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Although manually noting the precise sequence of steps taken allows for an analysis to be reproduced, the documentation can easily get out of sync with how the analysis was really performed in its final version. By instead specifying the full analysis workflow in a form that allows for direct execution, one can ensure that the specification matches the analysis that was (subsequently) performed, and that the analysis can be reproduced by yourself or others in an automated way. Such executable descriptions [10] might come in the form of simple shell scripts or makefiles [15], [16] at the command line, or in the form of stored workflows in a workflow management system [10], [11], [13], [17], [18].

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To allow efficient retrieval of details behind textual statements, we suggest that statements are connected to underlying results already from the time the statements are initially formulated (for instance in notes or emails). Such a connection can for instance be a simple file path to detailed results, or the ID of a result in an analysis framework, included within the text itself. For an even tighter integration, there are tools available to help integrate reproducible analyses directly into textual documents, such as Sweave [19], the GenePattern Word add-in [4], and Galaxy Pages [20]. These solutions can also subsequently be used in connection with publications, as discussed in the next rule.

The rise of high-throughput technologies in molecular biology has led to a massive amount of publicly available data. While computational method development has been a cornerstone of biomedical research for decades, the rapid technological progress in the wet lab makes it difficult for software development to keep pace. Wet lab scientists rely heavily on computational methods, especially since more research is now performed in silico. However, suitable tools do not always exist, and not everyone has the skills to write complex software. Computational biologists are required to close this gap, but they often lack formal training in software engineering. To alleviate this, several related challenges have been previously addressed in the Ten Simple Rules series, including reproducibility [1], effectiveness [2], and open-source development of software [3, 4].

To our knowledge, software development as part of scientific research is usually carried out by individuals or small teams with no more than two or three members. Hence, the responsibility of designing, implementing, testing, and documenting the code rests on few shoulders. Additionally, there is pressure to produce publishable results or, at least, to contribute analysis work to ongoing projects. Consequently, academic software is typically released as a prototype. We acknowledge that such a tool cannot adhere to and should not be judged by the standards that we take for granted for production grade software. However, widespread use of a tool is typically in the interest of a researcher. To this end, we propose ten simple rules that, in our experience, have a considerable impact on improving usability of scientific software.

Coronavirus Disease 2019 (COVID-19) manifests as extreme acute respiratory conditions caused by a novel beta coronavirus named severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) which is reported to be the seventh coronavirus to infect humans. Like other SARS-CoVs it has a large positive-stranded RNA genome. But, specific furin site in the spike protein, mutation prone and phylogenetically mess open reading frame1ab (Orf1ab) separates SARS-CoV-2 from other RNA viruses. Since the outbreak (February-March 2020), researchers, scientists, and medical professionals are inspecting all possible facts and aspects including its replication, detection, and prevention strategies. This led to the prompt identification of its basic biology, genome characterization, structural and expression based functional information of proteins, and utilization of this information in optimizing strategies to prevent its spread. This review summarizes the recent updates on the basic molecular biology of SARS-CoV-2 and prevention strategies undertaken worldwide to tackle COVID-19. This recent information can be implemented for the development and designing of therapeutics against SARS-CoV-2.

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Researchers also need a tool that can detect varying degrees of chaos (Glossary) in noisy recordings. In strongly chaotic systems, initially similar system states diverge faster than they do in weakly chaotic systems. And such varying degrees of chaos are predicted to occur in biology, with functional consequences. For example, a model of white blood cell concentrations in chronic granulocytic leukemia can display varying levels of chaos, and knowing how chaotic those concentrations are in actual leukemia patients could have important implications for health outcomes19. As another example, models of the human cortex predict that macro-scale cortical electrodynamics should be weakly chaotic during waking states and should be strongly chaotic under propofol anesthesia20; if this prediction is true, then detecting changing levels of chaos in large-scale brain activity could be useful for monitoring depth of anesthesia and for basic anesthesia research. Thus, it is imperative to develop tools that can not only determine that an experimental system is chaotic, but also tools to assess changing levels of chaos in a system.

Periodic white blood cell concentration. Inspired by the finding that chronic granulocytic leukemia involves apparently aperiodic oscillations in the concentration of circulating white blood cells77, Mackey and Glass19 study mathematical models of oscillating physiological control systems. They describe a simple mathematical model of the concentration of circulating white blood cells:

Chaotic logistic map. The logistic map is one of the simplest known systems that can exhibit both periodic and chaotic behavior. It was originally introduced by biologist Robert May52 as a discrete-time model of population growth. It is described by the following equation:

Neuron integrated circuit. The integrated circuit data analyzed in Table 5 are the same as those analyzed in Table 4. Because the circuit is a physical implementation of a known and simple two-dimensional system of equations, Uenuhara and colleagues used those equations to calculate the ground-truth largest Lyapunov exponents of the circuit in its three different states (periodic, strange non-chaotic, and chaotic), and report those largest Lyapunov exponents in their paper27.

If the NIAID Systems Biology Laboratory were a symphony orchestra, lab chief Ron Germain would be its conductor-musician. As conductor, Germain provides the necessary structure for tempo and harmony. As musician, he provides the immunology base, essentially the study of macrophages. He has recruited "orchestra" members for the lab who have the skills to work on their own but are also able to work together in the name of systems biology.

At that time, circa 2001, biology was rich in genomic data; proteomics had come of age; and immunologists had identified many cellular and even molecular components of the immune system. Yet predicting immune system behavior remained as elusive as ever.

Whether a systems biology lab could tease out answers was far from clear. But despite the risk, NIAID Director Anthony Fauci and Scientific Director Kathy Zoon committed a steady stream of resources. Together with Germain, they hoped for, and threw their energy into, a new approach to understanding the immune system that would better embrace experimental and computational techniques to explore connections in all their intricate glory.

Independently, the unit heads interact with labs at NIH and beyond to establish and incorporate systems biology methods. In true team spirit, they work together to attack the most basic elements of immunology such as a response to an infection or vaccination.

Aleksandra Nita-Lazar is developing new methods to obtain quantitative data that improve our understanding of cell biology and also funnel key information into model building. Her domain is the system-wide analysis of the proteome, which has fallen behind DNA analysis partly for want of the necessary tools.

Germain has seen systems biology labs in which collaborations are more opportunistic than routine, the shortsighted result of building a building, adding smart people, and hoping it all works out. His strategy instead has been to recruit individuals with the necessary skill sets to work on their own but also to work together in the name of systems biology. e24fc04721