Workflow for Data Analysis in Experimental and Computational Systems Biology: Using Python as ‘Glue’

Streamlined Data Processing for Determination of Equilibrium Dissociation Constants with Accurate Constant via Transient Incomplete Separation (ACTIS)

The determination of accurate equilibrium dissociation constants, K_d, of protein-small molecule complexes is important but challenging as all established methods have inherent sources of inaccuracy. Accurate Constant via Transient Incomplete Separation (ACTIS) is a new method for K_d determination using transient incomplete separation of the complex from the unbound small molecule in a pressure-driven flow inside a capillary. ACTIS is accurate, and its accuracy is invariant to variations in geometries of both the fluidic system and the flow. Furthermore, ACTIS is implemented using a simple fluidic system supporting its accuracy and providing a simple-to-follow/copy template for instrumentation. Despite the simple and robust instrumentation/acquisition, the current data processing workflow is cumbersome, time consuming, and prone to hard-to-trace human errors therefore hindering ACTIS' ability to become a practical reference method for K_d determination. This technical note describes a streamlined workflow for processing ACTIS data; the workflow is implemented as a set of open-source software tools called prACTISed (https://github.com/prACTISedProgram/prACTISed). These tools allow all steps of data processing to be performed in a fast and straightforward fashion. These practical software tools complement the simple instrumentation serving both developers and users of ACTIS.

SpinSPJ: a novel NMR scripting system to implement artificial intelligence and advanced applications

Background

Software for nuclear magnetic resonance (NMR) spectrometers offer general functionality of instrument control and data processing; these applications are often developed with non-scripting languages. NMR users need to flexibly integrate rapidly developing NMR applications with emerging technologies. Scripting systems offer open environments for NMR users to write custom programs. However, existing scripting systems have limited capabilities for both extending the functionality of NMR software’s non-script main program and using advanced native script libraries to support specialized application domains (e.g., biomacromolecules and metabolomics). Therefore, it is essential to design a novel scripting system to address both of these needs.

Result

Here, a novel NMR scripting system named SpinSPJ is proposed. It works as a plug-in in the Java based NMR spectrometer software SpinStudioJ. In the scripting system, both Java based NMR methods and original CPython based libraries are supported. A module has been developed as a bridge to integrate the runtime environments of Java and CPython. The module works as an extension in the CPython environment and interacts with Java via the Java Native Interface. Leveraging this bridge, Java based instrument control and data processing methods of SpinStudioJ can be called with the CPython style. Compared with traditional scripting systems, SpinSPJ better supports both extending the non-script main program and implementing advanced NMR applications with a rich variety of script libraries. NMR researchers can easily call functions of instrument control and data processing as well as developing complex functionality (such as multivariate statistical analysis, deep learning, etc.) with CPython native libraries.

Conclusion

SpinSPJ offers a user-friendly environment to implement custom functionality leveraging its powerful basic NMR and rich CPython libraries. NMR applications with emerging technologies can be easily integrated. The scripting system is free of charge and can be downloaded by visiting http://www.spinstudioj.net/spinspj.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04492-y.

The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data science

The environment, from microbial ecosystems to recycled resources, fluctuates dynamically due to many physical, chemical and biological factors, the profile of which reflects changes in overall state, such as environmental illness caused by a collapse of homeostasis. To evaluate and predict environmental health in terms of systemic homeostasis and resource balance, a comprehensive understanding of these factors requires an approach based on the "exposome paradigm", namely the totality of exposure to all substances. Furthermore, in considering sustainable development to meet global population growth, it is important to gain an understanding of both the circulation of biological resources and waste recycling in human society. From this perspective, natural environment, agriculture, aquaculture, wastewater treatment in industry, biomass degradation and biodegradable materials design are at the forefront of current research. In this respect, nuclear magnetic resonance (NMR) offers tremendous advantages in the analysis of samples of molecular complexity, such as crude bio-extracts, intact cells and tissues, fibres, foods, feeds, fertilizers and environmental samples. Here we outline examples to promote an understanding of recent applications of solution-state, solid-state, time-domain NMR and magnetic resonance imaging (MRI) to the complex evaluation of organisms, materials and the environment. We also describe useful databases and informatics tools, as well as machine learning techniques for NMR analysis, demonstrating that NMR data science can be used to evaluate the exposome in both the natural environment and human society towards a sustainable future.

Analyzing biological models and data sets using Jupyter notebooks as an alternate to laboratory-based exercises during COVID-19

Jupyter notebooks are widely used for data analysis across a large number of scientific disciplines. As a result of the COVID-19 pandemic, I developed a series of computational exercises using the Jupyter notebook to replace the laboratory exercises usually undertaken in my course. My students had no prior coding knowledge and therefore these exercises were structured in a "cookbook" format using the susceptible-infected-resistant model for disease, data from the Lenski long-term evolutionary experiment, and a fission yeast transcriptomic data set. Despite limited internet connectivity and on-line instruction, my students completed these computational exercises and then tested their own hypotheses. Because Jupyter notebooks can be annotated with text and images, student notebooks were submitted for assessment in the form of a structured scientific report. An advantage of this approach was that all the computational analyses presented in these reports could be easily replicated. The notebook and complete instructions used in my course are provided for others who want to adopt this approach.

Microcontroller-Based Direct Torque Control Servodrive

Robot technology has become an integral part of the automotive industry in several tasks such as material handling, welding, painting, and part assembly. Therefore, the knowledge and skills to control the electric motors in these manipulators are essential for undergraduate electrical engineering students. Currently, the digital signal processor (DSP) is the core chip in industrial motor-control drives; however, the implementation of DSP control algorithms can be quite challenging for an experienced programmer, even more so for the novice. Considerable research has been done on this topic, although authors usually focus on DSP-based motor drives using popular control techniques such as field-oriented control (FOC). Although highly efficient, this approach is usually reserved for postgraduate education due to its complex structure and functionality. In this paper, the authors present a modular servodrive design on a low-cost, general-purpose microcontroller using the direct torque control (DTC) method, an alternative known for greater simplicity and torque response, compared with FOC. The system design was based on Micropython language allowing the software structure to be more manageable and the code to be more understandable. This design will be useful to undergraduates and researchers with interests in motor control design.