Simple, Fast and Accurate Implementation of the Diffusion Approximation Algorithm for Stochastic Ion Channels with Multiple States

The phenomena that emerge from the interaction of the stochastic opening and closing of ion channels (channel noise) with the non-linear neural dynamics are essential to our understanding of the operation of the nervous system. The effects that channel noise can have on neural dynamics are generally studied using numerical simulations of stochastic models. Algorithms based on discrete Markov Chains (MC) seem to be the most reliable and trustworthy, but even optimized algorithms come with a non-negligible computational cost. Diffusion Approximation (DA) methods use Stochastic Differential Equations (SDE) to approximate the behavior of a number of MCs, considerably speeding up simulation times. However, model comparisons have suggested that DA methods did not lead to the same results as in MC modeling in terms of channel noise statistics and effects on excitability. Recently, it was shown that the difference arose because MCs were modeled with coupled activation subunits, while the DA was modeled using uncoupled activation subunits. Implementations of DA with coupled subunits, in the context of a specific kinetic scheme, yielded similar results to MC. However, it remained unclear how to generalize these implementations to different kinetic schemes, or whether they were faster than MC algorithms. Additionally, a steady state approximation was used for the stochastic terms, which, as we show here, can introduce significant inaccuracies. We derived the SDE explicitly for any given ion channel kinetic scheme. The resulting generic equations were surprisingly simple and interpretable - allowing an easy and efficient DA implementation. The algorithm was tested in a voltage clamp simulation and in two different current clamp simulations, yielding the same results as MC modeling. Also, the simulation efficiency of this DA method demonstrated considerable superiority over MC methods.Comment: 32 text pages, 10 figures, 1 supplementary text + figur

MuDelta: Delta-Oriented Mutation Testing at Commit Time

To effectively test program changes using mutation testing, one needs to use mutants that are relevant to the altered program behaviours. In view of this, we introduce MuDelta, an approach that identifies commit-relevant mutants; mutants that affect and are affected by the changed program behaviours. Our approach uses machine learning applied on a combined scheme of graph and vector-based representations of static code features. Our results, from 50 commits in 21 Coreutils programs, demonstrate a strong prediction ability of our approach; yielding 0.80 (ROC) and 0.50 (PR Curve) AUC values with 0.63 and 0.32 precision and recall values. These predictions are significantly higher than random guesses, 0.20 (PR-Curve) AUC, 0.21 and 0.21 precision and recall, and subsequently lead to strong relevant tests that kill 45%more relevant mutants than randomly sampled mutants (either sampled from those residing on the changed component(s) or from the changed lines). Our results also show that MuDelta selects mutants with 27% higher fault revealing ability in fault introducing commits. Taken together, our results corroborate the conclusion that commit-based mutation testing is suitable and promising for evolving software

Software fault tolerance using data diversity

Research on data diversity is discussed. Data diversity relies on a different form of redundancy from existing approaches to software fault tolerance and is substantially less expensive to implement. Data diversity can also be applied to software testing and greatly facilitates the automation of testing. Up to now it has been explored both theoretically and in a pilot study, and has been shown to be a promising technique. The effectiveness of data diversity as an error detection mechanism and the application of data diversity to differential equation solvers are discussed

Testing scientific software: techniques for automatic detection of metamorphic relations

2015 Spring.Includes bibliographical references.Scientific software plays an important role in critical decision making in fields such as the nuclear industry, medicine, and the military. Systematic testing of such software can help to ensure that it works as expected. Comprehensive, automated software testing requires an oracle to check whether the output produced by a test case matches the expected behavior of the program. But the challenges in creating suitable oracles limit the ability to perform automated testing of scientific software. For some programs, creating an oracle may be not possible since the correct output is not known a priori. Further, it may be impractical to implement an oracle for an arbitrary input due to the complexity of a program. The software testing community refers to such programs as non-testable. Many scientific programs fall into this category of non-testable programs, since they are either written to find answers that are previously unknown or they perform complex calculations. In this work, we developed techniques to automatically predict metamorphic relations by analyzing the program structure. These metamorphic relations can serve as automated partial test oracles in scientific software. Metamorphic testing is a method for automating the testing process for programs without test oracles. This technique operates by checking whether a program behaves according to a certain set of properties called metamorphic relations. A metamorphic relation is a relationship between multiple input and output pairs of the program. It specifies how the output should change following a specific change made to the input. A change in the output that differs from what is specified by the metamorphic relation indicates a fault in the program. Metamorphic testing can be effective in testing machine learning applications, bioinformatics programs, health-care simulations, partial differential equations and other programs. Unfortunately, finding appropriate metamorphic relations for use in metamorphic testing remains a labor intensive task that is generally performed by a domain expert or a programmer. In this work we applied novel machine learning based approaches to automatically derive metamorphic relations. We first evaluated the effectiveness of modeling the metamorphic relation prediction problem as a binary classification problem. We found that support vector machines are the most effective binary classifiers for predicting metamorphic relations. We also found that using walk-based graph kernels for feature extraction from graph-based program representations further improves the prediction accuracy. In addition, incorporating mathematical properties of operations in the graph kernel computation improves the prediction accuracy. Further, we found that control flow information of a function are more effective than data dependency information for predicting metamorphic relations. Finally we investigated the possibility of creating multi-label classifiers that can predict multiple metamorphic relations using a single classifier. Our empirical studies show that multi-label classifiers are not effective as binary classifiers for predicting metamorphic relations. Automated testing will make the testing process faster, reduce the testing cost and make it more reliable. Automated testing requires automated test oracles. Automatically discovering metamorphic relations is an important step towards automating oracle creation. Work presented here is the first attempt towards developing automated techniques for deriving metamorphic relations. Our work contributes toward automating the testing process of programs that face oracle problems

Limitations and challenges in protein stability prediction upon genome variations: towards future applications in precision medicine

Protein stability predictions are becoming essential in medicine to develop novel immunotherapeutic agents and for drug discovery. Despite the large number of computational approaches for predicting the protein stability upon mutation, there are still critical unsolved problems: 1) the limited number of thermodynamic measurements for proteins provided by current databases; 2) the large intrinsic variability of \u394\u394G values due to different experimental conditions; 3) biases in the development of predictive methods caused by ignoring the anti-symmetry of \u394\u394G values between mutant and native protein forms; 4) over-optimistic prediction performance, due to sequence similarity between proteins used in training and test datasets. Here, we review these issues, highlighting new challenges required to improve current tools and to achieve more reliable predictions. In addition, we provide a perspective of how these methods will be beneficial for designing novel precision medicine approaches for several genetic disorders caused by mutations, such as cancer and neurodegenerative diseases

Computational structure‐based drug design: Predicting target flexibility

The role of molecular modeling in drug design has experienced a significant revamp in the last decade. The increase in computational resources and molecular models, along with software developments, is finally introducing a competitive advantage in early phases of drug discovery. Medium and small companies with strong focus on computational chemistry are being created, some of them having introduced important leads in drug design pipelines. An important source for this success is the extraordinary development of faster and more efficient techniques for describing flexibility in three‐dimensional structural molecular modeling. At different levels, from docking techniques to atomistic molecular dynamics, conformational sampling between receptor and drug results in improved predictions, such as screening enrichment, discovery of transient cavities, etc. In this review article we perform an extensive analysis of these modeling techniques, dividing them into high and low throughput, and emphasizing in their application to drug design studies. We finalize the review with a section describing our Monte Carlo method, PELE, recently highlighted as an outstanding advance in an international blind competition and industrial benchmarks.We acknowledge the BSC-CRG-IRB Joint Research Program in Computational Biology. This work was supported by a grant from the Spanish Government CTQ2016-79138-R.J.I. acknowledges support from SVP-2014-068797, awarded by the Spanish Government.Peer ReviewedPostprint (author's final draft

TFCheck : A TensorFlow Library for Detecting Training Issues in Neural Network Programs

The increasing inclusion of Machine Learning (ML) models in safety critical systems like autonomous cars have led to the development of multiple model-based ML testing techniques. One common denominator of these testing techniques is their assumption that training programs are adequate and bug-free. These techniques only focus on assessing the performance of the constructed model using manually labeled data or automatically generated data. However, their assumptions about the training program are not always true as training programs can contain inconsistencies and bugs. In this paper, we examine training issues in ML programs and propose a catalog of verification routines that can be used to detect the identified issues, automatically. We implemented the routines in a Tensorflow-based library named TFCheck. Using TFCheck, practitioners can detect the aforementioned issues automatically. To assess the effectiveness of TFCheck, we conducted a case study with real-world, mutants, and synthetic training programs. Results show that TFCheck can successfully detect training issues in ML code implementations