ChemChains: a platform for simulation and analysis of biochemical networks aimed to laboratory scientists

Background  

New mathematical models of complex biological structures and computer simulation software allow modelers to simulate and analyze biochemical systems in silico and form mathematical predictions. Due to this potential predictive ability, the use of these models and software has the possibility to compliment laboratory investigations and help refine, or even develop, new hypotheses. However, the existing mathematical modeling techniques and simulation tools are often difficult to use by laboratory biologists without training in high-level mathematics, limiting their use to trained modelers.     

Theoretical and computational strategies for rational molecularly imprinted polymer design

The further evolution of molecularly imprinted polymer science and technology necessitates the development of robust predictive tools capable of handling the complexity of molecular imprinting systems. A combination of the rapid growth in computer power over the past decade and significant software developments have opened new possibilities for simulating aspects of the complex molecular imprinting process. We present here a survey of the current status of the use of in silico-based approaches to aspects of molecular imprinting. Finally, we highlight areas where ongoing and future efforts should yield information critical to our understanding of the underlying mechanisms sufficient to permit the rational design of molecularly imprinted polymers.     

A new quality management perspective for biodiversity conservation and research: Investigating Biospecimen Reporting for Improved Study Quality (BRISQ) and the Standard PRE-analytical Code (SPREC) using Natural History Museum and culture collections as case studies

The aims of this paper are to debate and raise awareness about the use of systematic, interconnected approaches for biodiversity collection curation by exploring the multi-disciplinary relevance of quality management tools developed by clinical biobanks. An appraisal of their best practices indicated the need for improved sample and process chain annotation as a significant number of historical collections used in medical research were of inadequate quality. This stimulated the creation of a new discipline, biospecimen science to develop quality management tools for clinical biobanks, two of which, Biospecimen Reporting for Improved Study Quality (BRISQ) and the Standard PRE-analytical Code (SPREC) report critical information about samples and process chain variables. Unprecedented advances in molecular-genetic and in silico technologies applied across the tree of life require international conservation networks to generate and share knowledge. This is used in biodiversity and systematics research, and to address the accelerating loss of species, including the sustainable use of bioresources. This review investigates the application of BRISQ and SPREC for biodiversity research and conservation using natural history, museum and living culture collections as case studies. The distinction between preservation and conservation is discussed with regard to process and storage treatments and how they impact on the usability of biospecimens and cultures. We conclude: (i) more rigorous approaches are needed for the quality management of biospecimens, bioresources and their associated sample and processing data to assure their fitness-for-purpose; and (ii) biospecimen science tools developed by clinical biobanks can be adapted to future-proof the quality of biodiversity collections and the reliability of molecular data generated from their use.     

Accomplishments in genome-scale in silico modeling for industrial and medical biotechnology

Driven by advancements in high-throughput biological technologies and the growing number of sequenced genomes, the construction of in silico models at the genome scale has provided powerful tools to investigate a vast array of biological systems and applications. Here, we review comprehensively the uses of such models in industrial and medical biotechnology, including biofuel generation, food production, and drug development. While the use of in silico models is still in its early stages for delivering to industry, significant initial successes have been achieved. For the cases presented here, genome-scale models predict engineering strategies to enhance properties of interest in an organism or to inhibit harmful mechanisms of pathogens. Going forward, genome-scale in silico models promise to extend their application and analysis scope to become a transformative tool in biotechnology.     

Amplification of intermethylated sites experimental design and results analysis with <Emphasis Type="Italic">AIMS in silico</Emphasis> computer software

Amplification of intermethylated sites (AIMS) is a powerful tool for differential methylation screening of genomes. Its applications have nevertheless been limited until recently for the absence of systemic approach to AIMS experimental design and of appropriate computer software for the analysis of AIMS results. We have developed AIMS in silico computer suggestion tool capable of predicting possible experimental outcomes, which assists in designing AIMS experiments depending on the research aims and available instrumentation, and in analyzing experimental results from the point of view of genomic locations of the DNA fragments under study. With AIMS in silico we have characterized qualitatively and quantitatively AIMS products obtainable under different conditions; to ease experimental design we demonstrate AIMS products hierarchical structure. We discuss examples of designing AIMS experiments and of results analysis as well as possible relative to AIMS alternative approaches to differential methylation screening. AIMS in silico computer software is intended to standardize AIMS applications and to turn it into one of the principal approaches towards cancer epigenomes studies as well as towards diagnostics in oncology, including early screening.     

In silico methods for predicting T-cell epitopes: Dr Jekyll or Mr Hyde?

In silico tools offer an attractive alternative strategy to the cumbersome experimental approaches to identify T-cell epitopes. These computational tools have metamorphosed over the years into complex algorithms that attempt to efficiently predict the binding of a plethora of peptides to HLA alleles. In recent years, the scientific community has embraced these techniques to reduce the burden of wet-laboratory experimentation. Although there are some splendid examples of the utility of these methods, there are also evidences where they fall short and remain inconsistent. Hence, are these computational tools ‘Dr Jekyll’ or ‘Mr Hyde’ to the researcher, who wishes to utilize them intrepidly? This article reviews the progress and pitfalls of the in silico tools that identify T-cell epitopes.     

A checkpoint-oriented cell cycle simulation model

Modeling and in silico simulations are of major conceptual and applicative interest in studying the cell cycle and proliferation in eukaryotic cells. In this paper, we present a cell cycle checkpoint-oriented simulator that uses agent-based simulation modeling to reproduce the dynamics of a cancer cell population in exponential growth. Our in silico simulations were successfully validated by experimental in vitro supporting data obtained with HCT116 colon cancer cells. We demonstrated that this model can simulate cell confluence and the associated elongation of the G1 phase. Using nocodazole to synchronize cancer cells at mitosis, we confirmed the model predictivity and provided evidence of an additional and unexpected effect of nocodazole on the overall cell cycle progression. We anticipate that this cell cycle simulator will be a potential source of new insights and research perspectives.     

Constraints-based genome-scale metabolic simulation for systems metabolic engineering

Random mutagenesis and selection approaches used traditionally for the development of industrial strains have largely been complemented by metabolic engineering, which allows purposeful modification of metabolic and cellular characteristics by using recombinant DNA and other molecular biological techniques. As systems biology advances as a new paradigm of research thanks to the development of genome-scale computational tools and high-throughput experimental technologies including omics, systems metabolic engineering allowing modification of metabolic, regulatory and signaling networks of the cell at the systems-level is becoming possible. In silico genome-scale metabolic model and its simulation play increasingly important role in providing systematic strategies for metabolic engineering. The in silico genome-scale metabolic model is developed using genomic annotation, metabolic reactions, literature information, and experimental data. The advent of in silico genome-scale metabolic model brought about the development of various algorithms to simulate the metabolic status of the cell as a whole. In this paper, we review the algorithms developed for the system-wide simulation and perturbation of cellular metabolism, discuss the characteristics of these algorithms, and suggest future research direction.     

Visualizing environmental correlates of species geographical range limits

Although many studies have treated aspects of species geographical distributions and numerous approaches exist for understanding overall ecological correlates of distributions, software tools for exploring environmental correlates of distributional limits are relatively few. We focused on the challenge of understanding spatial correlates of distributional limits, and developed an extension to arcview that provides a simple, univariate test and visualization for such explorations. The ‘Boundary U‐test’ seeks out environmental variables that show steep gradients associated with user‐defined boundaries across geography. We illustrate the tool and its applications with an example of the likely historical distribution of Mexican wolf (Canis lupus).     

Mining for novel antibiotics in the age of antimicrobial resistance

The misuse of antimicrobials is causing an alarming increase in the appearance of antibiotic-resistant microorganisms. In this context, the identification of novel antibiotics against new targets and with low rates of resistance development is a major global challenge. In this article, we highlight a number of recent articles that exploit a variety of in vitro, in vivo and in silico state-of-the-art approaches to identify and develop new antimicrobials. Rapid progress in this research field will be crucial to combating a global health problem, antimicrobial resistance, that is expected to be the leading cause of death by 2050.     

Mathematical and computational approaches can complement experimental studies of host–pathogen interactions

In addition to traditional and novel experimental approaches to study host–pathogen interactions, mathematical and computer modelling have recently been applied to address open questions in this area. These modelling tools not only offer an additional avenue for exploring disease dynamics at multiple biological scales, but also complement and extend knowledge gained via experimental tools. In this review, we outline four examples where modelling has complemented current experimental techniques in a way that can or has already pushed our knowledge of host–pathogen dynamics forward. Two of the modelling approaches presented go hand in hand with articles in this issue exploring fluorescence resonance energy transfer and two-photon intravital microscopy. Two others explore virtual or ' in silico ' deletion and depletion as well as a new method to understand and guide studies in genetic epidemiology. In each of these examples, the complementary nature of modelling and experiment is discussed. We further note that multi-scale modelling may allow us to integrate information across length (molecular, cellular, tissue, organism, population) and time (e.g. seconds to lifetimes). In sum, when combined, these compatible approaches offer new opportunities for understanding host–pathogen interactions.     

Shifting Regimes and Changing Interactions in the Lake Washington,U.S.A., Plankton Community from 1962–1994

Understanding how changing climate, nutrient regimes, and invasive species shift food web structure is critically important in ecology. Most analytical approaches, however, assume static species interactions and environmental effects across time. Therefore, we applied multivariate autoregressive (MAR) models in a moving window context to test for shifting plankton community interactions and effects of environmental variables on plankton abundance in Lake Washington, U.S.A. from 1962–1994, following reduced nutrient loading in the 1960s and the rise of Daphnia in the 1970s. The moving-window MAR (mwMAR) approach showed shifts in the strengths of interactions between Daphnia, a dominant grazer, and other plankton taxa between a high nutrient, Oscillatoria-dominated regime and a low nutrient, Daphnia-dominated regime. The approach also highlighted the inhibiting influence of the cyanobacterium Oscillatoria on other plankton taxa in the community. Overall community stability was lowest during the period of elevated nutrient loading and Oscillatoria dominance. Despite recent warming of the lake, we found no evidence that anomalous temperatures impacted plankton abundance. Our results suggest mwMAR modeling is a useful approach that can be applied across diverse ecosystems, when questions involve shifting relationships within food webs, and among species and abiotic drivers.     

Plankton dynamics under different climate conditions in tropical freshwater systems (a reply to the comment by Sarmento,Amado & Descy, )

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Genome-scale in silico aided metabolic analysis and flux comparisons of Escherichia coli to improve succinate production

In the post-genome era, it is one challenge to understand the cellular metabolism at the systematic levels. Mathematical modeling of microorganisms and subsequent computer simulation are effective tools for systems biology. In this paper, based on the genome-scale Escherichia coli stoichiometric model iJR904, through the GAMS linear programming package, the in silico maximal succinate yield was estimated to be 1.714 mol/mol glucose. When another two constraints were added, the maximal succinate yield dropped to 1.60 mol/mol glucose. Further analysis substantiated the uniqueness of the flux distribution under such constraints. After comparisons with the metabolic flux analysis (MFA) results computed from the wet experimental data of the three kinds of E. coli, three potential improvement target sites, the glucose phosphotransferase transport system, the pyruvate carboxylase, and the glyoxylate shunt, were identified and selected for the genetic modifications. All the three genetic modified strains showed increased succinate yield. The final strain TUQ19/pQZ6 had a high yield of 1.29 mol succinate/mol glucose and high productivity. The success of the above experiments proved that this in silico optimal succinate production pathway is reasonable and practical. This method may also be used as a general strategy to help enhance the yields of other favorable metabolites in E. coli.     

Utilizing systems biology to unravel stomatal function and the hierarchies underpinning its control

Stomata control the concomitant exchange of CO₂ and transpiration in land plants. While a constant supply of CO₂ is need to maintain the rate of photosynthesis, the accompanying water losses must be tightly regulated to prevent dehydration and undesired metabolic changes. The factors affecting stomatal movement are directly coupled with the cellular networks of guard cells. Although the guard cell has been used as a model for characterization of signaling pathways, several important questions about its functioning remain elusive. Current modeling approaches describe the stomatal conductance in terms of relatively few easy‐to‐measure variables being unsuitable for in silico design of genetic manipulation strategies. Here, we argue that a system biology approach, combining modeling and high‐throughput experiments, may be used to elucidate the mechanisms underlying stomata control and to determine targets for modulation of stomatal responses to environment. In support of our opinion, we review studies demonstrating how high‐throughput approaches have provided a systems‐view of guard cells. Finally, we emphasize the opportunities and challenges of genome‐scale modeling and large‐scale data integration for in silico manipulation of guard cell functions to improve crop yields, particularly under stress conditions which are of pertinence both to climate change and water use efficiency.     

BioJazz: in silico evolution of cellular networks with unbounded complexity using rule-based modeling

Systems biologists aim to decipher the structure and dynamics of signaling and regulatory networks underpinning cellular responses; synthetic biologists can use this insight to alter existing networks or engineer de novo ones. Both tasks will benefit from an understanding of which structural and dynamic features of networks can emerge from evolutionary processes, through which intermediary steps these arise, and whether they embody general design principles. As natural evolution at the level of network dynamics is difficult to study, in silico evolution of network models can provide important insights. However, current tools used for in silico evolution of network dynamics are limited to ad hoc computer simulations and models. Here we introduce BioJazz, an extendable, user-friendly tool for simulating the evolution of dynamic biochemical networks. Unlike previous tools for in silico evolution, BioJazz allows for the evolution of cellular networks with unbounded complexity by combining rule-based modeling with an encoding of networks that is akin to a genome. We show that BioJazz can be used to implement biologically realistic selective pressures and allows exploration of the space of network architectures and dynamics that implement prescribed physiological functions. BioJazz is provided as an open-source tool to facilitate its further development and use. Source code and user manuals are available at: http://oss-lab.github.io/biojazz and http://osslab.lifesci.warwick.ac.uk/BioJazz.aspx.     

Flux analysis and metabolomics for systematic metabolic engineering of microorganisms

Rational engineering of metabolism is important for bio-production using microorganisms. Metabolic design based on in silico simulations and experimental validation of the metabolic state in the engineered strain helps in accomplishing systematic metabolic engineering. Flux balance analysis (FBA) is a method for the prediction of metabolic phenotype, and many applications have been developed using FBA to design metabolic networks. Elementary mode analysis (EMA) and ensemble modeling techniques are also useful tools for in silico strain design. The metabolome and flux distribution of the metabolic pathways enable us to evaluate the metabolic state and provide useful clues to improve target productivity. Here, we reviewed several computational applications for metabolic engineering by using genome-scale metabolic models of microorganisms. We also discussed the recent progress made in the field of metabolomics and ¹³C-metabolic flux analysis techniques, and reviewed these applications pertaining to bio-production development. Because these in silico or experimental approaches have their respective advantages and disadvantages, the combined usage of these methods is complementary and effective for metabolic engineering.     

TranscriptomeBrowser 3.0: introducing a new compendium of molecular interactions and a new visualization tool for the study of gene regulatory networks

Background  

Deciphering gene regulatory networks by in silico approaches is a crucial step in the study of the molecular perturbations that occur in diseases. The development of regulatory maps is a tedious process requiring the comprehensive integration of various evidences scattered over biological databases. Thus, the research community would greatly benefit from having a unified database storing known and predicted molecular interactions. Furthermore, given the intrinsic complexity of the data, the development of new tools offering integrated and meaningful visualizations of molecular interactions is necessary to help users drawing new hypotheses without being overwhelmed by the density of the subsequent graph.