BIOCOMPUTATION: Some history and prospects

At first glance, biology and computer science are diametrically opposed sciences. Biology deals with carbon based life forms shaped by evolution and natural selection. Computer Science deals with electronic machines designed by engineers and guided by mathematical algorithms. In this brief paper, we review biologically inspired computing. We discuss several models of computation which have arisen from various biological studies. We show what these have in common, and conjecture how biology can still suggest answers and models for the next generation of computing problems. We discuss computation and argue that these biologically inspired models do not extend the theoretical limits on computation. We suggest that, in practice, biological models may give more succinct representations of various problems, and we mention a few cases in which biological models have proved useful. We also discuss the reciprocal impact of computer science on biology and cite a few significant contributions to biological science.     

Tackling the challenges of interdisciplinary bioscience

The ultimate goal for biology is to become a science that formulates our understanding of subcellular, cellular and multicellular systems in terms of quantitative, holistic models that are underpinned by the rigorous principles of the physical sciences and mathematics. This can only be achieved through interdisciplinary research that draws heavily on the expertise and technologies of the physical sciences, engineering, computation and mathematics. Here, I discuss the benefits and challenges (both intellectual and practical) of interdisciplinary bioscience.     

Computational methods in synthetic biology

We discuss how a theoretical synthetic biology research programme may liberate empiricism in biological sciences beyond the unaided human brain. Because synthetic biological systems are relatively small and largely independent of evolutionary contexts, they can be represented with mathematical models strongly founded on first principles of molecular biology and laws of statistical thermodynamics. A universal mathematical formalism for describing synthetic constructs may then be plausibly used to explain in unambiguous, quantitative terms how biological phenotypic complexity emerges as a result of well-defined biomolecular interactions. SynBioSS, a publicly available software package, is described that implements this mathematical formalism.     

保护生物学概要

保护生物学的形成是对生物危机的反应和生物科学迅速发展的结果。它是应用科学解决由于人类活动干扰或其它因素引起的物种、群落和生态系统出现的问题的新学科。其”目的是提供保护生物多样性的原理和工具“,其基础科学和应用科学的综合性交叉学科。系统学、生态学、生物地理学和种群生态学的原理和方法是保护生物学重要的理论和实践基础。     

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.     

The 'right' size in nanobiotechnology

The biological and physical sciences share a common interest in small structures (the definition of 'small' depends on the application, but can range from 1 nm to 1 mm). A vigorous trade across the borders of these areas of science is developing around new materials and tools (largely from the physical sciences) and new phenomena (largely from the biological sciences). The physical sciences offer tools for synthesis and fabrication of devices for measuring the characteristics of cells and sub-cellular components, and of materials useful in cell and molecular biology; biology offers a window into the most sophisticated collection of functional nanostructures that exists.     

Co-evolution of physical and social sciences in synthetic biology

Emerging technologies research often covers various perspectives in disciplines and research areas ranging from hard sciences, engineering, policymaking, and sociology. However, the interrelationship between these different disciplinary domains, particularly the physical and social sciences, often occurs many years after a technology has matured and moved towards commercialization. Synthetic biology may serve an exception to this idea, where, since 2000, the physical and the social sciences communities have increasingly framed their research in response to various perspectives in biological engineering, risk assessment needs, governance challenges, and the social implications that the technology may incur. This paper reviews a broad collection of synthetic biology literature from 2000–2016, and demonstrates how the co-development of physical and social science communities has grown throughout synthetic biology’s earliest stages of development. Further, this paper indicates that future co-development of synthetic biology scholarship will assist with significant challenges of the technology’s risk assessment, governance, and public engagement needs, where an interdisciplinary approach is necessary to foster sustainable, risk-informed, and societally beneficial technological advances moving forward.     

Simulation as experiment: a philosophical reassessment for biological modeling

Some scientific modelers suggest that complex simulation models that mimic biological processes should have a limited place in ecological and evolutionary studies. However, complex simulation models can have a role that is different from that of simpler models that are designed to be fit to data. Simulation can be viewed as another kind of experimental system and should be analyzed as such. Here, I argue that current discussions in the philosophy of science and in the physical sciences fields about the use of simulation as an experimental system have important implications for biology, especially complex sciences such as evolution and ecology. Simulation models can be used to mimic complex systems, but unlike nature, can be manipulated in ways that would be impossible, too costly or unethical to do in natural systems. Simulation can add to theory development and testing, can offer hypotheses about the way the world works and can give guidance as to which data are most important to gather experimentally.     

Fostering Systems Thinking in Biological Education Using the Example of Plant Hormones

Systems thinking is an increasingly recognized paradigm in education in both natural and social sciences, a particular focus being, naturally, in biology. This article argues that plant biology, and in particular, plant hormonal signaling, provides highly illustrative models for learning and teaching in a systems paradigm, because it offers examples of highly complex networks, ranging from the molecular‐ to ecosystem‐scale, and in addition lends itself to the use of real‐life biological objects.     

Biology in the secondary science curriculum

The search for a model of balanced science has not so far been based on a detailed analysis of the nature and effects of the science curriculum. The present paper attempts to begin to fill the gap, from the perspective of biology, using data from the Assessment of Perform-ance Unit (APU). It is shown that, in England, the uptake of the subject as an option outstrips that of the other sciences, with pupils quoting its intrinsic interest as their motivation. Biology serves to attract to science pupils of all abilities and girls in particular. The other sciences are not so successful in this sphere. However, performance data show that the study of biology is not as effective in enhancing science performance as are the physical sciences.     

Physical explanations and biological explanations,empirical laws and a priori laws

Philosophers intent upon characterizing the difference between physics and biology often seize upon the purported fact that physical explanations conform more closely to the covering law model than biological explanations. Central to this purported difference is the role of laws of nature in the explanations of these two sciences. However, I argue that, although certain important differences between physics and biology can be highlighted by differences between physical and biological explanations, these differences are not differences in the degree to which those explanations conform to the covering law model, which fits biology about as well as it does physics.     

Novel insights through the integration of structural and functional genomics data with protein networks

In recent years, major advances in genomics, proteomics, macromolecular structure determination, and the computational resources capable of processing and disseminating the large volumes of data generated by each have played major roles in advancing a more systems-oriented appreciation of biological organization. One product of systems biology has been the delineation of graph models for describing genome-wide protein-protein interaction networks. The network organization and topology which emerges in such models may be used to address fundamental questions in an array of cellular processes, as well as biological features intrinsic to the constituent proteins (or "nodes") themselves. However, graph models alone constitute an abstraction which neglects the underlying biological and physical reality that the network's nodes and edges are highly heterogeneous entities. Here, we explore some of the advantages of introducing a protein structural dimension to such models, as the marriage of conventional network representations with macromolecular structural data helps to place static node and edge constructs in a biologically more meaningful context. We emphasize that 3D protein structures constitute a valuable conceptual and predictive framework by discussing examples of the insights provided, such as enabling in silico predictions of protein-protein interactions, providing rational and compelling classification schemes for network elements, as well as revealing interesting intrinsic differences between distinct node types, such as disorder and evolutionary features, which may then be rationalized in light of their respective functions within networks.     

Physics,biology, and sociology: A reappraisal

To the extent that all biological phenomena are perceivable only through their physical manifestations, it may be justified to assume that all biological phenomena will be eventually represented in terms of physics; perhaps not of present day physics, but of some “extended” form of it. However, even if this should be correct, it must be kept in mind that representing individual biological phenomena in terms of physics is not the same as deducing from known physical laws the necessity of biological phenomena. Drawing an analogy from pure mathematics, it is possible that while every biological phenomenon may be represented in terms of physics, yet biological statements represent a class of “undecidable” statements within the framework of physics. Such a conjecture is reinforced by the history of physics itself and illustrated on several examples. The 19th century physicists tried in vain todeduce electromagnetic phenomena from mechanical ones. A similar situation may exist in regard to biological and social sciences. Quite generally, the possibility of representing a class B phenomena in terms of class A phenomena does not imply that the phenomena of class B can be deduced from those of class A. The consequences of the above on the relation between physics, biology, and sociology are studied. A tentative postulational formulation of basic biological principles are given and some consequences are discussed. It is pointed out that not only can the study of biological phenomena throw light on some physical phenomena, but that the study of social phenomena may be of value for the understanding of the structures and functions of living organisms. The possibility of a sort of “socionics” is indicated.     

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.     

An algebraic view of bacterial genome evolution

Rearrangements of bacterial chromosomes can be studied mathematically at several levels, most prominently at a local, or sequence level, as well as at a topological level. The biological changes involved locally are inversions, deletions, and transpositions, while topologically they are knotting and catenation. These two modelling approaches share some surprising algebraic features related to braid groups and Coxeter groups. The structural approach that is at the core of algebra has long found applications in sciences such as physics and analytical chemistry, but only in a small number of ways so far in biology. And yet there are examples where an algebraic viewpoint may capture a deeper structure behind biological phenomena. This article discusses a family of biological problems in bacterial genome evolution for which this may be the case, and raises the prospect that the tools developed by algebraists over the last century might provide insight to this area of evolutionary biology.     

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.     

A No-Holds-Barred Evolution Curriculum for Elementary and Junior High School Students

Understanding the basic mechanism of evolution by natural selection together with examples of how it works in nature is crucial for explaining and teaching the workings of biology and ecology to young students. Dobzhansky said it best in his advice to educators of biology: “Nothing in biology makes sense except in the light of evolution.” This premise is true at all levels of biology but especially so in the elementary years where foundations of science knowledge are laid. Elementary students are capable of learning cohesive and connected stories of biological principles and learning them within a no-holds-barred arena wherein concepts and processes usually reserved for high school years are taught with special care, appropriate exercises, and patient explanations. This story must include solid introductions to the fundamental principles of evolution by natural selection that are threaded within and alongside those of basic biology and ecology. This paper attempts to make the case for the inclusion of connected stories of biology in the earliest years of education and to include within that education the unifying theme of all biology and ecology studies—evolution.     

The Mathematical Basis of Mendelian Genetics

In a climate where increasing numbers of students are encouraged to pursue post-secondary education, the level of preparedness students have for college-level coursework is not far from the minds of all educators, especially high school teachers. Specifically within the biological sciences, introductory biology classes often serve as the gatekeeper or a pre-requisite for subsequent coursework in those fields and pre-professional programmes (eg pre-medicine or pre-veterinarian). Thus, how helpful high school science and mathematics experiences are in preparing students for their introductory biology classes is important and relevant for teachers, science educators and policy makers alike. This quantitative study looked at the association between students' high school science and mathematics experiences with introductory college biology performance. Using a nationally representative sample of US students (n?=?2667) enrolled in 33 introductory college biology courses, a multi-level statistical model was developed to analyse the association between high school educational experiences and the final course grade in introductory biology courses. Advanced high school science and mathematics coursework, an emphasis on a deep conceptual understanding of biology concepts and a prior knowledge of concepts addressed in well-structured laboratory investigations are all positively associated with students' achievement in introductory college biology.     

合成生物学及其在生物技术中的应用进展

合成生物学是一门21世纪生物学的新兴学科,它着眼生物科学与工程科学的结合,把生物系统当作工程系统"从下往上"进行处理,由"单元"(unit)到"部件"(device)再到"系统"(system)来设计,修改和组装细胞构件及生物系统.合成生物学是分子和细胞生物学、进化系统学、生物化学、信息学、数学、计算机和工程等多学科交叉的产物.目前研究应用包括两个主要方面:一是通过对现有的、天然存在的生物系统进行重新设计和改造,修改已存在的生物系统,使该系统增添新的功能.二是通过设计和构建新的生物零件、组件和系统,创造自然界中尚不存在的人工生命系统.合成生物学作为一门建立在基因组方法之上的学科,主要强调对创造人工生命形态的计算生物学与实验生物学的协同整合.必须强调的是,用来构建生命系统新结构、产生新功能所使用的组件单元既可以是基因、核酸等生物组件,也可以是化学的、机械的和物理的元件.本文跟踪合成生物学研究及应用,对其在DNA水平编程、分子修饰、代谢途径、调控网络和工业生物技术等方面的进展进行综述.