p300 is an obligate integrator of combinatorial transcription factor inputs.

Transcription coactivators are proteins or protein complexes that mediate transcription factor (TF) function. However, they lack DNA-binding capacity, prompting the question of how they engage target loci. Three non-exclusive hypotheses have been posited: coactivators are recruited by complexing with TFs, by binding histones through epigenetic reader domains, or by partitioning into condensates through their extensive intrinsically disordered regions. Using p300 as a prototypical coactivator, we systematically mutated its annotated domains and show by single-molecule tracking in live U2OS cells that coactivator-chromatin binding depends entirely on combinatorial binding of multiple TF-interaction domains. Furthermore, we demonstrate that acetyltransferase activity opposes p300-chromatin association and that the N-terminal TF-interaction domains regulate that activity. Single TF-interaction domains are insufficient for chromatin binding and regulation of catalytic activity, implying a principle that we speculate could broadly apply to eukaryotic gene regulation: a TF must act in coordination with other TFs to recruit coactivator activity.

"Structure"-function relationships in eukaryotic transcription factors: The role of intrinsically disordered regions in gene regulation.

Many principles of bacterial gene regulation have been foundational to understanding mechanisms of eukaryotic transcription. However, stark structural and functional differences exist between eukaryotic and bacterial transcription factors that complicate inferring properties of the eukaryotic system from that of bacteria. Here, we review those differences, focusing on the impact of intrinsically disordered regions on the thermodynamic and kinetic parameters governing eukaryotic transcription factor interactions-both with other proteins and with chromatin. The prevalence of unstructured domains in eukaryotic transcription factors as well as their known impact on function call for more sophisticated knowledge of what mechanisms they support. Using the evidence available to date, we posit that intrinsically disordered regions are necessary for the complex and integrative functions of eukaryotic transcription factors and that only by understanding their rich biochemistry can we develop a deep molecular understanding of their regulatory mechanisms.

The transcription factor activity gradient (TAG) model: contemplating a contact-independent mechanism for enhancer-promoter communication.

How distal cis-regulatory elements (e.g., enhancers) communicate with promoters remains an unresolved question of fundamental importance. Although transcription factors and cofactors are known to mediate this communication, the mechanism by which diffusible molecules relay regulatory information from one position to another along the chromosome is a biophysical puzzle-one that needs to be revisited in light of recent data that cannot easily fit into previous solutions. Here we propose a new model that diverges from the textbook enhancer-promoter looping paradigm and offer a synthesis of the literature to make a case for its plausibility, focusing on the coactivator p300.

A whole-cell computational model predicts phenotype from genotype.

Understanding how complex phenotypes arise from individual molecules and their interactions is a primary challenge in biology that computational approaches are poised to tackle. We report a whole-cell computational model of the life cycle of the human pathogen Mycoplasma genitalium that includes all of its molecular components and their interactions. An integrative approach to modeling that combines diverse mathematics enabled the simultaneous inclusion of fundamentally different cellular processes and experimental measurements. Our whole-cell model accounts for all annotated gene functions and was validated against a broad range of data. The model provides insights into many previously unobserved cellular behaviors, including in vivo rates of protein-DNA association and an inverse relationship between the durations of DNA replication initiation and replication. In addition, experimental analysis directed by model predictions identified previously undetected kinetic parameters and biological functions. We conclude that comprehensive whole-cell models can be used to facilitate biological discovery.

Self-guarding of MORC3 enables virulence factor-triggered immunity.

Pathogens use virulence factors to inhibit the immune system1. The guard hypothesis2,3 postulates that hosts monitor (or 'guard') critical innate immune pathways such that their disruption by virulence factors provokes a secondary immune response1. Here we describe a 'self-guarded' immune pathway in human monocytes, in which guarding and guarded functions are combined in one protein. We find that this pathway is triggered by ICP0, a key virulence factor of herpes simplex virus type 1, resulting in robust induction of anti-viral type I interferon (IFN). Notably, induction of IFN by ICP0 is independent of canonical immune pathways and the IRF3 and IRF7 transcription factors. A CRISPR screen identified the ICP0 target MORC34 as an essential negative regulator of IFN. Loss of MORC3 recapitulates the IRF3- and IRF7-independent IFN response induced by ICP0. Mechanistically, ICP0 degrades MORC3, which leads to de-repression of a MORC3-regulated DNA element (MRE) adjacent to the IFNB1 locus. The MRE is required in cis for IFNB1 induction by the MORC3 pathway, but is not required for canonical IFN-inducing pathways. As well as repressing the MRE to regulate IFNB1, MORC3 is also a direct restriction factor of HSV-15. Our results thus suggest a model in which the primary anti-viral function of MORC3 is self-guarded by its secondary IFN-repressing function-thus, a virus that degrades MORC3 to avoid its primary anti-viral function will unleash the secondary anti-viral IFN response.

Reusability and composability in process description maps: RAS-RAF-MEK-ERK signalling.

Detailed maps of the molecular basis of the disease are powerful tools for interpreting data and building predictive models. Modularity and composability are considered necessary network features for large-scale collaborative efforts to build comprehensive molecular descriptions of disease mechanisms. An effective way to create and manage large systems is to compose multiple subsystems. Composable network components could effectively harness the contributions of many individuals and enable teams to seamlessly assemble many individual components into comprehensive maps. We examine manually built versions of the RAS-RAF-MEK-ERK cascade from the Atlas of Cancer Signalling Network, PANTHER and Reactome databases and review them in terms of their reusability and composability for assembling new disease models. We identify design principles for managing complex systems that could make it easier for investigators to share and reuse network components. We demonstrate the main challenges including incompatible levels of detail and ambiguous representation of complexes and highlight the need to address these challenges.

Datanator: an integrated database of molecular data for quantitatively modeling cellular behavior.

Integrative research about multiple biochemical subsystems has significant potential to help advance biology, bioengineering and medicine. However, it is difficult to obtain the diverse data needed for integrative research. To facilitate biochemical research, we developed Datanator (https://datanator.info), an integrated database and set of tools for finding clouds of multiple types of molecular data about specific molecules and reactions in specific organisms and environments, as well as data about chemically-similar molecules and reactions in phylogenetically-similar organisms in similar environments. Currently, Datanator includes metabolite concentrations, RNA modifications and half-lives, protein abundances and modifications, and reaction rate constants about a broad range of organisms. Going forward, we aim to launch a community initiative to curate additional data. Datanator also provides tools for filtering, visualizing and exporting these data clouds. We believe that Datanator can facilitate a wide range of research from integrative mechanistic models, such as whole-cell models, to comparative data-driven analyses of multiple organisms.

RunBioSimulations: an extensible web application that simulates a wide range of computational modeling frameworks, algorithms, and formats.

Comprehensive, predictive computational models have significant potential for science, bioengineering, and medicine. One promising way to achieve more predictive models is to combine submodels of multiple subsystems. To capture the multiple scales of biology, these submodels will likely require multiple modeling frameworks and simulation algorithms. Several community resources are already available for working with many of these frameworks and algorithms. However, the variety and sheer number of these resources make it challenging to find and use appropriate tools for each model, especially for novice modelers and experimentalists. To make these resources easier to use, we developed RunBioSimulations (https://run.biosimulations.org), a single web application for executing a broad range of models. RunBioSimulations leverages community resources, including BioSimulators, a new open registry of simulation tools. These resources currently enable RunBioSimulations to execute nine frameworks and 44 algorithms, and they make RunBioSimulations extensible to additional frameworks and algorithms. RunBioSimulations also provides features for sharing simulations and interactively visualizing their results. We anticipate that RunBioSimulations will foster reproducibility, stimulate collaboration, and ultimately facilitate the creation of more predictive models.

SysMod: the ISCB community for data-driven computational modelling and multi-scale analysis of biological systems.

Computational models of biological systems can exploit a broad range of rapidly developing approaches, including novel experimental approaches, bioinformatics data analysis, emerging modelling paradigms, data standards and algorithms. A discussion about the most recent advances among experts from various domains is crucial to foster data-driven computational modelling and its growing use in assessing and predicting the behaviour of biological systems. Intending to encourage the development of tools, approaches and predictive models, and to deepen our understanding of biological systems, the Community of Special Interest (COSI) was launched in Computational Modelling of Biological Systems (SysMod) in 2016. SysMod's main activity is an annual meeting at the Intelligent Systems for Molecular Biology (ISMB) conference, which brings together computer scientists, biologists, mathematicians, engineers, computational and systems biologists. In the five years since its inception, SysMod has evolved into a dynamic and expanding community, as the increasing number of contributions and participants illustrate. SysMod maintains several online resources to facilitate interaction among the community members, including an online forum, a calendar of relevant meetings and a YouTube channel with talks and lectures of interest for the modelling community. For more than half a decade, the growing interest in computational systems modelling and multi-scale data integration has inspired and supported the SysMod community. Its members get progressively more involved and actively contribute to the annual COSI meeting and several related community workshops and meetings, focusing on specific topics, including particular techniques for computational modelling or standardisation efforts.

Simulating single-cell metabolism using a stochastic flux-balance analysis algorithm.

Stochasticity from gene expression in single cells is known to drive metabolic heterogeneity at the level of cellular populations, which is understood to have important consequences for issues such as microbial drug tolerance and treatment of human diseases like cancer. Despite considerable advancements in profiling the genomes, transcriptomes, and proteomes of single cells, it remains difficult to experimentally characterize their metabolism at the genome scale. Computational methods could bridge this gap toward a systems understanding of single-cell biology. To address this challenge, we developed stochastic simulation algorithm with flux-balance analysis embedded (SSA-FBA), a computational framework for simulating the stochastic dynamics of the metabolism of individual cells using genome-scale metabolic models with experimental estimates of gene expression and enzymatic reaction rate parameters. SSA-FBA extends the constraint-based modeling formalism of metabolic network modeling to the single-cell regime, enabling simulation when experimentation is intractable. We also developed an efficient implementation of SSA-FBA that leverages the topology of embedded flux-balance analysis models to significantly reduce the computational cost of simulation. As a preliminary case study, we built a reduced single-cell model of Mycoplasma pneumoniae and used SSA-FBA to illustrate the role of stochasticity on the dynamics of metabolism at the single-cell level.

Assessment of inorganic contamination of private wells and demonstration of effective filter-based reduction: A pilot-study in Stokes County, North Carolina.

Well water is the primary drinking source for nearly a quarter of North Carolina residents. Many communities across the state have been concerned about their well water quality and inorganic contamination. The "Well Empowered" study worked alongside a community in Stokes County, North Carolina to measure toxic metals in their well water as well as provide and test ZeroWater® filter pitchers in homes with arsenic (As) or lead (Pb) contamination. Multiple water samples, including a First Draw sample from the kitchen tap and a sample taken directly from the well, were collected from 39 homes in Stokes County. The samples were analyzed for 17 different inorganic contaminants, including As, boron (B), Pb, and manganese (Mn), using inductively coupled plasma mass spectrometry (ICP-MS). High concentrations of Pb along with copper (Cu), cadmium (Cd), and zinc (Zn) were only found in the First Draw sample and therefore likely originate in the home plumbing system while As, iron (Fe), and Mn were consistent across all samples and therefore are present in the groundwater. The low concentrations of B (<100 parts per billion (ppb)) make it unlikely that the source of As and Mn contamination was coal ash-derived. Out of the 39 homes, four had As levels exceeding the federal standard of 10 ppb and an additional two exceeded the Pb standard of 15 ppb. These homes were provided with a ZeroWater® filter pitcher and a water sample was taken pre- and post-filtration. The ZeroWater® filter removed 99% of As and Pb from the water, dropping the levels well below the drinking water standard levels. These ZeroWater® filter pitchers, while not a permanent solution, are a low-cost option for homeowners experiencing As or Pb contamination.

ATP Is an Allosteric Inhibitor of Coxsackievirus B3 Polymerase.

The RNA-dependent RNA polymerases from positive-strand RNA viruses, such as picornaviruses and flaviviruses, close their active sites for catalysis via a unique NTP-induced conformational change in the palm domain. Combined with a fully prepositioned templating nucleotide, this mechanism is error-prone and results in a distribution of random mutations in the viral progeny often described as a quasi-species. Here we examine the extent to which noncognate NTPs competitively inhibit single-cycle elongation by coxsackievirus B3 3D(pol), a polymerase that generates three to four mutations per 10 kb of RNA synthesized during viral infection. Using an RNA with a templating guanosine combined with 2-aminopurine fluorescence as a reporter for elongation, we find that the cognate CTP has a Km of 24 µM and the three noncognate nucleotides competitively inhibit the reaction with Kic values of 500 µM for GTP, 1300 µM for ATP, and 3000 µM for UTP. Unexpectedly, ATP also acted as an uncompetitive inhibitor with a Kiu of 1800 µM, resulting in allosteric modulation of 3D(pol) that slowed the polymerase elongation rate ≈4-fold. ATP uncompetitive inhibition required the ß- and γ-phosphates, and its extent was significantly diminished in two previously characterized low-fidelity polymerases. This led to further mutational analysis and the identification of a putative allosteric binding site below the NTP entry channel at the interface of conserved motifs A and D, although cocrystallization failed to reveal any density for bound ATP in this pocket. The potential role of an ATP allosteric effect during the virus life cycle is discussed.

Accelerated discovery via a whole-cell model.

To test the promise of whole-cell modeling to facilitate scientific inquiry, we compared growth rates simulated in a whole-cell model with experimental measurements for all viable single-gene disruption Mycoplasma genitalium strains. Discrepancies between simulations and experiments led to predictions about kinetic parameters of specific enzymes that we subsequently validated. These findings represent, to our knowledge, the first application of whole-cell modeling to accelerate biological discovery.

Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models.

Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype. However, even for simple bacteria, whole-cell models will contain thousands of parameters, many of which are poorly characterized or unknown. New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models. We organized the Dialogue for Reverse Engineering Assessments and Methods (DREAM) 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation algorithms for whole-cell models. We asked participants to identify a subset of parameters of a whole-cell model given the model's structure and in silico "experimental" data. Here we describe the challenge, the best performing methods, and new insights into the identifiability of whole-cell models. We also describe several valuable lessons we learned toward improving future challenges. Going forward, we believe that collaborative efforts supported by inexpensive cloud computing have the potential to solve whole-cell model parameter estimation.

Increased stray gas abundance in a subset of drinking water wells near Marcellus shale gas extraction.

Horizontal drilling and hydraulic fracturing are transforming energy production, but their potential environmental effects remain controversial. We analyzed 141 drinking water wells across the Appalachian Plateaus physiographic province of northeastern Pennsylvania, examining natural gas concentrations and isotopic signatures with proximity to shale gas wells. Methane was detected in 82% of drinking water samples, with average concentrations six times higher for homes <1 km from natural gas wells (P = 0.0006). Ethane was 23 times higher in homes <1 km from gas wells (P = 0.0013); propane was detected in 10 water wells, all within approximately 1 km distance (P = 0.01). Of three factors previously proposed to influence gas concentrations in shallow groundwater (distances to gas wells, valley bottoms, and the Appalachian Structural Front, a proxy for tectonic deformation), distance to gas wells was highly significant for methane concentrations (P = 0.007; multiple regression), whereas distances to valley bottoms and the Appalachian Structural Front were not significant (P = 0.27 and P = 0.11, respectively). Distance to gas wells was also the most significant factor for Pearson and Spearman correlation analyses (P < 0.01). For ethane concentrations, distance to gas wells was the only statistically significant factor (P < 0.005). Isotopic signatures (δ(13)C-CH4, δ(13)C-C2H6, and δ(2)H-CH4), hydrocarbon ratios (methane to ethane and propane), and the ratio of the noble gas (4)He to CH4 in groundwater were characteristic of a thermally postmature Marcellus-like source in some cases. Overall, our data suggest that some homeowners living <1 km from gas wells have drinking water contaminated with stray gases.

NetworkPainter: dynamic intracellular pathway animation in Cytobank.

BACKGROUND: High-throughput technologies such as flow and mass cytometry have the potential to illuminate cellular networks. However, analyzing the data produced by these technologies is challenging. Visualization is needed to help researchers explore this data. RESULTS: We developed a web-based software program, NetworkPainter, to enable researchers to analyze dynamic cytometry data in the context of pathway diagrams. NetworkPainter provides researchers a graphical interface to draw and "paint" pathway diagrams with experimental data, producing animated diagrams which display the activity of each network node at each time point. CONCLUSION: NetworkPainter enables researchers to more fully explore multi-parameter, dynamical cytometry data.

WholeCellKB: model organism databases for comprehensive whole-cell models.

Whole-cell models promise to greatly facilitate the analysis of complex biological behaviors. Whole-cell model development requires comprehensive model organism databases. WholeCellKB (http://wholecellkb.stanford.edu) is an open-source web-based software program for constructing model organism databases. WholeCellKB provides an extensive and fully customizable data model that fully describes individual species including the structure and function of each gene, protein, reaction and pathway. We used WholeCellKB to create WholeCellKB-MG, a comprehensive database of the Gram-positive bacterium Mycoplasma genitalium using over 900 sources. WholeCellKB-MG is extensively cross-referenced to existing resources including BioCyc, KEGG and UniProt. WholeCellKB-MG is freely accessible through a web-based user interface as well as through a RESTful web service.

The simulation experiment description markup language (SED-ML): language specification for level 1 version 5.

Modern biological research is increasingly informed by computational simulation experiments, which necessitate the development of methods for annotating, archiving, sharing, and reproducing the conducted experiments. These simulations increasingly require extensive collaboration among modelers, experimentalists, and engineers. The Minimum Information About a Simulation Experiment (MIASE) guidelines outline the information needed to share simulation experiments. SED-ML is a computer-readable format for the information outlined by MIASE, created as a community project and supported by many investigators and software tools. Level 1 Version 5 of SED-ML expands the ability of modelers to define simulations in SED-ML using the Kinetic Simulation Algorithm Onotoloy (KiSAO). While it was possible in Version 4 to define a simulation entirely using KiSAO, Version 5 now allows users to define tasks, model changes, ranges, and outputs using the ontology as well. SED-ML is supported by a growing ecosystem of investigators, model languages, and software tools, including various languages for constraint-based, kinetic, qualitative, rule-based, and spatial models, and many simulation tools, visual editors, model repositories, and validators. Additional information about SED-ML is available at https://sed-ml.org/.

WholeCellViz: data visualization for whole-cell models.

BACKGROUND: Whole-cell models promise to accelerate biomedical science and engineering. However, discovering new biology from whole-cell models and other high-throughput technologies requires novel tools for exploring and analyzing complex, high-dimensional data. RESULTS: We developed WholeCellViz, a web-based software program for visually exploring and analyzing whole-cell simulations. WholeCellViz provides 14 animated visualizations, including metabolic and chromosome maps. These visualizations help researchers analyze model predictions by displaying predictions in their biological context. Furthermore, WholeCellViz enables researchers to compare predictions within and across simulations by allowing users to simultaneously display multiple visualizations. CONCLUSION: WholeCellViz was designed to facilitate exploration, analysis, and communication of whole-cell model data. Taken together, WholeCellViz helps researchers use whole-cell model simulations to drive advances in biology and bioengineering.

Towards a whole-cell modeling approach for synthetic biology.

Despite rapid advances over the last decade, synthetic biology lacks the predictive tools needed to enable rational design. Unlike established engineering disciplines, the engineering of synthetic gene circuits still relies heavily on experimental trial-and-error, a time-consuming and inefficient process that slows down the biological design cycle. This reliance on experimental tuning is because current modeling approaches are unable to make reliable predictions about the in vivo behavior of synthetic circuits. A major reason for this lack of predictability is that current models view circuits in isolation, ignoring the vast number of complex cellular processes that impinge on the dynamics of the synthetic circuit and vice versa. To address this problem, we present a modeling approach for the design of synthetic circuits in the context of cellular networks. Using the recently published whole-cell model of Mycoplasma genitalium, we examined the effect of adding genes into the host genome. We also investigated how codon usage correlates with gene expression and find agreement with existing experimental results. Finally, we successfully implemented a synthetic Goodwin oscillator in the whole-cell model. We provide an updated software framework for the whole-cell model that lays the foundation for the integration of whole-cell models with synthetic gene circuit models. This software framework is made freely available to the community to enable future extensions. We envision that this approach will be critical to transforming the field of synthetic biology into a rational and predictive engineering discipline.