A two-way interface between limited Systems Biology Markup Language and R

Background  

Systems Biology Markup Language (SBML) is gaining broad usage as a standard for representing dynamical systems as data structures. The open source statistical programming environment R is widely used by biostatisticians involved in microarray analyses. An interface between SBML and R does not exist, though one might be useful to R users interested in SBML, and SBML users interested in R.     

SBML-PET-MPI: a parallel parameter estimation tool for Systems Biology Markup Language based models

Parameter estimation is crucial for the modeling and dynamic analysis of biological systems. However, implementing parameter estimation is time consuming and computationally demanding. Here, we introduced a parallel parameter estimation tool for Systems Biology Markup Language (SBML)-based models (SBML-PET-MPI). SBML-PET-MPI allows the user to perform parameter estimation and parameter uncertainty analysis by collectively fitting multiple experimental datasets. The tool is developed and parallelized using the message passing interface (MPI) protocol, which provides good scalability with the number of processors. AVAILABILITY: SBML-PET-MPI is freely available for non-commercial use at http://www.bioss.uni-freiburg.de/cms/sbml-pet-mpi.html or http://sites.google.com/site/sbmlpetmpi/.     

SBMLeditor: effective creation of models in the Systems Biology Markup Language (SBML)

Background  

The need to build a tool to facilitate the quick creation and editing of models encoded in the Systems Biology Markup language (SBML) has been growing with the number of users and the increased complexity of the language. SBMLeditor tries to answer this need by providing a very simple, low level editor of SBML files. Users can create and remove all the necessary bits and pieces of SBML in a controlled way, that maintains the validity of the final SBML file.     

SBML-PET: a Systems Biology Markup Language-based parameter estimation tool

The estimation of model parameters from experimental data remains a bottleneck for a major breakthrough in systems biology. We present a Systems Biology Markup Language (SBML) based Parameter Estimation Tool (SBML-PET). The tool is designed to enable parameter estimation for biological models including signaling pathways, gene regulation networks and metabolic pathways. SBML-PET supports import and export of the models in the SBML format. It can estimate the parameters by fitting a variety of experimental data from different experimental conditions. SBML-PET has a unique feature of supporting event definition in the SMBL model. SBML models can also be simulated in SBML-PET. Stochastic Ranking Evolution Strategy (SRES) is incorporated in SBML-PET for parameter estimation jobs. A classic ODE Solver called ODEPACK is used to solve the Ordinary Differential Equation (ODE) system. AVAILABILITY: http://sysbio.molgen.mpg.de/SBML-PET/. The website also contains detailed documentation for SBML-PET.     

Evolving a lingua franca and associated software infrastructure for computational systems biology: the Systems Biology Markup Language (SBML) project

Biologists are increasingly recognising that computational modelling is crucial for making sense of the vast quantities of complex experimental data that are now being collected. The systems biology field needs agreed-upon information standards if models are to be shared, evaluated and developed cooperatively. Over the last four years, our team has been developing the Systems Biology Markup Language (SBML) in collaboration with an international community of modellers and software developers. SBML has become a de facto standard format for representing formal, quantitative and qualitative models at the level of biochemical reactions and regulatory networks. In this article, we summarise the current and upcoming versions of SBML and our efforts at developing software infrastructure for supporting and broadening its use. We also provide a brief overview of the many SBML-compatible software tools available today.     

A Systems Biology Approach to Learning Autophagy

With its relevance to our understanding of eukaryotic cell function in the normal and disease state, autophagy is an important topic in modern cell biology; yet, few textbooks discuss autophagy beyond a two- or three-sentence summary. Here, we report an undergraduate/graduate class lesson for the in-depth presentation of autophagy using an active learning approach. By our method, students will work in small groups to solve problems and interpret an actual data set describing genes involved in autophagy. The problem-solving exercises and data set analysis will instill within the students a much greater understanding of the autophagy pathway than can be achieved by simple rote memorization of lecture materials; furthermore, the students will gain a general appreciation of the process by which data are interpreted and eventually formed into an understanding of a given pathway. As the data sets used in these class lessons are largely genomic and complementary in content, students will also understand first-hand the advantage of an integrative or systems biology study: No single data set can be used to define the pathway in full æ the information from multiple complementary studies must be integrated in order to recapitulate our present understanding of the pathways mediating autophagy. In total, our teaching methodology offers an effective presentation of autophagy as well as a general template for the discussion of nearly any signaling pathway within the eukaryotic kingdom.     

Evolution and RNA Relics. A Systems Biology View

The genetic code has evolved from its initial non-degenerate wobble version until reaching its present state of degeneracy. By using the stereochemical hypothesis, we revisit the problem of codon assignations to the synonymy classes of amino-acids. We obtain these classes with a simple classifier based on physico-chemical properties of nucleic bases, like hydrophobicity and molecular weight. Then we propose simple RNA (or more generally XNA, with X for D, P or R) ring structures that present, overlap included, one and only one codon by synonymy class as solutions of a combinatory variational problem. We compare these solutions to sequences of present RNAs considered as relics, with a high interspecific invariance, like invariant parts of (t)RNAs and micro-RNAs. We conclude by emphasizing some optimal properties of the genetic code.     

Functional Translational Readthrough: A Systems Biology Perspective

Translational readthrough (TR) has come into renewed focus because systems biology approaches have identified the first human genes undergoing functional translational readthrough (FTR). FTR creates functional extensions to proteins by continuing translation of the mRNA downstream of the stop codon. Here we review recent developments in TR research with a focus on the identification of FTR in humans and the systems biology methods that have spurred these discoveries.     

Systems Biology Elucidates Common Pathogenic Mechanisms between Nonalcoholic and Alcoholic-Fatty Liver Disease

The abnormal accumulation of fat in the liver is often related either to metabolic risk factors associated with metabolic syndrome in the absence of alcohol consumption (nonalcoholic fatty liver disease, NAFLD) or to chronic alcohol consumption (alcoholic fatty liver disease, AFLD). Clinical and histological studies suggest that NAFLD and AFLD share pathogenic mechanisms. Nevertheless, current data are still inconclusive as to whether the underlying biological process and disease pathways of NAFLD and AFLD are alike. Our primary aim was to integrate omics and physiological data to answer the question of whether NAFLD and AFLD share molecular processes that lead to disease development. We also explored the extent to which insulin resistance (IR) is a distinctive feature of NAFLD. To answer these questions, we used systems biology approaches, such as gene enrichment analysis, protein–protein interaction networks, and gene prioritization, based on multi-level data extracted by computational data mining. We observed that the leading disease pathways associated with NAFLD did not significantly differ from those of AFLD. However, systems biology revealed the importance of each molecular process behind each of the two diseases, and dissected distinctive molecular NAFLD and AFLD-signatures. Comparative co-analysis of NAFLD and AFLD clarified the participation of NAFLD, but not AFLD, in cardiovascular disease, and showed that insulin signaling is impaired in fatty liver regardless of the noxa, but the putative regulatory mechanisms associated with NAFLD seem to encompass a complex network of genes and proteins, plausible of epigenetic modifications. Gene prioritization showed a cancer-related functional map that suggests that the fatty transformation of the liver tissue is regardless of the cause, an emerging mechanism of ubiquitous oncogenic activation. In conclusion, similar underlying disease mechanisms lead to NAFLD and AFLD, but specific ones depict a particular disease signature that has a different impact on the systemic context.     

Systems Bioethics and Stem Cell Biology

The complexities of modern science are not adequately reflected in many bioethical discussions. This is especially problematic in highly contested cases where there is significant pressure to generate clinical applications fast, as in stem cell research. In those cases a more integrated approach to bioethics, which we call systems bioethics, can provide a useful framework to address ethical and policy issues. Much as systems biology brings together different experimental and methodological approaches in an integrative way, systems bioethics integrates aspects of the history and philosophy of science, social and political theory, and normative analysis with the science in question. In this paper we outline how a careful analysis of the science of stem cell research can help to refocus the discussions related to the clinical applications of stem cells. We show how inaccurate or inadequate scientific assumptions help to create a set of unrealistic expectations and badly inform ethical deliberations and policy development. Systems bioethics offers resources for moving beyond the current impasse.     

Systems Approaches to Biology and Disease Enable Translational Systems Medicine

The development and application of systems strategies to biology and disease are transforming medical research and clinical practice in an unprecedented rate.In the foreseeable future,clinicians,medical researchers,and ultimately the consumers and patients will be increasingly equipped with a deluge of personal health information,e.g.,whole genome sequences,molecular profiling of diseased tissues,and periodic multi-analyte blood testing of biomarker panels for disease and wellness.The convergence of these practices will enable accurate prediction of disease susceptibility and early diagnosis for actionable preventive schema and personalized treatment regimes tailored to each individual.It will also entail proactive participation from all major stakeholders in the health care system.We are at the dawn of predictive,preventive,personalized,and participatory(P4) medicine,the fully implementation of which requires marrying basic and clinical researches through advanced systems thinking and the employment of high-throughput technologies in genomics,proteomics,nanofluidics,single-cell analysis,and computation strategies in a highly-orchestrated discipline we termed translational systems medicine.     

系统生物学研究及其在医学中的应用

目前,系统生物学研究已初显端倪。生物学正从分子生物学走向系统生物学,由精细的分解研究转向系统的整体研究,由还原论的研究方法过渡到系统论的研究方法。简要论述了系统生物学的产生背景、结构和内容、研究思路和方法、与医学的关系等,重点介绍系统生物学的内容和研究方法,以及在疾病治疗和药物开发中的研究进展。     

Systems Biology and Genomics of Breast Cancer

It is now accepted that breast cancer is not a single disease, but instead it is composed of a spectrum of tumor subtypes with distinct cellular origins, somatic changes, and etiologies. Gene expression profiling using DNA microarrays has contributed significantly to our understanding of the molecular heterogeneity of breast tumor formation, progression, and recurrence. For example, at least two clinical diagnostic assays exist (i.e., OncotypeDX RS and Mammaprint®) that are able to predict outcome in patients using patterns of gene expression and predetermined mathematical algorithms. In addition, a new molecular taxonomy based upon the inherent, or “intrinsic,” biology of breast tumors has been developed; this taxonomy is called the “intrinsic subtypes of breast cancer,” which now identifies five distinct tumor types and a normal breast-like group. Importantly, the intrinsic subtypes of breast cancer predict patient relapse, overall survival, and response to endocrine and chemotherapy regimens. Thus, most of the clinical behavior of a breast tumor is already written in its subtype profile. Here, we describe the discovery and basic biology of the intrinsic subtypes of breast cancer, and detail how this interacts with underlying genetic alternations, response to therapy, and the metastatic process.     

分子系统生物学与细胞发生动力学

系统生物学采用系统理论和实验生物技术、计算机数学模型等方法整合研究动态生物系统网络.生物系统的结构理论和生物系统技术,研究基因组——生物体复杂系统与细胞分子网络系统的动态结构发生与进化,分析基因组的逻辑程序和人工设计原理.细胞信号传导、基因调控网络、代谢反应链和基因反馈调控的自组织化人工设计和基因、基因链、基因组人工合成等系统生物工程开发,可用于复杂疾病机理分析、药物分子筛选和转基因表达系统的生物反应器、纳米生物计算机等.