This article presents a topical review of coarse grain simulation techniques. First, we motivate these techniques with illustrative examples from biology and materials science. Next, approaches in the literature for increasing the efficiency of atomistic simulations are mentioned. Considerations related to a specific coarse grain modelling approach are discussed at length, and the consequences arising from the loss of detail are given. Finally, a large number of results are presented to give the reader a feeling for the types of problem which can be addressed.

Synthetic and natural peptide assemblies can possess transport or conductance activity across biomembranes through the formation of nanopores. The fundamental mechanisms of membrane insertion necessary for antimicrobial or synthetic pore formation are poorly understood. We observe a lipid-assisted mechanism for passive insertion into a model membrane from molecular dynamics simulations. The assembly used in the study, a generic nanotube functionalized with hydrophilic termini, is assisted in crossing the membrane core by transleaflet lipid flips. Lipid tails occlude a purely hydrophobic nanotube. The observed insertion mechanism requirements for hydrophobic-hydrophilic matching have implications for the design of synthetic channels and antibiotics.T he interaction between biological membranes and synthetic or natural macromolecules that have activity through the formation of transmembrane nanopores is intrinsic to the functioning of ion channels (1-8) and antimicrobial peptides (6, 9). It is surprising, therefore, that the insertion mechanism of amphiphilic molecules into (and even across) biomembranes is poorly understood (10-13). The use of fully atomistic computer simulations to probe the interactions of membrane proteins with lipid bilayers is of considerable current interest (8). However, despite many recent successes, such simulations are hampered by the accessible system size and timescale. To glean some insight into possible mechanisms associated with the membrane insertion process we have used molecular dynamics (MD) calculations to study the interaction of tubular macromolecules with a fully hydrated dimyristoylphosphatidylcholine (DMPC) lipid bilayer in its physiologically relevant liquid-crystalline phase. The calculations we report have been carried out by using a coarse-grain (CG) model, inspired by the model successfully used to study self-organizing surfactant systems (14). The tubular nanostructures we study are generic models for preassembled bundles of membrane-spanning proteins (3, 11), antimicrobial peptides (4), and cyclic peptides (1) and for a synthetic nanosyringe based on functionalizing a carbon nanotube (2, 15). Although many naturally existing ion channels and antimicrobial peptides form pores with a hydrophilic lumen, many systems exist, including viral ion channels, synthetic peptides, and nanodevices, for which the present model should be an adequate description.The present simulations involve molecular systems that represent Ϸ70,000 atoms altogether. Simulations with larger systems have been reported (8,16). The present CG approach, however, allows us to routinely calculate dynamics in the hundreds or thousands of nanoseconds and therefore allows us to study events that take place at longer scales than those currently accessible by all-atom calculations. Methods CG Model. In the CG model (17, 18), each DMPC lipid molecule consists of 13 interaction sites, eight of which are hydrophobic (four for each alkanoyl tail) and five of which are hydrophilic, three for the glycerol moiety ...

To better understand the role of surface chemical heterogeneity in natural nanoscale hydration, we study via molecular dynamics simulation the structure and thermodynamics of water confined between two protein-like surfaces. Each surface is constructed to have interactions with water corresponding to those of the putative hydrophobic surface of a melittin dimer, but is flattened rather than having its native ''cupped'' configuration. Furthermore, peripheral charged groups are removed. Thus, the role of a rough surface topography is removed, and results can be productively compared with those previously observed for idealized, atomically smooth hydrophilic and hydrophobic flat surfaces. The results indicate that the protein surface is less hydrophobic than the idealized counterpart. The density and compressibility of water adjacent to a melittin dimer is intermediate between that observed adjacent to idealized hydrophobic or hydrophilic surfaces. We find that solvent evacuation of the hydrophobic gap (cavitation) between dimers is observed when the gap has closed to sterically permit a single water layer. This cavitation occurs at smaller pressures and separations than in the case of idealized hydrophobic flat surfaces. The vapor phase between the melittin dimers occupies a much smaller lateral region than in the case of the idealized surfaces; cavitation is localized in a narrow central region between the dimers, where an apolar amino acid is located. When that amino acid is replaced by a polar residue, cavitation is no longer observed.confinement ͉ water ͉ biopolymer ͉ compressibility ͉ cavitation I t has long been accepted that the hydrophobic effect plays a key role in the stability of compact native protein structures (1-3). The protein contains hydrophobic regions which associate, at least in part, due to the favorable solvent-mediated free energy of aggregation of nonpolar moieties in an aqueous environment (2, 4, 5). Although experimental studies (6) suggest that this rationalization is valid, and theoretical work using model systems and realistic protein structures (7) confirms such observations, much is still unknown about the role and behavior of water near proteins and how the aqueous solvent contributes to protein structural stability at the molecular level. The main focus of the present work is to contribute to the understanding of water near, and between, nominally hydrophobic, but realistic, protein surfaces.When one turns to the molecular details of the mechanism of nonpolar aggregation in water, the picture is still not completely clear. The two limiting scenarios for events such as protein folding and directed self-assembly are summarized well in ref. 8, in the context of protein folding. The basic feature distinguishing these scenarios is the relationship between solute dehydration and solute spatial approach. In the traditional view, water is gradually reduced within and between the associating regions in a manner that is concerted with their spatial approach. In an alternative cavitation sce...

PySB is a framework for creating biological models as Python programs using a high-level, action-oriented vocabulary that promotes transparency, extensibility and reusability. PySB interoperates with many existing modeling tools and supports distributed model development.

An analysis of the structural and dynamical hydrogen bonding interactions at the lipid water interface from a 10 ns molecular dynamics simulation of a hydrated dimyristoylphosphatidylcholine (DMPC) lipid bilayer is presented. We find that the average number of hydrogen bonds per lipid oxygen atom varies depending on its position within the lipid. Radial distribution functions are reported for water interacting with lipid oxygen, nitrogen, and phosphorus atoms, as well as for lipid-lipid interactions. The extent of inter-and intramolecular lipid-water-lipid hydrogen bond bridges is explored along with charge pair associations among headgroups of different lipid molecules. We also examine the hydrogen bonding dynamics of water at the lipid surface. A picture emerges of a sticky interface where water that is hydrogen bonded to lipid oxygen atoms diffuses slowly. Hydrogen bonds between water and the double bonded lipid oxygen atoms are longer lived than those to single bonded lipid oxygen atoms, and hydrogen bonds between water and the tail lipid oxygen atoms are longer lived than those to headgroup oxygen atoms. The implications of these results for lateral proton transfer at the interface are also discussed.

Rule-based modeling was developed to address the limitations of traditional approaches for modeling chemical kinetics in cell signaling systems. These systems consist of multiple interacting biomolecules (e.g., proteins), which themselves consist of multiple parts (e.g., domains, linear motifs, and sites of phosphorylation). Consequently, biomolecules that mediate information processing generally have the potential to interact in multiple ways, with the number of possible complexes and post-translational modification states tending to grow exponentially with the number of binary interactions considered. As a result, only large reaction networks capture all possible consequences of the molecular interactions that occur in a cell signaling system, which is problematic because traditional modeling approaches for chemical kinetics (e.g., ordinary differential equations) require explicit network specification. This problem is circumvented through representation of interactions in terms of local rules. With this approach, network specification is implicit and model specification is concise. Concise representation results in a coarse graining of chemical kinetics, which is introduced because all reactions implied by a rule inherit the rate law associated with that rule. Coarse graining can be appropriate if interactions are modular, and the coarseness of a model can be adjusted as needed. Rules can be specified using specialized model-specification languages, and recently developed tools designed for specification of rule-based models allow one to leverage powerful software engineering capabilities. A rule-based model comprises a set of rules, which can be processed by general-purpose simulation and analysis tools to achieve different objectives (e.g., to perform either a deterministic or stochastic simulation).

In vitro cell proliferation assays are widely used in pharmacology, molecular biology, and drug discovery. Using theoretical modeling and experimentation, we show that current antiproliferative drug effect metrics suffer from time-dependent bias, leading to inaccurate assessments of parameters such as drug potency and efficacy. We propose the drug-induced proliferation (DIP) rate, the slope of the line on a plot of cell population doublings versus time, as an alternative, time-independent metric.

Adopting a systems approach, we devise a general workflow to define actionable subtypes in human cancers. Applied to small cell lung cancer (SCLC), the workflow identifies four subtypes based on global gene expression patterns and ontologies. Three correspond to known subtypes (SCLC-A, SCLC-N, and SCLC-Y), while the fourth is a previously undescribed ASCL1+ neuroendocrine variant (NEv2, or SCLC-A2). Tumor deconvolution with subtype gene signatures shows that all of the subtypes are detectable in varying proportions in human and mouse tumors. To understand how multiple stable subtypes can arise within a tumor, we infer a network of transcription factors and develop BooleaBayes, a minimally-constrained Boolean rule-fitting approach. In silico perturbations of the network identify master regulators and destabilizers of its attractors. Specific to NEv2, BooleaBayes predicts ELF3 and NR0B1 as master regulators of the subtype, and TCF3 as a master destabilizer. Since the four subtypes exhibit differential drug sensitivity, with NEv2 consistently least sensitive, these findings may lead to actionable therapeutic strategies that consider SCLC intratumoral heterogeneity. Our systems-level approach should generalize to other cancer types.