# Learning Systems Biology: Conceptual Considerations toward a Web-Based Learning Platform

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## Abstract

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## 1. Introduction

- Learning strategies.
- Mathematical aspects of web-based learning systems.
- Graph-theoretical aspects of hypertextual learning patterns.
- Theoretical aspects of e-learning platforms.
- Educational details of the web-based e-learning platform.

## 2. Learning Strategies

#### 2.1. Principle Outline

**Figure 1.**Visualization of the perception-action cycle. Here the animal is not only part of the environment but also part of its perception-action cycle.

## 3. Hypertext Learning and Graph-Theoretical Aspects

## 4. Aspects of E-Learning Platforms

- Ways of communicating among learners and between students and tutors using email, news groups, chat, forum, etc.
- Tools for learners, e.g., full text search within course materials, possibility to add annotations, possibility to create an individual learning environment, etc.
- Authoring tools such as web editors, modular design of course materials and development of animations using programming languages, etc.
- Adopt a recognized standard such as SCORM to organize and distribute the learning materials.
- Individual tools: Data management, appointment schedule, working schedule, address book etc.
- Database connections to libraries or other institutions for storing and processing the data.

- Composition of basic functionalities and desired operational area:
- Necessary and optional functions of the platform.
- Mission scenario: goals, requirements, individual wishes of tutors and learners, etc.
- Service ability and usability.

- Review of special and individual requirements:
- Hardware, software, administration, system integration.
- User interfaces: layout, languages, functions, etc.
- Cost factors.

- Decision making:
- Acquirement of a commercial learning platform using an own server.
- Acquirement of licenses from special service provider.
- Using learning platforms that are free of charge.
- In-house development of learning platforms.

- Non-technical factors of influence such as individual attitude and values of an institution, tutors, etc.

## 5. The Web-Based Learning Platform

#### 5.1. Course Design

- A text-based explanation of the course material in combination with multimedia illustrations, including pictures, video and animations to support the underlying text and enhance the learning experience.
- Each module will be accompanied by instructional video lectures in sync with presentation slides.
- Each of these components will be followed by self-assessment questions on the presented material.
- Development of programming skills by solving practical problems. Source code examples will be provided.
- A complete glossary of searchable keywords will allow a targeted selection of specific topics.

#### 5.2. Course Content

**Module 1—Statistical and Computational Biology:**In module 1 the students without a solid background in mathematics and statistics will receive an introduction to important topics necessary for Systems Biology. Basic analysis, linear algebra, statistics, network theory, differential equations and an introduction to the R programming language will be provided [46]. This should generate the general awareness by the students that regardless of the specific scientific questions one is working on, a computational component is always involved. Hence, mathematical and statistical basics are presented and examples involving programming are used to connect both skill sets seamlessly. Motivating examples are taken from modules 2 to 4 in order to demonstrate the utility and power of the discussed methods.

**Module 2—Network Biology:**Module 2 explains the students what Systems Biology is actually about. In order to communicate this in a clear way, various biological networks are introduced and explained [1,47]. For example, protein networks, transcriptional regulatory networks and metabolic networks are covered and their relation to molecular biology is established. In addition, the dynamic modeling of molecular processes (e.g., transcription regulation) is discussed. This will create an awareness of the fact that molecular processes are dynamic rather than static in time. From a methodological point of view, the students will extend and apply their knowledge in network theory and programming [48]. In addition, they will become familiar with databases and their usage, which can be used, e.g., to construct some of the discussed biological networks. Databases covered include BioGrid, BioCyc, DIP, IntAct, GO, Mips. Students will learn about basic database management and become familiar with several R packages that allow the access and manipulation of data in these databases.

**Module 3—Data in Systems Biology:**In module 3 important high-throughput data are discussed. These include data from microarray, proteomics and ChIP on chip experiments. It will be emphasized that these data allow a practical approach to Systems Biology studying molecular interactions on a genomic scale, and are hence vital for the successful translation of theoretical hypothesis into practical results. The basics of complex diseases such as cancer or cardiovascular disease are also discussed and contrasted to monogenetic diseases. The methodological skills that the students learn and extend include methods for an exploratory as well as confirmatory statistical analysis of data, e.g., hypothesis testing, multiple correction procedures, multivariate analysis, clustering, dimension reduction methods [7,49,50,51]. This will be complemented by various means to visualize these data. All methods will be discussed not only theoretically but also practically by providing source code in R demonstrating the various aspects of the discussed topics.

**Module 4—Large-scale data in Systems Biology:**Module 4 provides an extension of the topics from module 3. Its main purpose is to provide additional analysis methods that are appropriate to analyze large-scale high-throughput data from microarray experiments. Specifically, methods to detect pathological pathways from complex diseases are discussed. Also, approaches to infer regulatory networks from experimental as well as observations data are treated. Methodologically, advanced statistical analysis methods are discussed, including time series analysis and causal inference [7,52,53]. All methods are again discussed and used in the context of the R programming language, allowing to gain additional experience with this programming language.

#### 5.3. Usability

**Scenario 1:**The following outline of modules to attend is for students without prior knowledge in mathematics and statistics. For such students it is recommended to start with module 1, which will equip them with the necessary theoretical basics necessary to work on problems in Systems Biology. After module 1, either module 2 or module 3 can be attended. Both modules are on the same intermediate level covering complementing topics. Module 4 represents an advancement to module 3 and it is recommended to attend module 3 as preparation for this module. Overall, three courses are possible, $1\to 2\to 3\to 4$ or $1\to 3\to 2\to 4$ or $1\to 3\to 4$ (the number refers to the number of the module). The course $1\to 2\to 4$ is not recommended because students entering module 4 would not have the necessary basics with respect to the microarray technology, respectively the data resulting from such experiments.

**Scenario 2:**This course outline is for students familiar with basic mathematical and statistical methods, as covered in module 1. In this case, students may enter either module 2 or 3 directly without attending module 1 first. This results in the following three courses, $2\to 3\to 4$ or $3\to 2\to 4$ or $3\to 4$ (the number refers to the number of the module).

**Scenario 3:**Students that are already familiar with the mathematical and statistical methods needed to work on problems in Systems Biology and additionally have experience with data from microarray experiments may directly attend module 4. Not only is module 4 presented on an advanced methodological level, but also the biological topics covered are currently extended and improved almost annually. Hence, these topics are at the forefront of contemporary research within systems biology.

#### 5.4. Assessment

## 6. Conclusions

## Acknowledgements

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**MDPI and ACS Style**

Emmert-Streib, F.; Dehmer, M.; Lyardet, F. Learning Systems Biology: Conceptual Considerations toward a Web-Based Learning Platform. *Educ. Sci.* **2013**, *3*, 158-171.
https://doi.org/10.3390/educsci3020158

**AMA Style**

Emmert-Streib F, Dehmer M, Lyardet F. Learning Systems Biology: Conceptual Considerations toward a Web-Based Learning Platform. *Education Sciences*. 2013; 3(2):158-171.
https://doi.org/10.3390/educsci3020158

**Chicago/Turabian Style**

Emmert-Streib, Frank, Matthias Dehmer, and Fernando Lyardet. 2013. "Learning Systems Biology: Conceptual Considerations toward a Web-Based Learning Platform" *Education Sciences* 3, no. 2: 158-171.
https://doi.org/10.3390/educsci3020158