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\r\n\t[3] M. Kauranen, A. V. Zayats. Nonlinear Plasmonics. Nature Photonics, vol. 6, 2012, pp. 737-748.
\r\n\t[4] P. Dombi, Z. Pápa, J. Vogelsang et al. Strong-field nano-optics. Reviews of Modern Physics, vol. 92, 2020, pp. 025003-1 – 025003-66.
\r\n\t[5] N. C. Panoiu, W. E. I. Sha, D.Y. Lei, G.-C. Li. Nonlinear optics in plasmonic nanostructures. Journal of Optics, 20, 2018, pp. 1-36.
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The biological membrane is a selective barrier delineating the boundaries of cells and organizing cellular organelles into compartments. One of the major functions of the membrane is to regulate movements of water, ions, and certain constituents in and out of the cell or organelle. Singer and Nicholson described the membrane as a “fluid mosaic” model in which lipids and proteins freely diffuse in the membrane plane [1]. While the biological membrane is actually made up of 1.5- to 4-fold proteins by weight, it is usually described as phospholipids arranged in a bilayer.
\nThe lipid composition in membrane is highly specific with variation identified from membranes of different organisms, different cells of the same organism, and even different membranes of the same cell [2]. Generally, a lipid consists of a polar head group and hydrophobic tail region where a cell may have over a thousand different lipid species. Formation of bilayers and other lipid structures such as micelles occurs as the hydrophobic tails orientates away from the aqueous environment while the polar head groups interact favorably with water. Changes in the bilayer structures can affect the function of the membrane, membrane-embedded proteins and complexes. The surrounding environment can greatly affect the structure and functions of proteins, much like the effects of water on water-soluble proteins. The membrane produces a complex environment for embedded proteins, and membrane thickness, fluidity, charge, curvature, and phase have been demonstrated to play a role in protein structure and function determination [3].
\nThe structure and properties of membranes are a complex matter and cannot be easily described by a single-lipid molecule alone. Molecular dynamics (MD) simulations offer a viable alternative to study the properties of membrane and lipid-forming structures such as vesicles. This review serves as an introduction to currently available membrane lipid force fields and recent advances in membrane simulations.
\nIn general, all-atom (AT), united-atom (UA), and coarse-grained (CG) are the three-membrane lipid force fields. Figure 1 illustrates the AT, UA and CG force field of a lipid by spherical representation.
\nRepresentation of 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) with (a) atomistic (all-atom; AT), (b) united-atom (UA), and (c) coarse-grain (CG) force fields as van der Waals spheres.
AT MD simulation represents every atom in the system as a single interaction site. Figure 2 shows the example of AT simulation system of Salmonella enterica ser. Typhi TolC protein in POPE. To date, Chemistry at HARvard Macromolecular Mechanics (CHARMM) and Assisted Model Building with Energy Refinement (AMBER) are the only fully AT force field parameterization available for lipids. Prior to the development of CHARMM36, CHARMM27r was widely used for membrane simulations [4, 5]. Simulations using CHARMM27r require a large positive surface tension (30–40 dyn/cm) to achieve the experimentally determined surface area per lipid (APL). However, theoretical considerations of self-assembled systems and macroscopic black lipid bilayers [6] indicate that the surface tension of bilayers are about zero to several dyn/cm even when undulations are taken into account [7]. Therefore, simulations of lipid bilayers using CHARMM27 and CHARMM27r shrinks to a near gel phase state without the use of surface tension. Additionally, CHARMM27 and CHARMM27r also failed to reproduce the experimental deuterium order parameters, SCD in the glycerol and upper chain regions [4]. A wide range of glycerophospolipids exhibit splitting in the carbon 2 of the aliphatic chain and carbon 1 for glycerols, but this observation cannot be replicated when simulations were performed with CHARMM27 or CHARMM27r [8]. This may affect conclusions of interactions of lipids with surface-active agents drawn from MD simulations. Besides that, the area compressibility modulus, KA, was underestimated, the head group region was underhydrated, the electron density in the bilayer midplane was underestimated while the frequency dependence of the 13C NMR T1 of the acyl chains near the head group was overestimated.
\nAll-atom (AT) simulation of Salmonella enterica ser. Typhi TolC protein in POPE.
These major weaknesses of the CHARMM27r force field eventually motivated the development of CHARMM36. CHARMM36 corrected most of the existing problems with lipid force fields, most importantly allowing the simulation of lipid bilayer without the use of surface tension and improved the reproducibility of the experimental deuterium order parameters in the glycerol and upper chain regions of phospholipids [8]. A comparative study of lipid force fields by Piggot and colleagues found that CHARMM36 was the only force field that accurately reproduced the experimental order parameters of carbon 2 in both acyl chains of 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) and 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (POPC) [9]. The other properties of the membrane were reasonably well reproduced. However, there are several limitations that have to be considered while pursuing CHARMM36. One is the approximate treatment of long-range Lennard-Jones (LJ) forces for bilayers. Simulations augmented with long-range LJ forces using 3D-isotropic periodic sum/discrete fast Fourier transform (3D-IPS/DFFT) may improve results for lipid monolayers but this increases surface tension, therefore making it less suited for bilayer simulations. The recommendation for bilayer simulations is to use particle-mesh Ewald (PME) with rc = 10 or 12 Å and no long-range correction for the LJ term, but these setting will cause underestimation of the surface tension of lipid monolayers.
\nAMBER force fields were generally less used for membrane protein simulation due to the lack of a specific parameter set for lipids. However, the general AMBER force field (GAFF) which was originally parameterized for the simulation of arbitrary organic molecules with pre-existing AMBER force fields have been shown to reproduce lipid bilayer parameters satisfactorily [10]. GAFF has been tested on a range of lipids, including 1,2-dimyristoyl-d54-sn-glycero-3-phosphocholine (DMPC), 2-dioleoyl-sn-glycero-3-phosphocholine (DOPC), 1,2-dilauroyl-sn-glycero-3-phosphocholine (DLPC), DPPC, POPC, and 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine (POPE) [11, 12]. The use of surface tension is necessary to achieve the correct APL for POPC [10], DOPC [13], and DMPC [12]. In order to better fit GAFF for lipid molecules, Dickson and colleagues set to re-parameterize the LJ terms for acyl chain carbons and hydrogens as the initial GAFF LJ terms were developed for proteins, nucleic acids, and small organic molecules [11].
\nIn addition, torsion parameters were also re-optimized using high-level quantum chemical data with “Paramfit.” This strategy has allowed a tensionless simulation of the lipids in the isothermal-isobaric (NPT) ensemble, achieving high level of agreement with experiment data for volume per lipid (VL) and thickness value within 5% of experiment [11]. However, the APL for POPE lipids is lower than expected and thickness value was overestimated by 10% [11]. The force field has also been shown to be able to reproduce the data on large lipid bilayers containing 288 lipids, with little changes in the APL, VL, and bilayer thickness [11]. The comparison with CHARMM27 and Berger force field also displayed GAFF\'s ability to reproduce the order asymmetry found at the beginning of lipid acyl chains [13]. Prior to the development of CHARMM36, GAFF was the only force field capable of capturing the carbon-2 deuterium order parameter splitting [13], although it has also produced lower surface APL and higher deuterium order parameters compared to experimental data [10, 12, 13]. Even so, compatibility of GAFF with previous AMBER force fields makes it a suitable choice for the simultaneous simulation of membrane, protein, and organic molecule simulation [13].
\nA recently developed Lipid14 force field with an updated Lipid11 head group and tail group charges and parameters enabled proper tensionless simulation of lipid bilayers in AMBER [14]. Lipid14 LJ and torsion parameters were modified to reproduce the experimental density, ρ and heat of vaporization, ∆Hvap of alkanes of different chain lengths by fitting the CH2-CH2-CH2-CH2 torsion to ab initio data using “Paramfit” and altering the LJ and torsion simultaneously. Lipid14 force field has a modular nature that allows new lipid species to be added into the force field by constructing them from head group and tail group “building blocks.” Testing of Lipid14 on DLPC, DMPC, DPPC, DOPC, POPC, and POPE showed that APL for all simulations was within 3% of the experimental value, with the exception of POPE. The APL was closer to the older experimental value of 56.6 Å2 [15] than the more current APL of 59–60 Å2 [16]. The VL was found to be within 5% of experimental value which was acceptable, although it may be considered a slight underestimation [14].
\nFurthermore, the isothermal area compressibility modulus, KA falls close to the experiment, again with the exception of POPE, which has a higher KA with a large standard deviation value. The authors suggested that implementation of other barostats into AMBER may improve KA values as the Berendsen method for pressure control is not ideal for simulations in which volume fluctuations is an important parameter that is capable of influencing the outcome of the simulation [14]. The Luzzati thickness, DB, which is calculated using the z-dimension of the simulation box and the integral probability distribution of the water density along the z-axis was slightly underestimated for saturated lipids, which implies more water penetration into the hydrophobic region of the bilayer of these lipids. Lipid14 was also able to reproduce the experimental order parameter trend, including the splitting of the sn-2 chain from the sn-1 chain and the drop at the carbon-9 and carbon-10 positions of POPC and POPE lipids, which is the cis double bond. Results from GPU repeats and CPU runs were also consistent.
\nRecent analysis applying full AT force field have reached timescales of up to tens of nanoseconds, with ambitious simulations pushing the microsecond mark, e.g. to characterize the interaction of the multiple sclerosis synthetic biomarker CSF114(Glc) with the membrane bilayer [17], probing of the huntingtin Htt17 membrane anchor on a POPC bilayer [18], mixing of lipids [19], and membrane-binding mechanism of the yeast Osh4 peripheral membrane protein [20]. Even though microsecond simulations have been reported, the accessibility of this approach is limited as many biologically relevant phenomena may require sampling time beyond the microsecond timescale and investigators may not have access to intensive computational resources. Given that lipid reorganization is quite slow, a properly equilibrated membrane may require 20–40 ns of simulation time. Convergence of membrane protein simulations still remains a question, as 100 ns of simulation may still be insufficient to fully describe e.g. rhodopsin loop fluctuations in a membrane [21]. Indeed, since all atomic-level interactions are retained and time-steps for integration of Newtonian motions are in the femtosecond range, AT simulations could be a time consuming and computationally expensive practice. The issue of convergence has been rectified in part by running several long simulations and using non-equilibrium sampling method or steered MD to calculate the observables of interest. A series of extended simulations (~100 ns) on the voltage sensor domain of potassium channels revealed the importance of lipid phosphates in accommodating the significantly charged S4 helix [22]. Using umbrella sampling, potential of mean force has been calculated for permeation and effect of a potassium ion on the second ion passing through gramicidin A channel [23].
\nRegardless, AT simulations still provided the highest level of detail and reliability when it comes to quantitative prediction of properties such as motional timescales or interaction strengths. Such details recently provided insightful interactions between protein and membrane, such as the rearrangement of amino acid side chains and local bilayer deformation due to hydrophobic mismatch and how the hydrophobic membrane layer accommodates charged arginine side chain of outer membrane phospholipase A [24, 25].
\nThe UA representation of lipids simplifies the carbon tails of the lipid by associating the aliphatic carbon and its hydrogen atoms into a single particle. Because the non-polar hydrogen atoms are treated implicitly, the number of interaction sites per lipid can be reduced by two third. The computational costs for simulations of such membrane systems become relatively cheap as the 60% of the pairwise interactions in the membrane is reduced. The model lipid DPPC can be represented by 50 particles in UA force field, but needed 130 interaction sites in an AT force field. Since limited physical information may be collected from explicit acyl chain hydrogens, it became desirable to utilize UA force fields for membrane with an AT protein force field for membrane protein simulation [26, 27].
\nThe UA lipid models parameterized by Berger et al. (1997) were one of the most popular lipid force field for lipids and were originally developed by Essex and colleagues [28] from the Optimized Potentials for Liquid Simulations (OPLSs) UA force field. Bonded parameters of the Berger lipids were obtained from the GROMOS87 force field (note: GROMOS is the GROningen Molecular Simulation package), the acyl chains used Ryckaert-Bellemans dihedral parameters whereas the van der Waals terms were from OPLS and atomic partial charges were from Chiu and colleagues\' calculations [29]. Berger and colleagues further optimized the LJ parameters of the lipid tails based on thermodynamic data of pentadecane [30]. Berger lipid parameters were recommended if one desires maximal sampling due to its fast diffusion and good simulation efficiency [9]. For membrane protein simulations, Berger lipids are commonly used with OPLS and GROMOS [27]. It has also been demonstrated to be compatible with AMBER99SB and could give marginally better free energy calculations than the widely used OPLS/Berger combination [31]. While simulations of membrane proteins using such hybrid parameters have been validated by various groups [32–34], the combination of different force fields require care. Protein lipid interactions in Berger-OPLS and Berger-GROMOS have been found to be overestimated and result in drastic changes of lipid properties upon protein insertion [13, 27].
\nA CHARMM UA representation of the acyl chains is also available, and compatible for simulations with AT CHARMM protein force fields [26]. The CHARMM UA model was derived from C27 phospholipids, where explicit hydrogen atoms of the acyl chains were replaced with a UA representation. The force field, called C27-UA, retains the accuracy of the AT counterpart and provides a practical alternative when used in simulations of proteins and other compounds described by C27. C27-UA was parameterized by fitting to experimental data and AT simulations of liquid phase model systems (pentadecane for saturated hydrocarbon chains, cis-5-decene for monounsaturated chains and methyl hexanoate for the ester region). Simulation of POPC bilayer with C27-UA was comparable to C27 and reproduced experimental NMR and X-ray diffraction data, including electron density profile and carbon-deuterium order parameter. The free energy profiles of transfer of ethane, methanol, and water across a water-dodecane interface were identical in C27-UA and C27 simulation, suggesting that the force field is capable of simulating mixed system containing UA lipids and AT proteins and organic molecules described by the standard CHARMM force field. However, it also retained C27\'s feature of requiring a positive surface tension to be able to reproduce the experimental APL.
\nSeveral GROMOS parameters are used in membrane simulations, such as 43A1-S3 [35], 53A6 [36], and Kukol\'s modification of the 53A6 parameters [37]. 43A1-S3 is an extension and modification of the 43A1 force field designed to improve the properties of lipid membranes in simulations. 43A1-S3 employs charges from Chiu et al. [29] while van der Waals and dihedral parameters were modified from 43A1 to improve hydrocarbon and choline head group dynamics [38]. GROMOS 43A1-S3 accurately reproduces many properties of DPPC bilayers, including APL, lipid diffusion, and the order parameter [39]. The 53A6 parametrization also uses Chiu et al.\'s charges [29] and LJ parameters of the choline methyls and phosphate ester oxygen atoms, thus providing good agreement for the APL, density profile, and order parameter for saturated and unsaturated acyl chain PC lipids [40, 41]. Even with improvements introduced, the isothermal area compressibility modulus was still overestimated. Meanwhile, Kukol reparametrize 53A6 following reports that the force field failed to reproduce DPPC satisfactorily [37]. He used Chiu\'s charges and increased the van der Waals radii of the carbonyl carbons of DPPC, DMPC, POPC, and POPG. He also employed non-standard GROMOS dihedral parameters for the double bond in the unsaturated lipids. This results in fairly good agreement with experimentally determined properties with the exception of order parameters for the sn-2 oleoyl chains. A comparative force field study by Piggot et al. recommended against using 43A1-S3 for POPC membranes and Kukol\'s 53A6 POPC parameters [9]. This is because 43A1-S3 could not reliably reproduce the drop in order parameter at the carbon 10 of oleoyl chains whereas Kukol\'s POPC parameters showed several disagreements with experimental value in terms of membrane thickness and order parameter [9].
\nWith the advances in computational power, large-scale simulation projects using UA representation of the membrane region have been able to reach the microsecond mark. Access to such timescale allowed the observation of structural rearrangement and changes in hydrogen bonding pattern in an integral Kv1.2 channel embedded in a hyperpolarized membrane [42]. On a shorter timescale, CorA magnesium transport channel has been probed to undergo conformational changes to a putative open state in 110 ns [43].
\nCG simulations are being widely used to investigate phenomenon occurring in timescales not accessible by AT simulation. In a CG simulation, 3–4 heavy atoms (non-H) are grouped together and represented by a single particle. For example, a DMPC lipid consisting of 130 atoms can be represented by 12 interaction sites [44]. The choline moiety is modeled with a single positively charged particle, the phosphate group with a negatively charged particle, the glycerol linkages with two nonpolar particles, while the lipid chains are modeled with 4 apolar particles each [45].
\nEarly CG approaches were typically parameterized based on comparison to AT simulations by using inverted Monte Carlo schemes [46–48] or force matching [49], which aims to reproduce the structural details at a particular state for a particular system. Instead, the MARTINI (note: MARTINI is a CG force field developed by Marrink and coworkers [44]) CG force field calibrated the building blocks of biomolecules against thermodynamic data, particularly the oil/water partitioning coefficients so that there is no need to re-parameterize the model each time. A consistent, atomic level compatible CG force field will be beneficial for multi-scale applications. In MARTINI, an average of four heavy atoms were represented by a single interaction site, with the exception of ring structures which has 2 or 3 ring atoms mapped to a CG bead. The beads are then distinguished into four main types, i.e., polar, nonpolar, apolar, and charged. MARTINI was able to reproduce the properties of lipid bilayers on a semiquantitative level. These are including the APL, the distribution of groups across the membrane and the bending and area compression moduli.
\nMARTINI 2.0 further improved the stress profile across the lipid bilayer and its tendency to form pores. Additionally, the free energy of lipid desorption and lipid flip-flop across the bilayer agreed well with AT simulations and the condensing effect of cholesterol on the APL can be reproduced. MARTINI achieved 5- to 10-fold faster sampling of the configurational space of liquid hydrocarbons and the lipid tails inside a bilayer compared to AT force fields [44]. Among others, MARTINI has allowed applications in simulations of vesicle formation and fusion [50, 51], phase transition of lipid bilayers [52], and the structure and dynamics of membrane-protein assemblies [53, 54]. Currently, MARTINI has also been supplemented with an implicit solvent force field, named Dry MARTINI, which provided 1–2 order speed up and is expected to find application in simulations of large membranes containing millions of lipids [55].
\nThe ELBA (an acronym for “electrostatic-based” by Orsi and Essex [56]) force field, so named because it is electrostatics based, offers another alternative for CG simulations of lipids and lipid bilayers. The ELBA model features two approaches in contrast to other CG methods. Notably, LJ interactions are treated using standard Lorentz-Berthelot mixing rules, similar to AT force fields. This is due to the explicit treatment of lipid electrostatics and water dipoles whereby a relative dielectric constant of unity (ɛr = 1) was used to model their interactions. In addition, a realistic dipolar water model based on a simpler soft sticky dipole potential, i.e. the Stockmayer potential is used in ELBA, which helped provide a correct diffusion coefficient of lipids in the liquid phase, a point often overestimated by other CG models. It also shows 15 and 200 times speed improvement over UA and AT models, respectively. Validation has been performed on DOPC, DOPE, and gel phase DSPC, whereby ELBA satisfactorily reproduced several fundamental experimental properties including APL, volume per lipid, curvature elastics constants, electrostatic potential distribution, electrostatic diffusion constant, and lipid diffusion coefficient. At the time of the development, ELBA was only available with the authors\' in-house software, and it has since been implemented in LAMMPS [57]. However, there has yet to be a current parameterization for protein and other organic molecules, potentially limiting its use in mixed systems.
\nCG models for proteins are available and have been adopted into protein-bilayer and peptide-bilayer systems [58, 59]. The advantage in employing CG models is the improved speed and size. As CG force fields have a reduced degree of freedom and removed high-frequency motions such as hydrogen bond vibrations, integration time step could be pushed up to 20–40 fs and highly increases the accessible timescale by 100-fold. Therefore, it is possible to access phenomenon occurring at timescales not accessible to classical simulations, for example, membrane protein aggregation, demonstrated by the formation and domain-specific distribution of Ras proteins in plasma membranes [60]. CG simulation has also been used to self-assemble lipid bilayers around membrane proteins, providing information into protein positioning in the bilayer [61]. Comparison of membrane positioning for six proteins in the CG approach with available experimental data showed high qualitative similarity, even though there are discrepancies from different lipid composition used in the simulation and experiments [61].
\nNevertheless, due to simplification of the representations, loss of details is inevitable. Two approaches have been employed to probe for detailed interactions while simulating in CG membrane models is by mixed AT-CG simulation and multiscaling. To elucidate small molecules, membrane peptides and proteins at high resolution, some approaches have used mixed CG-AT systems by simulating the ligand and protein atomistically in a CG membrane [62, 63]. Such mixed descriptions can be likened to quantum mechanical/molecular mechanical (QM/MM) simulations that have achieved considerable success in the past decade (see [64, 65] for reviews regarding usage of QM/MM with proteins). A major consideration in building a mixed AT-CG system is the parameterization and definition of the interactions at the AT-CG interface. As many CG force fields are derived from AT simulations, force-matching procedures can be used to derive effective pairwise CG force field from AT simulations. In AT-CG approach, all atoms simulation of the whole system is first carried out, the system will be subsequently divided into AT and CG parts whereby the most interesting part of the system will retain the atomistic details. The effective AT-CG force field is subsequently obtained by treating the AT and CG parts equally in the force matching procedure [63].
\nCHARMM-GUI PACE CG Builder [66] was developed for the modeling of large and complex biological system. It utilizes the mixed UA/CG where the PACE force field was used for protein in UA format and Martini CG force field for the water, ions and lipids. Thus, the number of atoms in a system can be reduced by the factor of 10. Analysis showed the PACE/MARTINI hybrid simulation has most of the proteins in the root means square deviation (RMSD) of less than 3 Å.
\nOn the other hand, several methods have been developed for conversion of CG system to AT representation through fragment-based approach [67], simulated annealing [68], and force-matching [49]. These methods, termed the multiscale approach allows the use of CG simulations to explore membrane-lipid interactions, which, after a sufficient equilibration period is converted back to AT model simulation for detailed characterization [67].
\nThe availability of many different force fields and parameters for a range of lipid molecules has made it easier to construct systems consisting of mixed lipids. Therefore, the lipid-converter tool is beneficial to easily adapt a system between force fields [69]. The lipid-converter tool can be used in command line by defining a PDB or Gromacs coordinate file and is also available as a web server [70]. The tool currently supports the Berger, GROMOS 43A1-S3, GROMOS 53A6, GROMOS 53A7, CHARMM36, OPLS-UA, and Lipid11. Thus, Stockholm lipids that are compatible with AMBER force field and used CHARMM nomenclature are also supported by this extension. Moreover, lipid converter may be useful to build non-conventional systems as it can generate asymmetric lipid distribution and even label leaflets in curved systems like vesicles.
\nTowards the end of automating the process of building heterogeneous membrane, the CHARMM-GUI Membrane Builder [71] is a useful tool to generate coordinates for membrane models and protein/membrane systems [72–74]. The membrane builder offers a selection of commonly used lipid models in addition to cholesterol which can be customized according to concentration, APL, hydration number, and thickness of the water layer [73].
\nAlternatively, a new web server MemGen is able to automatically set up lipid membrane simulation systems without restrictions in force fields, lipid types, or MD simulation software [75]. The user can upload one or more lipid structure files as well as amphiphilic molecules such as alcohol or detergent. A compact representation of each lipid aligned along the z-axis is generated by building GAFF topology of each lipid using ACPYPE, then applying simulated annealing with constant-force pulling on the head group and tail atoms as well as position-restraining potentials with Gromacs. The server subsequently hydrates the membrane with a number of water molecules, which can also be specified by the user. After the addition of counter ions or sodium chloride, a PDB format of the final structure is available for download. However, it must be noted that MemGen provides highly ordered, unphysical configurations which requires careful equilibration of at least 10 ns. In addition, it is also unable to produce asymmetric bilayers with different composition of lipids in the two monolayers.
\niMembrane is another useful web-based tool which can predict the orientation of a membrane protein within the membrane [76]. Early approaches use a two-state membrane model or a simple hydrophobic slab to model the orientation of a membrane protein in the membrane. Scott and colleagues developed a CG MD to simulate membrane proteins in the presence of membrane lipids which self-assemble into a lipid bilayer [61]. Using the simulation results, iMembrane can predict the orientation of proteins of homologous structure or sequence. BLAST is first performed against the CGDB database for any input sequence or structure. Matches are subsequently realigned to the query using MUSCLE [77] for sequence realignment or “MAMMOTH” [78] for structure super-positioning. Residues in the query are then annotated as N (not in contact with the membrane), H (in contact with polar head group of the membrane lipids), or T (in contact with the lipid hydrophobic tails).
\nAs MD simulations for membrane and membrane protein systems became widespread, many groups began developing tools to allow more efficient analysis of the MD trajectories. APL is an important indicator of the membrane phase and stability of the simulation. “GridMAT-MD” is a Perl program which can calculate the APL as well as thickness of a membrane [79]. For bilayer thickness calculation, the user can define a reference atom (such as the lipid phosphate, P atom) and the program first uses the upper leaflet as a reference and assign a paired lipid in the lower leaflet with the upper leaflet based on proximity in the x- and y-direction. The z-distance between the two points is calculated. The program then repeats the same step using the bottom leaflet as reference, and the two results are averaged, and written to a generic ASCII.dat file. Meanwhile, APL calculation of lipid-only systems can be as simple as taking the box size divided by the number of lipids in the upper or lower leaflet. Calculation of APL for membrane-protein systems are not as simple, and “GridMAT-MD” solves the problem by assigning protein atoms found within the lipid head groups to grid points then subtracts the total protein area from the size of the system. As of version 2.0, “GridMAT-MD” can now calculate the bilayer thickness and APL for multi “.pdb” or “.gro” files.
\nMori and colleagues proposed a more sophisticated method for calculating the APL using Voronoi tessellation and Monte Carlo simulation [80]. Coordinates of center of mass for each lipid molecules and coordinates for protein atoms located between the maximum and minimum z-coordinates for the monolayer are projected onto the XY plane. Two-dimensional Voronoi analysis is subsequently performed for the lipids only. The APL for non-boundary lipids is the area of the Voronoi polygon where the lipid center of mass is located. The APL for boundary lipids can be determined by using a Monte Carlo integration method where the lipid region is probed by randomly shooting a pseudoparticle into the lipid Voronoi polygon. Thus, the APL for the boundary lipid is the product of the area of the Voronoi polygon, and the probability of the shot missing a protein atom. This method finds application in analysis of membrane-protein system and can differentiate between boundary and non-boundary lipids.
\nNot only that, analysis of other membrane properties became easier with the development of “Membrainy,” an intelligent membrane analysis tool that can provide the calculation of various membrane-specific properties for planar bilayer trajectories [81]. This include APL, order parameter, head group orientation, lipid mixing/demixing entropy, time evolution of the transmembrane voltage, 2D surface map generation, gel percentage, membrane thickness, detection of lipid flip-flop and annular shell lipid analysis. While the program has been primarily designed for use with Gromacs MD package, it is also compatible with pdb trajectory from other MD packages. Currently, it is implemented with CHARMM36, Berger/GROMOS87 and Martini v2.0 force fields, but is also expandable to include other force fields and trajectory formats. Output graphs can be readable by the Grace plotting software.
\nOther than that, MEMBPLUGIN [82] is another tool to study the MD trajectories of membrane-protein and complex membrane structures. This is a plugin in Visual MD package to measure biophysical properties in the simulated membranes.
\nBeyond the improvements in computational power, force field developments, and CG methodologies, more accurate representations of the membrane continued to evolve. The biological membrane is a complex entity composed of numerous lipid species such as phosphatidylcholines, phosphatidylethanolamines, and phosphatidylserine. Other molecules such as cholesterols, sphingomyelins, and cardiolipins also play a role in regulating membrane structure and function. In the outer membrane of many species of Gram-negative bacteria, the presence of lipopolysaccharide in the upperleaflet modulates the insertion, folding, and dynamics of outer membrane proteins within the membrane. Available tools for generate mixed membrane bilayers are including CHARMM-GUI Membrane Builder [73] and MemBuilder [83]. CHARMM-GUI Membrane Builder and MemBuilder supports a total of 32 and 18 different lipids types, respectively.
\nStraatsma and Soares first reported the simulation of the outer membrane protein OprF in an asymmetric outer membrane with a lipopolysaccharide and phospholipids, describing the saccharides component using GLYCAM parameters [84]. Holdbrook et al. performed simulations of the Haemphilus influenza Hia autotransporter domain in LPS and a realistic outer membrane inner leaflet which comprises 1-myristoyl 2-palmitoleoyl phosphatidylethanolamine (DMPE) lipid [85]. Comparison with simulations of the autotransporter in simpler, single species DMPC lipid model showed that the DMPC membrane accurately replicated the membrane thickness of the outer membrane and reproduced similar dynamics of the protein in asymmetric LPS/MPoPE membrane [85]. The realistic bilayer, however, revealed a patch of positive lysine and arginine residues on the extracellular mouth of Hia that interact regularly with phosphate and sugar groups of the LPS and are suggested to anchor Hia within the outer membrane [85].
\nThe continuous update and improvement of atomistic force fields expanded the types of lipid molecules which could be simulated and increased the accuracy to better match experimental data. Depending on the level of detail of the simulation, UA force fields are an excellent alternative to balance between accuracy and speed. By using CG force fields approaches, sampling and size limitations may be tackled efficiently. Ultimately, advances in computational power and hardware have improved the timescale and system size where MD can be employed. In membrane simulations, the microsecond mark has been reached and simulations are slowly becoming routine work to complement experimental results. In addition, various web server tools and useful analysis programs have been developed to aid membrane simulation analysis. Further advances in lipid force fields will make it possible to characterize membrane structures in greater time and physical scale.
\nThis work was funded by the Malaysia Ministry of Higher Education Fundamental Research Grant Scheme (FRGS) (203/CIPPM/6711439) and the Higher Institutions Center of Excellence (HICoE) Grant. S. W. Leong would also like to thank the Malaysia Ministry of Higher Education for MyBrain Science scholarship.
\nThe mankind has relied on different sources of energy during its economic development throughout the centuries. Whereas coal has been the main energy source in the nineteenth century, oil was in twentieth one. The possible scenarios for remediation of greenhouse effect due to carbon dioxide released by energy production and industry are rendered to minimization of emissions and its recycling. The latter is accomplished by the production of energy sources and chemicals of practical importance from carbon dioxide.
The emission minimization consists in two approaches: replacement of the fossil fuels by renewable ones (solar, wind energies, biomass, etc.) or improvement of energy efficiency in all human activities in different ways. The distribution of energy sources for the European Union for the year 2016 is shown in Figure 1. One can see that the share of renewables is bigger than the powerful nuclear energy with a leading role in energy production. The biggest part (more than 60%) of the renewable energy sources is assigned to the biomass and waste utilization.
Production of primary energy, EU-28, 2016 (% of total, based on tons of oil equivalent). Source: Eurostat (nrg_100a) and (nrg_107a) [1].
One of the ways to cope with the problem of carbon dioxide emissions is to close the carbon cycle using renewable fuels from presently grown biomass, by recycling the released carbon dioxide by the present vegetation by photosynthesis. This is the philosophy of biomass utilization as energy source. The most spread biofuels in the present period are biogas, produced by anaerobic digestion of organic waste, bioethanol, produced from cereals and/or lignocellulosic residues and biodiesel, produced by trans-esterification of lipids with methanol or ethanol.
In this review, we shall concentrate ourselves to the application of biogas as renewable energy source and also as a feedstock for the production of chemicals and other fuels.
Biogas is produced by anaerobic digestion of organic matter of natural origin [2, 3, 4]. The main advantage of this process consists in the combined environmental and energy effect.
Biogas consists mainly of methane, carbon dioxide, and traces of hydrogen sulfide and mercaptanes, as well as residual amounts of oxygen and nitrogen. Small amounts of ethane and hydrogen are possible too. Biogas is obtained by anaerobic digestion of organic waste of biologic origin. The most exploited ones are of agricultural origin (manure, poultry litter, hay, and straw) [5], from food industry, stillage from ethanol production [6], landfill gas, activated sludge from wastewater treatment plants, etc. One of the simplest and the mostly spread flow sheets for biogas production and utilization is shown in Figure 2 [7].
Illustration of biogas cycle, formation, and applications. Scheme taken from [7].
The main fuel in the scheme, shown in Figure 2, is biogas, utilized for energy (thermal one and electricity) or fuel for transport. The carbon dioxide released after combustion is absorbed by the vegetation by photosynthesis, thus closing the carbon cycle. The residual sludge from the digester is rich of organic nitrogen, and therefore, it is suitable for fertilizing the soil.
In the past, biogas has been widely spread as an energy source in the households in the countries of Africa and Asia. Although quite primitive as design, the anaerobic digesters have solved the problems with autonomous energy supply for many households in India, Pakistan, Indo-China, etc.
Later, biogas became very important and essential share as energy source for the countries in Western Europe and Northern America. Besides heating, biogas is now more frequently used for the production of electricity and transport fuel in many municipalities. It is already added to the pipelines for natural gas distribution of household purposes.
A new trend in biogas production and utilization is the so-called biorefinery concept. This concept not only presumes the use of renewable biomass as energy source but also combines it with the production of chemicals, such as plastics, solvents, and synthetic fuels [8]. An example for this is the Danish Bioethanol Concept presented by Zafar [9]. It comprises the ethanol production from lignocellulosic biomass with biogas production of the stillage and cellulose waste. The residual cellulose waste is additionally recycled after wet-oxidation for additional conversion into biogas. A detailed review on biogas applications is published recently by Sawyerr et al. [10].
The variety of anaerobic digesters for biogas production is very broad: from the very primitive pits to most sophisticated bioreactors, such as the floating drum reactor, the upflow anaerobic sludge blanket (UASB) reactor [11, 12, 13], and multistage bioreactor with separated compartments [14, 15]. The choice for anaerobic digester depends on the origin of substrate, and the intermediates are converted during the consecutive steps of hydrolysis, acidification, acetogenesis, and final methanation. In case an accumulation of fatty acids takes place, the reactor with separated compartments is preferable. The most exploited digester for biogas production from domestic waste, activated sludge, and manure is the UASB reactor.
The mostly used substrates for biogas production are the manure from cattle, pigs, and poultry litter. This application competes with the traditional use of manure for soil fertilization. When the amounts of manure prevail the demand for fertilization, biogas production is welcome because double problem is solved: on the one hand, the waste is destroyed and removed, and on the other hand, renewable energy is produced saving money and contributing for carbon cycle closing. That is why attention is paid to the utilization of cattle dung, lignocellulose waste, waste from food and beverage processing, activated sludge from wastewater treatment plants, and household solid waste with landfill gas use. The waste treatment is associated with energy production and reduction of the energy demand of the main enterprise.
Crude glycerol is the main residue from biodiesel production. The amount of this waste product is about 10% from the produced fuel. The poor quality of this glycerol, containing water, potassium hydroxide, and some methanol makes it non-suitable for market purposes even after purification. One alternative utilization of this residual glycerol is in its direct conversion into biogas, thus supplying the biodiesel plant with energy simultaneously. However, as a very simple and digestible substrate, glycerol yields large amounts of organic acids as intermediates, leading to strong inhibition of methanogenic bacteria [16, 17, 18]. That is why glycerol must be used as substrate for biogas production very cautiously with the addition of small amounts, thus making this process with little practical use. It is reported, however, that small additions of glycerol to other basic substrates, i.e. manure, can boost biogas production, as reported by Robra et al. [19] and Astals et al. [20].
Food industry is also a good source for biogas production.
Traditional biogas contains approximately 60% (vol.) methane, almost 40% carbon dioxide, small amounts of ethane and hydrogen (less than 0.5% together), hydrogen sulfide and mercaptanes (some ppm), humidity, and traces of oxygen. Its net energy capacity is ca. 24 MJ/nm3 at methane content of 60% (vol.). The first and most direct use of biogas is for heating purposes for maintenance of the equipment and the farm, where the animal dung is treated. The same applies for its use for domestic purposes, besides heating, e.g., cooking and lighting, as firstly used in Asian and African countries.
Another more sophisticated use of the biogas heating capacity is its utilization as heat energy in beverage and ethanol production. There the stillage remaining after distillation is recycled for biogas production. The resulting biogas is combusted for boiler heating and for energy for operation of distillation columns. Thus, the problems with the treatment of the residual stillage are solved by conversion into biogas, thus mitigating the problems with energy supply and spending. Calculations show that in some cases, stillage utilization as biogas can cover almost the whole energy demand for heating the distillation process. Besides these straightforward applications, biogas is also injected into the grid for natural gas supply for domestic use [21, 22]. For this purpose, a preliminary scrubbing of the carbon dioxide and sulfur compounds is necessary.
Biogas is suitable for generation of electric power in combination with heat recovery. Usually the gas is combusted in engines with internal combustion coupled to turbine. The released heat (being around 60% of the utilized energy) is used for heating purposes for maintenance of the anaerobic digester or for household needs. This method is widely applied for the treatment of activated sludge, a residue from municipal wastewater treatment plants [23, 24].
Electricity production by gas turbines can be applied by biogas as a fuel, thus replacing the natural gas for small-scale applications (or power within 25–100 kW).
The use of biogas as a fuel for civil transport and road vehicles instead of natural gas is already spread in Western Europe and the United States [25]. There are many vehicles in Sweden operating on biogas in the urban public transport [26].
Another very attractive application of biogas for electricity production is its use in fuel cells. The specialized cells for these purposes are described briefly by O’Hayre et al. [27]. Prior to biogas feed, carbon dioxide and sulfur compounds must be removed by scrubbing to avoid corrosion and catalyst poisoning and to rise the gas energy capacity. A sketch of such a fuel cell is shown in Figure 3, cf. [28].
Principal sketch of methane-driven fuel cell, from [28].
The classic process for methane-driven fuel cells is to convert catalytically by steam reforming methane into a mixture of carbon monoxide and hydrogen and to use the latter in a traditional hydrogen/oxygen fuel cell to generate electricity. The advantages of fuel cell applications with methane as a fuel compared to the traditional heat power stations consist in their higher efficiency, clean waste gases (containing almost only carbon dioxide), and higher efficiency at low loads than the gas turbine equipment [29]. Moreover, the released heat can be utilized for different purposes; the main one is to maintain the temperature regime in the fuel cell. There are many practical applications of these methods. It is already widely commercialized. A disadvantage of this method is the necessity of consequent reactions of steam reforming and carbon monoxide removal as well as the operation at high temperatures (about 750°C), being harmful for the metal parts of the equipment [30, 31]. Higher temperatures are preferred to avoid coke deposition on the catalyst [31].
There are new efforts to lower the operation temperature to 500°C in order to keep the equipment durability [32, 33]. Another improvement of the technology is to use the mixture of carbon monoxide and hydrogen as a fuel simultaneously, thus simplifying the whole process, but applying new catalytic process.
The most attractive option is to convert methane (biogas, respectively) into electricity in one step, thus avoiding the steam reforming and carbon dioxide removal. There are some new studies showing direct catalytic oxidation of methane in the anodic space of solid oxide fuel cells (SOFCs), with direct activation of the C-H bonds in the methane molecule [28, 34, 35, 36]. A platinum catalyst was used for this purpose at low temperatures, e.g., 80°C. However, the catalyst deactivates, and the process is limited by methane diffusion in the anodic space. As a result, the power density is still low for practical use.
Besides as a fuel, biogas could be used as a feedstock for synthetic organic fuel production. There are studies claiming for biogas recovery as fuels applying catalytic auto-reforming. Another approach is the dry reforming consisting in converting the equimolar mixture of methane and carbon dioxide into synthesis gas (an equimolar mixture of carbon monoxide and hydrogen).
Afterward, this synthesis gas is converted into a mixture of light hydrocarbons by the catalytic Fischer-Tropsch process. The resulting Fischer-Tropsch process yields liquid hydrocarbon fuels (methanol and dimethyl ether). The intrinsically high-energy density of these fuels and their transportability make them highly desirable. Such synthetic fuels do not contain any sulfur. In addition, methanol (arguably the “simplest” synthetic carbonaceous fuel) is a candidate both as a hydrogen source for a fuel cell vehicle and indeed as a transport fuel, and dimethyl ether is viewed as a “superclean” diesel fuel [36]. It is well known that methanol is a starting material in chemical industry. It is a liquid at room temperature and has much easier storage and transport capabilities than alternatives such as methane and hydrogen. Methanol is used as solvent, gasoline additive, and a chemical feedstock for production of biodiesel and other chemicals of high value. Therefore, the wide application of methanol motivates its large-scale production, which is ever increasing.
However, presently, the dominant technology of methanol is a two-step catalytic process, which is too expensive. A large number of industrial-scale chemical manufacturing processes are currently operated worldwide on the basis of strongly endothermic chemical reactions. The steam reforming of hydrocarbons to yield syngas and hydrogen is a classic example:
The above, highly endothermic reaction is used worldwide for the high-volume production of “merchant hydrogen” in the gas, food, and fertilizer industries, i.e., other portions of energy have to be spent with the consequent air pollution by carbon dioxide.
At present, a relevant technology for methanol production resides in the transformation of CO2 and CH4 to molecules having industrial added values. Among such technologies, a great attention is focused on the production of synthesis gas (gaseous mixture of CO and H2) that constitutes a versatile building block for subsequent production of methanol or chemical intermediates in petrochemical industries. Methanol is still produced on a world scale from synthesis gas, which is combination of varying amounts of H2, CO, and CO2 (at 200–300°C, 50–100 bar), which is itself product of steam reforming of methane (SRM; at ca. 800°C over Ni-based catalyst), followed by further conversion processes such as Fischer-Tropsch (FT) synthesis. This two-step process incurs high energy and capital demands. Additionally, this process gives many other light and heavy weight co-products along with the methanol product. Therefore, additional energy and cost in the conventional methanol plants are directed to the separation of these coproducts from methanol prior to the final deposition of product.
The direct synthesis of methanol from syngas requires a H2/CO ratio of about 2 [37, 38]. Since the syngas produced by dry reforming of methane (DRM) is too poor of H2 (H2/CO ≤ 1) to be fed to a FT synthesis unit, the bi-reforming of methane (BRM), combining DRM with steam reforming of methane (SRM) (H2/CO = 3) and the utilization of the most important two greenhouse gases CH4 and CO2 with water, may yield a syngas with ratio close to 2, the so-called “metgas”:
To date, only one plant with the combination of steam and dry reforming has been recently demonstrated by the Japan Oil, Gas, and Metals National Cooperation. No other industrial technology for DRM has been developed because the selection and design of suitable reforming catalyst remain an important challenge. Ni-based catalysts are the most attractive candidates for large-scale industrial applications due to their high activity in DRM and SRM [39, 40, 41, 42, 43], low cost, and wide availability compared to noble metals. However, they are sensitive to deactivation caused by the metal particles sintering and carbon formation at high reaction temperature of reforming processes. Development of selective and coke-resistance modified Ni-based reforming catalysts is a key challenge for successful application of bi-reforming for methanol production. Modifying Ni catalysts with suitable promoters and supported on reducible metal oxide carriers will give the opportunity to develop active and stable catalysts for bi-reforming of methane.
A “super-dry” CH4 reforming reaction for enhanced CO production from CH4 and CO2 was developed [44]. Ni/MgAl2O4 was used as a CH4 reforming catalyst, Fe2O3/MgAl2O4 was used as a solid oxygen carrier, and CaO/Al2O3 was used as a CO2 sorbent. The isothermal coupling of these three different processes resulted in a higher CO production than conventional dry reforming by avoiding back reactions with water. Equation (3) shows the global reaction of this two-step process, in which CO and H2O are inherently separated because of the two-step process configuration:
It is important to note that despite the apparently higher endothermic effect of the super-dry reforming process than conventional DRM (Eq. 1), the required heat input per mole CO2 converted is much lower (110 kJ/mol CO2 compared to 247 kJ/mol CO2). Finally, given the availability of a renewable source of H2, applications are possible where CO and H2 can be combined in different ratios for the formation of chemicals or fuels [45, 46]. Indeed, an efficient and separate production of high purity CO and H2 would further establish the role of syngas as a versatile and flexible platform mixture.
All these methods and techniques are applicable when biogas is available. Some other applications are described briefly below.
First of all, biogas must be purified for sulfur compounds prior to its use [47]. Afterward, methane and carbon dioxide have to be separated by membrane processes using gas-liquid systems [48] or swing pressure adsorption [49]. Once methane and carbon dioxide are separated, each of them has its own route for further application. Besides the already mentioned applications as a fuel for transport and energy purposes, dry reforming and steam reforming to obtain synthesis gas, the purified methane can be converted into light hydrocarbons, e.g., ethane and ethylene by advanced methods, like the so-called VYJ process [50, 51, 52, 53]. By this method, methane is converted in one step into ethylene by catalytic or electrocatalytic reaction [54, 55, 56].
High yields up to 88% in total are attained [50]. The rest of nonreacted methane is trapped in molecular sieves and recycled to the reactor [50, 53, 54]. In this way, the use of methane reaches 97% with an ethylene yield of 85% [50].
As ethylene is a basic feedstock for the mostly spread polymerizations and many value-added chemicals, it is clear that this way of biogas utilization is quite promising one.
The usual criteria for the feasibility of an anaerobic digestion technology are the type of digester, the operation temperature, the necessary retention time of the substrate in the reactor, the substrate acidity (the initial pH value), and the presence of certain chemicals in the inlet slurry.
However, the most important one is energy demand for the biogas formation and the energy potential of the produced biogas.
There are two typical temperature ranges for biogas production: mesophilic one (at 30–35°C) and thermophilic one (at 55–60°C). Different genera of methanogenic microorganisms are capable to accomplish the processes in those two cases. The advantages of the thermophilic regime are in the higher production rate and the lack of pathogens in the outlet slurry. However, the energy input for maintenance of this regime is higher than for the mesophilic one.
The question of the energy demand for any industrial process is of crucial importance for its economic reliability. The same applies to biogas production.
There are some methodologies for the estimation of the feasibility of biogas production [57, 58]. They all involve the demand of heat for temperature maintenance and electricity for mechanical operations (stirring, pumping, and transport) and comparison to the energy yield after anaerobic digestion.
Generally, the operations for a certain flowsheet are separated into production processes and support ones. The production processes in the considered case are the reception of the substrate and its storage, pre-treatment of feed (dilution, pH adjustment, acid hydrolysis, etc.), and anaerobic digestion with biogas production. The removal of the digestate and its storage and processing are also included. This set of processes is called as Level 1 [57].
Once biogas is produced, it could be used for direct heat and/or electricity production and supplied to customers or for own use (Level 2). More sophisticated operations, such as gas cleaning, upgrading (i.e., removal of carbon dioxide), and compressing the upgraded gas, are required if the gas will be distributed by the gas distribution grid or for some chemical applications.
The methodologies for energy demand evaluation consist in the inventory of all such processes and auxiliary ones with their energy demand per unit production (i.e., amount of produced biogas with certain energy potential). Then, the overall energy demand is compared to the biogas yield with its energy potential, and the percentage of the energy input to the overall yield is a measure for feasibility of the studied technology.
The structures of the energy demand for different flow sheets and the weight of different subprocesses depend on the substrate properties (particles size, chemical structure and content, moisture, and total solid content) and the amount to be treated, the digester construction and design.
Berglund and Borjesson [58] proposed a methodology based on the life-cycle perspective including the energy required for the production of the substrates (including crop growth, harvesting, transport, etc.). The energy efficiency is defined by the ratio of the energy input to the energy yield of the produced biogas. It was found that the energy input corresponds mainly to 15–40% of the energy content of the produced biogas. The subprocesses of extensive handling of raw materials may lead to considerably increase the energy input and thus to undermine the feasibility of the entire technology.
In case the gas will be used as a feedstock for other chemical applications (e.g., dry reforming and steam reforming), the operational costs of the processes at Levels 1 and 2 have to be compared to the operational costs for the chemical processes and the prices of the produced chemicals or other final products.
The main disadvantage of biomass produced fuels is the inevitable release of CO2 in the atmosphere after combustion. Therefore, big efforts are made in the recent years for remediation of this adverse effect of greenhouse gas. The best way to cope with this problem is the natural assimilation by the vegetation by photosynthesis, but it is not sufficient due to the very large emissions from industrial sources, energy production, transport, and household. That is why many other methods are proposed and studied in the recent years.
One of them is the direct use of pure carbon dioxide as a solvent in supercritical extraction in the pharmaceutical industry. However, this application is limited and cannot be a substantial solution of the problem. There are many efforts to recycle carbon dioxide to produce different organic chemicals: formic acid, methanol, dimethyl-ether, poly-carbonates, acrylic acid, etc. [59, 60]. All of these methods are applicable for the residual carbon dioxide after separation from biogas. Therefore, not only methane but also carbon dioxide in biogas is valuable source of energy and value-added product.
The data presented here illustrate one of the very important biorefinery approaches to produce simultaneous energy and value-added chemicals from biomass, thus reducing the demand of fossil fuels and resulting in overloading of atmosphere by greenhouse gases. The same applies to the water and soil pollution, since those resulting from biomass processing are nature compatible and facilitate the formation of close energy and material cycle. One of the ways to do it is biogas production from such waste.
At the end, we can say that biogas extends its area of application leading simultaneously to protect the environment by waste treatment, natural gas, and fossil fuel saving, as well as to replace, at least partially, the oil as a feedstock for organic value-added products.
This work was supported by the Bulgarian Ministry of Education and Science under the National Research Program Eplus: Low Carbon Energy for the Transport and Households, grant agreement D01-214/2018.
The authors declare no conflict of interest.
Edited by Jan Oxholm Gordeladze, ISBN 978-953-51-3020-8, Print ISBN 978-953-51-3019-2, 336 pages,
\nPublisher: IntechOpen
\nChapters published March 22, 2017 under CC BY 3.0 license
\nDOI: 10.5772/61430
\nEdited Volume
This book serves as a comprehensive survey of the impact of vitamin K2 on cellular functions and organ systems, indicating that vitamin K2 plays an important role in the differentiation/preservation of various cell phenotypes and as a stimulator and/or mediator of interorgan cross talk. Vitamin K2 binds to the transcription factor SXR/PXR, thus acting like a hormone (very much in the same manner as vitamin A and vitamin D). Therefore, vitamin K2 affects a multitude of organ systems, and it is reckoned to be one positive factor in bringing about "longevity" to the human body, e.g., supporting the functions/health of different organ systems, as well as correcting the functioning or even "curing" ailments striking several organs in our body.
\\n\\nChapter 1 Introductory Chapter: Vitamin K2 by Jan Oxholm Gordeladze
\\n\\nChapter 2 Vitamin K, SXR, and GGCX by Kotaro Azuma and Satoshi Inoue
\\n\\nChapter 3 Vitamin K2 Rich Food Products by Muhammad Yasin, Masood Sadiq Butt and Aurang Zeb
\\n\\nChapter 4 Menaquinones, Bacteria, and Foods: Vitamin K2 in the Diet by Barbara Walther and Magali Chollet
\\n\\nChapter 5 The Impact of Vitamin K2 on Energy Metabolism by Mona Møller, Serena Tonstad, Tone Bathen and Jan Oxholm Gordeladze
\\n\\nChapter 6 Vitamin K2 and Bone Health by Niels Erik Frandsen and Jan Oxholm Gordeladze
\\n\\nChapter 7 Vitamin K2 and its Impact on Tooth Epigenetics by Jan Oxholm Gordeladze, Maria A. Landin, Gaute Floer Johnsen, Håvard Jostein Haugen and Harald Osmundsen
\\n\\nChapter 8 Anti-Inflammatory Actions of Vitamin K by Stephen J. Hodges, Andrew A. Pitsillides, Lars M. Ytrebø and Robin Soper
\\n\\nChapter 9 Vitamin K2: Implications for Cardiovascular Health in the Context of Plant-Based Diets, with Applications for Prostate Health by Michael S. Donaldson
\\n\\nChapter 11 Vitamin K2 Facilitating Inter-Organ Cross-Talk by Jan O. Gordeladze, Håvard J. Haugen, Gaute Floer Johnsen and Mona Møller
\\n\\nChapter 13 Medicinal Chemistry of Vitamin K Derivatives and Metabolites by Shinya Fujii and Hiroyuki Kagechika
\\n"}]'},components:[{type:"htmlEditorComponent",content:'This book serves as a comprehensive survey of the impact of vitamin K2 on cellular functions and organ systems, indicating that vitamin K2 plays an important role in the differentiation/preservation of various cell phenotypes and as a stimulator and/or mediator of interorgan cross talk. Vitamin K2 binds to the transcription factor SXR/PXR, thus acting like a hormone (very much in the same manner as vitamin A and vitamin D). Therefore, vitamin K2 affects a multitude of organ systems, and it is reckoned to be one positive factor in bringing about "longevity" to the human body, e.g., supporting the functions/health of different organ systems, as well as correcting the functioning or even "curing" ailments striking several organs in our body.
\n\nChapter 1 Introductory Chapter: Vitamin K2 by Jan Oxholm Gordeladze
\n\nChapter 2 Vitamin K, SXR, and GGCX by Kotaro Azuma and Satoshi Inoue
\n\nChapter 3 Vitamin K2 Rich Food Products by Muhammad Yasin, Masood Sadiq Butt and Aurang Zeb
\n\nChapter 4 Menaquinones, Bacteria, and Foods: Vitamin K2 in the Diet by Barbara Walther and Magali Chollet
\n\nChapter 5 The Impact of Vitamin K2 on Energy Metabolism by Mona Møller, Serena Tonstad, Tone Bathen and Jan Oxholm Gordeladze
\n\nChapter 6 Vitamin K2 and Bone Health by Niels Erik Frandsen and Jan Oxholm Gordeladze
\n\nChapter 7 Vitamin K2 and its Impact on Tooth Epigenetics by Jan Oxholm Gordeladze, Maria A. Landin, Gaute Floer Johnsen, Håvard Jostein Haugen and Harald Osmundsen
\n\nChapter 8 Anti-Inflammatory Actions of Vitamin K by Stephen J. Hodges, Andrew A. Pitsillides, Lars M. Ytrebø and Robin Soper
\n\nChapter 9 Vitamin K2: Implications for Cardiovascular Health in the Context of Plant-Based Diets, with Applications for Prostate Health by Michael S. Donaldson
\n\nChapter 11 Vitamin K2 Facilitating Inter-Organ Cross-Talk by Jan O. Gordeladze, Håvard J. Haugen, Gaute Floer Johnsen and Mona Møller
\n\nChapter 13 Medicinal Chemistry of Vitamin K Derivatives and Metabolites by Shinya Fujii and Hiroyuki Kagechika
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