Systems biology, integrative biology, predictive biology

Towards an Analytical Biology

This article draws a perspective on the increasingly unavoidable question of whether steps can be taken in genomics and biology at large to move them more rapidly towards more analytical and deductive biology, akin to similar developments that occurred in other natural sciences, such as physics and chemistry, centuries ago. It provides a summary of recent advances in other relevant sciences in the last 3 decades that are likely to pull it in that direction in the next decade or so, as well as what methods and tools will make it possible.

Multidimensional Landscape of SA-AKI Revealed by Integrated Proteomics and Metabolomics Analysis

Sepsis-associated acute kidney injury (SA-AKI) is a severe and life-threatening condition with high morbidity and mortality among emergency patients, and it poses a significant risk of chronic renal failure. Clinical treatments for SA-AKI remain reactive and non-specific, lacking effective diagnostic biomarkers or treatment targets. In this study, we established an SA-AKI mouse model using lipopolysaccharide (LPS) and performed proteomics and metabolomics analyses. A variety of bioinformatic analyses, including gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), protein and protein interactions (PPI), and MetaboAnalyst analysis, were conducted to investigate the key molecules of SA-AKI. Integrated proteomics and metabolomics analysis revealed that sepsis led to impaired renal mitochondrial function and metabolic disorders. Immune-related pathways were found to be activated in kidneys upon septic infection. The catabolic products of polyamines accumulated in septic kidneys. Overall, our integrated analysis provides a multidimensional understanding of SA-AKI and identifies potential pathways for this condition.

Bionic Design Model for Co-creative Product Innovation Based on Deep Generative and BID

Bio-inspired design (BID) is an abstract process, if we can visualize the process of fusing abstract biological inspiration with figurative product shapes, and combine it with artificial intelligence technology to express the designer’s creativity, it will greatly improve the efficiency and accuracy of product shape bionic design. To address this problem, we combine BID with deep generative (DG) model to build a co-creative deep generative bio-inspired design (DGBID) model. Firstly, the designers used perceptual engineering and eye-movement experiments to select the bionic creature that best fits the bionic product and the suitable bionic product and bionic image, respectively. Then, the images are embedded into the potential space of StyleGAN, and the potential relationship between the two is visualized using StyleGAN’s image morphing technique, which generates a new bionic fusion scheme. Finally, the contour lines of the solution are extracted as a reference, the designer is involved in the optimization of the scheme as a sketch, and the hand-drawn sketch is transformed into a real product solution using style migration techniques. The entire bionic design experiment process is a co-creative approach with artificial intelligence technology as the lead and designer participation. The feasibility of the method is verified using the side view of a car as a bionic product. The results show that the integration of bionic technology with deep generative model technology can accelerate the innovation and development of bionic products and provide designers with design references and rapid-generation tools.

Pursuit of precision medicine: Systems biology approaches in Alzheimer's disease mouse models

Alzheimer's disease (AD) is a complex disease that is mediated by numerous factors and manifests in various forms. A systems biology approach to studying AD involves analyses of various body systems, biological scales, environmental elements, and clinical outcomes to understand the genotype to phenotype relationship that potentially drives AD development. Currently, there are many research investigations probing how modifiable and nonmodifiable factors impact AD symptom presentation. This review specifically focuses on how imaging modalities can be integrated into systems biology approaches using model mouse populations to link brain level functional and structural changes to disease onset and progression. Combining imaging and omics data promotes the classification of AD into subtypes and paves the way for precision medicine solutions to prevent and treat AD.

Metabolomics Analysis of the Development of Sepsis and Potential Biomarkers of Sepsis-Induced Acute Kidney Injury

Sepsis-induced acute kidney injury (SI-AKI) is a serious condition in critically ill patients. Currently, the diagnosis is based on either elevated serum creatinine levels or oliguria, which partially contribute to delayed recognition of AKI. Metabolomics is a potential approach for identifying small molecule biomarkers of kidney diseases. Here, we studied serum metabolomics alterations in rats with sepsis to identify early biomarkers of sepsis and SI-AKI. A rat model of SI-AKI was established by intraperitoneal injection of lipopolysaccharide (LPS). Thirty Sprague-Dawley (SD) rats were randomly divided into the control (CT) group and groups treated for 2 hours (LPS2) and 6 hours (LPS6) with LPS (10 rats per group). Nontargeted metabolomics screening was performed on the serum samples from the control and SI-AKI groups. Combined multivariate and univariate analysis was used for pairwise comparison of all groups to identify significantly altered serum metabolite levels in early-stage AKI in rats with sepsis. Orthogonal partial least squares discriminant analysis (OPLS-DA) showed obvious separation between the CT and LPS2 groups, CT and LPS6 groups, and LPS2 and LPS6 groups. All comparisons of the groups identified a series of differential metabolites according to the threshold defined for potential biomarkers. Intersections and summaries of these differential metabolites were used for pathway enrichment analysis. The results suggested that sepsis can cause an increase in systemic aerobic and anaerobic metabolism, an impairment of the oxygen supply, and uptake and abnormal fatty acid metabolism. Changes in the levels of malic acid, methionine sulfoxide, and petroselinic acid were consistently measured during the progression of sepsis. The development of sepsis was accompanied by the development of AKI, and these metabolic disorders are directly or indirectly related to the development of SI-AKI.

Using optimal control to understand complex metabolic pathways

BACKGROUND

Optimality principles have been used to explain the structure and behavior of living matter at different levels of organization, from basic phenomena at the molecular level, up to complex dynamics in whole populations. Most of these studies have assumed a single-criteria approach. Such optimality principles have been justified from an evolutionary perspective. In the context of the cell, previous studies have shown how dynamics of gene expression in small metabolic models can be explained assuming that cells have developed optimal adaptation strategies. Most of these works have considered rather simplified representations, such as small linear pathways, or reduced networks with a single branching point, and a single objective for the optimality criteria.

RESULTS

Here we consider the extension of this approach to more realistic scenarios, i.e. biochemical pathways of arbitrary size and structure. We first show that exploiting optimality principles for these networks poses great challenges due to the complexity of the associated optimal control problems. Second, in order to surmount such challenges, we present a computational framework which has been designed with scalability and efficiency in mind, including mechanisms to avoid the most common pitfalls. Third, we illustrate its performance with several case studies considering the central carbon metabolism of S. cerevisiae and B. subtilis. In particular, we consider metabolic dynamics during nutrient shift experiments.

CONCLUSIONS

We show how multi-objective optimal control can be used to predict temporal profiles of enzyme activation and metabolite concentrations in complex metabolic pathways. Further, we also show how to consider general cost/benefit trade-offs. In this study we have considered metabolic pathways, but this computational framework can also be applied to analyze the dynamics of other complex pathways, such as signal transduction or gene regulatory networks.

Prediction of Adipose Browning Capacity by Systematic Integration of Transcriptional Profiles

Activation and recruitment of thermogenic cells in human white adipose tissues ("browning") can counteract obesity and associated metabolic disorders. However, quantifying the effects of therapeutic interventions on browning remains enigmatic. Here, we devise a computational tool, named ProFAT (profiling of fat tissue types), for quantifying the thermogenic potential of heterogeneous fat biopsies based on prediction of white and brown adipocyte content from raw gene expression datasets. ProFAT systematically integrates 103 mouse-fat-derived transcriptomes to identify unbiased and robust gene signatures of brown and white adipocytes. We validate ProFAT on 80 mouse and 97 human transcriptional profiles from 14 independent studies and correctly predict browning capacity upon various physiological and pharmacological stimuli. Our study represents the most exhaustive comparative analysis of public data on adipose biology toward quantification of browning after personalized medical intervention. ProFAT is freely available and should become increasingly powerful with the growing wealth of transcriptomics data.

Construction and Clarification of Dynamic Gene Regulatory Network of Cancer Cell Cycle via Microarray Data

Background

Cell cycle is an important clue to unravel the mechanism of cancer cells. Recently, expression profiles of cDNA microarray data of Cancer cell cycle are available for the information of dynamic interactions among Cancer cell cycle related genes. Therefore, it is more appealing to construct a dynamic model for gene regulatory network of Cancer cell cycle to gain more insight into the infrastructure of gene regulatory mechanism of cancer cell via microarray data.

Results

Based on the gene regulatory dynamic model and microarray data, we construct the whole dynamic gene regulatory network of Cancer cell cycle. In this study, we trace back upstream regulatory genes of a target gene to infer the regulatory pathways of the gene network by maximum likelihood estimation method. Finally, based on the dynamic regulatory network, we analyze the regulatory abilities and sensitivities of regulatory genes to clarify their roles in the mechanism of Cancer cell cycle.

Conclusions

Our study presents a systematically iterative approach to discern and characterize the transcriptional regulatory network in Hela cell cycle from the raw expression profiles. The transcription regulatory network in Hela cell cycle can also be confirmed by some experimental reviews. Based on our study and some literature reviews, we can predict and clarify the E2F target genes in G1/S phase, which are crucial for regulating cell cycle progression and tumorigenesis. From the results of the network construction and literature confirmation, we infer that MCM4, MCM5, CDC6, CDC25A, UNG and E2F2 are E2F target genes in Hela cell cycle.

Analysis of Drag Reduction Methods and Mechanisms of Turbulent

Turbulent flow is a difficult issue in fluid dynamics, the rules of which have not been totally revealed up to now. Fluid in turbulent state will result in a greater frictional force, which must consume great energy. Therefore, it is not only an important influence in saving energy and improving energy utilization rate but also an extensive application prospect in many fields, such as ship domain and aerospace. Firstly, bionic drag reduction technology is reviewed and is a hot research issue now, the drag reduction mechanism of body surface structure is analyzed, such as sharks, earthworms, and dolphins. Besides, we make a thorough study of drag reduction characteristics and mechanisms of microgrooved surface and compliant wall. Then, the relevant drag reduction technologies and mechanisms are discussed, focusing on the microbubbles, the vibrant flexible wall, the coating, the polymer drag reduction additives, superhydrophobic surface, jet surface, traveling wave surface drag reduction, and the composite drag reduction methods. Finally, applications and advancements of the drag reduction technology in turbulence are prospected.

Medium term water deficit elicits distinct transcriptome responses in Eucalyptus species of contrasting environmental origin

Background

Climatic and edaphic conditions over geological timescales have generated enormous diversity of adaptive traits and high speciation within the genus Eucalyptus (L. Hér.). Eucalypt species occur from high rainfall to semi-arid zones and from the tropics to latitudes as high as 43°S. Despite several morphological and metabolomic characterizations, little is known regarding gene expression differences that underpin differences in tolerance to environmental change. Using species of contrasting taxonomy, morphology and physiology (E. globulus and E. cladocalyx), this study combines physiological characterizations with ‘second-generation’ sequencing to identify key genes involved in eucalypt responses to medium-term water limitation.

Results

One hundred twenty Million high-quality HiSeq reads were created from 14 tissue samples in plants that had been successfully subjected to a water deficit treatment or a well-watered control. Alignment to the E. grandis genome saw 23,623 genes of which 468 exhibited differential expression (FDR < 0.01) in one or both ecotypes in response to the treatment. Further analysis identified 80 genes that demonstrated a significant species-specific response of which 74 were linked to the ‘dry’ species E. cladocalyx where 23 of these genes were uncharacterised. The majority (approximately 80%) of these differentially expressed genes, were expressed in stem tissue. Key genes that differentiated species responses were linked to photoprotection/redox balance, phytohormone/signalling, primary photosynthesis/cellular metabolism and secondary metabolism based on plant metabolic pathway network analysis.

Conclusion

These results highlight a more definitive response to water deficit by a ‘dry’ climate eucalypt, particularly in stem tissue, identifying key pathways and associated genes that are responsible for the differences between ‘wet’ and ‘dry’ climate eucalypts. This knowledge provides the opportunity to further investigate and understand the mechanisms and genetic variation linked to this important environmental response that will assist with genomic efforts in managing native populations as well as in tree improvement programs under future climate scenarios.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3664-z) contains supplementary material, which is available to authorized users.

Electric fish genomics: Progress, prospects, and new tools for neuroethology

Electric fish have served as a model system in biology since the 18th century, providing deep insight into the nature of bioelectrogenesis, the molecular structure of the synapse, and brain circuitry underlying complex behavior. Neuroethologists have collected extensive phenotypic data that span biological levels of analysis from molecules to ecosystems. This phenotypic data, together with genomic resources obtained over the past decades, have motivated new and exciting hypotheses that position the weakly electric fish model to address fundamental 21^st century biological questions. This review article considers the molecular data collected for weakly electric fish over the past three decades, and the insights that data of this nature has motivated. For readers relatively new to molecular genetics techniques, we also provide a table of terminology aimed at clarifying the numerous acronyms and techniques that accompany this field. Next, we pose a research agenda for expanding genomic resources for electric fish research over the next 10years. We conclude by considering some of the exciting research prospects for neuroethology that electric fish genomics may offer over the coming decades, if the electric fish community is successful in these endeavors.

Systems Biology for Smart Crops and Agricultural Innovation: Filling the Gaps between Genotype and Phenotype for Complex Traits Linked with Robust Agricultural Productivity and Sustainability

In recent years, rapid developments in several omics platforms and next generation sequencing technology have generated a huge amount of biological data about plants. Systems biology aims to develop and use well-organized and efficient algorithms, data structure, visualization, and communication tools for the integration of these biological data with the goal of computational modeling and simulation. It studies crop plant systems by systematically perturbing them, checking the gene, protein, and informational pathway responses; integrating these data; and finally, formulating mathematical models that describe the structure of system and its response to individual perturbations. Consequently, systems biology approaches, such as integrative and predictive ones, hold immense potential in understanding of molecular mechanism of agriculturally important complex traits linked to agricultural productivity. This has led to identification of some key genes and proteins involved in networks of pathways involved in input use efficiency, biotic and abiotic stress resistance, photosynthesis efficiency, root, stem and leaf architecture, and nutrient mobilization. The developments in the above fields have made it possible to design smart crops with superior agronomic traits through genetic manipulation of key candidate genes.

Proteomic approaches and identification of novel therapeutic targets for alcoholism

Recent studies have shown that gene regulation is far more complex than previously believed and does not completely explain changes at the protein level. Therefore, the direct study of the proteome, considerably different in both complexity and dynamicity to the genome/transcriptome, has provided unique insights to an increasing number of researchers. During the past decade, extraordinary advances in proteomic techniques have changed the way we can analyze the composition, regulation, and function of protein complexes and pathways underlying altered neurobiological conditions. When combined with complementary approaches, these advances provide the contextual information for decoding large data sets into meaningful biologically adaptive processes. Neuroproteomics offers potential breakthroughs in the field of alcohol research by leading to a deeper understanding of how alcohol globally affects protein structure, function, interactions, and networks. The wealth of information gained from these advances can help pinpoint relevant biomarkers for early diagnosis and improved prognosis of alcoholism and identify future pharmacological targets for the treatment of this addiction.

When integration fails: Prokaryote phylogeny and the tree of life

Much is being written these days about integration, its desirability and even its necessity when complex research problems are to be addressed. Seldom, however, do we hear much about the failure of such efforts. Because integration is an ongoing activity rather than a final achievement, and because today's literature about integration consists mostly of manifesto statements rather than precise descriptions, an examination of unsuccessful integration could be illuminating to understand better how it works. This paper will examine the case of prokaryote phylogeny and its apparent failure to achieve integration within broader tree-of-life accounts of evolutionary history (often called 'universal phylogeny'). Despite the fact that integrated databases exist of molecules pertinent to the phylogenetic reconstruction of all lineages of life, and even though the same methods can be used to construct phylogenies wherever the organisms fall on the tree of life, prokaryote phylogeny remains at best only partly integrated within tree-of-life efforts. I will examine why integration does not occur, compare it with integrative practices in animal and other eukaryote phylogeny, and reflect on whether there might be different expectations of what integration should achieve. Finally, I will draw some general conclusions about integration and its function as a 'meta-heuristic' in the normative commitments guiding scientific practice.

Challenges and solutions in the development of genomic biomarker panels: a systematic phased approach

In the post-genome era, high throughput gene expression profiling has been successfully used to develop genomic biomarker panels (GBP) that can be integrated into clinical decision making. The development of GBPs in the context of personalized medicine is a scientifically challenging and resource-intense process. It needs to be accomplished in a systematic phased approach to address biological variation related to a clinical phenotype (e.g. disease etiology, gender, etc.) and minimize technical variation (noise). Here we present the methodological aspects of GBP development based on the experience of the Cardiac Allograft Rejection Gene Expression Observation (CARGO) study, a study that lead to the development of a molecular classifier for rejection screening in heart transplant patients.

Complexity and the reductionism-holism debate in systems biology

Reductionism has largely influenced the development of science, culminating in its application to molecular biology. An increasing number of novel research findings have, however, shattered this view, showing how the molecular-reductionist approach cannot entirely handle the complexity of biological systems. Within this framework, the advent of systems biology as a new and more integrative field of research is described, along with the form which has taken on the debate of reductionism versus holism. Such an issue occupies a central position in systems biology, and nonetheless it is not always clearly delineated. This partly occurs because different dimensions (ontological, epistemological, methodological) are involved, and yet the concerned ones often remain unspecified. Besides, within systems biology different streams can be distinguished depending on the degree of commitment to embrace genuine systemic principles. Some useful insights into the future development of this discipline might be gained from the tradition of complexity and self-organization. This is especially true with regards the idea of self-reference, which incorporated into the organizational scheme is able to generate autonomy as an emergent property of the biological whole.

Semantic Web meets Integrative Biology: a survey

Integrative Biology (IB) uses experimental or computational quantitative technologies to characterize biological systems at the molecular, cellular, tissue and population levels. IB typically involves the integration of the data, knowledge and capabilities across disciplinary boundaries in order to solve complex problems. We identify a series of bioinformatics problems posed by interdisciplinary integration: (i) data integration that interconnects structured data across related biomedical domains; (ii) ontology integration that brings jargons, terminologies and taxonomies from various disciplines into a unified network of ontologies; (iii) knowledge integration that integrates disparate knowledge elements from multiple sources; (iv) service integration that build applications out of services provided by different vendors. We argue that IB can benefit significantly from the integration solutions enabled by Semantic Web (SW) technologies. The SW enables scientists to share content beyond the boundaries of applications and websites, resulting into a web of data that is meaningful and understandable to any computers. In this review, we provide insight into how SW technologies can be used to build open, standardized and interoperable solutions for interdisciplinary integration on a global basis. We present a rich set of case studies in system biology, integrative neuroscience, bio-pharmaceutics and translational medicine, to highlight the technical features and benefits of SW applications in IB.

BioNetwork Bench: Database and Software for Storage, Query, and Analysis of Gene and Protein Networks

Gene and protein networks offer a powerful approach for integration of the disparate yet complimentary types of data that result from high-throughput analyses. Although many tools and databases are currently available for accessing such data, they are left unutilized by bench scientists as they generally lack features for effective analysis and integration of both public and private datasets and do not offer an intuitive interface for use by scientists with limited computational expertise. We describe BioNetwork Bench, an open source, user-friendly suite of database and software tools for constructing, querying, and analyzing gene and protein network models. It enables biologists to analyze public as well as private gene expression; interactively query gene expression datasets; integrate data from multiple networks; store and selectively share the data and results. Finally, we describe an application of BioNetwork Bench to the assembly and iterative expansion of a gene network that controls the differentiation of retinal progenitor cells into rod photoreceptors. The tool is available from http://bionetworkbench.sourceforge.net/

Molecularization in nutritional science: a view from philosophy of science

SCOPE

Over the past decade, a trend toward molecularization, which could be observed in almost all bioscientific disciplines, now appears to have also developed in nutritional science. However, molecular nutrition research gives birth to a series of questions. Therefore, we take a look at the epistemological foundation of (molecular) nutritional science.

METHODS AND RESULTS

We (i) analyze the scientific status of (molecular) nutritional science and its position in the canon of other scientific disciplines, (ii) focus on the cognitive aims of nutritional science in general and (iii) on the chances and limits of molecular nutrition research in particular. By taking up the thoughts of an earlier work, we are analyzing (molecular) nutritional science from a strictly realist and emergentist-naturalist perspective.

CONCLUSION

Methodologically, molecular nutrition research is bound to a microreductive research approach. We emphasize, however, that it need not be a radical microreductionism whose scientific reputation is not the best. Instead we favor moderate microreductionism, which combines reduction with integration. As mechanismic explanations are one of the primary aims of factual sciences, we consider it as the task of molecular nutrition research to find profound, i.e. molecular-mechanismic, explanations for the conditions, characteristics and changes of organisms related to the organism-nutrition environment interaction.

Systems biology: the next frontier for bioinformatics

Biochemical systems biology augments more traditional disciplines, such as genomics, biochemistry and molecular biology, by championing (i) mathematical and computational modeling; (ii) the application of traditional engineering practices in the analysis of biochemical systems; and in the past decade increasingly (iii) the use of near-comprehensive data sets derived from 'omics platform technologies, in particular "downstream" technologies relative to genome sequencing, including transcriptomics, proteomics and metabolomics. The future progress in understanding biological principles will increasingly depend on the development of temporal and spatial analytical techniques that will provide high-resolution data for systems analyses. To date, particularly successful were strategies involving (a) quantitative measurements of cellular components at the mRNA, protein and metabolite levels, as well as in vivo metabolic reaction rates, (b) development of mathematical models that integrate biochemical knowledge with the information generated by high-throughput experiments, and (c) applications to microbial organisms. The inevitable role bioinformatics plays in modern systems biology puts mathematical and computational sciences as an equal partner to analytical and experimental biology. Furthermore, mathematical and computational models are expected to become increasingly prevalent representations of our knowledge about specific biochemical systems.

Chronic lung diseases

Chronic lung diseases often have high morbidity and mortality rate and have posed a serious threat to human health. The incidence of many chronic lung diseases such as asthma has been on the rise in the past decade, which causes serious economic burden. Despite many efforts which employed traditional experimental approaches to elucidate the mechanisms of the diseases have been made, little is known about the pathogenesis of complex lung diseases. Systems biology approaches which aim to integrate and analyze information gathered from multiple sources offer a great opportunity to examine complex human diseases from a new angle. Many attempts have been made using high-throughput technologies such as microarrays to study chronic lung diseases; although compared with the full-fledged systems biology approach, research strategies employed in most of these investigations still have much room to improve, promising findings have already emerged from these efforts, which demonstrates the potential of implementing systems biology in pulmonary biomedical research.

Aspects of experimental design for plant metabolomics experiments and guidelines for growth of plant material

Experiments involve the deliberate variation of one or more factors in order to provoke responses, the identification of which then provides the first step towards functional knowledge. Because environmental, biological, and/or technical noise is unavoidable, biological experiments usually need to be designed. Thus, once the major sources of experimental noise have been identified, individual samples can be grouped, randomised, and/or pooled. Like other 'omics approaches, metabolomics is characterised by the numbers of analytes largely exceeding sample number. While this unprecedented singularity in biology dramatically increases false discovery, experimental error can nevertheless be decreased in plant metabolomics experiments. For this, each step from plant cultivation to data acquisition needs to be evaluated in order to identify the major sources of error and then an appropriate design can be produced, as with any other experimental approach. The choice of technology, the time at which tissues are harvested, and the way metabolism is quenched also need to be taken into consideration, as they decide which metabolites can be studied. A further recommendation is to document data and metadata in a machine readable way. The latter should also describe every aspect of the experiment. This should provide valuable hints for future experimental design and ultimately give metabolomic data a second life. To facilitate the identification of critical steps, a list of items to be considered before embarking on time-consuming and costly metabolomic experiments is proposed.

The Human Oral Microbiome Database: a web accessible resource for investigating oral microbe taxonomic and genomic information

The human oral microbiome is the most studied human microflora, but 53% of the species have not yet been validly named and 35% remain uncultivated. The uncultivated taxa are known primarily from 16S rRNA sequence information. Sequence information tied solely to obscure isolate or clone numbers, and usually lacking accurate phylogenetic placement, is a major impediment to working with human oral microbiome data. The goal of creating the Human Oral Microbiome Database (HOMD) is to provide the scientific community with a body site-specific comprehensive database for the more than 600 prokaryote species that are present in the human oral cavity based on a curated 16S rRNA gene-based provisional naming scheme. Currently, two primary types of information are provided in HOMD—taxonomic and genomic. Named oral species and taxa identified from 16S rRNA gene sequence analysis of oral isolates and cloning studies were placed into defined 16S rRNA phylotypes and each given unique Human Oral Taxon (HOT) number. The HOT interlinks phenotypic, phylogenetic, genomic, clinical and bibliographic information for each taxon. A BLAST search tool is provided to match user 16S rRNA gene sequences to a curated, full length, 16S rRNA gene reference data set. For genomic analysis, HOMD provides comprehensive set of analysis tools and maintains frequently updated annotations for all the human oral microbial genomes that have been sequenced and publicly released. Oral bacterial genome sequences, determined as part of the Human Microbiome Project, are being added to the HOMD as they become available. We provide HOMD as a conceptual model for the presentation of microbiome data for other human body sites.

Database URL: http://www.homd.org

Rule-based spatial modeling with diffusing, geometrically constrained molecules

Background

We suggest a new type of modeling approach for the coarse grained, particle-based spatial simulation of combinatorially complex chemical reaction systems. In our approach molecules possess a location in the reactor as well as an orientation and geometry, while the reactions are carried out according to a list of implicitly specified reaction rules. Because the reaction rules can contain patterns for molecules, a combinatorially complex or even infinitely sized reaction network can be defined.

For our implementation (based on LAMMPS), we have chosen an already existing formalism (BioNetGen) for the implicit specification of the reaction network. This compatibility allows to import existing models easily, i.e., only additional geometry data files have to be provided.

Results

Our simulations show that the obtained dynamics can be fundamentally different from those simulations that use classical reaction-diffusion approaches like Partial Differential Equations or Gillespie-type spatial stochastic simulation. We show, for example, that the combination of combinatorial complexity and geometric effects leads to the emergence of complex self-assemblies and transportation phenomena happening faster than diffusion (using a model of molecular walkers on microtubules). When the mentioned classical simulation approaches are applied, these aspects of modeled systems cannot be observed without very special treatment. Further more, we show that the geometric information can even change the organizational structure of the reaction system. That is, a set of chemical species that can in principle form a stationary state in a Differential Equation formalism, is potentially unstable when geometry is considered, and vice versa.

Conclusions

We conclude that our approach provides a new general framework filling a gap in between approaches with no or rigid spatial representation like Partial Differential Equations and specialized coarse-grained spatial simulation systems like those for DNA or virus capsid self-assembly.

Novel application of fluorescence lifetime and fluorescence microscopy enables quantitative access to subcellular dynamics in plant cells

BACKGROUND

Optical and spectroscopic technologies working at subcellular resolution with quantitative output are required for a deeper understanding of molecular processes and mechanisms in living cells. Such technologies are prerequisite for the realisation of predictive biology at cellular and subcellular level. However, although established in the physical sciences, these techniques are rarely applied to cell biology in the plant sciences.

PRINCIPAL FINDINGS

Here, we present a combined application of one-chromophore fluorescence lifetime microscopy and wavelength-selective fluorescence microscopy to analyse the function of a GFP fusion of the Brassinosteroid Insensitive 1 Receptor (BRI1-GFP) with high spatial and temporal resolution in living Arabidopsis cells in their tissue environment. We show a rapid, brassinolide-induced cell wall expansion and a fast BR-regulated change in the BRI1-GFP fluorescence lifetime in the plasmamembrane in vivo. Both cell wall expansion and changes in fluorescence lifetime reflect early BR-induced and BRI1-dependent physiological or signalling processes. Our experiments also show the potential of one-chromophore fluorescence lifetime microscopy for the in vivo monitoring of the biochemical and biophysical subcellular environment using GFP fusion proteins as probes.

SIGNIFICANCE

One-chromophore fluorescence lifetime microscopy, combined with wavelength-specific fluorescence microscopy, opens up new frontiers for in vivo dynamic and quantitative analysis of cellular processes at high resolution which are not addressable by pure imaging technologies or transmission electron microscopy.

Biological processes, properties and molecular wiring diagrams of candidate low-penetrance breast cancer susceptibility genes

Background

Recent advances in whole-genome association studies (WGASs) for human cancer risk are beginning to provide the part lists of low-penetrance susceptibility genes. However, statistical analysis in these studies is complicated by the vast number of genetic variants examined and the weak effects observed, as a result of which constraints must be incorporated into the study design and analytical approach. In this scenario, biological attributes beyond the adjusted statistics generally receive little attention and, more importantly, the fundamental biological characteristics of low-penetrance susceptibility genes have yet to be determined.

Methods

We applied an integrative approach for identifying candidate low-penetrance breast cancer susceptibility genes, their characteristics and molecular networks through the analysis of diverse sources of biological evidence.

Results

First, examination of the distribution of Gene Ontology terms in ordered WGAS results identified asymmetrical distribution of Cell Communication and Cell Death processes linked to risk. Second, analysis of 11 different types of molecular or functional relationships in genomic and proteomic data sets defined the "omic" properties of candidate genes: i/ differential expression in tumors relative to normal tissue; ii/ somatic genomic copy number changes correlating with gene expression levels; iii/ differentially expressed across age at diagnosis; and iv/ expression changes after BRCA1 perturbation. Finally, network modeling of the effects of variants on germline gene expression showed higher connectivity than expected by chance between novel candidates and with known susceptibility genes, which supports functional relationships and provides mechanistic hypotheses of risk.

Conclusion

This study proposes that cell communication and cell death are major biological processes perturbed in risk of breast cancer conferred by low-penetrance variants, and defines the common omic properties, molecular interactions and possible functional effects of candidate genes and proteins.

Multiway modeling and analysis in stem cell systems biology

Background

Systems biology refers to multidisciplinary approaches designed to uncover emergent properties of biological systems. Stem cells are an attractive target for this analysis, due to their broad therapeutic potential. A central theme of systems biology is the use of computational modeling to reconstruct complex systems from a wealth of reductionist, molecular data (e.g., gene/protein expression, signal transduction activity, metabolic activity, etc.). A number of deterministic, probabilistic, and statistical learning models are used to understand sophisticated cellular behaviors such as protein expression during cellular differentiation and the activity of signaling networks. However, many of these models are bimodal i.e., they only consider row-column relationships. In contrast, multiway modeling techniques (also known as tensor models) can analyze multimodal data, which capture much more information about complex behaviors such as cell differentiation. In particular, tensors can be very powerful tools for modeling the dynamic activity of biological networks over time. Here, we review the application of systems biology to stem cells and illustrate application of tensor analysis to model collagen-induced osteogenic differentiation of human mesenchymal stem cells.

Results

We applied Tucker1, Tucker3, and Parallel Factor Analysis (PARAFAC) models to identify protein/gene expression patterns during extracellular matrix-induced osteogenic differentiation of human mesenchymal stem cells. In one case, we organized our data into a tensor of type protein/gene locus link × gene ontology category × osteogenic stimulant, and found that our cells expressed two distinct, stimulus-dependent sets of functionally related genes as they underwent osteogenic differentiation. In a second case, we organized DNA microarray data in a three-way tensor of gene IDs × osteogenic stimulus × replicates, and found that application of tensile strain to a collagen I substrate accelerated the osteogenic differentiation induced by a static collagen I substrate.

Conclusion

Our results suggest gene- and protein-level models whereby stem cells undergo transdifferentiation to osteoblasts, and lay the foundation for mechanistic, hypothesis-driven studies. Our analysis methods are applicable to a wide range of stem cell differentiation models.

A global pathway crosstalk network

MOTIVATION

Given the complex nature of biological systems, pathways often need to function in a coordinated fashion in order to produce appropriate physiological responses to both internal and external stimuli. Therefore, understanding the interaction and crosstalk between pathways is important for understanding the function of both cells and more complex systems.

RESULTS

We have developed a computational approach to detect crosstalk among pathways based on protein interactions between the pathway components. We built a global mammalian pathway crosstalk network that includes 580 pathways (covering 4753 genes) with 1815 edges between pathways. This crosstalk network follows a power-law distribution: P(k) approximately k(-)(gamma), gamma = 1.45, where P(k) is the number of pathways with k neighbors, thus pathway interactions may exhibit the same scale-free phenomenon that has been documented for protein interaction networks. We further used this network to understand colorectal cancer progression to metastasis based on transcriptomic data.

CONTACT

yong.2.li@gsk.com

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The oestrogen-dependent biology of breast cancer. Sensitivity and resistance to aromatase inhibitors revisited: a molecular perspective

Endocrine treatment of breast cancer was the first molecular targeted anti-cancer therapy to reach clinical practice. Among the several options that share the common denomination of hormonal treatment, aromatase inhibitors (AIs) in the postmenopausal setting show the highest efficacy rates. These drugs have become the standard of care both in the advanced and adjuvant scenarios. Nevertheless resistance to AIs either upfront or after initial clinical response is almost a universal feature whenever tumour excision is not possible. Multiple reports have established the role of alternative pro-growth signalling pathways in the acquisition of resistance to the oestradiol deprivation that AIs produce. However the first clinical trials addressing the double blockade of both the oestrogen and other growing factor pathways raise some concerns on the efficacy of this approach. This review presents the evidence on the molecular events underpinning the response and resistance to AIs and suggests some key issues to consider when designing clinical research projects in this context.

Scale relativity theory and integrative systems biology: 1. Founding principles and scale laws

In these two companion papers, we provide an overview and a brief history of the multiple roots, current developments and recent advances of integrative systems biology and identify multiscale integration as its grand challenge. Then we introduce the fundamental principles and the successive steps that have been followed in the construction of the scale relativity theory, and discuss how scale laws of increasing complexity can be used to model and understand the behaviour of complex biological systems. In scale relativity theory, the geometry of space is considered to be continuous but non-differentiable, therefore fractal (i.e., explicitly scale-dependent). One writes the equations of motion in such a space as geodesics equations, under the constraint of the principle of relativity of all scales in nature. To this purpose, covariant derivatives are constructed that implement the various effects of the non-differentiable and fractal geometry. In this first review paper, the scale laws that describe the new dependence on resolutions of physical quantities are obtained as solutions of differential equations acting in the scale space. This leads to several possible levels of description for these laws, from the simplest scale invariant laws to generalized laws with variable fractal dimensions. Initial applications of these laws to the study of species evolution, embryogenesis and cell confinement are discussed.

Proteome informatics for cancer research: from molecules to clinic

Proteomics offers the most direct approach to understand disease and its molecular biomarkers. Biomarkers denote the biological states of tissues, cells, or body fluids that are useful for disease detection and classification. Clinical proteomics is used for early disease detection, molecular diagnosis of disease, identification and formulation of therapies, and disease monitoring and prognostics. Bioinformatics tools are essential for converting raw proteomics data into knowledge and subsequently into useful applications. These tools are used for the collection, processing, analysis, and interpretation of the vast amounts of proteomics data. Management, analysis, and interpretation of large quantities of raw and processed data require a combination of various informatics technologies such as databases, sequence comparison, predictive models, and statistical tools. We have demonstrated the utility of bioinformatics in clinical proteomics through the analysis of the cancer antigen survivin and its suitability as a target for cancer immunotherapy.

The study of socioethical issues in systems biology

Systems biology is the rapidly growing and heavily funded successor science to genomics. Its mission is to integrate extensive bodies of molecular data into a detailed mathematical understanding of all life processes, with an ultimate view to their prediction and control. Despite its high profile and widespread practice, there has so far been almost no bioethical attention paid to systems biology and its potential social consequences. We outline some of systems biology's most important socioethical issues by contrasting the concept of systems as dynamic processes against the common static interpretation of genomes. New issues arise around systems biology's capacities for in silico testing, changing cultural understandings of life, synthetic biology, and commercialization. We advocate an interdisciplinary and interactive approach that integrates social and philosophical analysis and engages closely with the science. Overall, we argue that systems biology socioethics could stimulate new ways of thinking about socioethical studies of life sciences.

Integrative analysis of a cancer somatic mutome

BACKGROUND

The consecutive acquisition of genetic alterations characterizes neoplastic processes. As a consequence of these alterations, molecular interactions are reprogrammed in the context of highly connected and regulated cellular networks. The recent identification of the collection of somatically mutated genes in breast tumors (breast cancer somatic "mutome") allows the comprehensive study of its function and organization in complex networks.

RESULTS

We analyzed functional genomic data (loss of heterozygosity, copy number variation and gene expression in breast tumors) and protein binary interactions from public repositories to identify potential novel components of neoplastic processes, the functional relationships between them, and to examine their coordinated function in breast cancer pathogenesis. This analysis identified candidate tumor suppressors and oncogenes, and new genes whose expression level predicts survival rate in breast cancer patients. Mutome network modeling using different types of pathological and healthy functional relationships unveils functional modules significantly enriched in genes or proteins (genes/proteins) with related biological process Gene Ontology terms and containing known breast cancer-related genes/proteins.

CONCLUSION

This study presents a comprehensive analysis of the breast somatic mutome, highlighting those genes with a higher probability of playing a determinant role in tumorigenesis and better defining molecular interactions related to the neoplastic process.

Optical molecular imaging for systems biology: from molecule to organism

The development of highly efficient analytical methods capable of probing biological systems at system level is an important task that is required in order to meet the requirements of the emerging field of systems biology. Optical molecular imaging (OMI) is a very powerful tool for studying the temporal and spatial dynamics of specific biomolecules and their interactions in real time in vivo. In this article, recent advances in OMI are reviewed extensively, such as the development of molecular probes that make imaging brighter, more stable and more informative (e.g., FPs and semiconductor nanocrystals, also referred to as quantum dots), the development of imaging approaches that provide higher resolution and greater tissue penetration, and applications for measuring biological events from molecule to organism level, including gene expression, protein and subcellular compartment localization, protein activation and interaction, and low-mass molecule dynamics. These advances are of great significance in the field of biological science and could also be applied to disease diagnosis and pharmaceutical screening. Further developments in OMI for systems biology are also proposed.

MACSIMS: multiple alignment of complete sequences information management system

BACKGROUND

In the post-genomic era, systems-level studies are being performed that seek to explain complex biological systems by integrating diverse resources from fields such as genomics, proteomics or transcriptomics. New information management systems are now needed for the collection, validation and analysis of the vast amount of heterogeneous data available. Multiple alignments of complete sequences provide an ideal environment for the integration of this information in the context of the protein family.

RESULTS

MACSIMS is a multiple alignment-based information management program that combines the advantages of both knowledge-based and ab initio sequence analysis methods. Structural and functional information is retrieved automatically from the public databases. In the multiple alignment, homologous regions are identified and the retrieved data is evaluated and propagated from known to unknown sequences with these reliable regions. In a large-scale evaluation, the specificity of the propagated sequence features is estimated to be >99%, i.e. very few false positive predictions are made. MACSIMS is then used to characterise mutations in a test set of 100 proteins that are known to be involved in human genetic diseases. The number of sequence features associated with these proteins was increased by 60%, compared to the features available in the public databases. An XML format output file allows automatic parsing of the MACSIM results, while a graphical display using the JalView program allows manual analysis.

CONCLUSION

MACSIMS is a new information management system that incorporates detailed analyses of protein families at the structural, functional and evolutionary levels. MACSIMS thus provides a unique environment that facilitates knowledge extraction and the presentation of the most pertinent information to the biologist. A web server and the source code are available at http://bips.u-strasbg.fr/MACSIMS/.

Emerging Corynebacterium glutamicum systems biology

Corynebacterium glutamicum is widely used for the biotechnological production of amino acids. Amino acid producing strains have been improved classically by mutagenesis and screening as well as in a rational manner using recombinant DNA technology. Metabolic flux analysis may be viewed as the first systems approach to C. glutamicum physiology since it combines isotope labeling data with metabolic network models of the biosynthetic and central metabolic pathways. However, only the complete genome sequence of C. glutamicum and post-genomics methods such as transcriptomics and proteomics have allowed characterizing metabolic and regulatory properties of this bacterium on a truly global level. Besides transcriptomics and proteomics, metabolomics and modeling approaches have now been established. Systems biology, which uses systematic genomic, proteomic and metabolomic technologies with the final aim of constructing comprehensive and predictive models of complex biological systems, is emerging for C. glutamicum. We will present current developments that advanced our insight into fundamental biology of C. glutamicum and that in the future will enable novel biotechnological applications for the improvement of amino acid production.

Genetics in the age of systems biology

Systems biology has become a fashionable label for a new generation of large-scale experiments. This essay explores how classical approaches such as forward genetics fit into this emerging framework.