We join the increasing call to take computational education of life science students a step further, beyond teaching mere programming and employing existing software tools. We describe a new course, focusing on enriching the curriculum of life science students with abstract, algorithmic, and logical thinking, and exposing them to the computational “culture.” The design, structure, and content of our course are influenced by recent efforts in this area, collaborations with life scientists, and our own instructional experience. Specifically, we suggest that an effective course of this nature should: (1) devote time to explicitly reflect upon computational thinking processes, resisting the temptation to drift to purely practical instruction, (2) focus on discrete notions, rather than on continuous ones, and (3) have basic programming as a prerequisite, so students need not be preoccupied with elementary programming issues. We strongly recommend that the mere use of existing bioinformatics tools and packages should not replace hands-on programming. Yet, we suggest that programming will mostly serve as a means to practice computational thinking processes. This paper deals with the challenges and considerations of such computational education for life science students. It also describes a concrete implementation of the course and encourages its use by others.

Modeling and analysis of genetic regulatory networks is essential both for better understanding their dynamic behavior and for elucidating and refining open issues. We hereby present a discrete computational model that effectively describes the transient and sequential expression of a network of genes in a representative developmental pathway. Our model system is a transcriptional cascade that includes positive and negative feedback loops directing the initiation and progression through meiosis in budding yeast. The computational model allows qualitative analysis of the transcription of early meiosis-specific genes, specifically, Ime2 and their master activator, Ime1. The simulations demonstrate a robust transcriptional behavior with respect to the initial levels of Ime1 and Ime2. The computational results were verified experimentally by deleting various genes and by changing initial conditions. The model has a strong predictive aspect, and it provides insights into how to distinguish among and reason about alternative hypotheses concerning the mode by which negative regulation through Ime1 and Ime2 is accomplished. Some predictions were validated experimentally, for instance, showing that the decline in the transcription of IME1 depends on Rpd3, which is recruited by Ime1 to its promoter. Finally, this general model promotes the analysis of systems that are devoid of consistent quantitative data, as is often the case, and it can be easily adapted to other developmental pathways.budding yeast ͉ computational modeling ͉ meiosis ͉ systems biology ͉ transcriptional networks

BackgroundTGF-β signaling is a cellular pathway that functions in most cells and has been shown to play a role in multiple processes, such as the immune response, cell differentiation and proliferation. Recent evidence suggests a possible interaction between TGF-β signaling and the molecular circadian oscillator. The current study aims to characterize this interaction in the zebrafish at the molecular and behavioral levels, taking advantage of the early development of a functional circadian clock and the availability of light-entrainable clock-containing cell lines.ResultsSmad3a, a TGF-β signaling-related gene, exhibited a circadian expression pattern throughout the brain of zebrafish larvae. Both pharmacological inhibition and indirect activation of TGF-β signaling in zebrafish Pac-2 cells caused a concentration dependent disruption of rhythmic promoter activity of the core clock gene Per1b. Inhibition of TGF-β signaling in intact zebrafish larvae caused a phase delay in the rhythmic expression of Per1b mRNA. TGF-β inhibition also reversibly disrupted, phase delayed and increased the period of circadian rhythms of locomotor activity in zebrafish larvae.ConclusionsThe current research provides evidence for an interaction between the TGF-β signaling pathway and the circadian clock system at the molecular and behavioral levels, and points to the importance of TGF-β signaling for normal circadian clock function. Future examination of this interaction should contribute to a better understanding of its underlying mechanisms and its influence on a variety of cellular processes including the cell cycle, with possible implications for cancer development and progression.

Modeling and simulation of biological networks is an effective and widely used research methodology. The Biological Network Simulator (BioNSi) is a tool for modeling biological networks and simulating their discrete-time dynamics, implemented as a Cytoscape App. BioNSi includes a visual representation of the network that enables researchers to construct, set the parameters, and observe network behavior under various conditions. To construct a network instance in BioNSi, only partial, qualitative biological data suffices. The tool is aimed for use by experimental biologists and requires no prior computational or mathematical expertise. BioNSi is freely available at http://bionsi.wix.com/bionsi , where a complete user guide and a step-by-step manual can also be found.

BackgroundBench biologists often do not take part in the development of computational models for their systems, and therefore, they frequently employ them as “black-boxes”. Our aim was to construct and test a model that does not depend on the availability of quantitative data, and can be directly used without a need for intensive computational background.ResultsWe present a discrete transition model. We used cell-cycle in budding yeast as a paradigm for a complex network, demonstrating phenomena such as sequential protein expression and activity, and cell-cycle oscillation. The structure of the network was validated by its response to computational perturbations such as mutations, and its response to mating-pheromone or nitrogen depletion. The model has a strong predicative capability, demonstrating how the activity of a specific transcription factor, Hcm1, is regulated, and what determines commitment of cells to enter and complete the cell-cycle.ConclusionThe model presented herein is intuitive, yet is expressive enough to elucidate the intrinsic structure and qualitative behavior of large and complex regulatory networks. Moreover our model allowed us to examine multiple hypotheses in a simple and intuitive manner, giving rise to testable predictions. This methodology can be easily integrated as a useful approach for the study of networks, enriching experimental biology with computational insights.