Co-creation environment with cloud virtual reality and real-time artificial intelligence toward the design of molecular robots.

This paper describes the design philosophy for our cloud-based virtual reality (VR) co-creation environment (CCE) for molecular modeling. Using interactive VR simulation can provide enhanced perspectives in molecular modeling for intuitive live demonstration and experimentation in the CCE. Then the use of the CCE can enhance knowledge creation by bringing people together to share and create ideas or knowledge that may not emerge otherwise. Our prototype CCE discussed here, which was developed to demonstrate our design philosophy, has already enabled multiple members to log in and touch virtual molecules running on a cloud server with no noticeable network latency via real-time artificial intelligence techniques. The CCE plays an essential role in the rational design of molecular robot parts, which consist of bio-molecules such as DNA and protein molecules.

Utility of constraints reflecting system stability on analyses for biological models.

Simulating complex biological models consisting of multiple ordinary differential equations can aid in the prediction of the pharmacological/biological responses; however, they are often hampered by the availability of reliable kinetic parameters. In the present study, we aimed to discover the properties of behaviors without determining an optimal combination of kinetic parameter values (parameter set). The key idea was to collect as many parameter sets as possible. Given that many systems are biologically stable and resilient (BSR), we focused on the dynamics around the steady state and formulated objective functions for BSR by partial linear approximation of the focused region. Using the objective functions and modified global cluster Newton method, we developed an algorithm for a thorough exploration of the allowable parameter space for biological systems (TEAPS). We first applied TEAPS to the NF-κB signaling model. This system shows a damped oscillation after stimulation and seems to fit the BSR constraint. By applying TEAPS, we found several directions in parameter space which stringently determines the BSR property. In such directions, the experimentally fitted parameter values were included in the range of the obtained parameter sets. The arachidonic acid metabolic pathway model was used as a model related to pharmacological responses. The pharmacological effects of nonsteroidal anti-inflammatory drugs were simulated using the parameter sets obtained by TEAPS. The structural properties of the system were partly extracted by analyzing the distribution of the obtained parameter sets. In addition, the simulations showed inter-drug differences in prostacyclin to thromboxane A2 ratio such that aspirin treatment tends to increase the ratio, while rofecoxib treatment tends to decrease it. These trends are comparable to the clinical observations. These results on real biological models suggest that the parameter sets satisfying the BSR condition can help in finding biologically plausible parameter sets and understanding the properties of biological systems.

Modeling a Microtubule Filaments Mesh Structure from Confocal Microscopy Imaging.

This study introduces a modeling method for a supermolecular structure of microtubules for the development of a force generation material using motor proteins. 3D imaging by confocal laser scanning microscopy (CLSM) was used to obtain 3D volume density data. The density data were then interpreted by a set of cylinders with the general-purpose 3D modeling software Blender, and a 3D network structure of microtubules was constructed. Although motor proteins were not visualized experimentally, they were introduced into the model to simulate pulling of the microtubules toward each other to yield shrinking of the network, resulting in contraction of the artificial muscle. From the successful force generation simulation of the obtained model structure of artificial muscle, the modeling method introduced here could be useful in various studies for potential improvements of this contractile molecular system.

Adaptation of Patterns of Motile Filaments under Dynamic Boundary Conditions.

Boundary conditions are important for pattern formation in active matter. However, it is still not well-understood how alterations in the boundary conditions (dynamic boundary conditions) impact pattern formation. To elucidate the effect of dynamic boundary conditions on the pattern formation by active matter, we investigate an in vitro gliding assay of microtubules on a deformable soft substrate. The dynamic boundary conditions were realized by applying mechanical stress through stretching and compression of the substrate during the gliding assay. A single cycle of stretch-and-compression (relaxation) of the substrate induces perpendicular alignment of microtubules relative to the stretch axis, whereas repeated cycles resulted in zigzag patterns of microtubules. Our model shows that the orientation angles of microtubules correspond to the direction to attain smooth movement without buckling, which is further amplified by the collective migration of the microtubules. Our results provide an insight into understanding the rich dynamics in self-organization arising in active matter subjected to time-dependent boundary conditions.

Significance of Data Selection in Deep Learning for Reliable Binding Mode Prediction of Ligands in the Active Site of CYP3A4.

For rational drug design, it is essential to predict the binding mode of protein-ligand complexes. Although various machine learning-based models have been reported that use convolutional neural networks (deep learning) to predict binding modes from three-dimensional structures, there are few detailed reports on how best to construct and use datasets. Here, we examined how different datasets affected the prediction of the binding mode of CYP3A4 by a three-dimensional neural network when the number of crystal structures for the target protein was limited. We used four different training datasets: one large, general dataset containing various protein complexes and three smaller, more specific datasets containing complexes with CYP3A4-like pockets, complexes with CYP3A4-binding ligands, and complexes with CYP protein family members. We then trained models with different combinations of datasets with or without subsequent fine-tuning and evaluated the binding mode prediction performance of each model. The best receiver operating characteristic (ROC) area under the curve (AUC) model with respect to area under the receiver operating characteristic curve was obtained by training with a combination of the general protein and CYP family datasets. However, the ROC AUC-recall balanced model was obtained by training with this combination of datasets followed by fine-tuning with the CYP3A4-binding ligands dataset. Our results suggest that datasets that balance protein functionality and data size are important for optimizing binding mode prediction performance. In addition, datasets with large median binding pocket sizes may be important for the binding mode prediction specifically of CYP3A4.

Stabilization of microtubules by cevipabulin.

We report the utility of cevipabulin as a stabilizing agent for microtubules. Cevipabulin-stabilized microtubules were more flexible compared to the microtubules stabilized by paclitaxel, the most commonly used microtubule stabilizing agent. Similar to the paclitaxel-stabilized microtubules, cevipabulin-stabilized microtubules were driven by kinesins in an in vitro gliding assay. The velocity of cevipabulin-stabilized microtubules was significantly higher than that of paclitaxel-stabilized microtubules. These findings will enrich the variety of microtubules with difference in mechanical and dynamic properties and widen their applications in nanotechnology.

Artificial Smooth Muscle Model Composed of Hierarchically Ordered Microtubule Asters Mediated by DNA Origami Nanostructures.

DNA has been well-known for its applications in programmable self-assembly of materials. Nonetheless, utility of DNA origami, which offers more opportunity to realize complicated operations, has been very limited. Here we report self-assembly of a biomolecular motor system, microtubule-kinesin mediated by DNA origami nanostructures. We demonstrate that a rodlike DNA origami motif facilitates self-assembly of microtubules into asters. A smooth-muscle like molecular contraction system has also been realized using the DNA origami in which self-assembled microtubules exhibited fast and dynamic contraction in the presence of kinesins through an energy dissipative process. This work provides potential nanotechnological applications of DNA and biomolecular motor proteins.

Accurate Estimation of In Vivo Inhibition Constants of Inhibitors and Fraction Metabolized of Substrates with Physiologically Based Pharmacokinetic Drug-Drug Interaction Models Incorporating Parent Drugs and Metabolites of Substrates with Cluster Newton Method.

The accurate estimation of "in vivo" inhibition constants (K i) of inhibitors and fraction metabolized (f m) of substrates is highly important for drug-drug interaction (DDI) prediction based on physiologically based pharmacokinetic (PBPK) models. We hypothesized that analysis of the pharmacokinetic alterations of substrate metabolites in addition to the parent drug would enable accurate estimation of in vivo K i and f m Twenty-four pharmacokinetic DDIs caused by P450 inhibition were analyzed with PBPK models using an emerging parameter estimation method, the cluster Newton method, which enables efficient estimation of a large number of parameters to describe the pharmacokinetics of parent and metabolized drugs. For each DDI, two analyses were conducted (with or without substrate metabolite data), and the parameter estimates were compared with each other. In 17 out of 24 cases, inclusion of substrate metabolite information in PBPK analysis improved the reliability of both K i and f m Importantly, the estimated K i for the same inhibitor from different DDI studies was generally consistent, suggesting that the estimated K i from one study can be reliably used for the prediction of untested DDI cases with different victim drugs. Furthermore, a large discrepancy was observed between the reported in vitro K i and the in vitro estimates for some inhibitors, and the current in vivo K i estimates might be used as reference values when optimizing in vitro-in vivo extrapolation strategies. These results demonstrated that better use of substrate metabolite information in PBPK analysis of clinical DDI data can improve reliability of top-down parameter estimation and prediction of untested DDIs.

Construction of artificial cilia from microtubules and kinesins through a well-designed bottom-up approach.

Self-organized structures of biomolecular motor systems, such as cilia and flagella, play key roles in the dynamic processes of living organisms, like locomotion or the transportation of materials. Although fabrication of such self-organized structures from reconstructed biomolecular motor systems has attracted much attention in recent years, a systematic construction methodology is still lacking. In this work, through a bottom-up approach, we fabricated artificial cilia from a reconstructed biomolecular motor system, microtubule/kinesin. The artificial cilia exhibited a beating motion upon the consumption, by the kinesins, of the chemical energy obtained from the hydrolysis of adenosine triphosphate (ATP). Several design parameters, such as the length of the microtubules, the density of the kinesins along the microtubules, the depletion force among the microtubules, etc., have been identified, which permit tuning of the beating frequency of the artificial cilia. The beating frequency of the artificial cilia increases upon increasing the length of the microtubules, but declines for the much longer microtubules. A high density of the kinesins along the microtubules is favorable for the beating motion of the cilia. The depletion force induced bundling of the microtubules accelerated the beating motion of the artificial cilia and increased the beating frequency. This work helps understand the role of self-assembled structures of the biomolecular motor systems in the dynamics of living organisms and is expected to expedite the development of artificial nanomachines, in which the biomolecular motors may serve as actuators.

Constraint-based perturbation analysis with cluster Newton method: a case study of personalized parameter estimations with irinotecan whole-body physiologically based pharmacokinetic model.

BACKGROUND: Drug development considering individual varieties among patients becomes crucial to improve clinical development success rates and save healthcare costs. As a useful tool to predict individual phenomena and correlations among drug characteristics and individual varieties, recently, whole-body physiologically based pharmacokinetic (WB- PBPK) models are getting more attention. WB-PBPK models generally have a lot of drug-related parameters that need to be estimated, and the estimations are difficult because the observed data are limited. Furthermore, parameter estimation in WB-PBPK models may cause overfitting when applying to individual clinical data such as urine/feces drug excretion for each patient in which Cluster Newton Method (CNM) is applicable for parameter estimation. In order to solve this issue, we came up with the idea of constraint-based perturbation analysis of the CNM. The effectiveness of our approach is demonstrated in the case of irinotecan WB-PBPK model using common organ-specific tissue-plasma partition coefficients (Kp) among the patients as constraints in WB-PBPK parameter estimation. RESULTS: We find strong correlations between age, renal clearance and liver functions in irinotecan WB-PBPK model with personalized physiological parameters by observing the distributions of optimized values of strong convergence drug-related parameters using constraint-based perturbation analysis on CNM. The constraint-based perturbation analysis consists of the following three steps: (1) Estimation of all drug-related parameters for each patient; the parameters include organ-specific Kp. (2) Fixing suitable values of Kp for each organ among all patients identically. (3) Re-estimation of all drug-related parameters other than Kp by using the fixed values of Kp as constraints of CNM. CONCLUSIONS: Constraint-based perturbation analysis could yield new findings when using CNM with appropriate constraints. This method is a new technique to find suitable values and important insights that are masked by CNM without constraints.

Sensing surface mechanical deformation using active probes driven by motor proteins.

Studying mechanical deformation at the surface of soft materials has been challenging due to the difficulty in separating surface deformation from the bulk elasticity of the materials. Here, we introduce a new approach for studying the surface mechanical deformation of a soft material by utilizing a large number of self-propelled microprobes driven by motor proteins on the surface of the material. Information about the surface mechanical deformation of the soft material is obtained through changes in mobility of the microprobes wandering across the surface of the soft material. The active microprobes respond to mechanical deformation of the surface and readily change their velocity and direction depending on the extent and mode of surface deformation. This highly parallel and reliable method of sensing mechanical deformation at the surface of soft materials is expected to find applications that explore surface mechanics of soft materials and consequently would greatly benefit the surface science.

Depletion force induced collective motion of microtubules driven by kinesin.

Collective motion is a fascinating example of coordinated behavior of self-propelled objects, which is often associated with the formation of large scale patterns. Nowadays, the in vitro gliding assay is being considered a model system to experimentally investigate various aspects of group behavior and pattern formation by self-propelled objects. In the in vitro gliding assay, cytoskeletal filaments F-actin or microtubules are driven by the surface immobilized associated biomolecular motors myosin or dynein respectively. Although the F-actin/myosin or microtubule/dynein system was found to be promising in understanding the collective motion and pattern formation by self-propelled objects, the most widely used biomolecular motor system microtubule/kinesin could not be successfully employed so far in this regard. Failure in exhibiting collective motion by kinesin driven microtubules is attributed to the intrinsic properties of kinesin, which was speculated to affect the behavior of individual gliding microtubules and mutual interactions among them. In this work, for the first time, we have demonstrated the collective motion of kinesin driven microtubules by regulating the mutual interaction among the gliding microtubules, by employing a depletion force among them. Proper regulation of the mutual interaction among the gliding microtubules through the employment of the depletion force was found to allow the exhibition of collective motion and stream pattern formation by the microtubules. This work offers a universal means for demonstrating the collective motion using the in vitro gliding assay of biomolecular motor systems and will help obtain a meticulous understanding of the fascinating coordinated behavior and pattern formation by self-propelled objects.

Implementing a modeling software for animated protein-complex interactions using a physics simulation library.

To better understand the behaviors and structural dynamics of proteins within a cell, novel software tools are being developed that can create molecular animations based on the findings of structural biology. This study proposes our method developed based on our prototypes to detect collisions and examine the soft-body dynamics of molecular models. The code was implemented with a software development toolkit for rigid-body dynamics simulation and a three-dimensional graphics library. The essential functions of the target software system included the basic molecular modeling environment, collision detection in the molecular models, and physical simulations of the movement of the model. Taking advantage of recent software technologies such as physics simulation modules and interpreted scripting language, the functions required for accurate and meaningful molecular animation were implemented efficiently.

Molecular robots with sensors and intelligence.

CONSPECTUS: What we can call a molecular robot is a set of molecular devices such as sensors, logic gates, and actuators integrated into a consistent system. The molecular robot is supposed to react autonomously to its environment by receiving molecular signals and making decisions by molecular computation. Building such a system has long been a dream of scientists; however, despite extensive efforts, systems having all three functions (sensing, computation, and actuation) have not been realized yet. This Account introduces an ongoing research project that focuses on the development of molecular robotics funded by MEXT (Ministry of Education, Culture, Sports, Science and Technology, Japan). This 5 year project started in July 2012 and is titled "Development of Molecular Robots Equipped with Sensors and Intelligence". The major issues in the field of molecular robotics all correspond to a feedback (i.e., plan-do-see) cycle of a robotic system. More specifically, these issues are (1) developing molecular sensors capable of handling a wide array of signals, (2) developing amplification methods of signals to drive molecular computing devices, (3) accelerating molecular computing, (4) developing actuators that are controllable by molecular computers, and (5) providing bodies of molecular robots encapsulating the above molecular devices, which implement the conformational changes and locomotion of the robots. In this Account, the latest contributions to the project are reported. There are four research teams in the project that specialize on sensing, intelligence, amoeba-like actuation, and slime-like actuation, respectively. The molecular sensor team is focusing on the development of molecular sensors that can handle a variety of signals. This team is also investigating methods to amplify signals from the molecular sensors. The molecular intelligence team is developing molecular computers and is currently focusing on a new photochemical technology for accelerating DNA-based computations. They also introduce novel computational models behind various kinds of molecular computers necessary for designing such computers. The amoeba robot team aims at constructing amoeba-like robots. The team is trying to incorporate motor proteins, including kinesin and microtubules (MTs), for use as actuators implemented in a liposomal compartment as a robot body. They are also developing a methodology to link DNA-based computation and molecular motor control. The slime robot team focuses on the development of slime-like robots. The team is evaluating various gels, including DNA gel and BZ gel, for use as actuators, as well as the body material to disperse various molecular devices in it. They also try to control the gel actuators by DNA signals coming from molecular computers.

Estimation of feasible solution space using Cluster Newton Method: application to pharmacokinetic analysis of irinotecan with physiologically-based pharmacokinetic models.

BACKGROUND: To facilitate new drug development, physiologically-based pharmacokinetic (PBPK) modeling methods receive growing attention as a tool to fully understand and predict complex pharmacokinetic phenomena. As the number of parameters to reproduce physiological functions tend to be large in PBPK models, efficient parameter estimation methods are essential. We have successfully applied a recently developed algorithm to estimate a feasible solution space, called Cluster Newton Method (CNM), to reveal the cause of irinotecan pharmacokinetic alterations in two cancer patient groups. RESULTS: After improvements in the original CNM algorithm to maintain parameter diversities, a feasible solution space was successfully estimated for 55 or 56 parameters in the irinotecan PBPK model, within ten iterations, 3000 virtual samples, and in 15 minutes (Intel Xeon E5-1620 3.60GHz × 1 or Intel Core i7-870 2.93GHz × 1). Control parameters or parameter correlations were clarified after the parameter estimation processes. Possible causes in the irinotecan pharmacokinetic alterations were suggested, but they were not conclusive. CONCLUSIONS: Application of CNM achieved a feasible solution space by solving inverse problems of a system containing ordinary differential equations (ODEs). This method may give us reliable insights into other complicated phenomena, which have a large number of parameters to estimate, under limited information. It is also helpful to design prospective studies for further investigation of phenomena of interest.

On experiences of i2b2 (Informatics for integrating biology and the bedside) database with Japanese clinical patients' data.

Informatics for Integrating Biology and the Bedside (i2b2) is a database system to facilitate sharing and reuse of clinical patients' data collected in individual hospitals. The i2b2 provides an ontology based object-oriented database system with highly simple and flexible database schema which enables us to integrate clinical patients' data from different laboratories and different hospitals. 392 patients' data including carcinoma and non-carcinoma specimens from cancer patients are transported from the Integrated Clinical Omics Database (iCOD) to the i2b2 database for a feasibility study to check applicability of i2b2 ontology and database schema on Japanese clinical patients' data. No modification is required for the i2b2 data model to deal with Japanese characters. Some modification of ontology is required to integrate biomedical information extracted from the cancer patients' data. We believe that the i2b2 system will be practical infrastructure to integrate Japanese clinical databases if appropriate disease ontology for Japanese patients is provided.

The phenotype and genotype experiment object model (PaGE-OM): a robust data structure for information related to DNA variation.

Torrents of genotype-phenotype data are being generated, all of which must be captured, processed, integrated, and exploited. To do this optimally requires the use of standard and interoperable "object models," providing a description of how to partition the total spectrum of information being dealt with into elemental "objects" (such as "alleles," "genotypes," "phenotype values," "methods") with precisely stated logical interrelationships (such as "A objects are made up from one or more B objects"). We herein propose the Phenotype and Genotype Experiment Object Model (PaGE-OM; www.pageom.org), which has been tested and implemented in conjunction with several major databases, and approved as a standard by the Object Management Group (OMG). PaGE-OM is open-source, ready for use by the wider community, and can be further developed as needs arise. It will help to improve information management, assist data integration, and simplify the task of informatics resource design and construction for genotype and phenotype data projects.

Coincidence between transcriptome analyses on different microarray platforms using a parametric framework.

A parametric framework for the analysis of transcriptome data is demonstrated to yield coincident results when applied to data acquired using two different microarray platforms. Microarrays are widely employed to acquire transcriptome information, and several platforms of chips are currently in use. However, discrepancies among studies are frequently reported, particularly among those performed using different platforms, casting doubt on the reliability of collected data. The inconsistency among observations can be largely attributed to differences among the analytical frameworks employed for data analysis. The existing frameworks are based on different philosophies and yield different results, but all involve normalization against a standard determined from the data to be analyzed. In the present study, a parametric framework based on a strict model for normalization is applied to data acquired using several slide-glass-type chips and GeneChip. The model is based on a common statistical characteristic of microarray data, and each set of chip data is normalized on the basis of a linear relationship with this model. In the proposed framework, the expressional changes observed and genes selected are coincident between platforms, achieving superior universality of data compared to other frameworks.

Drug interaction prediction using ontology-driven hypothetical assertion framework for pathway generation followed by numerical simulation.

BACKGROUND: In accordance with the increasing amount of information concerning individual differences in drug response and molecular interaction, the role of in silico prediction of drug interaction on the pathway level is becoming more and more important. However, in view of the interferences for the identification of new drug interactions, most conventional information models of a biological pathway would have limitations. As a reflection of real world biological events triggered by a stimulus, it is important to facilitate the incorporation of known molecular events for inferring (unknown) possible pathways and hypothetic drug interactions. Here, we propose a new Ontology-Driven Hypothetic Assertion (OHA) framework including pathway generation, drug interaction detection, simulation model generation, numerical simulation, and hypothetic assertion. Potential drug interactions are detected from drug metabolic pathways dynamically generated by molecular events triggered after the administration of certain drugs. Numerical simulation enables to estimate the degree of side effects caused by the predicted drug interactions. New hypothetic assertions of the potential drug interactions and simulation are deduced from the Drug Interaction Ontology (DIO) written in Web Ontology Language (OWL). RESULTS: The concept of the Ontology-Driven Hypothetic Assertion (OHA) framework was demonstrated with known interactions between irinotecan (CPT-11) and ketoconazole. Four drug interactions that involved cytochrome p450 (CYP3A4) and albumin as potential drug interaction proteins were automatically detected from Drug Interaction Ontology (DIO). The effect of the two interactions involving CYP3A4 were quantitatively evaluated with numerical simulation. The co-administration of ketoconazole may increase AUC and Cmax of SN-38(active metabolite of irinotecan) to 108% and 105%, respectively. We also estimates the potential effects of genetic variations: the AUC and Cmax of SN-38 may increase to 208% and 165% respectively with the genetic variation UGT1A1*28/*28 which reduces the expression of UGT1A1 down to 30%. CONCLUSION: These results demonstrate that the Ontology-Driven Hypothetic Assertion framework is a promising approach for in silico prediction of drug interactions. The following future researches for the in silico prediction of individual differences in the response to the drug and drug interactions after the administration of multiple drugs: expansion of the Drug Interaction Ontology for other drugs, and incorporation of virtual population model for genetic variation analysis, as well as refinement of the pathway generation rules, the drug interaction detection rules, and the numerical simulation models.

Microchromosomal deletions involving SCN1A and adjacent genes in severe myoclonic epilepsy in infancy.

PURPOSE: Genetic abnormalities of the gene encoding alpha1 subunit of the sodium channel (SCN1A), which can be detected by direct sequencing, are present in more than 60% of patients with severe myoclonic epilepsy in infancy (SMEI) or its borderline phenotype (SMEB). Microchromosomal deletions have been recently reported as additional causes of SMEI. This study examines whether such microdeletions are associated with SMEI as well as with SMEB. METHODS: We recruited patients with SMEI (n = 35) and SMEB (n = 34), who were confirmed previously to have no mutations of SCN1A by direct sequencing. Microdeletions were sought by multiplex ligation-dependent probe amplification (MLPA), and then confirmed and characterized by fluorescence in situ hybridization (FISH) and array-based comparative genomic hybridization (aCGH), respectively. RESULTS: Heterozygous multiple exonic deletions were identified in 7/35 SMEI patients (20%) and 0/34 SMEB patients (0%), with a net frequency of 10.1% (7/69 patients). Deletions were confirmed by FISH and aCGH analysis. The concomitant deletions of adjacent genes were revealed by aCGH. None of the parents who agreed to undergo the analysis had such deletions suggesting that the deletions were de novo. The phenotypes of patients with the deletions were indistinguishable from those of SMEI resulting from point mutations. DISCUSSION: Our findings indicate that microchromosomal deletion, often involving not only SCN1A but also several adjacent genes, is associated with core SMEI. As microchromosomal deletion cannot be anticipated by the phenotypes or detected by conventional methods, genetic abnormalities in SMEI should be carefully sought by techniques that can detect microdeletions.