Academic literature on the topic 'Systems biology, density functional theory, computational modeling'

A new, computationally efficient, complex modeling approach is presented for the quantification of the local and average atomic structure, nanostructure and microstructure of an Au0.25Cu0.75 alloy. High-resolution X-ray powder diffraction and whole pattern fitting show that the sample is phase pure, with isotropic lattice strain and a distribution of equiaxed crystallites of mean size 144 (11) nm, where each crystallite has on average four twin boundaries and an average of three deformation faults per four crystallites. Both small- and large-box model optimizations were used to extract local and long-range information from the pair distribution function. The large-box, 640 000-atom-ensemble optimization approach applied herein relies on differential evolution optimization and shows that the alloy has chemical short-range ordering, with correlation parameters of −0.26 (2) and 0.36 (8) in the first and second correlation shells, respectively. Locally, there is a 1.45 (8)% tetragonal distortion which on average results in a cubic atomic structure. The isotropic lattice strain is a result of atom-pair-dependent bond lengths, following the trend d Au—Au > d Au—Cu > d Cu—Cu, highlighted by density functional theory calculations. This approach is generalizable and should be extensible to other disordered systems, allowing for quantification of localized structure deviations.

Transcranial direct current stimulation (tDCS) has been shown to evoke hemodynamics response; however, the mechanisms have not been investigated systematically using systems biology approaches. Our study presents a grey-box linear model that was developed from a physiologically detailed multi-compartmental neurovascular unit model consisting of the vascular smooth muscle, perivascular space, synaptic space, and astrocyte glial cell. Then, model linearization was performed on the physiologically detailed nonlinear model to find appropriate complexity (Akaike information criterion) to fit functional near-infrared spectroscopy (fNIRS) based measure of blood volume changes, called cerebrovascular reactivity (CVR), to high-definition (HD) tDCS. The grey-box linear model was applied on the fNIRS-based CVR during the first 150 seconds of anodal HD-tDCS in eleven healthy humans. The grey-box linear models for each of the four nested pathways starting from tDCS scalp current density that perturbed synaptic potassium released from active neurons for Pathway 1, astrocytic transmembrane current for Pathway 2, perivascular potassium concentration for Pathway 3, and voltage-gated ion channel current on the smooth muscle cell for Pathway 4 were fitted to the total hemoglobin concentration (tHb) changes from optodes in the vicinity of 4x1 HD-tDCS electrodes as well as on the contralateral sensorimotor cortex. We found that the tDCS perturbation Pathway 3 presented the least mean square error (MSE, median <2.5%) and the lowest Akaike information criterion (AIC, median -1.726) from the individual grey-box linear model fitting at the targeted-region. Then, minimal realization transfer function with reduced-order approximations of the grey-box model pathways was fitted to the ensemble average tHb time series. Again, Pathway 3 with nine poles and two zeros (all free parameters), provided the best Goodness of Fit of 0.0078 for Chi-Square difference test of nested pathways. Therefore, our study provided a systems biology approach to investigate the initial transient hemodynamic response to tDCS based on fNIRS tHb data. Future studies need to investigate the steady-state responses, including steady-state oscillations found to be driven by calcium dynamics, where transcranial alternating current stimulation may provide frequency-dependent physiological entrainment for system identification. We postulate that such a mechanistic understanding from system identification of the hemodynamics response to transcranial electrical stimulation can facilitate adequate delivery of the current density to the neurovascular tissue under simultaneous portable imaging in various cerebrovascular diseases.

At Nano-scale level, innovative biomedical techniques are developed in advanced drug delivery systems and targeted Nano-therapy. Ultrathin needles provide a low invasive and highly selective means for molecular delivery and cell manipulation. This article studies the geometry and the stability of Boron Nano-Bucket (B16 Cluster of Bucket Shape) and B15-Li complex by using computational modeling methods. The equilibrium geometry of Boron Nano-Bucket and BNB-Li complex in the ground state have been determined and analyzed by Density functional theory (DFT) employing 6-311 G (d, p) as the basis set. The frontier orbital HOMO-LUMO gap, Chemical Softness, Chemical Hardness have also been calculated to understand its complete Chemical Properties. In this study, we have also performed BNB-Li complex interaction with drug Resorcinol. The binding character interactive species have been determined by NBO and AIM analysis. From these studies, we can say that BNB and BNB-Li complex may also potentially able to stabilize ions around their structure like Carbon Nano Niddle (CNN) in future. The polar characteristics of CNN and their ability to carry ionic species, Li doped Boron Nano-Bucket might be suitable to act as drug carrier through nonpolar biologic media.

The 3e′ orbitals of cyclopropane have the correct symmetry to interact with low-lying unoccupied orbitals of π-acceptor substituents and maximum overlap occurs when the two orbital systems are parallel, i.e. when the π-acceptor bisects the ring in projection down the substituent bond. Since the cyclopropyl group is a common component of active pharmaceutical and agrochemical ingredients, it is important that these strong conjugative interactions are well modelled by computational techniques, and clearly represented in experimental crystal structures. Here we show that torsion angle distributions derived from crystal structure data in the Cambridge Structural Database are in excellent correspondence with torsional energy profiles computed using density functional theory (DFT) for a range of substituents: —COOR, —CONR 2, —NO2, vinyl and phenyl. We also show that crystal structure information is invaluable in modelling conformations of compounds that contain multiply substituted rings, where steric interactions require some substituents to adopt energetically disfavoured conformations. Further, conjugative interactions with π-acceptors lead to significant asymmetry in the cyclopropane ring bond lengths and again the experimental and computational results are in excellent agreement. Such asymmetry effects are additive, and this explains bond-length variations in cyclopropane rings bearing two or more π-acceptor substituents.

Dendrodendritic interactions between excitatory mitral cells and inhibitory granule cells in the olfactory bulb create a dense interaction network, reorganizing sensory representations of odors and, consequently, perception. Large-scale computational models are needed for revealing how the collective behavior of this network emerges from its global architecture. We propose an approach where we summarize anatomical information through dendritic geometry and density distributions which we use to calculate the connection probability between mitral and granule cells, while capturing activity patterns of each cell type in the neural dynamical systems theory of Izhikevich. In this way, we generate an efficient, anatomically and physiologically realistic large-scale model of the olfactory bulb network. Our model reproduces known connectivity between sister vs. non-sister mitral cells; measured patterns of lateral inhibition; and theta, beta, and gamma oscillations. The model in turn predicts testable relationships between network structure and several functional properties, including lateral inhibition, odor pattern decorrelation, and LFP oscillation frequency. We use the model to explore the influence of cortex on the olfactory bulb, demonstrating possible mechanisms by which cortical feedback to mitral cells or granule cells can influence bulbar activity, as well as how neurogenesis can improve bulbar decorrelation without requiring cell death. Our methodology provides a tractable tool for other researchers.

Modeling and investigation of thermodynamic characteristics of spatially-finite metallic systems is an essential task of modern nanophysics. We show that the widely used DFT (density functional theory) is less efficient than the QST (quantum-statistical theory) approach.

In spite of being rare, actinide elements provide the building blocks for many fascinating condensed-matter systems, both from an experimental and theoretical perspective. Experimental observations of actinide materials are difficult because of rarity, toxicity, radioactivity, and even safety and security. Theory, on the other hand, has its own challenges. Complex crystal and electronic structures are often encountered in actinide materials, as well as pronounced electron correlation effects. Consequently, theoretical modeling of actinide materials and their 5f electronic states is very difficult. Here, we review recent theoretical efforts to describe and sometimes predict the behavior of actinide materials and complexes, such as phase stability including density functional theory (DFT), DFT in conjunction with an additional Coulomb repulsion U (DFT+U), and DFT in combination with dynamical mean-field theory (DFT+DMFT).

Recent years have seen vast improvements in the ability of rigorous quantum-mechanical methods to treat systems of interest to molecular biology. In this review article, we survey common computational methods used to study such large, weakly bound systems, starting from classical simulations and reaching to quantum chemistry and density functional theory. We sketch their underlying frameworks and investigate their strengths and weaknesses when applied to potentially large biomolecules. In particular, density functional theory — a framework that can treat thousands of atoms on firm theoretical ground — can now accurately describe systems dominated by weak van der Waals interactions. This newfound ability has rekindled interest in using this tried-and-true approach to investigate biological systems of real importance. In this review, we focus on some new methods within the density functional theory that allow for accurate inclusion of the weak interactions that dominate binding in biological macromolecules. Recent work utilizing these methods to study biologically relevant systems will be highlighted, and a vision for the future of density functional theory within molecular biology will be discussed.

Density functional theory (DFT) has been applied to modeling molecular interactions in water for over three decades. The ubiquity of water in chemical and biological processes demands a unified understanding of its physics, from the single molecule to the thermodynamic limit and everything in between. Recent advances in the development of data-driven and machine-learning potentials have accelerated simulation of water and aqueous systems with DFT accuracy. However, anomalous properties of water in the condensed phase, where a rigorous treatment of both local and non-local many-body (MB) interactions is in order, are often unsatisfactory or partially missing in DFT models of water. In this review, we discuss the modeling of water and aqueous systems based on DFT and provide a comprehensive description of a general theoretical/computational framework for the development of data-driven many-body potentials from DFT reference data. This framework, coined MB-DFT, readily enables efficient many-body molecular dynamics (MD) simulations of small molecules, in both gas and condensed phases, while preserving the accuracy of the underlying DFT model. Theoretical considerations are emphasized, including the role that the delocalization error plays in MB-DFT potentials of water and the possibility to elevate DFT and MB-DFT to near-chemical-accuracy through a density-corrected formalism. The development of the MB-DFT framework is described in detail, along with its application in MB-MD simulations and recent extension to the modeling of reactive processes in solution within a quantum mechanics/MB molecular mechanics (QM/MB-MM) scheme, using water as a prototypical solvent. Finally, we identify open challenges and discuss future directions for MB-DFT and QM/MB-MM simulations in condensed phases.

In the active-site cavity of vanadium haloperoxidases vanadate as the prosthetic group is solely fixed by one covalent bond to a histidine residue and embedded in a supramolecular environment of extensive hydrogen bonds. Structural aspects of relevant vanadium complexes with supramolecular interactions, including assemblies with chiral hosts, are presented. The importance of hydrogen-bonding relays is presented together with relevant examples. The reactivity of related functional mimics containing vanadium and molybdenum toward the oxidation of thioethers is described. Computational modeling based on density functional theory (DFT) is used for the investigation of model systems. The resulting implications for structure and function of vanadium haloperoxidases, including their substrate and cofactor specificity, are discussed.

The aim of the thesis is the development of computational models of the enzymatic activity of biomolecules at different scales. Parallel investigations have been carried out at a quantum level to study the reactivity of an enzyme from an electronic point of view, and at a systemic level using simulation techniques to determine the role of enzymes in the network of cellular reactions. Starting from the lowest complexity level, the thesis begins with two computational studies with the aims of describing the molecular mechanism of the catalytic reaction between amavadin and methyl mercaptoacetate and the structural coordination between angiogenin and copper ion, respectively. Since in both cases transition metals are involved, Density Functional Theory (DFT) computations and Quantum Theory of Atoms in Molecules (QTAIM) analysis of the electron density were used. The first study regards the electronic description of the enzymatic mechanism by which molecule amavadin, an unusual octa-coordinated VIV complex isolated from Amanita muscaria mushrooms, catalyzes the oxidation of some thiols to the corresponding disulfides. An experimental work by G.da Silva et al. proposed an inner-sphere mechanism for the reaction (J. Am. Chem. Soc. 1996, 118:7568–7573.) but the electronic mechanism was not identified. In the first step of our investigation, the stereoelectronic features of the V(IV) (inactive) and V(V) (active) states of amavadin were determined by means of DFT. Then, the formation of the VV complex with methyl mercaptoacetate (MMA), which has been chosen as a prototypical substrate, has been characterized both thermodynamically and kinetically. DFT results reveal that protonation of VV amavadin at a carboxylate oxygen not directly involved in the V coordination, favors MMA binding into the first coordination sphere of vanadium, by substitution of the amavadin carboxylate oxygen with that of the substrate and formation of an S–H···O hydrogen bond interaction. The latter interaction can promote SH deprotonation and binding of the thiolate group to vanadium. The kinetic and thermodynamic feasibility of the V(V)–MMA intermediates formation is in agreement, along with electrochemical experimental data, also with the biological role exerted by amavadin. Finally, the presence of an ester functional group as an essential requisite for MMA oxidation has been rationalized. Moreover, the results proposed an indirect evidence on the role of the vanadium (in its d0 active state) based catalyst protonation. The second studies focused on the structural and spectroscopic properties of the complexes formed by Cu2+ and the peptide fragments Ac-PHREN-NH2, which encompasses the putative cell binding domain of angiogenin, as well as its Ac-PHREN-NH2 variant. Analysis of structures, relative energies and EPR parameters has allowed to conclude that the metal coordination environment at pH 8 is formed by a nitrogen atom of His, two deprotonated amide groups, a water molecule and an oxygen atom from the COO- side chain of Glu, in nice agreement with recent experimental results [Dalton Trans, 2010, 39:10678]. Moreover, DFT results allowed to reveal that the Glu sidechain of the Ac-PHREN-NH2 peptide is coordinated in equatorial position, fully disclosing the effects of Cu2+ binding on the structural properties of this key angiogenin portion. We have also investigated the configuration space of the E→Q mutated system Ac-PHRQN-NH2/Cu2+·H2O In this case, computational results led to the conclusion that the H2O molecule is coordinated in equatorial position and the oxygen atom of the carbonyl group of glutamine is weakly coordinated in apical position. Using as a reference the recent experimental results reported by Bonomo and collaborators [Dalton Trans, 2010, 39:10678], we have used classical MM/MD calculations followed by DFT optimizations to explore the configurational space of the Ac-PHREN-NH2/Cu2+·H2O complex. Both of these DFT studies have been carried out in the laboratory of professor Piercarlo Fantucci, Department of Biotechnology and Biosciences, University of Milan-Bicocca. These two works lead to the following publications: “DFT characterization of key intermediates in thiols oxidation catalyzed by amavadin”, Dalton Transaction, 40 (30): 7704-7712, 2011. “Copper coordination to the putative cell binding site of angiogenin”. A DFT investigation., Inorg.Chemistry. Accepted. Acquired some modeling skills at the molecular level, we decided to add another layer of complexity to our investigation and we decided to test if a correlation could exist between the peculiar structure of a protein and a biological effect at a cellular level. The fundamental biological system of the Ras/Sos signaling activation pathway has been chosen for our study. Based on two different previous studies we developed a model that can correlate bistability and the microdomains clustering in this small network. In fact Das et al. (Cell 2009, 136:337-351, 2009) reported that the Sos-dependent Ras activation in lymphocytes, beyond stimulus, causes a digital (on/off) response that implies a positive feedback loop in the regulation mechanism of Sos activity. The deterministic model developed by the authors demonstrates that for low or high levels of Sos there is only one possible state correlated to low or high levels of active Ras, respectively. For intermediate values of Sos, three states of Ras activity are generated (two states are stable and could be simultaneously observed, the third is unstable and slightly perturbated). Bistability occurs because system lies in the lower state until this is no longer possible and then jumps to an high Ras activity correlated state. An interconnected work reproduced the same biological system in a minimal 2D lattice model in order to investigate the relevance of the microdomains clustering in the signaling pathway. It was reported that positive feedback, in the presence of slow diffusion, results in clustering of activated molecules on the plasma membrane and rapid spatial spreading as the front of the cluster of the newly formed membrane bounded Ras-GTP propagates with a constant velocity dependent on the feedback strength (J.Chem.Phys, 2009, 130:245102). Our work, implemented in the software Smoldyn (Phys.Biol, 2000, 1:137-151), presents a new description of both the above mentioned models by utilizing brownian dynamic stochastic simulations and spatial discretization. It describes how the microdomains clustering of the components of the network can induce bistability. Interestingly it has recently been found a correlation between the activation levels of components of networks that show ultrasensitivity (such as the Ras/SOS regulatory network) and the Fermi-Dirac statistics generally used to measure the probability of the distribution of the states of a system of particles (PNAS, 2010, 107:1247-1252). We are carrying out further investigation to test if this kind of description, in which the molecules of the systems can be treated as analogous of fermion gas, could be useful to identify the statistic distribution of the states of a bistable system. This study has being carried out in the laboratory of professor Piercarlo Fantucci, Department of Biotechnology and Biosciences, University of Milan-Bicocca. In the last part of the thesis we were finally able to deal with a whole cellular network. We focused on the heterotrimeric Gq/11 protein signaling network which is known to be critical for evoking varied physiological functions including sensory perceptions, behavioral and mood regulation, regulation of the immune system activity and inflammation. Dysfunctions of the Gq/11 network can lead to diseases like cancers and immune system deficiencies. A detailed network topology of the various pathways that emanate from Gq/11 and that allow signal to flow from the receptor to multiple transcription factors such as AP1, c-fos, c-jun, SRF and CREB has been developed. We have then implemented a multi compartment ordinary differential equation model using the software Virtual Cell to analyze signal flow from the plasma membrane through the cytoplasm to the nucleus. The obtained time courses of activation of key components of the system, such as small GTPases, MAPkinases and transcription factors are in accordance with experimental data provided by the S.Gutkind Lab (Oral and Pharyngeal Cancer Branch, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, Maryland, USA). The obtained numerical data will soon be implemented in dynamic graphs to identify the key regulatory motifs of the information processing through the Gq/11 protein signaling network that are controlling the temporal and spatial activity of transcription factors such as SRF, c-jun, c-fos, AP1 and CREB. This study has being carried out in the laboratory of the Professor Ravi Iyengar (Department of Pharmacology and Systems Therapeutics) of the Mount Sinai School of Medicine, New York, USA. The present thesis allowed us to study four systems each at a different level of complexity, according to the biological question we wanted to answer. The models we proposed constitute another demonstration of the importance of quantitative modeling in biology. This work also suggests that even if all the approaches we used were mathematically different to each other, they share a common methodology. This implies that an underlying correlation runs through all the scales of modeling.

Molecular Orbital Calculations for Biological Systems is a hands-on guide to computational quantum chemistry and its applications in organic chemistry, biochemistry, and molecular biology. With improvements in software, molecular modeling techniques are now becoming widely available; they are increasingly used to complement experimental results, saving significant amounts of lab time. Common applications include pharmaceutical research and development; for example, ab initio and semi-empirical methods are playing important roles in peptide investigations and in drug design. The opening chapters provide an introduction for the non-quantum chemist to the basic quantum chemistry methods, ab initio, semi-empirical, and density functionals, as well as to one of the main families of computer programs, the Gaussian series. The second part then describes current research which applies quantum chemistry methods to such biological systems as amino acids, peptides, and anti-cancer drugs. Throughout the authors seek to encourage biochemists to discover aspects of their own research which might benefit from computational work. They also show that the methods are accessible to researchers from a wide range of mathematical backgrounds. Combining concise introductions with practical advice, this volume will be an invaluable tool for research on biological systems.

Recent advances in nanofabrication technology have facilitated the development of single-walled carbon nanotube (SWCNT) arrays with long-range order across macroscopic dimensions. However, an accurate generalized method of modeling these systems has yet to be realized. A multiscale computational approach combining first principles methods based on density functional theory (DFT) and extensions thereof to account for excited electron states, and classical electrodynamics simulations is described and applied to calculations of the optical properties of macroscopic SWCNT arrays. The first-principles approach includes the use of the GW and Bethe-Saltpeter methods, and the accuracy of these approximations is assessed through evaluation of the absorption spectra of individual SWCNTs. The fundamental mechanisms for the unique characteristics of extremely low reflectivity and high absorptance in the near IR are delineated. Furthermore, opportunities to tune the optical properties of the macroscopic array are explored.