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.     

Modeling and simulation of biological systems from image data

This essay provides an introduction to the terminology, concepts, methods, and challenges of image‐based modeling in biology. Image‐based modeling and simulation aims at using systematic, quantitative image data to build predictive models of biological systems that can be simulated with a computer. This allows one to disentangle molecular mechanisms from effects of shape and geometry. Questions like “what is the functional role of shape” or “how are biological shapes generated and regulated” can be addressed in the framework of image‐based systems biology. The combination of image quantification, model building, and computer simulation is illustrated here using the example of diffusion in the endoplasmic reticulum.     

Systems biology,emergence and antireductionism

This study explores the conceptual history of systems biology and its impact on philosophical and scientific conceptions of reductionism, antireductionism and emergence. Development of systems biology at the beginning of 21st century transformed biological science. Systems biology is a new holistic approach or strategy how to research biological organisms, developed through three phases. The first phase was completed when molecular biology transformed into systems molecular biology. Prior to the second phase, convergence between applied general systems theory and nonlinear dynamics took place, hence allowing the formation of systems mathematical biology. The second phase happened when systems molecular biology and systems mathematical biology, together, were applied for analysis of biological data. Finally, after successful application in science, medicine and biotechnology, the process of the formation of modern systems biology was completed.Systems and molecular reductionist views on organisms were completely opposed to each other. Implications of systems and molecular biology on reductionist–antireductionist debate were quite different. The analysis of reductionism, antireductionism and emergence issues, in the era of systems biology, revealed the hierarchy between methodological, epistemological and ontological antireductionism. Primarily, methodological antireductionism followed from the systems biology. Only after, epistemological and ontological antireductionism could be supported.     

计算系统生物学:理论、方法及在药物研发中的应用

计算系统生物学是一个多学科交叉的新兴领域,旨在通过整合海量数据建立其生物系统相互作用的复杂网络。数据的整合和模型的建立需要发展合适的数学方法和软件工具,这也是计算系统生物学的主要任务。生物系统模型有助于从整体上理解生物体的内在功能和特性。同时,生物网络模型在药物研发中的应用也越来越受到制药企业以及新药研发机构的重视,如用于特异性药物作用靶点的预测和药物毒性评估等。该文简要介绍计算系统生物学的常见网络和计算模型,以及建立模型所用的研究方法,并阐述其在建模和分析中的作用及面临的问题和挑战。     

生物信息学技术进展

生物信息学是一门对生物信息进行采集、储存、传递、检索、分析和解读的学科,它已经渗透于现代生物学、数学、信息学、计算科学、统计学、物理、化学各个方面。本概述和分析了生物信息学研究中的一些方法。     

3D Modelling of Biological Systems for Biomimetics

1　IntroductionBasedonthereviewofthepreviousworkof 3Dgeometricalmodellingtechniquesandsystemsdevelopedforindustrial,medicalandanimationapplications,thispaperdiscussestheproblemsassociatedwiththeexist ingtechniquesandsystems ,especiallywhenappliedto3Dmodellingof plants ,insectsandanimalsforbiomimeticsresearchanddevelopment .Then ,paperproposessomeareasofresearchinterestsin 3Dmod ellingofplants ,insectsandanimalsforBiomimetics .Toavoidtherepeating ,inthispaper ,biologicalobjectswillbeusedtorep…     

Bridging the gaps in systems biology

Systems biology aims at creating mathematical models, i.e., computational reconstructions of biological systems and processes that will result in a new level of understanding—the elucidation of the basic and presumably conserved “design” and “engineering” principles of biomolecular systems. Thus, systems biology will move biology from a phenomenological to a predictive science. Mathematical modeling of biological networks and processes has already greatly improved our understanding of many cellular processes. However, given the massive amount of qualitative and quantitative data currently produced and number of burning questions in health care and biotechnology needed to be solved is still in its early phases. The field requires novel approaches for abstraction, for modeling bioprocesses that follow different biochemical and biophysical rules, and for combining different modules into larger models that still allow realistic simulation with the computational power available today. We have identified and discussed currently most prominent problems in systems biology: (1) how to bridge different scales of modeling abstraction, (2) how to bridge the gap between topological and mechanistic modeling, and (3) how to bridge the wet and dry laboratory gap. The future success of systems biology largely depends on bridging the recognized gaps.     

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.     

传统中国医药与现代系统生物学研究理论的殊途同归

系统生物学是研究一个生物系统中所有组成成分(基因、mRNA、蛋白质等)的构成与组分之间相互关系的学科,近年来,系统生物学作为后基因组学时代研究的一个重要内容,已广泛深入到生命科学和医药学的各个领域。而作为中国传统医学而言,似乎与之相去甚远,然而当我们对这两个新老学科基础理论进行比较时,我们发现:传统中国医药与现代系统生物学研究理论的殊途同归。有鉴于此,本文论述了系统生物学和中医学的思想起源、相互联系,基于系统生物学的发展、研究思路和方法,阐述了生物学由还原论的研究方法过渡到系统论的研究方法,强调对生命现象从系统和整体的层次进行研究和把握,对传统中医学研究方法的变革起到了推动作用,最后对系统生物学在中医药学未来发展进行了评价。     

Systems biology in animal sciences

Systems biology is a rapidly expanding field of research and is applied in a number of biological disciplines. In animal sciences, omics approaches are increasingly used, yielding vast amounts of data, but systems biology approaches to extract understanding from these data of biological processes and animal traits are not yet frequently used. This paper aims to explain what systems biology is and which areas of animal sciences could benefit from systems biology approaches. Systems biology aims to understand whole biological systems working as a unit, rather than investigating their individual components. Therefore, systems biology can be considered a holistic approach, as opposed to reductionism. The recently developed 'omics' technologies enable biological sciences to characterize the molecular components of life with ever increasing speed, yielding vast amounts of data. However, biological functions do not follow from the simple addition of the properties of system components, but rather arise from the dynamic interactions of these components. Systems biology combines statistics, bioinformatics and mathematical modeling to integrate and analyze large amounts of data in order to extract a better understanding of the biology from these huge data sets and to predict the behavior of biological systems. A 'system' approach and mathematical modeling in biological sciences are not new in itself, as they were used in biochemistry, physiology and genetics long before the name systems biology was coined. However, the present combination of mass biological data and of computational and modeling tools is unprecedented and truly represents a major paradigm shift in biology. Significant advances have been made using systems biology approaches, especially in the field of bacterial and eukaryotic cells and in human medicine. Similarly, progress is being made with 'system approaches' in animal sciences, providing exciting opportunities to predict and modulate animal traits.     

系统生物学——生命科学的新领域

系统生物学是继基因组学、蛋白质组学之后一门新兴的生物学交叉学科,代表21世纪生物学的未来.最近,系统生物学研究机构纷纷成立.在研究上,了解一个复杂的生物系统需要整合实验和计算方法.基因组学和蛋白质组学中的高通量方法为系统生物学发展提供了大量的数据.计算生物学通过数据处理、模型构建和理论分析,成为系统生物学发展的一个必不可缺、强有力的工具.在应用上,系统生物学代表新一代医药开发和疾病防治的方向.     

Taking the example of computer systems engineering for the analysis of biological cell systems

In this paper, we discuss the potential for the use of engineering methods that were originally developed for the design of embedded computer systems, to analyse biological cell systems. For embedded systems as well as for biological cell systems, design is a feature that defines their identity. The assembly of different components in designs of both systems can vary widely. In contrast to the biology domain, the computer engineering domain has the opportunity to quickly evaluate design options and consequences of its systems by methods for computer aided design and in particular design space exploration. We argue that there are enough concrete similarities between the two systems to assume that the engineering methodology from the computer systems domain, and in particular that related to embedded systems, can be applied to the domain of cellular systems. This will help to understand the myriad of different design options cellular systems have. First we compare computer systems with cellular systems. Then, we discuss exactly what features of engineering methods could aid researchers with the analysis of cellular systems, and what benefits could be gained.     

Taking the example of computer systems engineering for the analysis of biological cell systems

In this paper, we discuss the potential for the use of engineering methods that were originally developed for the design of embedded computer systems, to analyse biological cell systems. For embedded systems as well as for biological cell systems, design is a feature that defines their identity. The assembly of different components in designs of both systems can vary widely. In contrast to the biology domain, the computer engineering domain has the opportunity to quickly evaluate design options and consequences of its systems by methods for computer aided design and in particular design space exploration. We argue that there are enough concrete similarities between the two systems to assume that the engineering methodology from the computer systems domain, and in particular that related to embedded systems, can be applied to the domain of cellular systems. This will help to understand the myriad of different design options cellular systems have. First we compare computer systems with cellular systems. Then, we discuss exactly what features of engineering methods could aid researchers with the analysis of cellular systems, and what benefits could be gained.     

Systems biology and cancer: promises and perils

Systems biology uses systems of mathematical rules and formulas to study complex biological phenomena. In cancer research there are three distinct threads in systems biology research: modeling biology or biophysics with the goal of establishing plausibility or obtaining insights, modeling based on statistics, bioinformatics, and reverse engineering with the goal of better characterizing the system, and modeling with the goal of clinical predictions. Using illustrative examples we discuss these threads in the context of cancer research.     

Biological systems modeling and analysis: a biomolecular technique of the twenty-first century.

It is proposed that computational systems biology should be considered a biomolecular technique of the twenty-first century, because it complements experimental biology and bioinformatics in unique ways that will eventually lead to insights and a depth of understanding not achievable without systems approaches. This article begins with a summary of traditional and novel modeling techniques. In the second part, it proposes concept map modeling as a useful link between experimental biology and biological systems modeling and analysis. Concept map modeling requires the collaboration between biologist and modeler. The biologist designs a regulated connectivity diagram of processes comprising a biological system and also provides semi-quantitative information on stimuli and measured or expected responses of the system. The modeler converts this information through methods of forward and inverse modeling into a mathematical construct that can be used for simulations and to generate and test new hypotheses. The biologist and the modeler collaboratively interpret the results and devise improved concept maps. The third part of the article describes software, BST-Box, supporting the various modeling activities.     

The use of evolutionary biology concepts for genome annotation

The past decade has seen the completion of numerous whole-genome sequencing projects, began with bacterial genomes and continued with eukaryotic species from different phyla: fungi, plants and animals. Besides, more biological information are produced and are shared thanks to information exchange systems, and more biological concepts, as well as more bioinformatics tools, are available. In this article, we will describe how the evolutionary biology concepts, as well as computer science, are useful for a better understanding of biology in general and genome annotation in particular. The genome annotation process consists of taking the raw DNA produced, for example, by the genome sequencing projects, adding the layers of analysis and interpretation necessary to extract its biological significance and placing it in the context of our understanding of biological processes. Genome annotation is a multistep process falling into two broad categories: structural and functional annotation.     

Neural system modeling and simulation using Hybrid Functional Petri Net

The Petri net formalism has been proved to be powerful in biological modeling. It not only boasts of a most intuitive graphical presentation but also combines the methods of classical systems biology with the discrete modeling technique. Hybrid Functional Petri Net (HFPN) was proposed specially for biological system modeling. An array of well-constructed biological models using HFPN yielded very interesting results. In this paper, we propose a method to represent neural system behavior, where biochemistry and electrical chemistry are both included using the Petri net formalism. We built a model for the adrenergic system using HFPN and employed quantitative analysis. Our simulation results match the biological data well, showing that the model is very effective. Predictions made on our model further manifest the modeling power of HFPN and improve the understanding of the adrenergic system. The file of our model and more results with their analysis are available in our supplementary material.