A first attempt to bring computational biology into advanced high school biology classrooms

Computer science has become ubiquitous in many areas of biological research, yet most high school and even college students are unaware of this. As a result, many college biology majors graduate without adequate computational skills for contemporary fields of biology. The absence of a computational element in secondary school biology classrooms is of growing concern to the computational biology community and biology teachers who would like to acquaint their students with updated approaches in the discipline. We present a first attempt to correct this absence by introducing a computational biology element to teach genetic evolution into advanced biology classes in two local high schools. Our primary goal was to show students how computation is used in biology and why a basic understanding of computation is necessary for research in many fields of biology. This curriculum is intended to be taught by a computational biologist who has worked with a high school advanced biology teacher to adapt the unit for his/her classroom, but a motivated high school teacher comfortable with mathematics and computing may be able to teach this alone. In this paper, we present our curriculum, which takes into consideration the constraints of the required curriculum, and discuss our experiences teaching it. We describe the successes and challenges we encountered while bringing this unit to high school students, discuss how we addressed these challenges, and make suggestions for future versions of this curriculum.We believe that our curriculum can be a valuable seed for further development of computational activities aimed at high school biology students. Further, our experiences may be of value to others teaching computational biology at this level. Our curriculum can be obtained at http://ecsite.cs.colorado.edu/?page_id=149#biology or by contacting the authors.     

Undergraduate structural biology education: A shift from users to developers of computation and simulation tools

The use of theory and simulation in undergraduate education in biochemistry, molecular biology, and structural biology is now common, but the skills students need and the curriculum instructors have to train their students are evolving. The global pandemic and the immediate switch to remote instruction forced instructors to reconsider how they can use computation to teach concepts previously approached with other instructional methods. In this review, we survey some of the curricula, materials, and resources for instructors who want to include theory, simulation, and computation in the undergraduate curriculum. There has been a notable progression from teaching students to use discipline-specific computational tools to developing interactive computational tools that promote active learning to having students write code themselves, such that they view computation as another tool for solving problems. We are moving toward a future where computational skills, including programming, data analysis, visualization, and simulation, will no longer be considered an optional bonus for students but a required skill for the 21st century STEM (Science, Technology, Engineering, and Mathematics) workforce; therefore, all physical and life science students should learn to program in the undergraduate curriculum.     

Crossing over...Markov meets Mendel

Chromosomal crossover is a biological mechanism to combine parental traits. It is perhaps the first mechanism ever taught in any introductory biology class. The formulation of crossover, and resulting recombination, came about 100 years after Mendel's famous experiments. To a great extent, this formulation is consistent with the basic genetic findings of Mendel. More importantly, it provides a mathematical insight for his two laws (and corrects them). From a mathematical perspective, and while it retains similarities, genetic recombination guarantees diversity so that we do not rapidly converge to the same being. It is this diversity that made the study of biology possible. In particular, the problem of genetic mapping and linkage-one of the first efforts towards a computational approach to biology-relies heavily on the mathematical foundation of crossover and recombination. Nevertheless, as students we often overlook the mathematics of these phenomena. Emphasizing the mathematical aspect of Mendel's laws through crossover and recombination will prepare the students to make an early realization that biology, in addition to being experimental, IS a computational science. This can serve as a first step towards a broader curricular transformation in teaching biological sciences. I will show that a simple and modern treatment of Mendel's laws using a Markov chain will make this step possible, and it will only require basic college-level probability and calculus. My personal teaching experience confirms that students WANT to know Markov chains because they hear about them from bioinformaticists all the time. This entire exposition is based on three homework problems that I designed for a course in computational biology. A typical reader is, therefore, an instructional staff member or a student in a computational field (e.g., computer science, mathematics, statistics, computational biology, bioinformatics). However, other students may easily follow by omitting the mathematically more elaborate parts. I kept those as separate sections in the exposition.     

Design and implementation of an introductory course for computer applications in molecular genetics

Formal training in computational biology was initiated at Wayne State University in 1990 to meet the needs of the faculty. This was still at a time when the molecular databases and analysis tools could be housed in what is now equivalent to a modern but dated desktop computer. In 1995 the course was expanded to include graduate students to provide these senior students with a foundation in computational biology. This course has armed our students with a requisite set of basic skills that are necessary for a successful career in molecular genetics. It is now an integral component of the graduate program of the Center for Molecular Medicine and Genetics and our experiences in course delivery have been detailed (BioInformatics Methods and Protocols, S. Misener and S. A. Krawetz, eds., Humana Press, Totowa, NJ, 2000.). The course was expanded to a campus-wide unlimited enrollment program for the summer of 2000 to address the needs of our student body. In this review we present our experience with delivering a multidisciplinary campuswide computational biology course to a new and widely diverse student body.     

Szallasi Zoltan,Stelling Jörg,Periwal Vipul: System Modeling in Cellular Biology: From Concepts to Nuts and Bolts.

"System Modeling in Cellular Biology: From Concepts to Nuts and Bolts" by Szallasi, Stelling and Periwal introduces the relevant concepts, terminology, and techniques of this field of science. It emphasises the modelling and computational challenges of taking a multidisciplinary approach to biology. This book provides a comprehensive introduction to systems biology and will form a valuable resource for students, teachers and researchers from both experimental and theoretical disciplines.     

Ten simple rules for designing and running a computing minor for bio/chem students

Science students increasingly need programming and data science skills to be competitive in the modern workforce. However, at our university (San Francisco State University), until recently, almost no biology, biochemistry, and chemistry students (from here bio/chem students) completed a minor in computer science. To change this, a new minor in computing applications, which is informally known as the Promoting Inclusivity in Computing (PINC) minor, was established in 2016. Here, we present the lessons we learned from our experience in a set of 10 rules. The first 3 rules focus on setting up the program so that it interests students in biology, chemistry, and biochemistry. Rules 4 through 8 focus on how the classes of the program are taught to make them interesting for our students and to provide the students with the support they need. The last 2 rules are about what happens “behind the scenes” of running a program with many people from several departments involved.     

Trends in computational biology: a summary based on a RECOMB plenary lecture, 1999.

Computational biology, a term coined from analogy to the role of computing in the physical sciences, is now coming into its own as a major element of contemporary biological and biomedical research. Information science and computational science provide essential tools for next generation biological science efforts, from focusing the direction of experimental studies to providing knowledge and insight that can not otherwise be obtained. Going beyond the revolution in biology reflected in the successes of the genome project and driven by the power of molecular biology techniques, computational approaches will provide an underpinning for the integration of broad disciplines for development of a quantitative systems approach to understanding the mechanisms in the life of the cell.     

The Quantitative Methods Boot Camp: Teaching Quantitative Thinking and Computing Skills to Graduate Students in the Life Sciences

The past decade has seen a rapid increase in the ability of biologists to collect large amounts of data. It is therefore vital that research biologists acquire the necessary skills during their training to visualize, analyze, and interpret such data. To begin to meet this need, we have developed a “boot camp” in quantitative methods for biology graduate students at Harvard Medical School. The goal of this short, intensive course is to enable students to use computational tools to visualize and analyze data, to strengthen their computational thinking skills, and to simulate and thus extend their intuition about the behavior of complex biological systems. The boot camp teaches basic programming using biological examples from statistics, image processing, and data analysis. This integrative approach to teaching programming and quantitative reasoning motivates students’ engagement by demonstrating the relevance of these skills to their work in life science laboratories. Students also have the opportunity to analyze their own data or explore a topic of interest in more detail. The class is taught with a mixture of short lectures, Socratic discussion, and in-class exercises. Students spend approximately 40% of their class time working through both short and long problems. A high instructor-to-student ratio allows students to get assistance or additional challenges when needed, thus enhancing the experience for students at all levels of mastery. Data collected from end-of-course surveys from the last five offerings of the course (between 2012 and 2014) show that students report high learning gains and feel that the course prepares them for solving quantitative and computational problems they will encounter in their research. We outline our course here which, together with the course materials freely available online under a Creative Commons License, should help to facilitate similar efforts by others.This is part of the PLOS Computational Biology Education collection.     

Using a course-long theme for inquiry-based laboratories in a comparative physiology course

I developed an inquiry-based laboratory model that uses a central theme throughout the semester to develop in undergraduate biology majors the skills required for conducting science while introducing them to modern and classical physiological techniques. The physiology laboratory uses a goal-oriented approach, with students working cooperatively in small groups to answer basic biological questions. The student teams work to develop skills associated with experimental design, data analysis, written and oral communication, science literacy, and critical thinking. The laboratory curriculum is a research-based model that offers the advantage of students asking open-ended questions by use of a variety of techniques. For the students and instructor alike, this presents an exciting and challenging approach for learning physiology and basic biological principles. Another advantage of this laboratory model is that it is flexible and adaptable; the central theme can be any that the instructor chooses, and the goals and techniques developed are based on student and instructor needs and interests. Students who have completed this model at Loyola College in Maryland have become equipped with the skills essential for any area of the biological sciences and, most importantly, showed elevated excitement and commitment to learning.     

Executable cell biology

Computational modeling of biological systems is becoming increasingly important in efforts to better understand complex biological behaviors. In this review, we distinguish between two types of biological models--mathematical and computational--which differ in their representations of biological phenomena. We call the approach of constructing computational models of biological systems 'executable biology', as it focuses on the design of executable computer algorithms that mimic biological phenomena. We survey the main modeling efforts in this direction, emphasize the applicability and benefits of executable models in biological research and highlight some of the challenges that executable biology poses for biology and computer science. We claim that for executable biology to reach its full potential as a mainstream biological technique, formal and algorithmic approaches must be integrated into biological research. This will drive biology toward a more precise engineering discipline.     

Modelling,abstraction, and computation in systems biology: A view from computer science

Systems biology is centrally engaged with computational modelling across multiple scales and at many levels of abstraction. Formal modelling, precise and formalised abstraction relationships, and computation also lie at the heart of computer science—and over the past decade a growing number of computer scientists have been bringing their discipline's core intellectual and computational tools to bear on biology in fascinating new ways. This paper explores some of the apparent points of contact between the two fields, in the context of a multi-disciplinary discussion on conceptual foundations of systems biology.     

Demystifying computer science for molecular ecologists

In this age of data‐driven science and high‐throughput biology, computational thinking is becoming an increasingly important skill for tackling both new and long‐standing biological questions. However, despite its obvious importance and conspicuous integration into many areas of biology, computer science is still viewed as an obscure field that has, thus far, permeated into only a few of the biology curricula across the nation. A national survey has shown that lack of computational literacy in environmental sciences is the norm rather than the exception [Valle & Berdanier (2012) Bulletin of the Ecological Society of America, 93, 373–389]. In this article, we seek to introduce a few important concepts in computer science with the aim of providing a context‐specific introduction aimed at research biologists. Our goal was to help biologists understand some of the most important mainstream computational concepts to better appreciate bioinformatics methods and trade‐offs that are not obvious to the uninitiated.     

Integrating Interactive Computational Modeling in Biology Curricula

While the use of computer tools to simulate complex processes such as computer circuits is normal practice in fields like engineering, the majority of life sciences/biological sciences courses continue to rely on the traditional textbook and memorization approach. To address this issue, we explored the use of the Cell Collective platform as a novel, interactive, and evolving pedagogical tool to foster student engagement, creativity, and higher-level thinking. Cell Collective is a Web-based platform used to create and simulate dynamical models of various biological processes. Students can create models of cells, diseases, or pathways themselves or explore existing models. This technology was implemented in both undergraduate and graduate courses as a pilot study to determine the feasibility of such software at the university level. First, a new (In Silico Biology) class was developed to enable students to learn biology by “building and breaking it” via computer models and their simulations. This class and technology also provide a non-intimidating way to incorporate mathematical and computational concepts into a class with students who have a limited mathematical background. Second, we used the technology to mediate the use of simulations and modeling modules as a learning tool for traditional biological concepts, such as T cell differentiation or cell cycle regulation, in existing biology courses. Results of this pilot application suggest that there is promise in the use of computational modeling and software tools such as Cell Collective to provide new teaching methods in biology and contribute to the implementation of the “Vision and Change” call to action in undergraduate biology education by providing a hands-on approach to biology.     

Involving undergraduates in the annotation and analysis of global gene expression studies: creation of a maize shoot apical meristem expression database

Through a multi-university and interdisciplinary project we have involved undergraduate biology and computer science research students in the functional annotation of maize genes and the analysis of their microarray expression patterns. We have created a database to house the results of our functional annotation of >4400 genes identified as being differentially regulated in the maize shoot apical meristem (SAM). This database is located at http://sam.truman.edu and is now available for public use. The undergraduate students involved in constructing this unique SAM database received hands-on training in an intellectually challenging environment, which has prepared them for graduate and professional careers in biological sciences. We describe our experiences with this project as a model for effective research-based teaching of undergraduate biology and computer science students, as well as for a rich professional development experience for faculty at predominantly undergraduate institutions.     

Teaching cell biology to nonscience majors through forensics, or how to design a killer course

Nonscience majors often do not respond to traditional lecture-only biology courses. However, these students still need exposure to basic biological concepts. To accomplish this goal, forensic science was paired with compatible cell biology subjects. Several topics such as human development and molecular biology were found to fulfill this purpose. Another goal was to maximize the hands-on experience of the nonscience major students. This objective was fulfilled by specific activities such as fingerprinting and DNA typing. One particularly effective teaching tool was a mock murder mystery complete with a Grand Jury trial. Another objective was to improve students' attitudes toward science. This was successful in that students felt more confident in their own scientific abilities after taking the course. In pre/post tests, students answered four questions about their ability to conduct science. All four statements showed a positive shift after the course (p values ranging from.001 to.036, df = 23; n = 24). The emphasis on experiential pedagogy was also shown to increase critical thinking skills. In pre/post testing, students in this course significantly increased their performance on critical thinking assessment tests from 33.3% correct to 45.3% (p =.008, df = 4; n = 24).     

An investigative laboratory course in human physiology using computer technology and collaborative writing

Active investigative student-directed experiences in laboratory science are being encouraged by national science organizations. A growing body of evidence from classroom assessment supports their effectiveness. This study describes four years of implementation and assessment of an investigative laboratory course in human physiology for 65 second-year students in sports medicine and biology at a small private comprehensive college. The course builds on skills and abilities first introduced in an introductory investigations course and introduces additional higher-level skills and more complex human experimental models. In four multiweek experimental modules, involving neuromuscular, reflex, and cardiovascular physiology, by use of computerized hardware/software with a variety of transducers, students carry out self-designed experiments with human subjects and perform data collection and analysis, collaborative writing, and peer editing. In assessments, including standard course evaluations and the Salgains Web-based evaluation, student responses to this approach are enthusiastic, and gains in their skills and abilities are evident in their comments and in improved performance.     

Computational Modeling,Formal Analysis,and Tools for Systems Biology

As the amount of biological data in the public domain grows, so does the range of modeling and analysis techniques employed in systems biology. In recent years, a number of theoretical computer science developments have enabled modeling methodology to keep pace. The growing interest in systems biology in executable models and their analysis has necessitated the borrowing of terms and methods from computer science, such as formal analysis, model checking, static analysis, and runtime verification. Here, we discuss the most important and exciting computational methods and tools currently available to systems biologists. We believe that a deeper understanding of the concepts and theory highlighted in this review will produce better software practice, improved investigation of complex biological processes, and even new ideas and better feedback into computer science.     

DNA计算机的研究和展望

DNA计算机是计算机科学和分子生物学互相结合、互相渗透而产生的新兴交叉研究领域.目前已取得较大进展.DNA计算机是以编码的DNA序列为运算对象,通过分子生物学的运算操作以解决复杂的数学难题.DNA计算机的重要特点是信息容量的巨量性和密集性,和处理操作的高度并行性,通过强力搜索策略迅速得出正确的答案,从而使其运算速度大大超过常规计算机的计算速度.介绍了DNA计算机的近期进展和工作原理及其分子生物学的运算操作过程.并对DNA计算机的未来发展前景及在生物信息学中的意义,进行了分析和讨论.