A report of BioSysBio 2009, the IET conference on Synthetic Biology, Systems Biology and Bioinformatics, Cambridge, UK, 23-25 March 2009.

Systems biology aims at achieving a system-level understanding of living organisms and applying this knowledge to various fields such as synthetic biology, metabolic engineering, and medicine. System-level understanding of living organisms can be derived from insight into: (i) system structure and the mechanism of biological networks such as gene regulation, protein interactions, signaling, and metabolic pathways; (ii) system dynamics of biological networks, which provides an understanding of stability, robustness, and transduction ability through system identification, and through system analysis methods; (iii) system control methods at different levels of biological networks, which provide an understanding of systematic mechanisms to robustly control system states, minimize malfunctions, and provide potential therapeutic targets in disease treatment; (iv) systematic design methods for the modification and construction of biological networks with desired behaviors, which provide system design principles and system simulations for synthetic biology designs and systems metabolic engineering. This review describes current developments in systems biology, systems synthetic biology, and systems metabolic engineering for engineering and biology researchers. We also discuss challenges and future prospects for systems biology and the concept of systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering.

The molecular pathways that regulate megakaryocyte production have historically been identified through multiple candidate gene approaches. Several transcription factors critical for generating megakaryocytes were identified by promoter analysis of megakaryocyte-specific genes, and their biological roles then verified by gene knockout studies; for example, GATA-1, NF-E2, and RUNX1 were identified in this way. In contrast, other transcription factors important for megakaryopoiesis were discovered through a systems approach; for example, c-Myb was found to be critical for the erythroid versus megakaryocyte lineage decision by genome-wide loss-of-function studies. The regulation of the levels of these transcription factors is, for the most part, cell intrinsic, although that assumption has recently been challenged. Epigenetics also impacts megakaryocyte gene expression, mediated by histone acetylation and methylation. Several cytokines have been identified to regulate megakaryocyte survival, proliferation, and differentiation, most prominent of which is thrombopoietin. Upon binding to its receptor, the product of the c-Mpl proto-oncogene, thrombopoietin induces a conformational change that activates a number of secondary messengers that promote cell survival, proliferation, and differentiation, and down-modulate receptor signaling. Among the best studied are the signal transducers and activators of transcription (STAT) proteins; phosphoinositol-3-kinase; mitogen-activated protein kinases; the phosphatases PTEN, SHP1, SHP2, and SHIP1; and the suppressors of cytokine signaling (SOCS) proteins. Additional signals activated by these secondary mediators include mammalian target of rapamycin; β(beta)-catenin; the G proteins Rac1, Rho, and CDC42; several transcription factors, including hypoxia-inducible factor 1α(alpha), the homeobox-containing proteins HOXB4 and HOXA9, and a number of signaling mediators that are reduced, including glycogen synthase kinase 3α(alpha) and the FOXO3 family of forkhead proteins. More recently, systematic interrogation of several aspects of megakaryocyte formation have been conducted, employing genomics, proteomics, and chromatin immunoprecipitation (ChIP) analyses, among others, and have yielded many previously unappreciated signaling mechanisms that regulate megakaryocyte lineage determination, proliferation, and differentiation. This chapter focuses on these pathways in normal and neoplastic megakaryopoiesis, and suggests areas that are ripe for further study.

A report on the Fifth International Workshop on Bioinformatics and Systems Biology, Berlin, Germany, 22-25 August 2005.

A report of the 3rd International Symposium on Optimization and Systems Biology, 20-22 September 2009, Zhangjiajie, China.

No abstract available

AutoAnalyze is a highly customizable framework for the visualization and analysis of large-scale model graphs. Originally developed for use in the automotive domain, it also supports efficient computation within molecular networks represented by reaction equations. A static analysis approach is used for efficient treatment-condition-specific simulation. The chosen method relies on the computation of a global network data-flow resulting from the evaluation of individual genetic data. The approach facilitates complex analyses of biological components from a molecular network under specific therapeutic perturbations, as demonstrated in a case study. In addition to simulating the complex networks in a stable and reproducible way, kinetic constants can also be fine-tuned using a genetic algorithm and built-in statistical tools.

Maximisation of the ratio of normal tissue preservation and tumour cell reduction is the main concept of radiotherapy alone or combined with chemo-, immuno- or biologically targeted therapy. The foremost parameter influencing this ratio is radiation sensitivity and its modulation towards a more efficient killing of tumour cells and a better preservation of normal tissue at the same time is the overall aim of modern therapy schemas. Nevertheless, this requires a deep understanding of the molecular mechanisms of radiation sensitivity in order to identify its key players as potential therapeutic targets. Moreover, the success of conventional approaches that tried to statistically associate altered radiation sensitivity with any molecular phenotype such as gene expression proofed to be somewhat limited since the number of clinically used targets is rather sparse. However, currently a paradigm shift is taking place from pure frequentistic association analysis to the rather holistic systems biology approach that seeks to mathematically model the system to be investigated and to allow the prediction of an altered phenotype as the function of one single or a signature of biomarkers. Integrative systems biology also considers the data from different molecular levels such as the genome, transcriptome or proteome in order to partially or fully comprehend the causal chain of molecular mechanisms. An example for the application of this concept currently carried out at the Clinical Cooperation Group "Personalized Radiotherapy in Head and Neck Cancer" of the Helmholtz-Zentrum München and the LMU Munich is described. This review article strives for providing a compact overview on the state of the art of systems biology, its actual challenges, potential applications, chances and limitations in radiation oncology research working towards improved personalised therapy concepts using this relatively new methodology.

Vaccines are the most effective tools to prevent infectious diseases and to minimize their impact on humans or animals. Despite the successful development of vaccines that are able to elicit potent and protective immune responses, the majority of vaccines have been so far developed empirically and mechanistic events leading to protective immune responses are often poorly understood. This hampers the development of new prophylactic as well as therapeutic vaccines for infectious diseases and cancer. Biological correlates of immune-mediated protection are currently based on standard readout such as antibody titres and ELISPOT assays. The development of successful vaccines for difficult settings, such as infectious agents leading to chronic infection (HIV, HCV...) or cancer, calls for novel 'readout systems' or 'correlates' of immune-mediated protection that would reliably predict immune responses to novel vaccines in vivo. Systems biology offers a new approach to vaccine design that is based upon understanding the molecular network mobilized by vaccination. Systems vaccinology approaches investigate more global correlates of successful vaccination, beyond the specific immune response to the antigens administered, providing new methods for measuring early vaccine efficacy and ultimately generating hypotheses for understanding the mechanisms that underlie successful immunogenicity. Using functional genomics, specific molecular signatures of individual vaccine can be identified and used as predictors of vaccination efficiency. The immune response to vaccination involves the coordinated induction of master transcription factors that leads to the development of a broad, polyfunctional and persistent immune response integrating all effector cells of the immune systems.

A report of the 6th International Conference on Computational Methods in Systems Biology, Rostock, Germany, 12-15 October 2008.

Reproducibility of scientific results is a key element of science and credibility. The lack of reproducibility across many scientific fields has emerged as an important concern. In this piece, we assess mathematical model reproducibility and propose a scorecard for improving reproducibility in this field.

A report on the Tenth International Conference on Intelligent Systems for Molecular Biology (ISMB), Edmonton, Canada, 3-7 August 2002.

Elucidation of pathogenicity mechanisms of the most important human-pathogenic fungi, Aspergillus fumigatus and Candida albicans, has gained great interest in the light of the steadily increasing number of cases of invasive fungal infections. A key feature of these infections is the interaction of the different fungal morphotypes with epithelial and immune effector cells in the human host. Because of the high level of complexity, it is necessary to describe and understand invasive fungal infection by taking a systems biological approach, i.e., by a comprehensive quantitative analysis of the non-linear and selective interactions of a large number of functionally diverse, and frequently multifunctional, sets of elements, e.g., genes, proteins, metabolites, which produce coherent and emergent behaviors in time and space. The recent advances in systems biology will now make it possible to uncover the structure and dynamics of molecular and cellular cause-effect relationships within these pathogenic interactions. We review current efforts to integrate omics and image-based data of host-pathogen interactions into network and spatio-temporal models. The modeling will help to elucidate pathogenicity mechanisms and to identify diagnostic biomarkers and potential drug targets for therapy and could thus pave the way for novel intervention strategies based on novel antifungal drugs and cell therapy.

The National Heart, Lung, and Blood Institute (NHLBI) has recognized the importance of the systems biology approach for understanding normal physiology and perturbations associated with heart, lung, blood, and sleep diseases and disorders. In 2006, NHLBI announced the Exploratory Program in Systems Biology program, followed in 2010 by the NHLBI Systems Biology Collaborations program. The goal of these programs is to support collaborative teams of investigators in using experimental and computational strategies to integrate the component parts of biological networks and pathways into computational models that are based firmly on and validated using experimental data. These validated models are then applied to gain insights into the mechanisms of altered system function in disease, to generate novel hypotheses regarding these mechanisms that can be tested experimentally, and to then use the results of experiments to refine the models. This perspective reviews the history of dedicated systems biology programs at NHLBI and reviews some promising directions for future research in this area.

Systems Biology has emerged as an exciting research approach in molecular biology and functional genomics that involves a systematic use of genomic, proteomic, and metabolomic technologies for the construction of network-based models of biological processes. These endeavors, collectively referred to as systems biology establish a paradigm by which to systematically interrogate, model, and iteratively refine our knowledge of the regulatory events within a cell. Here, we present a new systems approach, integrating DNA and transcript expression information, specifically designed to identify transcriptional networks governing the macrophage immune response to lipopolysaccharide (LPS). Using this approach, we are not only able to infer a global macrophage transcriptional network, but also time-specific sub-networks that are dynamically active across the LPS response. We believe that our system biological approach could be useful for identifying other complex networks mediating immunological responses.

Natural systems are, almost by definition, heterogeneous: this can be either a boon or an obstacle to be overcome, depending on the situation. Traditionally, when constructing mathematical models of these systems, heterogeneity has typically been ignored, despite its critical role. However, in recent years, stochastic computational methods have become commonplace in science. They are able to appropriately account for heterogeneity; indeed, they are based around the premise that systems inherently contain at least one source of heterogeneity (namely, intrinsic heterogeneity). In this mini-review, we give a brief introduction to theoretical modelling and simulation in systems biology and discuss the three different sources of heterogeneity in natural systems. Our main topic is an overview of stochastic simulation methods in systems biology. There are many different types of stochastic methods. We focus on one group that has become especially popular in systems biology, biochemistry, chemistry and physics. These discrete-state stochastic methods do not follow individuals over time; rather they track only total populations. They also assume that the volume of interest is spatially homogeneous. We give an overview of these methods, with a discussion of the advantages and disadvantages of each, and suggest when each is more appropriate to use. We also include references to software implementations of them, so that beginners can quickly start using stochastic methods for practical problems of interest.

Many alterations of innate and adaptive immunity are common in the aging population, which reflect a deterioration of the immune system, and have lead to the terms "immune aging" or "immunosenescence". Systems Biology aims to the comprehensive knowledge of the structure, dynamics, control and design that define a given biological system. Systems Biology benefits from the continuous advances in the omics sciences, based on high-throughput and high-content technologies, as well as on bioinformatic tools for data mining and integration. The Systems Biology approach is becoming gradually used to propose and to test comprehensive models of aging, both at the level of the immune system and the whole organism. In this way, immune aging may be described by a dynamic view of the states and interactions of every individual cell and molecule of the immune system and their role in the context of aging and longevity. This mini-review presents a panoramics of the current strategies, tools and challenges for applying Systems Biology to immune aging.

The human gut microbiota performs essential functions for host and well-being, but has also been linked to a variety of disease states, e.g., obesity and type 2 diabetes. The mammalian body fluid and tissue metabolomes are greatly influenced by the microbiota, with many health-relevant metabolites being considered 'mammalian-microbial co-metabolites'. To systematically investigate this complex host-microbial co-metabolism, a systems biology approach integrating high-throughput data and computational network models is required. Here, we review established top-down and bottom-up systems biology approaches that have successfully elucidated relationships between gut microbiota-derived metabolites and host health and disease. We focus particularly on the constraint-based modeling and analysis approach, which enables the prediction of mechanisms behind metabolic host-microbe interactions on the molecular level. We illustrate that constraint-based models are a useful tool for the contextualization of metabolomic measurements and can further our insight into host-microbe interactions, yielding, e.g., in potential novel drugs and biomarkers.

The success of genome-scale models (GEMs) can be attributed to the high-quality, bottom-up reconstructions of metabolic, protein synthesis, and transcriptional regulatory networks on an organism-specific basis. Such reconstructions are biochemically, genetically, and genomically structured knowledge bases that can be converted into a mathematical format to enable a myriad of computational biological studies. In recent years, genome-scale reconstructions have been extended to include protein structural information, which has opened up new vistas in systems biology research and empowered applications in structural systems biology and systems pharmacology.

No abstract available