High-Dimensional Parameter Search Method to Determine Force Field Mixing Terms in Molecular Simulations.

Molecular dynamics (MD) force fields for lipids and ions are typically developed independently of one another. In simulations consisting of both lipids and ions, lipid-ion interaction energies are estimated using a predefined set of mixing rules for Lennard-Jones (LJ) interactions. This, however, does not guarantee their reliability. In fact, compared to the quantum mechanical reference data, Lorentz-Berthelot mixing rules substantially underestimate the binding energies of Na+ ions with small-molecule analogues of lipid headgroups, yielding errors on the order of 80 and 130 kJ/mol, respectively, for methyl acetate and diethyl phosphate. Previously, errors associated with mixing force fields have been reduced using approaches such as "NB-fix" in which LJ interactions are computed using explicit cross terms rather than those from mixing rules. Building on this idea, we derive explicit lipid-ion cross terms that also may implicitly include many-body cooperativity effects. Additionally, to account for the interdependency between cross terms, we optimize all cross terms simultaneously by performing high-dimensional searches using our ParOpt software. The cross terms we obtain reduce the errors due to mixing rules to below 10 kJ/mol. MD simulation of the lipid bilayer conducted using these optimized cross terms resolves the structural discrepancies between our previous simulations and small-angle X-ray and neutron scattering experiments. These results demonstrate that simulations of lipid bilayers with ions that are accurate up to structural data from scattering experiments can be performed without explicit polarization terms. However, it is worth noting that such NB-fix cross terms are not based on any physical principle; a polarizable lipid model would be more realistic and is still desired. Our approach is generic and can be applied to improve the accuracies of simulations employing mixed force fields.

Transferable interactions of Li+ and Mg2+ ions in polarizable models.

Therapeutic implications of Li+, in many cases, stem from its ability to inhibit certain Mg2+-dependent enzymes, where it interacts with or substitutes for Mg2+. The underlying details of its action are, however, unknown. Molecular simulations can provide insights, but their reliability depends on how well they describe relative interactions of Li+ and Mg2+ with water and other biochemical groups. Here, we explore, benchmark, and recommend improvements to two simulation approaches: the one that employs an all-atom polarizable molecular mechanics (MM) model and the other that uses a hybrid quantum and MM implementation of the quasi-chemical theory (QCT). The strength of the former is that it describes thermal motions explicitly and that of the latter is that it derives local contributions from electron densities. Reference data are taken from the experiment, and also obtained systematically from CCSD(T) theory, followed by a benchmarked vdW-inclusive density functional theory. We find that the QCT model predicts relative hydration energies and structures in agreement with the experiment and without the need for additional parameterization. This implies that accurate descriptions of local interactions are essential. Consistent with this observation, recalibration of local interactions in the MM model, which reduces errors from 10.0 kcal/mol to 1.4 kcal/mol, also fixes aqueous phase properties. Finally, we show that ion-ligand transferability errors in the MM model can be reduced significantly from 10.3 kcal/mol to 1.2 kcal/mol by correcting the ligand's polarization term and by introducing Lennard-Jones cross-terms. In general, this work sets up systematic approaches to evaluate and improve molecular models of ions binding to proteins.

Interactions of Monovalent and Divalent Cations at Palmitoyl-Oleoyl-Phosphatidylcholine Interface.

Li+ is a biologically active and medically important cation. Experiments show that Li+ modulates some phospholipid bilayer properties in a manner similar to divalent cations, rather than other monovalent cations. We previously performed a comparative simulation study of the interaction of several monovalent cations with palmitoyl-oleoyl-phosphatidylcholine bilayers and reported that Li+ exhibited the highest association with lipids and formed a unique tetrahedral coordinated structure with lipid head groups. Here we extend these studies to two biologically important divalent cations, Mg2+ and Ca2+, and observe that, just like monovalent cations, Mg2+ and Ca2+ reduce bilayer areas and increase chain order. Bilayer area changes induced by cations are strongly correlated with the amount of charge inside the headgroup region; however, Mg2+ and Li+ are clear outliers. At the same time though, Mg2+ adsorption in the bilayer is the smallest among all cations, which is in contrast to Li+ that binds strongly to lipids. In fact, in contrast to all other cations, Mg2+ remains fully hydrated in the lipid headgroup region. However, Li+ and Mg2+ share high overlap between their inner-shell coordination topologies. This suggests that Li+ can structurally replace Mg2+, which is bound to other biomolecules with up to fourfold coordination, provided such replacement is energetically feasible. We compute structural topologies and compare them quantitatively using a new weighted-graphs-based method. Finally, we find that the specificity of cation interaction with lipid head groups exhibit consistent trend with the solvation shell energetics of ions in lipid headgroup and bulk water regions.

An assessment of true and false positive detection rates of stepwise epistatic model selection as a function of sample size and number of markers.

Association studies have been successful at identifying genomic regions associated with important traits, but routinely employ models that only consider the additive contribution of an individual marker. Because quantitative trait variability typically arises from multiple additive and non-additive sources, utilization of statistical approaches that include main and two-way interaction marker effects of several loci in one model could lead to unprecedented characterization of these sources. Here we examine the ability of one such approach, called the Stepwise Procedure for constructing an Additive and Epistatic Multi-Locus model (SPAEML), to detect additive and epistatic signals simulated using maize and human marker data. Our results revealed that SPAEML was capable of detecting quantitative trait nucleotides (QTNs) at sample sizes as low as n = 300 and consistently specifying signals as additive and epistatic for larger sizes. Sample size and minor allele frequency had a major influence on SPAEML's ability to distinguish between additive and epistatic signals, while the number of markers tested did not. We conclude that SPAEML is a useful approach for providing further elucidation of the additive and epistatic sources contributing to trait variability when applied to a small subset of genome-wide markers located within specific genomic regions identified using a priori analyses.

Towards a Unified Understanding of Lithium Action in Basic Biology and its Significance for Applied Biology.

Lithium has literally been everywhere forever, since it is one of the three elements created in the Big Bang. Lithium concentration in rocks, soil, and fresh water is highly variable from place to place, and has varied widely in specific regions over evolutionary and geologic time. The biological effects of lithium are many and varied. Based on experiments in which animals are deprived of lithium, lithium is an essential nutrient. At the other extreme, at lithium ingestion sufficient to raise blood concentration significantly over 1 mM/, lithium is acutely toxic. There is no consensus regarding optimum levels of lithium intake for populations or individuals-with the single exception that lithium is a generally accepted first-line therapy for bipolar disorder, and specific dosage guidelines for sufferers of that condition are generally agreed on. Epidemiological evidence correlating various markers of social dysfunction and disease vs. lithium level in drinking water suggest benefits of moderately elevated lithium compared to average levels of lithium intake. In contrast to other biologically significant ions, lithium is unusual in not having its concentration in fluids of multicellular animals closely regulated. For hydrogen ions, sodium ions, potassium ions, calcium ions, chloride ions, and magnesium ions, blood and extracellular fluid concentrations are closely and necessarily regulated by systems of highly selective channels, and primary and secondary active transporters. Lithium, while having strong biological activity, is tolerated over body fluid concentrations ranging over many orders of magnitude. The lack of biological regulation of lithium appears due to lack of lithium-specific binding sites and selectivity filters. Rather lithium exerts its myriad physiological and biochemical effects by competing for macromolecular sites that are relatively specific for other cations, most especially for sodium and magnesium. This review will consider what is known about the nature of this competition and suggest using and extending this knowledge towards the goal of a unified understanding of lithium in biology and the application of that understanding in medicine and nutrition.

Effects of Lithium and Other Monovalent Ions on Palmitoyl Oleoyl Phosphatidylcholine Bilayer.

Interactions of monovalent salts with lipid membranes are explored with molecular dynamics (MD) simulations. The simulations included the monovalent ions Na+ and K+, for their importance in physiology, Li+ for its small size and importance in several medical conditions including bipolar disorder, and Rb+ for its large size. All simulations included Cl- as counterions. One bilayer was simulated without salt as a control. Palmitoyl oleoyl phosphatidylcholine (POPC) bilayers experienced reductions in area per lipid with the addition of salt; the smaller the ion the smaller the area, with the exception of Li+. Li+ exhibited unique binding affinities between phosphates and sn-2 carbonyls that lowered the order of the top part of sn-2 chain, which increased the area per lipid, compared to other ionic simulations. Further, we observe that monovalent salts alter bilayer properties through structural changes and not so much through the changes in surface potential.

Conservation in Mammals of Genes Associated with Aggression-Related Behavioral Phenotypes in Honey Bees.

The emerging field of sociogenomics explores the relations between social behavior and genome structure and function. An important question is the extent to which associations between social behavior and gene expression are conserved among the Metazoa. Prior experimental work in an invertebrate model of social behavior, the honey bee, revealed distinct brain gene expression patterns in African and European honey bees, and within European honey bees with different behavioral phenotypes. The present work is a computational study of these previous findings in which we analyze, by orthology determination, the extent to which genes that are socially regulated in honey bees are conserved across the Metazoa. We found that the differentially expressed gene sets associated with alarm pheromone response, the difference between old and young bees, and the colony influence on soldier bees, are enriched in widely conserved genes, indicating that these differences have genomic bases shared with many other metazoans. By contrast, the sets of differentially expressed genes associated with the differences between African and European forager and guard bees are depleted in widely conserved genes, indicating that the genomic basis for this social behavior is relatively specific to honey bees. For the alarm pheromone response gene set, we found a particularly high degree of conservation with mammals, even though the alarm pheromone itself is bee-specific. Gene Ontology identification of human orthologs to the strongly conserved honey bee genes associated with the alarm pheromone response shows overrepresentation of protein metabolism, regulation of protein complex formation, and protein folding, perhaps associated with remodeling of critical neural circuits in response to alarm pheromone. We hypothesize that such remodeling may be an adaptation of social animals to process and respond appropriately to the complex patterns of conspecific communication essential for social organization.

Building a knowledge-based statistical potential by capturing high-order inter-residue interactions and its applications in protein secondary structure assessment.

The rapidly increasing number of protein crystal structures available in the Protein Data Bank (PDB) has naturally made statistical analyses feasible in studying complex high-order inter-residue correlations. In this paper, we report a context-based secondary structure potential (CSSP) for assessing the quality of predicted protein secondary structures generated by various prediction servers. CSSP is a sequence-position-specific knowledge-based potential generated based on the potentials of mean force approach, where high-order inter-residue interactions are taken into consideration. The CSSP potential is effective in identifying secondary structure predictions with good quality. In 56% of the targets in the CB513 benchmark, the optimal CSSP potential is able to recognize the native secondary structure or a prediction with Q3 accuracy higher than 90% as best scored in the predicted secondary structures generated by 10 popularly used secondary structure prediction servers. In more than 80% of the CB513 targets, the predicted secondary structures with the lowest CSSP potential values yield higher than 80% Q3 accuracy. Similar performance of CSSP is found on the CASP9 targets as well. Moreover, our computational results also show that the CSSP potential using triplets outperforms the CSSP potential using doublets and is currently better than the CSSP potential using quartets.

Sampling multiple scoring functions can improve protein loop structure prediction accuracy.

Accurately predicting loop structures is important for understanding functions of many proteins. In order to obtain loop models with high accuracy, efficiently sampling the loop conformation space to discover reasonable structures is a critical step. In loop conformation sampling, coarse-grain energy (scoring) functions coupling with reduced protein representations are often used to reduce the number of degrees of freedom as well as sampling computational time. However, due to implicitly considering many factors by reduced representations, the coarse-grain scoring functions may have potential insensitivity and inaccuracy, which can mislead the sampling process and consequently ignore important loop conformations. In this paper, we present a new computational sampling approach to obtain reasonable loop backbone models, so-called the Pareto optimal sampling (POS) method. The rationale of the POS method is to sample the function space of multiple, carefully selected scoring functions to discover an ensemble of diversified structures yielding Pareto optimality to all sampled conformations. The POS method can efficiently tolerate insensitivity and inaccuracy in individual scoring functions and thereby lead to significant accuracy improvement in loop structure prediction. We apply the POS method to a set of 4-12-residue loop targets using a function space composed of backbone-only Rosetta and distance-scale finite ideal-gas reference (DFIRE) and a triplet backbone dihedral potential developed in our lab. Our computational results show that in 501 out of 502 targets, the model sets generated by POS contain structure models are within subangstrom resolution. Moreover, the top-ranked models have a root mean square deviation (rmsd) less than 1 A in 96.8, 84.1, and 72.2% of the short (4-6 residues), medium (7-9 residues), and long (10-12 residues) targets, respectively, when the all-atom models are generated by local optimization from the backbone models and are ranked by our recently developed Pareto optimal consensus (POC) method. Similar sampling effectiveness can also be found in a set of 13-residue loop targets.

ResidueFinder: extracting individual residue mentions from protein literature.

BACKGROUND: The revolution in molecular biology has shown how protein function and structure are based on specific sequences of amino acids. Thus, an important feature in many papers is the mention of the significance of individual amino acids in the context of the entire sequence of the protein. MutationFinder is a widely used program for finding mentions of specific mutations in texts. We report on augmenting the positive attributes of MutationFinder with a more inclusive regular expression list to create ResidueFinder, which finds mentions of native amino acids as well as mutations. We also consider parameter options for both ResidueFinder and MutationFinder to explore trade-offs between precision, recall, and computational efficiency. We test our methods and software in full text as well as abstracts. RESULTS: We find there is much more variety of formats for mentioning residues in the entire text of papers than in abstracts alone. Failure to take these multiple formats into account results in many false negatives in the program. Since MutationFinder, like several other programs, was primarily tested on abstracts, we found it necessary to build an expanded regular expression list to achieve acceptable recall in full text searches. We also discovered a number of artifacts arising from PDF to text conversion, which we wrote elements in the regular expression library to address. Taking into account those factors resulted in high recall on randomly selected primary research articles. We also developed a streamlined regular expression (called "cut") which enables a several hundredfold speedup in both MutationFinder and ResidueFinder with only a modest compromise of recall. All regular expressions were tested using expanded F-measure statistics, i.e., we compute Fß for various values of where the larger the value of ß the more recall is weighted, the smaller the value of ß the more precision is weighted. CONCLUSIONS: ResidueFinder is a simple, effective, and efficient program for finding individual residue mentions in primary literature starting with text files, implemented in Python, and available in SourceForge.net. The most computationally efficient versions of ResidueFinder could enable creation and maintenance of a database of residue mentions encompassing all articles in PubMed.

A biophysical model for integration of electrical, osmotic, and pH regulation in the human bronchial epithelium.

A dynamical biophysical model for the functioning of an epithelium is presented. This model integrates the electrical and osmotic behaviors of the epithelium, taking into account intracellular conditions. The specific tissue modeled is the human bronchial epithelium, which is of particular interest, as it is the location of the most common lethal symptoms of cystic fibrosis. The model is implemented in a modular form to facilitate future application of the code to other epithelial tissue by inputting different transporters, channels, and geometric parameters. The model includes pH regulation as an integral component of overall regulation of epithelial function, through the interdependence of pH, bicarbonate concentration, and current. The procedures for specification, the validation of the model, and parametric studies are presented using available experimental data of cultured human bronchial epithelium. Parametric studies are performed to elucidate a), the contribution of basolateral chloride channels to the short-circuit current functional form, and b), the role that regulation of basolateral potassium conductance plays in epithelial function.

Improving predicted protein loop structure ranking using a Pareto-optimality consensus method.

BACKGROUND: Accurate protein loop structure models are important to understand functions of many proteins. Identifying the native or near-native models by distinguishing them from the misfolded ones is a critical step in protein loop structure prediction. RESULTS: We have developed a Pareto Optimal Consensus (POC) method, which is a consensus model ranking approach to integrate multiple knowledge- or physics-based scoring functions. The procedure of identifying the models of best quality in a model set includes: 1) identifying the models at the Pareto optimal front with respect to a set of scoring functions, and 2) ranking them based on the fuzzy dominance relationship to the rest of the models. We apply the POC method to a large number of decoy sets for loops of 4- to 12-residue in length using a functional space composed of several carefully-selected scoring functions: Rosetta, DOPE, DDFIRE, OPLS-AA, and a triplet backbone dihedral potential developed in our lab. Our computational results show that the sets of Pareto-optimal decoys, which are typically composed of approximately 20% or less of the overall decoys in a set, have a good coverage of the best or near-best decoys in more than 99% of the loop targets. Compared to the individual scoring function yielding best selection accuracy in the decoy sets, the POC method yields 23%, 37%, and 64% less false positives in distinguishing the native conformation, indentifying a near-native model (RMSD < 0.5A from the native) as top-ranked, and selecting at least one near-native model in the top-5-ranked models, respectively. Similar effectiveness of the POC method is also found in the decoy sets from membrane protein loops. Furthermore, the POC method outperforms the other popularly-used consensus strategies in model ranking, such as rank-by-number, rank-by-rank, rank-by-vote, and regression-based methods. CONCLUSIONS: By integrating multiple knowledge- and physics-based scoring functions based on Pareto optimality and fuzzy dominance, the POC method is effective in distinguishing the best loop models from the other ones within a loop model set.

An improved united atom force field for simulation of mixed lipid bilayers.

We introduce a new force field (43A1-S3) for simulation of membranes by the Gromacs simulation package. Construction of the force fields is by standard methods of electronic structure computations for bond parameters and charge distribution and specific volumes and heats of vaporization for small-molecule components of the larger lipid molecules for van der Waals parameters. Some parameters from the earlier 43A1 force field are found to be correct in the context of these calculations, while others are modified. The validity of the force fields is demonstrated by correct replication of X-ray form factors and NMR order parameters over a wide range of membrane compositions in semi-isotropic NTP 1 atm simulations. 43-A1-S3 compares favorably with other force fields used in conjunction with the Gromacs simulation package with respect to the breadth of phenomena that it accurately reproduces.

Simulation of charge transport in ion channels and nanopores with anisotropic permittivity.

Ion channels are part of nature's solution for regulating biological environments. Every ion channel consists of a chain of amino acids carrying a strong and sharply varying permanent charge, folded in such a way that it creates a nanoscopic aqueous pore spanning the otherwise mostly impermeable membranes of biological cells. These naturally occurring proteins are particularly interesting to device engineers seeking to understand how such nanoscale systems realize device-like functions. Availability of high-resolution structural information from X-ray crystallography, as well as large-scale computational resources, makes it possible to conduct realistic ion channel simulations. In general, a hierarchy of simulation methodologies is needed to study different aspects of a biological system like ion channels. Biology Monte Carlo (BioMOCA), a three-dimensional coarse-grained particle ion channel simulator, offers a powerful and general approach to study ion channel permeation. BioMOCA is based on the Boltzmann Transport Monte Carlo (BTMC) and Particle-Particle-Particle-Mesh (P(3)M) methodologies developed at the University of Illinois at Urbana-Champaign. In this paper we briefly discuss the various approaches to simulating ion flow in channel systems that are currently being pursued by the biophysics and engineering communities, and present the effect of having anisotropic dielectric constants on ion flow through a number of nanopores with different effective diameters.

Systems Biology Understanding of the Effects of Lithium on Cancer.

Lithium has many widely varying biochemical and phenomenological effects, suggesting that a systems biology approach is required to understand its action. Multiple lines of evidence point to lithium as a significant factor in development of cancer, showing that understanding lithium action is of high importance. In this paper we undertake first steps toward a systems approach by analyzing mutual enrichment between the interactomes of lithium-sensitive enzymes and the pathways associated with cancer. This work integrates information from two important databases, STRING, and KEGG pathways. We find that for the majority of cancer pathways the mutual enrichment is statistically highly significant, reinforcing previous lines of evidence that lithium is an important influence on cancer.

Rationally designing antisense therapy to keep up with evolving bacterial resistance.

Antisense molecules used as antibiotics offer the potential to keep up with acquired resistance, by redesigning the sequence of an antisense. Once bacteria acquire resistance by mutating the targeted sequence, new antisense can readily be designed by using sequence information of a target gene. However, antisense molecules require additional delivery vehicles to get into bacteria and be protected from degradation. Based on progress in the last few years it appears that, while redesigning or finding new delivery vehicle will be more difficult than redesigning the antisense cargo, it will perhaps be less difficult than finding new conventional small molecule antibiotics. In this study we propose a protocol that maximizes the combined advantages of engineered delivery vehicle and antisense cargo by decreasing the immediate growth advantage to the pathogen of mutating the entry mechanisms and increasing the advantage to the pathogen of antisense target mutations. Using this protocol, we show by computer simulation an appropriately designed antisense therapy can potentially be effective many times longer than conventional antibiotics before succumbing to resistance. While the simulations describe an in-vitro situation, based on comparison with other in-vitro studies on acquired resistance we believe the advantages of the combination antisense strategy have the potential to provide much more sustainability in vivo than conventional antibiotic therapy.

Systems Biology Understanding of the Effects of Lithium on Affective and Neurodegenerative Disorders.

Lithium has many widely varying biochemical and phenomenological effects, suggesting that a systems biology approach is required to understand its action. Multiple lines of evidence point to lithium intake and consequent blood levels as important determinants of incidence of neurodegenerative disease, showing that understanding lithium action is of high importance. In this paper we undertake first steps toward a systems approach by analyzing mutual enrichment between the interactomes of lithium-sensitive enzymes and the pathways associated with affective and neurodegenerative disorders. This work integrates information from two important databases, STRING and KEGG pathways. We find that for the majority of neurodegenerative disorders the mutual enrichment is many times greater than chance, reinforcing previous lines of evidence that lithium is an important influence on incidence of neurodegeneration. Our work suggests rational prioritization for which disorders are likely to be most sensitive to lithium and identifies genes that are likely to be useful targets for therapy adjunct to lithium.

Fourier Analysis of Conservation Patterns in Protein Secondary Structure.

Residue conservation is a common observation in alignments of protein families, underscoring positions important in protein structure and function. Though many methods measure the level of conservation of particular residue positions, currently we do not have a way to study spatial oscillations occurring in protein conservation patterns. It is known that hydrophobicity shows spatial oscillations in proteins, which is characterized by computing the hydrophobic moment of the protein domains. Here, we advance the study of moments of conservation of protein families to know whether there might exist spatial asymmetry in the conservation patterns of regular secondary structures. Analogous to the hydrophobic moment, the conservation moment is defined as the modulus of the Fourier transform of the conservation function of an alignment of related protein, where the conservation function is the vector of conservation values at each column of the alignment. The profile of the conservation moment is useful in ascertaining any periodicity of conservation, which might correlate with the period of the secondary structure. To demonstrate the concept, conservation in the family of potassium ion channel proteins was analyzed using moments. It was shown that the pore helix of the potassium channel showed oscillations in the moment of conservation matching the period of the α-helix. This implied that one side of the pore helix was evolutionarily conserved in contrast to its opposite side. In addition, the method of conservation moments correctly identified the disposition of the voltage sensor of voltage-gated potassium channels to form a 310 helix in the membrane.

Three-Dimensional Encapsulation of Saccharomyces cerevisiae in Silicate Matrices Creates Distinct Metabolic States as Revealed by Gene Chip Analysis.

In order to design hybrid cellular/synthetic devices such as sensors and vaccines, it is important to understand how the metabolic state of living cells changes upon physical confinement within three-dimensional (3D) matrices. We analyze the gene expression patterns of stationary phase Saccharomyces cerevisiae (S. cerevisiae) cells encapsulated within three distinct nanostructured silica matrices and relate those patterns to known naturally occurring metabolic states. Silica encapsulation methods employed were lipid-templated mesophase silica thin films formed by cell-directed assembly (CDA), lipid-templated mesophase silica particles formed by spray drying (SD), and glycerol-doped silica gel monoliths prepared from an aqueous silicate (AqS+g) precursor solution. It was found that the cells for all three-encapsulated methods enter quiescent states characteristic of response to stress, albeit to different degrees and with differences in detail. By the measure of enrichment of stress-related gene ontology categories, we find that the AqS+g encapsulation is more amenable to the cells than CDA and SD encapsulation. We hypothesize that this differential response in the AqS+g encapsulation is related to four properties of the encapsulating gel: (1) oxygen permeability, (2) relative softness of the material, (3) development of a protective sheath around individual cells (visible in TEM micrographs vide infra), and (4) the presence of glycerol in the gel, which has been previously noted to serve as a protectant for encapsulated cells and can serve as the sole carbon source for S. cerevisiae under aerobic conditions. This work represents a combination of experiment and analysis aimed at the design and development of 3D encapsulation procedures to induce, and perhaps control, well-defined physiological behaviors.

Digital biology: an emerging and promising discipline.

This article examines the role of computation and quantitative methods in modern biomedical research to identify emerging scientific, technical, policy and organizational trends. It identifies common concerns and practices in the emerging community of computationally-oriented bio-scientists by reviewing a national symposium, Digital Biology: the Emerging Paradigm, held at the National Institutes of Health in Bethesda, Maryland, November 6th and 7th 2003. This meeting showed how biomedical computing promises scientific breakthroughs that will yield significant health benefits. Three key areas that define the emerging discipline of digital biology are: scientific data integration, multi-scale modeling and networked science. Each area faces unique technical challenges and information policy issues that must be addressed as the field matures. Here we summarize the emergent challenges and offer suggestions to academia, industry and government on how best to expand the role of computation in their scientific activities.