Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges

Background: Systems biology has embraced computational modeling in response to the quantitative nature and increasing scale of contemporary data sets. The onslaught of data is accelerating as molecular profiling technology evolves. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) is a community effort to catalyze discussion about the design, application, and assessment of systems biology models through annual reverse-engineering challenges. Methodology and Principal Findings: We describe our assessments of the four challenges associated with the third DREAM conference which came to be known as the DREAM3 challenges: signaling cascade identification, signaling response prediction, gene expression prediction, and the DREAM3 in silico network challenge. The challenges, based on anonymized data sets, tested participants in network inference and prediction of measurements. Forty teams submitted 413 predicted networks and measurement test sets. Overall, a handful of best-performer teams were identified, while a majority of teams made predictions that were equivalent to random. Counterintuitively, combining the predictions of multiple teams (including the weaker teams) can in some cases improve predictive power beyond that of any single method. Conclusions: DREAM provides valuable feedback to practitioners of systems biology modeling. Lessons learned from the predictions of the community provide much-needed context for interpreting claims of efficacy of algorithms described in the scientific literature

Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models

Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype. However, even for simple bacteria, whole-cell models will contain thousands of parameters, many of which are poorly characterized or unknown. New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models. We organized the Dialogue for Reverse Engineering Assessments and Methods (DREAM) 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation algorithms for whole-cell models. We asked participants to identify a subset of parameters of a whole-cell model given the model’s structure and in silico “experimental” data. Here we describe the challenge, the best performing methods, and new insights into the identifiability of whole-cell models. We also describe several valuable lessons we learned toward improving future challenges. Going forward, we believe that collaborative efforts supported by inexpensive cloud computing have the potential to solve whole-cell model parameter estimation

DISASSEMBLE/ANALYZE/ASSEMBLE: HOW A HANDS-ON ENGINEERING PROJECT AFFECTS HIGH SCHOOL GIRLS\u27 SCIENCE/ENGINEERING SELF-EFFICACY, INTEREST AND CAREER CONSIDERATIONS

Engineering education as part of the K-12 curriculum can be an effective instructional tool and its benefits include improved science and mathematical achievement as well as an increased interest and understanding of the engineering field, especially for female students. However, there is a serious lack of research-based engineering curriculum being used at the middle and high school levels and lessons most often rely on building or construction competitions. Over the past decade or two, many well-known colleges have implemented a reverse engineering instructional unit known as Disassemble/Analyze/Assemble projects within their introductory engineering courses. These units have been shown to improve students’ understanding of mechanical and technical processes as well as increase students’ engineering interest and motivation, especially for female students. The purpose of this study is to implement at the high school level a Disassemble/Analyze/Assemble (DAA) project using computers, handheld fans, and LED lights and to determine if and how this unit affects female students’ self-efficacy, science and engineering interest and career aspirations. Using Social Cognitive Theory for the theoretical framework, nine female students were chosen for the study using stratified purposeful sampling. Semi-structured interviews were conducted before and after the DAA unit. Data was analyzed using an a priori directed approach to content analysis described by Hseih and Shannon (2005). This research study showed that the DAA unit appeared to increase female students’ science/engineering self-efficacy and interest as the unit provided multiple opportunities for the students to problem solve and make cognitive connections with previously learned science concepts. Students did not show any changes in their career considerations after the DAA unit. There was no statistically significant difference between the male and female mean scores on the Purdue Spatial Visual Test: Rotations (Guay, 1976; Yoon, 2011). KEYWORDS: Engineering Education, Science Education, Engineering Interest, Science Self-Efficacy, Science Interes

Towards Adversarial Malware Detection: Lessons Learned from PDF-based Attacks

Malware still constitutes a major threat in the cybersecurity landscape, also due to the widespread use of infection vectors such as documents. These infection vectors hide embedded malicious code to the victim users, facilitating the use of social engineering techniques to infect their machines. Research showed that machine-learning algorithms provide effective detection mechanisms against such threats, but the existence of an arms race in adversarial settings has recently challenged such systems. In this work, we focus on malware embedded in PDF files as a representative case of such an arms race. We start by providing a comprehensive taxonomy of the different approaches used to generate PDF malware, and of the corresponding learning-based detection systems. We then categorize threats specifically targeted against learning-based PDF malware detectors, using a well-established framework in the field of adversarial machine learning. This framework allows us to categorize known vulnerabilities of learning-based PDF malware detectors and to identify novel attacks that may threaten such systems, along with the potential defense mechanisms that can mitigate the impact of such threats. We conclude the paper by discussing how such findings highlight promising research directions towards tackling the more general challenge of designing robust malware detectors in adversarial settings

An Experiment in Refactoring an Object Oriented CASE Tool

Colloque avec actes et comité de lecture.This paper describes experience gained and lessons learned from restructuring OODesigner, a Computer Aided Software Engineering (CASE) tool that supports Object Modelling Technique (OMT). This tool supports a wide range of features such as constructing the three models of OMT, managing information repository, documenting class resources, automatically generating C++ and Java code, reverse engineering C++ and Java code, searching and reusing classes in the corresponding repository and collecting metrics data. A version 1.x of OODesigner has been developed for 3 years since 1994. Although this version was developed using OMT (i.e. the tool has been designed using OMT) and C++, we recognized the potential maintenance problems that originated from the ill-designed class architecture. Thus that version was totally restructured, resulting in a new version that is easier to maintain than the old one. In this paper, we briefly describe the tool's functionality, its development process and its refactoring process, emphasizing the fact that the refactoring of the tool is conducted using the tool itself. Then we discuss lesson learned from these processes and we exhibit some comparative measurements of the developed versions

PLoS Comput Biol

Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype. However, even for simple bacteria, whole-cell models will contain thousands of parameters, many of which are poorly characterized or unknown. New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models. We organized the Dialogue for Reverse Engineering Assessments and Methods (DREAM) 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation algorithms for whole-cell models. We asked participants to identify a subset of parameters of a whole-cell model given the model's structure and in silico "experimental" data. Here we describe the challenge, the best performing methods, and new insights into the identifiability of whole-cell models. We also describe several valuable lessons we learned toward improving future challenges. Going forward, we believe that collaborative efforts supported by inexpensive cloud computing have the potential to solve whole-cell model parameter estimation.5DP1LM01150-05/DP/NCCDPHP CDC HHS/United StatesDP1 LM011510/LM/NLM NIH HHS/United StatesDP1 OD006413/OD/NIH HHS/United States26020786PMC444741

Safe Routes to School State Network Project Final Report 2007-2009: Making Change through Partners and Policies

The Safe Routes to School National Partnership launched the State Network Project in 2007 to influence state-level Safe Routes to School implementation and to leverage additional resources and build a supportive environment through other state-level policies. The 2007 -- 2009 Report describes the approach and structure of the Partnership's State Network and Local School Projects in 10 jurisdictions (CA, DC, GA, IL, KY, LA, NY, OK, TX and VA). The networks were selected primarily based on high levels of childhood obesity, diversity and low income communities. The report highlights the progress achieved at state and local levels over three years, including major accomplishments, lessons learned, and next steps

Perfect Snap

Taking group photos during important events is a common practice. Group photos are taken to remember cheerful times when people had an opportunity to meet many other people. However, an unappealing facial expression of one person can easily ruin the entire photo. Capturing the wrong moments when a person doesn’t look attractive can leave him/ her displeased from the complete event experience. A solution is to develop a mobile app that captures the moment when everyone is smiling with eyes wide open. Our solution aims to develop an iPhone app that will preclude users from worrying about not having a great group picture