Multiscale Computational Methods Research Articles

Application of scalable sensor-assisted multi-scale computational methods in the simulation of micro mechanical behavior of composite materials

Micro mechanics involves the examination of the mechanical behavior of heterogeneous materials, taking into account inhomogeneities such as voids, fractures, and inclusions, and building on mathematical models developed. Composites bring engineering design barriers despite their strengths. Composite manufacturing and processing need specialized equipment, strategies, and labor, ensuring they are challenging and expensive. Mechanical behavior deals with how a composite material performs if faced with mechanical effects and action. Scalable sensor-assisted multi-scale computational Methods (SS-MSCM) are used to investigate topics ranging from the molecular basis of soot production in combustion to how molecule-level flaws influence macroscopic mechanical qualities. Carbon fiber reinforced polymers (CFRP) are generated by mixing graphene fiber with a resin, like vinyl ester and epoxy, to render a composite material with superior performance to the component ingredients. Hence, SS-MSCM-CFRP has Improved mechanical qualities achieved by incorporating nano-reinforcements, including carbon nanofibers and graphene nanoplates, into the CFRP matrix: enhanced flexural and compressive strengths, energy absorption upon impact, toughness to fracture, and interlaminar bonding. Composite materials feature excellent mechanical qualities like high strength and stiffness, fatigue resistance, and durability. It is possible to insert scalable sensors throughout the manufacturing process, which enables real-time monitoring of structural health, strain, and other factors. Scalable sensor-assisted multi-scale computational methods offer enhanced accuracy, real-time monitoring, and cost-effectiveness by integrating sensor data with computational models, improving predictions and failure mechanism insights. However, they face limitations like sensor dependency, computational complexity, data integration challenges, and high implementation costs, leading to potential discrepancies between simulation and experimental results. Important qualities include corrosion resistance, thermal conductivity, and electrical conductivity. As the composite materials develop to satisfy the established mechanical stress and temperature conditions, they offer high durability and strength.

Assessing the accuracy and efficacy of multiscale computational methods in predicting reaction mechanisms and kinetics of SN2 reactions and Claisen rearrangement

This study investigates the application of quantum mechanical (QM) and multiscale computational methods in understanding the reaction mechanisms and kinetics of SN2 reactions involving methyl iodide with NH2OH and NH2O−, as well as the Claisen rearrangement of 8-(vinyloxy)dec-9-enoate. Our aim is to evaluate the accuracy and effectiveness of these methods in predicting experimental outcomes for these organic reactions. We achieve this by employing QM-only calculations and several hybrids of QM and molecular mechanics (MM) methods, namely QM/MM, QM1/QM2, and QM1/QM2/MM methodologies. For the SN2 reactions, our results demonstrate the importance of explicitly including solvent effects in the calculations to accurately reproduce the transition state geometry and energetics. The multiscale methods, particularly QM/MM and QM1/QM2, show promising performance in predicting activation energies. Moreover, we observe that the size of the MM active region significantly affects the accuracy of calculated activation energies, highlighting the need for careful consideration during the setup of multiscale calculations. In the case of the Claisen rearrangement, both QM-only and multiscale methods successfully reproduce the proposed reaction mechanism. However, the activation free energies calculated using a continuum solvation model, based on single-point calculations of QM-only structures, fail to account for solvent effects. On the other hand, multiscale methods more accurately capture the impact of solvents on activation free energies, with systematic error correction enhancing the accuracy of the results. Furthermore, we introduce a Python code for setting up multiscale calculations with ORCA, which is available on GitHub at https://github.com/iranimehdi/pdbtoORCA.

Bound ion effects: Using machine learning method to study the kinesin Ncd’s binding with microtubule

Drosophila Ncd proteins are motor proteins that play important roles in spindle organization. Ncd and the tubulin dimer are highly charged. Thus, it is crucial to investigate Ncd-tubulin dimer interactions in the presence of ions, especially ions that are bound or restricted at the Ncd-tubulin dimer binding interfaces. To consider the ion effects, widely used implicit solvent models treat ions implicitly in the continuous solvent environment without focusing on the individual ions’ effects. But highly charged biomolecules such as the Ncd and tubulin dimer may capture some ions at highly charged regions as bound ions. Such bound ions are restricted to their binding sites; thus, they can be treated as part of the biomolecules. By applying multiscale computational methods, including the machine-learning-based Hybridizing Ions Treatment-2 program, molecular dynamics simulations, DelPhi, and DelPhiForce, we studied the interaction between the Ncd motor domain and the tubulin dimer using a hybrid solvent model, which considers the bound ions explicitly and the other ions implicitly in the solvent environment. To identify the importance of treating bound ions explicitly, we also performed calculations using the implicit solvent model without considering the individual bound ions. We found that the calculations of the electrostatic features differ significantly between those of the hybrid solvent model and the pure implicit solvent model. The analyses show that treating bound ions at highly charged regions explicitly is crucial for electrostatic calculations. This work proposes a machine-learning-based approach to handle the bound ions using the hybrid solvent model. Such an approach is not only capable of handling kinesin-tubulin complexes but is also appropriate for other highly charged biomolecules, such as DNA/RNA, viral capsid proteins, etc.

To elucidate the effect of alloying elements for enhanced nitriding of aluminum: A multiscale computational study

The limitations of aluminum (Al) are discussed in certain industrial applications, owing to its low hardness and wear resistance. To overcome these limitations, surface modification techniques, such as the formation of aluminum nitride (AlN) layers on the Al surface, have been explored. However, conventional surface-nitriding processes have low productivity, and it is essential to develop a cost-effective method to ensure higher nitriding rates. This paper presents the results of a multiscale computational study on the atomic-scale mechanisms involved in gas nitriding processes and the effects of alloying elements on reaction kinetics using density functional theory calculations. This study also provides insights into the thermodynamic stability and kinetics of the process, contributing to the design and optimization of the nitriding process for the formation of AlN on Al surfaces using Thermo-Calc. and microkinetic analyses. This study demonstrates the potential of multiscale computational methods for advancing the understanding of surface modification techniques and their applications for nitriding on the surface of Al and suggests the best alloying element to enhance the nitriding rate.

How the Conformational Movement of the Substrate Drives the Regioselective C–N Bond Formation in P450 TleB: Insights from Molecular Dynamics Simulations and Quantum Mechanical/Molecular Mechanical Calculations

P450 TleB catalyzes the oxidative cyclization of the dipeptide N-methylvalyl-tryptophanol into indolactam V through selective intramolecular C-H bond amination at the indole C4 position. Understanding its catalytic mechanism is instrumental for the engineering or design of P450-catalyzed C-H amination reactions. Using multiscale computational methods, we show that the reaction proceeds through a diradical pathway, involving a hydrogen atom transfer (HAT) from N1-H to Cpd I, a conformational transformation of the substrate radical species, and a second HAT from N13-H to Cpd II. Intriguingly, the conformational transformation is found to be the key to enabling efficient and selective C-N coupling between N13 and C4 in the subsequent diradical coupling reaction. The underlined conformational transformation is triggered by the first HAT, which proceeds with an energy-demanding indole ring flip and is followed by the facile approach of the N13-H group to Cpd II. Detailed analysis shows that the internal electric field (IEF) from the protein environment plays key roles in the transformation process, which not only provides the driving force but also stabilizes the flipped conformation of the indole radical. Our simulations provide a clear picture of how the P450 enzyme can smartly modulate the selective C-N coupling reaction. The present findings are in line with all available experimental data, highlighting the crucial role of substrate dynamics in controlling this highly valuable reaction.

High temperature stability and transport characteristics of hydrogen in alumina via multiscale computation

The impact of hydrogen charge states on the stability and transport characteristics of hydrogen interstitials in alumina polymorphs is evaluated by multiscale computational methods including density functional theory (DFT), ab initio molecular dynamics (AIMD) and machine learned force fields. Thermodynamic calculations show that the protonic Hi+1 interstitial is the most stable defect species for most values of the electronic bandgap in both α and amorphous alumina (Al2O3). Further, active learned Gaussian approximation potentials (GAP) were developed using AIMD data to study temperature dependent long time proton diffusion in alumina. Diffusivity calculations from GAP-MD simulations are found to be comparable with of the AIMD data, while being ∼340 times faster and scalable to larger systems. Comparisons with diffusivity values for other interstitial charge states (Hi0 and Hi−1) and published experimental literature indicate that Hi+1 diffusion is the likely mechanism of hydrogen transport. A good agreement is obtained between Hi+1 diffusivity calculated in α-Al2O3 from DFT: 5.05 × 10−3 exp(-0.81 eV/kB/T) cm2/s and reported experiment: 9.7 ×10−4 exp(-0.83 eV/kB/T) cm2/s. Computationally and experimentally calculated energy barriers (0.81 and 0.83 eV respectively) only differ by 2.5%. Similarly, the pre-exponential diffusion coefficients only differ by 0.5 orders of magnitude. Moreover, the diffusivity of Hi+1 in amorphous Al2O3 in the 1000–2000 K range is calculated to be 2.53 × 10−2 exp(-0.89 eV/k B/T), just one order of magnitude higher than the corresponding value in α-Al2O3. This suggests that local structural disorder does not significantly affect the energy landscape and diffusion behavior of Hi+1 in Al2O3. Overall, these results show promise for the application of alumina polymorphs as hydrogen permeation barriers.

Computational Study on E-Hooks of Tubulins in the Binding Process with Kinesin

Cargo transport within cells is essential to healthy cells, which requires microtubules-based motors, including kinesin. The C-terminal tails (E-hooks) of alpha and beta tubulins of microtubules have been proven to play important roles in interactions between the kinesins and tubulins. Here, we implemented multi-scale computational methods in E-hook-related analyses, including flexibility investigations of E-hooks, binding force calculations at binding interfaces between kinesin and tubulins, electrostatic potential calculations on the surface of kinesin and tubulins. Our results show that E-hooks have several functions during the binding process: E-hooks utilize their own high flexibilities to increase the chances of reaching a kinesin; E-hooks help tubulins to be more attractive to kinesin. Besides, we also observed the differences between alpha and beta tubulins: beta tubulin shows a higher flexibility than alpha tubulin; beta tubulin generates stronger attractive forces (about twice the strengths) to kinesin at different distances, no matter with E-hooks in the structure or not. Those facts may indicate that compared to alpha tubulin, beta tubulin contributes more to attracting and catching a kinesin to microtubule. Overall, this work sheds the light on microtubule studies, which will also benefit the treatments of neurodegenerative diseases, cancer treatments, and preventions in the future.

Impurity Controlled near Infrared Surface Plasmonic in AlN.

In this work, we used multi-scale computational simulation methods combined with density functional theory (DFT) and finite element analysis (FEA) in order to study the optical properties of substitutional doped aluminium nitride (AlN). There was strong surface plasmon resonance (SPR) in the near-infrared region of AlN substituted with different alkali metal doping configurations. The strongest electric field strength reached 109 V/m. There were local exciton and charge transfer exciton behaviours in some special doping configurations. These research results not only improve the application of multi-scale computational simulations in quantum surface plasmons, but also promote the application of AlN in the field of surface-enhanced linear and non-linear optical spectroscopy.

Multiscale computations and artificial intelligent models of electrochemical performance in Li‐ion battery materials

AbstractLi‐ion battery (LIB) is widely used as one of renewable energy resources for powerful electronics and electric vehicles. The main challenge in developing next‐generation LIB is to further improve the energy density, rate capability, and cycling stability of electrode and electrolyte materials. With the rapid development of computational science, the material design has changed from the traditional trial‐and‐error approach to integrated database‐based computation. Multiscale computational methods and machine learning (ML) not only speed up the development of new materials, but also provide insight into the intrinsic relationship between the microscopic composition and macroscopic performance of materials. In this review, we revealed the correlation of electrochemical activity of LIB materials with the response of energy to variable parameters such as charge‐transfer number and Li‐ion diffusion coordinates. Based on the structure–property relationship, we reviewed multiscale calculation and ML methods for electrochemical properties in LIB materials. A comparison for various applied methods was outlined to provide a method‐selecting reference in LIB material design. It is expected that a deep combination of multiscale computations, experimental data, ML should be more powerful methods for discovering new LIB materials.This article is categorized under: Structure and Mechanism > Computational Materials Science Electronic Structure Theory > Density Functional Theory Structure and Mechanism > Reaction Mechanisms and Catalysis

Multiphase Microreactors Based on Liquid–Liquid and Gas–Liquid Dispersions Stabilized by Colloidal Catalytic Particles

AbstractPickering emulsions, foams, bubbles, and marbles are dispersions of two immiscible liquids or of a liquid and a gas stabilized by surface‐active colloidal particles. These systems can be used for engineering liquid–liquid–solid and gas–liquid–solid microreactors for multiphase reactions. They constitute original platforms for reengineering multiphase reactors towards a higher degree of sustainability. This Review provides a systematic overview on the recent progress of liquid–liquid and gas–liquid dispersions stabilized by solid particles as microreactors for engineering eco‐efficient reactions, with emphasis on biobased reagents. Physicochemical driving parameters, challenges, and strategies to (de)stabilize dispersions for product recovery/catalyst recycling are discussed. Advanced concepts such as cascade and continuous flow reactions, compartmentalization of incompatible reagents, and multiscale computational methods for accelerating particle discovery are also addressed.

Multiphase Microreactors Based on Liquid-Liquid and Gas-Liquid Dispersions Stabilized by Colloidal Catalytic Particles.

Pickering emulsions, foams, bubbles, and marbles are dispersions of two immiscible liquids or of a liquid and a gas stabilized by surface‐active colloidal particles. These systems can be used for engineering liquid–liquid–solid and gas–liquid–solid microreactors for multiphase reactions. They constitute original platforms for reengineering multiphase reactors towards a higher degree of sustainability. This Review provides a systematic overview on the recent progress of liquid–liquid and gas–liquid dispersions stabilized by solid particles as microreactors for engineering eco‐efficient reactions, with emphasis on biobased reagents. Physicochemical driving parameters, challenges, and strategies to (de)stabilize dispersions for product recovery/catalyst recycling are discussed. Advanced concepts such as cascade and continuous flow reactions, compartmentalization of incompatible reagents, and multiscale computational methods for accelerating particle discovery are also addressed.

First-Principles Calculations for Stable β-Ti–Mo Alloys Using Cluster-Plus-Glue-Atom Model

Construction of suitable structural models in order to account for chemical short-range orders is the reason behind the difficult multi-scale computational simulation methods for solid solutions. Herein, using Ti–Mo alloys as representative, we used our cluster-plus-glue-atom model to address the chemical short-range orders for body-center cubic lattice. In accordance with the atomic interaction mode, an Mo solute atom would prefer 14 Ti solvent atoms as its nearest neighbors, forming a rhombic-dodecahedral cluster, and some next outer-shell Mo and Ti atoms would serve as the glue atoms, which is formulated as [Mo–Ti14](Mo,Ti)x. The number of glue atoms x corresponds to different spatial distribution of the clusters. One of the formula having good stability is [Mo–Ti14]Mo, i.e., with one Mo as the glue atom. To verify its stability, mechanical properties and electronic density of state are obtained using the first-principles calculations and the Young’s modulus agrees with the experimental values. Also the formulated structural unit [Mo–Ti14]Mo is indeed verified by the cluster expansion method. This work then confirms the existence of simple structural unit covering the nearest neighbors and a few next outer-shell atoms for the Ti–Mo alloy of high structural stability.

2019 Boys-Rahman Award to Gregory Voth

Gregory Voth, the Haig P. Papazian Distinguished Service Professor of Chemistry at the University of Chicago, is the recipient of the 2019 S. F. Boys–A. Rahman Award from the Royal Society of Chemistry for his development and application of powerful multiscale theoretical and computational methods in the study of liquids, materials, biomolecules, and quantum mechanical systems. The award honors outstanding innovative research in computational chemistry. Voth also studies charge transport in water and biomolecules and the behavior of room-temperature ionic liquids and other complex materials. Please send announcements of awards to l_wang@acs.org.

Dynamics and Catalytic Mechanism of Histone Demethylase PHF8

PHF8 is histone lysine demethylase that demethylates dimethylated lysine residues in H3 histone protein. The enzyme is involved in key events of the epigenetic regulation and has been linked to mental retardation and other pathologies. While success in understanding structure‐function relationships of this enzyme was achieved using X‐ray crystallographic, biochemical and mutagenic studies, so far there is no knowledge about its reaction mechanism and the effect of the conformational dynamics on the enzyme structure and functions.In order to provide understanding into this missing areas, we performed combination from molecular dynamics simulations (MD), Electronic Structure (ES) and Combined Quantum Mechanics and Molecular Mechanics (QM/MM) Calculations. The MD studies provided atomistic insights into the effects of the conformational dynamics and collective motions into enzyme structure, interactions with the co‐substrates. The QM/MM studies revealed the mechanistic aspect of the oxygen activation processes as well the hydrogen abstraction (HAT) and rebound hydroxylation steps. Furthermore, HAT step is calculated to be the rate‐limiting step. We explored the effects of conformational dynamics and effects of mutations on the HAT step. The results demonstrate the potential of multiscale computational methods to provide an insight that complement the experimental methods.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

A Hybrid Experimental-Computational Modeling Framework for Cardiovascular Device Testing.

Significant advances in biomedical science often leverage powerful computational and experimental modeling platforms. We present a framework named physiology simulation coupled experiment ("PSCOPE") that can capitalize on the strengths of both types of platforms in a single hybrid model. PSCOPE uses an iterative method to couple an in vitro mock circuit to a lumped-parameter numerical simulation of physiology, obtaining closed-loop feedback between the two. We first compared the results of Fontan graft obstruction scenarios modeled using both PSCOPE and an established multiscale computational fluid dynamics method; the normalized root-mean-square error values of important physiologic parameters were between 0.1% and 2.1%, confirming the fidelity of the PSCOPE framework. Next, we demonstrate an example application of PSCOPE to model a scenario beyond the current capabilities of multiscale computational methods-the implantation of a Jarvik 2000 blood pump for cavopulmonary support in the single-ventricle circulation; we found that the commercial Jarvik 2000 controller can be modified to produce a suitable rotor speed for augmenting cardiac output by approximately 20% while maintaining blood pressures within safe ranges. The unified modeling framework enables a testing environment which simultaneously operates a medical device and performs computational simulations of the resulting physiology, providing a tool for physically testing medical devices with simulated physiologic feedback.

The Coordination and Regulation of Axonemal Dynein

Molecular motor proteins drive the motility behind cellular dynamics. Recent advances in structural biology and single-molecule biophysics have led to a detailed understanding of many motor protein mechanisms. However, two understudied areas include the regulation and the coordination of motor proteins, particularly axonemal dyneins, which are the motors that drive the motility of eukaryotic cilia and flagella. Here we reconstitute motor motility in vitro using purified protein components to study the regulation of axonemal dynein by the biochemistry of the microtubules upon which they act. We find that axonemal dynein's motile properties are regulated differently depending on the source of tubulin in the experiments and the post translational modification of these microtubules. We also apply multi-scale computational methods to dynein microtubule binding domains and show how electrostatic interactions affect multiple aspects of dynein's motility. These effects include how interactions with the highly charged C-terminal tails, the location of many post-translational modifications of microtubules, could regulate motility. Finally, we show how these regulation mechanisms could lead to cooperation amongst teams of axonemal dynein motors and coordination of the eukaryotic ciliary beat.

A Review of Multiscale Computational Methods in Polymeric Materials.

Polymeric materials display distinguished characteristics which stem from the interplay of phenomena at various length and time scales. Further development of polymer systems critically relies on a comprehensive understanding of the fundamentals of their hierarchical structure and behaviors. As such, the inherent multiscale nature of polymer systems is only reflected by a multiscale analysis which accounts for all important mechanisms. Since multiscale modelling is a rapidly growing multidisciplinary field, the emerging possibilities and challenges can be of a truly diverse nature. The present review attempts to provide a rather comprehensive overview of the recent developments in the field of multiscale modelling and simulation of polymeric materials. In order to understand the characteristics of the building blocks of multiscale methods, first a brief review of some significant computational methods at individual length and time scales is provided. These methods cover quantum mechanical scale, atomistic domain (Monte Carlo and molecular dynamics), mesoscopic scale (Brownian dynamics, dissipative particle dynamics, and lattice Boltzmann method), and finally macroscopic realm (finite element and volume methods). Afterwards, different prescriptions to envelope these methods in a multiscale strategy are discussed in details. Sequential, concurrent, and adaptive resolution schemes are presented along with the latest updates and ongoing challenges in research. In sequential methods, various systematic coarse-graining and backmapping approaches are addressed. For the concurrent strategy, we aimed to introduce the fundamentals and significant methods including the handshaking concept, energy-based, and force-based coupling approaches. Although such methods are very popular in metals and carbon nanomaterials, their use in polymeric materials is still limited. We have illustrated their applications in polymer science by several examples hoping for raising attention towards the existing possibilities. The relatively new adaptive resolution schemes are then covered including their advantages and shortcomings. Finally, some novel ideas in order to extend the reaches of atomistic techniques are reviewed. We conclude the review by outlining the existing challenges and possibilities for future research.

A computational study of self-assembled hexapeptide inhibitors against amyloid-β (Aβ) aggregation.

The fibrillation and deposition of amyloid-β (Aβ) peptides in human brains are pathologically linked to Alzheimer's disease (AD). Development of different inhibitors (peptides, organic molecules, and nanoparticles) to prevent Aβ aggregation becomes a promising therapeutic strategy for AD treatment. We recently propose a "like-interacts-like" design principle to computationally design/screen and experimentally validate a new set of hexapeptide inhibitors with completely different sequences from the Aβ sequence. These hexapeptide inhibitors inhibit Aβ aggregation and reduce Aβ-induced cytotoxicity. However, inhibitory mechanisms of these hexapeptides and the underlying interactions between hexapeptides and Aβ remain unclear. Herein we apply multi-scale computational methods (quantum-chemical calculations, molecular docking and explicit-solvent molecular dynamic simulation) to explore the structure, dynamics, and interaction between 3 identified hexapeptides (CTLWWG, GTVWWG, and CTIYWG) and different Aβ-derived fragments and an Aβ17-42 pentamer. When interacting with 6 Aβ-derived fragments, 3 hexapeptide inhibitors show stronger interactions with two lysine-included fragments (16KLVFFA21 and 27NKGAII33) than other fragments, indicating different sequence-specific interactions with Aβ. When interacting with the Aβ17-42 pentamer, the 3 peptides show similar binding modes and interaction mechanisms by preferentially binding to the edge of the Aβ17-42 pentamer to potentially block the Aβ elongation pathway. This work provides structural-based binding information on further modification and optimization of these peptide inhibitors to experimentally enhance their inhibitory abilities against Aβ aggregation.

Multi-scale Modeling in Clinical Oncology: Opportunities and Barriers to Success.

Hierarchical processes spanning several orders of magnitude of both space and time underlie nearly all cancers. Multi-scale statistical, mathematical, and computational modeling methods are central to designing, implementing and assessing treatment strategies that account for these hierarchies. The basic science underlying these modeling efforts is maturing into a new discipline that is close to influencing and facilitating clinical successes. The purpose of this review is to capture the state-of-the-art as well as the key barriers to success for multi-scale modeling in clinical oncology. We begin with a summary of the long-envisioned promise of multi-scale modeling in clinical oncology, including the synthesis of disparate data types into models that reveal underlying mechanisms and allow for experimental testing of hypotheses. We then evaluate the mathematical techniques employed most widely and present several examples illustrating their application as well as the current gap between pre-clinical and clinical applications. We conclude with a discussion of what we view to be the key challenges and opportunities for multi-scale modeling in clinical oncology.

A Perspective on Implementing a Quantitative Systems Pharmacology Platform for Drug Discovery and the Advancement of Personalized Medicine

Drug candidates exhibiting well-defined pharmacokinetic and pharmacodynamic profiles that are otherwise safe often fail to demonstrate proof-of-concept in phase II and III trials. Innovation in drug discovery and development has been identified as a critical need for improving the efficiency of drug discovery, especially through collaborations between academia, government agencies, and industry. To address the innovation challenge, we describe a comprehensive, unbiased, integrated, and iterative quantitative systems pharmacology (QSP)–driven drug discovery and development strategy and platform that we have implemented at the University of Pittsburgh Drug Discovery Institute. Intrinsic to QSP is its integrated use of multiscale experimental and computational methods to identify mechanisms of disease progression and to test predicted therapeutic strategies likely to achieve clinical validation for appropriate subpopulations of patients. The QSP platform can address biological heterogeneity and anticipate the evolution of resistance mechanisms, which are major challenges for drug development. The implementation of this platform is dedicated to gaining an understanding of mechanism(s) of disease progression to enable the identification of novel therapeutic strategies as well as repurposing drugs. The QSP platform will help promote the paradigm shift from reactive population-based medicine to proactive personalized medicine by focusing on the patient as the starting and the end point.