A chronology of plankton dynamics in silico: how computer models have been used to study marine ecosystems

Research on plankton ecology in the oceans has traditionally been conducted via two scientific approaches: in situ (in the field) and in vitro (in the laboratory). There is, however, a third approach: exploring plankton dynamics in silico, or using computer models as tools to study marine ecosystems. Models have been used for this purpose for over 60 years, and the innovations and implementations of historical studies provide a context for how future model applications can continue to advance our understanding. To that end, this paper presents a chronology of the in silico approach to plankton dynamics, beginning with modeling pioneers who worked in the days before computers. During the first 30 years of automated computation, plankton modeling focused on formulations for biological processes and investigations of community structure. The changing technological context and conceptual paradigms of the late-1970s and 1980s resulted in simulations becoming more widespread research tools for biological oceanographers. This period saw rising use of models as hypothesis-testing tools, and means of exploring the effects of circulation on spatial distributions of organisms. Continued computer advances and increased availability of data in the 1990s allowed old approaches to be applied to old and new problems, and led to developments of new approaches. Much of the modeling in the new millennium so far has incorporated these sophistications, and many cutting-edge applications have come from a new generation of plankton scientists who were trained by modeling gurus of previous eras. The future directions for modeling plankton dynamics are rooted in the historical studies.     

Crius: A novel fragment‐based algorithm of de novo substrate prediction for enzymes

The study of enzyme substrate specificity is vital for developing potential applications of enzymes. However, the routine experimental procedures require lot of resources in the discovery of novel substrates. This article reports an in silico structure‐based algorithm called Crius, which predicts substrates for enzyme. The results of this fragment‐based algorithm show good agreements between the simulated and experimental substrate specificities, using a lipase from Candida antarctica (CALB), a nitrilase from Cyanobacterium syechocystis sp. PCC6803 (Nit6803), and an aldo‐keto reductase from Gluconobacter oxydans (Gox0644). This opens new prospects of developing computer algorithms that can effectively predict substrates for an enzyme.     

<Emphasis Type="Italic">In silico</Emphasis> panning for a non-competitive peptide inhibitor

Background  

Peptide ligands have tremendous therapeutic potential as efficacious drugs. Currently, more than 40 peptides are available in the market for a drug. However, since costly and time-consuming synthesis procedures represent a problem for high-throughput screening, novel procedures to reduce the time and labor involved in screening peptide ligands are required. We propose the novel approach of 'in silico panning' which consists of a two-stage screening, involving affinity selection by docking simulation and evolution of the peptide ligand using genetic algorithms (GAs). In silico panning was successfully applied to the selection of peptide inhibitor for water-soluble quinoprotein glucose dehydrogenase (PQQGDH).     

A computational protocol to predict suitable redox mediators for substitution of NAD(P)H in P450 monooxygenases

P450s catalyze a wide spectrum of stereo- and regioselective reactions like hydroxylations, epoxidations and dehydrogenations. Therefore biotransformations with P450s are of great relevance to organic synthesis. The use of isolated enzymes offers advantages over the use of whole cells. A key issue for catalytic applications of isolated P450s is the demand for a continuous electron supply to the heme-group. Mediator driven bioelectrocatalysis can help to overcome this problem.For mediator driven bioelectrocatalysis the identification of a suitable mediator is crucial for the fast development of an efficient electro enzymatic process. To this end we have developed a computational screening method based on using freely available software. Calculated electron transfer rates were compared with measured product formation rates. The novel in silico procedure allows a faster identification of suitable mediators for electrochemically driven P450 catalyzed reactions and can be used as screening tool. It may also lead to a massive reduction of experimental effort for the development of bioelectrochemical reaction systems in the future.     

Global methylation screening in the Arabidopsis thaliana and Mus musculus genome: applications of virtual image restriction landmark genomic scanning (Vi-RLGS)

Understanding the role of ‘epigenetic’ changes such as DNA methylation and chromatin remodeling has now become critical in understanding many biological processes. In order to delineate the global methylation pattern in a given genomic DNA, computer software has been developed to create a virtual image of restriction landmark genomic scanning (Vi-RLGS). When using a methylation- sensitive enzyme such as NotI as the restriction landmark, the comparison between real and in silico RLGS profiles of the genome provides a methylation map of genomic NotI sites. A methylation map of the Arabidopsis genome was created that could be confirmed by a methylation-sensitive PCR assay. The method has also been applied to the mouse genome. Although a complete methylation map has not been completed, a region of methylation difference between two tissues has been tested and confirmed by bisulfite sequencing. Vi-RLGS in conjunction with real RLGS will make it possible to develop a more complete map of genomic sites that are methylated or demethylated as a consequence of normal or abnormal development.     

Discovery of DNA Hypermethylation Using a DHPLC Screening Strategy

Promoter hypermethylation is recognized as a hallmark of human cancer, in addition to conventional mechanisms of gene inactivation. As such, many new technologies have been developed over the past two decades to uncover novel targets of methylation and decipher complex epigenetic patterns. However, many of these are either labour intensive or provide limited data, confined to oligonucleotide hybridization sequences or enzyme cleavage sites and cannot be easily applied to screening large sets of sequences or samples. We present an application of denaturing high performance liquid chromatography (DHPLC), which relies on bisulfite modification of genomic DNA, for methylation screening. We validated DHPLC as a methylation screening tool using GSTP1, a well known target of methylation in prostate cancer. We developed an in silico approach to identify potential targets of promoter hypermethylation in prostate cancer. Using DHPLC, we screened two of these targets LGALS3 and SMAD4 for methylation. We show that DHPLC has an application as a fast, sensitive, quantitative and cost effective method for screening novel targets or DNA samples for DNA methylation.     

<Emphasis Type="Italic">In silico</Emphasis> physical mapping software for the <Emphasis Type="Italic">Triticum aestivum</Emphasis> genome

The large size of the Triticum aestivum genome makes it unlikely that a complete genome sequence for wheat will be available in the near future. Exploiting the conserved genome organization between wheat and rice and existing genomic resources, we have constructed in silico physical mapping software for wheat, assigning a gross physical location(s) into chromosome bins to 22,626 representative wheat gene sequences. To validate the predictions from the software we compared the predicted locations of ten ESTs to their positions experimentally determined by SNP marker analysis. Six of the sequences were correctly positioned on the map including four that demonstrated a high level of colinearity with their orthologous rice genomic region. This tool will facilitate the development of molecular markers for regions of interest and the creation of map-based cloning strategies in areas demonstrating high levels of sequence conservation and organization between wheat and rice.     

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.     

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.     

Validation of 2D‐animated pictures as an investigative tool in the behavioural sciences: A case study with a West African cichlid fish,Pelvicachromis pulcher

Virtual stimuli represent an increasingly popular tool in the study of animal behaviour. Modern techniques have the potential to simplify and improve traditional experiments using live stimuli. However, the increasing availability of diverse techniques is associated with problems and limitations. Although many new methods have been developed, their validation remains largely untested. In the present study, we therefore performed two experiments to test whether 2‐D animations of predators and conspecifics elicit biologically appropriate behavioural responses in male rainbow kribs, Pelvicachromis pulcher. Individual responses towards a sympatric natural fish predator, Parachanna obscura, were tested using live predators and still colour photographs, animated using PowerPoint©. Compared to control trials (empty aquarium and white computer screen, respectively), individuals decreased their activity in response to both live and animated predators. We found no difference in activity between live and animation trials. Further, we tested individual aggression (frequency of aggressive behaviours) exhibited towards live and animated conspecifics. Individual aggressive behaviours shown towards live and animated conspecifics were positively correlated. Moreover, an individual's mean distance towards the opponent was a suitable proxy for individual aggression permitting the facilitation and standardisation of an individual's aggression through the use of a tracking software compared with the more laborious, traditional manual assessment. Our results show that simple, inexpensive animation techniques have the potential to provide an easy‐to‐apply and useful technological advance in animal behaviour research.     

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.     

Molecular characterization of <Emphasis Type="Italic">Bifidobacterium longum</Emphasis> biovar <Emphasis Type="Italic">longum</Emphasis> NAL8 plasmids and construction of a novel replicon screening system

In this study, we performed molecular characterization and sequence analysis of three plasmids from the human intestinal isolate Bifidobacterium longum biovar longum NAL8 and developed a novel vector screening system. Plasmids pNAL8H (10 kb) and pNAL8M (4.9 kb) show close sequence similarity to and the same gene organization as the already characterized B. longum plasmids. The B. longum plasmid pNAC1 was identified as being most closely related to pNAL8L (3.5 kb). However, DNA sequence analysis suggested that direct repeat-rich sites could have promoted several recombination events to diversify the two plasmid molecules. We verified the likely rolling circle replication of plasmid pNAL8L and studied the phylogenetic relationship in all the Bifidobacterium plasmids fully sequenced to date based on in silico comparative sequence analysis of their replication proteins and iteron regions. Our transformation experiments confirmed that the ColE1 replication origin from high-copy-number pUC vectors could interfere with the replication apparatus of Bifidobacterium plasmids and give rise to false positive clones. As a result, we developed a system suitable for avoiding possible interference by other functional replication modules on the vector and for screening functional replicons from wild-type plasmids. Electronic supplementary material Supplementary material is available in the online version of this article at and is accessible for authorized users only.     

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.     

A low‐cost,computer‐controlled robotic flower system for behavioral experiments

Human observations during behavioral studies are expensive, time‐consuming, and error prone. For this reason, automatization of experiments is highly desirable, as it reduces the risk of human errors and workload. The robotic system we developed is simple and cheap to build and handles feeding and data collection automatically. The system was built using mostly off‐the‐shelf components and has a novel feeding mechanism that uses servos to perform refill operations. We used the robotic system in two separate behavioral studies with bumblebees (Bombus terrestris): The system was used both for training of the bees and for the experimental data collection. The robotic system was reliable, with no flight in our studies failing due to a technical malfunction. The data recorded were easy to apply for further analysis. The software and the hardware design are open source. The development of cheap open‐source prototyping platforms during the recent years has opened up many possibilities in designing of experiments. Automatization not only reduces workload, but also potentially allows experimental designs never done before, such as dynamic experiments, where the system responds to, for example, learning of the animal. We present a complete system with hardware and software, and it can be used as such in various experiments requiring feeders and collection of visitation data. Use of the system is not limited to any particular experimental setup or even species.     

Efficient high‐throughput biological process characterization: Definitive screening design with the Ambr250 bioreactor system

The burgeoning pipeline for new biologic drugs has increased the need for high‐throughput process characterization to efficiently use process development resources. Breakthroughs in highly automated and parallelized upstream process development have led to technologies such as the 250‐mL automated mini bioreactor (ambr250?) system. Furthermore, developments in modern design of experiments (DoE) have promoted the use of definitive screening design (DSD) as an efficient method to combine factor screening and characterization. Here we utilize the 24‐bioreactor ambr250? system with 10‐factor DSD to demonstrate a systematic experimental workflow to efficiently characterize an Escherichia coli (E. coli) fermentation process for recombinant protein production. The generated process model is further validated by laboratory‐scale experiments and shows how the strategy is useful for quality by design (QbD) approaches to control strategies for late‐stage characterization. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:1388–1395, 2015     

Mathematical models of cell factories: moving towards the core of industrial biotechnology

Industrial biotechnology involves the utilization of cell factories for the production of fuels and chemicals. Traditionally, the development of highly productive microbial strains has relied on random mutagenesis and screening. The development of predictive mathematical models provides a new paradigm for the rational design of cell factories. Instead of selecting among a set of strains resulting from random mutagenesis, mathematical models allow the researchers to predict in silico the outcomes of different genetic manipulations and engineer new strains by performing gene deletions or additions leading to a higher productivity of the desired chemicals. In this review we aim to summarize the main modelling approaches of biological processes and illustrate the particular applications that they have found in the field of industrial microbiology.     

In vivo,in vitro and in silico: an open space for the development of microbe-based applications of synthetic biology

Living systems are studied using three complementary approaches: living cells, cell-free systems and computer-mediated modelling. Progresses in understanding, allowing researchers to create novel chassis and industrial processes rest on a cycle that combines in vivo, in vitro and in silico studies. This design–build–test–learn iteration loop cycle between experiments and analyses combines together physiology, genetics, biochemistry and bioinformatics in a way that keeps going forward. Because computer-aided approaches are not directly constrained by the material nature of the entities of interest, we illustrate here how this virtuous cycle allows researchers to explore chemistry which is foreign to that present in extant life, from whole chassis to novel metabolic cycles. Particular emphasis is placed on the importance of evolution.