by the efforts seeking to identify genes that underlie complex genetic disorders. The latest efforts in this area will be described and reviewed.