Image Processing & Analysis
TR&D4: Image-derived modeling
(This project was TR&D3 in the previous funding period.)
The overall goal for TR&D4 is to make maximal use of the information in biological images to enable the construction of predictive, multiscale models of structure and dynamics at the subcellular and cellular level.
Vision
We will create and enhance tools intended to be useful for understanding how cell organization is created and maintained and how that organization differs from cell type to cell type and during disease. As a unifying principle for these efforts, we construct generative models of cell organization, rather than simply describing specific aspects. We seek to have these models capture the reality underlying collections of images or movies, such that computing using the models can produce new results much as computing on molecular structure models can be used to obtain results on protein dynamics (as discussed in TR&D1). Generative models have distinct advantages over the discriminative and descriptive approaches usually used. They attempt to make use of all information in images, rather than to just extract selected descriptors (features). Further, features are dependent upon the specifics of image acquisition, while generative models can be compared across different microscopes and laboratories. They are also combinable and reusable, in that models can be linked together to make predictions about new relationships, and models for organelle shape and distribution learned for one cell type can be provisionally extended to new cell types.
The proposed work makes use of best available methods in machine learning and computer vision, including advanced inference methods and convolutional neural nets (so called “deep learning” methods). The work builds on the extensive progress that has been made under what was Aim 1 of TR&D3 in the prior funding period, which resulted in eleven publications that acknowledged P41 support.
Background and Motivation
The tools to be developed and improved will be especially useful for studies of how cell organization is created and maintained and how that organization differs from cell type to cell type and during disease. While existing software primarily provides descriptions of images, the focus of this project is on construction of generative models of cell organization. Generative models are learned from a collection of images and are capable of producing new images that are statistically equivalent to the images used for training. These models have distinct advantages over discriminative or descriptive approaches. They attempt to make use of all information in images, rather than to just extract selected descriptors or features. While features are not useful for comparing and communicating results between different laboratories due to their dependence upon the specifics of image acquisition, generative models capture the underlying reality that gave rise to images and can therefore be compared across different microscopes and laboratories. They are also combinable and reusable, in that models can be linked together to make predictions about new relationships, and models for organelle shape and distribution learned for one cell type can be provisionally extended to new cell types.
Work during the prior funding led to the development of extensive generative model capabilities that were incorporated into the open source CellOrganizer system. We propose to build upon this work to build new capabilities for constructing models that consider the extensive interrelationships between organelles and structures in cells, and for modeling the dynamics of proteins and organelles. In conjunction with TR&D3, we will also develop new methods for using images to constrain estimation of the affinities between components of a biological system. Lastly, we will develop new approaches for constructing models from both electron and fluorescence microscope images.
Example synthetic 3D image for HeLa cells The image shows the nuclear membrane in red, the cell membrance in blue, and lysosomal membranes in green. It was randomly generated from a model learned from real 3D microscope images using the approach described in Peng and Murphy. (Peng,T. and R.F.Murphy. 2011. Image-derived, three-dimensional generative models of cellular organization. Cytometry A 79:383-391.)
Specific aims
- To develop and extend methods for learning image-derived deep dynamic generative models of cell organization
This aim will extend capabilities created under Aim 1 of TR&D3 of the prior grant. A major focus will be on developing approaches to learn the full set of dependencies between multiple organelles from only selected measurements (we refer to these as deep models because there can be many layers of dependencies). A second focus will be learning dynamic models either from short movies or from large collections of cells at individual time points. We will also facilitate CellOrganizer use by providing ability to run it on PSC servers and by producing a Python version; this will also facilitate integration with software from other TR&Ds.
Subaims
1.1. Develop methods for constructing deep point process models that more completely capture dependencies between organelle or structure locations
1.2. Develop improved methods for comparing generative cell models
1.3. Develop improved methods for modeling and inferring dynamics
1.4. Establish pipelines for automated analysis of movies of two-color transcription in living cells
1.5. Implement methods for constructing models of heterogeneous cell populations
1.6. Develop Galaxy workflows for important CellOrganizer functions for deployment under the PSC Bridges system
- To develop software to estimate molecular affinities from fluorescence microscope (FM) movies
In combination with proposed new developments in TR&D1 and TR&D3, this aim will provide an important new capability for cell modeling and simulation: the ability to guide model choice and parameter estimation with image-derived models. The first subaim seeks to identify interactions that may be important to the process being studied as a way of reducing the number of species that need to be considered and produce a tentative reaction diagram to be used during the second subaim. That subaim then seeks to estimate the apparent affinities of these interactions. The software produced by each subaim can be useful without the other: learning putative causal relationships can motivate studies of underlying mechanisms not involving simulations, and estimating affinities using images can be done for reaction diagrams created from independent knowledge of likely interactions (such as from immunoprecipitations).
Subaims
2.1. Develop analysis tools for experimentally testing models of putative spatiotemporal dependencies
2.2. Incorporate methods for constructing models of vesicle composition and movement
2.3. Develop methods for estimating apparent affinities between components by inverse modeling using constraints.
- To develop software to create generative cell models combining information from electron microscope (EM) and FM images
This aim will bridge the gap between the higher spatial resolution of EM and the high throughput and live cell capabilities of FM. The goal will be to learn a generative model for high resolution structures in EM images that can be inserted into places in which FM images indicate that that structure is likely to be present.
Subaims
3.1. Semantic nonlinear regression of high-resolution EM structures from FM data
3.2. Learning low-dimensional embeddings of organelle shape
3.3. Constructing regression pipeline that makes use of unpaired EM / FM images.
An illustration of a model of the distribution of Arp3 in the helper T cell [being built in collaboration with DBP4]. Left: 40 seconds before immunological synapse formation. Going across rows left to right and progressing top to bottom, each subimage shows a cross section of the 3D protein distribution averaged across 17 cells. Each slice is perpendicular to the synapse (the face of the half ellipsoid template) and shows the synapse as the top edge of the shape shown. The synapse has not yet formed, so actin branching is occurring uniformly all around the periphery of the cell. Right: Model at the time of immunological synapse formation. Actin branching appears to be concentrated near the synapse as expected.
Molecular Modeling
TR&D1: Molecular modeling and simulations: Bridging molecular and cellular scales
TR&D1's overarching goal is to develop, implement, integrate and apply computational technology toward meeting the emerging needs for structure-based modeling of mesoscopic- and/or omics-scale dynamics, and to establish a platform that synergistically interfaces with the technologies developed in the other TR&Ds.
Vision
There is a growing need to understand molecular events at the mesoscopic time scale - microseconds-to-seconds, for systems containing 10s-to-100s of proteins/subunits, which current methods usually fail to represent with adequate structural and spatial complexity. We also have new challenges with 'omics'-scale data, which could be best tackled by advanced algorithms and high performance computing resources. Significant progress was made during the past funding period in the TR&D1 project, evidenced by 38 publications by TR&D1 members that acknowledged the P41 support. We developed and disseminated novel computational technology, and helped accelerate biomedical research driven by two DBPs. Many tools that we developed in the past decade, rooted in fundamental concepts of statistical mechanics, spectral graph theoretical methods and machine learning, can now be substantively advanced to meet emerging needs and challenges.
Time scales sampled by molecular (MD) and subcellular (MCell) simulations. The intermediate regime, mesoscale, is poorly sampled. Elastic network models aim at filling the gap between those scales.
Background and Motivation
The last decade has seen the creation of a remarkably inventive array of approaches for 4D modeling of biomolecular systems, using coarse-grained models and enhanced-sampling methods, as well as spatiotemporally realistic approaches at cellular scale. However, “mesoscale” systems such as large multi-protein complexes and subcellular structures, and “omics-scale” systems like chromatin have received significantly less attention. There is a growing need to develop computational technology for structure-based mesoscopic- and spatially resolved omics-scale modeling. Several methodologies already developed by TR&D1 investigators show great promise for meeting this need. These include the methods and tools based on elastic network models (ENMs) and implemented in the ProDy Application Programming Interface (API) developed for modeling supramolecular systems dynamics, and the Armatus software developed for identifying topological associated domains in chromosomes. Our research and development activities are driven by four Driving Biomedical Projects that focus on the complex interactions controlling neurotransmission and neurosignaling events (DBP1-3), and on constructing a spatial dynamic map of transcription and chromatin structure (DBP6). We are working together with all three other TR&Ds to meet the multiscale challenges of the investigated complex systems and processes.
Specific aims
- Advancing and implementing the methodology for treating the structure, dynamics and interactions of multimeric proteins and multiprotein assemblies
We extend the capabilities of our widely used ProDy API, to generate elastic network models (ENMs) of various levels of granularity for biomolecular complexes/assemblies in their subcellular environment, interacting with lipids, substrates, and ions. We take advantage of existing databases of structures and interactions, and the methods we developed during the past term such as coMD, weighted-ensemble(WE)-based HPC methods, in addition to our two-decade long experience on the development and use of ENMs for biomolecular systems dynamics.
- Extending the computing capabilities of TR&D1 to model chromosomal structure, dynamics and function
This aim is driven by DBP6.
We extend the capabilities of ProDy, and its underlying GNM theory and methods, to model the chromosomal structure and dynamics from pairwise contacts measured using chromosome conformation capture measurements (3C), and benchmark our model against data at the forefront of genomic sciences. We extend our existing software to add the capability to find functional spatial arrangements from imaging of pairs of genomic loci generated in collaboration with TR&D4. In particular, we develop techniques to identify co-localized transcription and bursty transcription events. We integrate the tools developed in the two subaims to provide a user-friendly platform for multiscale analysis of genome-scale structure/contact data.
- Further development of TR&D1 technology to ensure efficient integration of all software within TR&D1 and interoperability with those developed at TR&D2-4 and other resources
The goal is to promote the efficient usage of our tools by the broader community and to provide a platform that bridges between molecular and cellular simulations. We integrate and automate of ProDy modules methods and protocols, developed in TR&D1 aims 1 and 2, and implement the interfaces to enable interoperability with the software developed by TR&D1 members including Armatus59 for identifying topologically associated domains on the chromosomes. We ensure the interoperability of TR&D1 tools with, the major software MCell, BioNetGen and CellOrganizer, being developed in the respective TR&Ds 2 - 4 toward building a computational platform for integrated structural cell biology.
Cell Modeling
TR&D2: Cell modeling
The goal of TR&D2 is to provide an extensible, state-of-the-art model building and simulation platform for spatially realistic simulation and analysis of cellular and subcellular biochemistry, meeting the diverse needs of biologists, and which interfaces synergistically with the technologies developed in TR&Ds 1, 3, and 4.
Vision
Cellular systems are profoundly difficult to understand because of the interplay between spatial, biochemical and molecular complexity that occurs on multiple levels of organization, from macromolecular assemblies to synapse architecture to neural circuits. Current cell simulation tools are just beginning to address the complexity that spans these levels in an integrated fashion. Major advances in simulation tools are needed to enable the development of models that capture the required level of detail and at the same time remain computationally tractable. The MCell/CellBlender platform for cell modeling developed in TR&D2 is expressly designed to fulfill these needs, providing insight and understanding of complex cellular systems.
Background and Motivation
Scientific discovery is driven by testable hypotheses which derive from our intuition and questions surrounding our current understanding of reality. But when daunting complexity confounds our intuition we struggle to conceive new hypotheses and the cycle of discovery grinds to a halt. Computational models allow investigators to probe the complex relationships between biological components, obtain new insights and intuition -- the genesis of new hypotheses. Models are often developed in an iterative fashion where results from an initial computational experiment is compared against results of bench experiments. The model is then refined appropriately, simulated again and the cycle continues until satisfactory agreement is reached. This iterative process can lead to new insights, and these in turn can be tested by further experimentation.
Monte Carlo Simulation of Cellular Microphysiology. MCell is a highly successful modeling tool for realistic simulation of cellular signaling in the complex 3D subcellular microenvironment in and around living cells – what we call cellular microphysiology. At such small subcellular scales the familiar macroscopic concept of concentration is not useful and stochastic behavior dominates. MCell uses highly optimized Monte Carlo algorithms to track discrete molecules in space and time as they diffuse and interact with other effector molecules such as membrane channels, receptors, transporters or enzymes. CellBlender is the 3D CAD system we have developed over the previous funding period. CellBlender is an extension for the popular 3D content creation software Blender (blender.org) which transforms Blender into a sophisticated platform allowing researchers to build complex 3D cellular models and explore, visualize, and analyze their dynamics as computed by MCell.
Our modeling approach employs a technique we call “computational reconstitution”– attempting to recapitulate the structure and function of a cellular system from its component parts, including molecules, reaction networks, subcellular organelles, and cellular membrane architecture. The MCell/CellBlender platform for cell modeling and simulation was designed expressly for this purpose. With MCell/CellBlender, cellular systems of extraordinary scope and complexity can be reconstituted and new insights obtained by observing how microscopic interactions and organization give rise to macroscopic behaviors. Most importantly, because models constructed this way include rich mechanistic detail, predictions from simulations constitute testable hypotheses at the biochemical and molecular biological level.
Specific Aims
- Expand Simulation Capabilities for MCell
Subaims
1.1. Spatially structured, multi-state multi-component molecules. We will work with TR&D3 to add spatial structure to an expanded BioNetGen language, and create new algorithms for network-free, particle-based spatial simulations into MCell.
1.2. State-dependent dynamic geometries. We will improve MCell’s dynamic geometry algorithms by making the geometry update rules be a function of the simulation state.
1.3. Parallel MCell - We will parallelize the MCell code to run both in a multithreaded and/or multiprocess fashion to obtain good speedups when simulating models large enough to be efficiently decomposed into many subvolumes. This will dramatically shorten the cycle of model modification and evaluation, and allow researchers to explore model space much more rapidly.
- Create a Geometry Preparation Pipeline for MCell/CellBlender We will streamline the process of preparing simulation-quality 3D meshes obtained from stacks of images, especially electron-microscopic images, and obtained from CellOrganizer (TR&D4). We will develop an intuitive and flexible GUI in CellBlender that harnesses the power of advanced algorithms and expert knowledge.
Subaims
2.1. Improved workflow for high quality alignment of 3DEM datasets. Accurate image registration is a critical first step in transforming raw serial-section EM image sets into geometric forms needed for accurate geometric analysis and MCell simulations.
2.2. Improved workflow for transforming segmented structures into computational quality meshes. We will integrate several disjoint tools and process steps into CellBlender to create a coherent and streamlined process. This workflow will also benefit TR&D3 Subaim 1.3.
- Expand interfaces for MCell and CellBlender
Subaims
3.1. libMCell API for advanced simulation event-scheduler/event-handler. This will enable advanced capabilities such as simulation-state-dependent dynamic geometry and multiscale/hybrid simulations via coupling with external physics engines (e.g TR&D3 Subaims 1.2 and 1.3).
3.2. libMCell API for model building from C and Python. Expose a set of high-level routines, which will allow users to create and simulate MCell models via API calls alone, i.e., without invoking any parsed MDL description of the model.
3.3. Interface to Web/Cloud computing resources for MCell/CellBlender. We will create an interface for advanced simulation control of Web/Cloud computing resources to enable parameter sweep/estimation applications, closely coordinated with TR&D3.
Network Modeling
TR&D3: Network modeling
The goal of TR&D3 Network-based approaches have yielded many novel insights, in particular, the identification of common network motifs such as feedback and feedforward loops that give rise to specific classes of signal processing characteristics – like switching, oscillation, and adaptation.
Vision
Cellular systems are profoundly difficult to understand because of the interplay between spatial, biochemical and molecular complexity that occurs on multiple levels of organization, from macromolecular assemblies to synapse architecture to neural circuits. Current cell simulation tools are just beginning to address the complexity that spans these levels in an integrated fashion. Major advances in simulation tools are needed to enable the development of models that capture the required level of detail and at the same time remain computationally tractable. The MCell/CellBlender platform for cell modeling developed in TR&D2 is expressly designed to fulfill these needs, providing insight and understanding of complex cellular systems.
Background and Motivation
The integration of the network modeling component within MMBioS offers numerous opportunities for further developing RBM capabilities and to demonstrating their effectiveness for providing mechanistic insight into complex regulatory networks. A number of specific aims will be pursued to achieve these goals: Accelerated methods for stochastic simulation of rule-based models. Most of the parameters governing the behavior of network models cannot be measured independently but must instead be determined by solving the inverse problem to obtain the parameters from experimental data, which demands many orders of magnitude more computational effort that computing a single trajectory. The structure of rule-based models opens the door to development of more efficient simulation methods that have not been previously explored.
Novel coarse-grained spatial modeling capabilities MCell simulations are prohibitively expensive in terms of computing time and memory for many applications, particularly those that require simulation of protein copy numbers on the scale of a whole cell. Simulations at this scale require more coarse-grained spatial resolution for computational efficiency, which we will develop in Aims 1.2 and 1.3. These coarse-grained network-free simulators will enable parameter estimation and other intensive analysis tasks to be performed on spatially-resolved models, such as those developed in DBP 4 and 8 in collaboration with TR&D4 and TR&D1.
Novel interfaces for model construction, visualization, and comparative analysis Large-scale modeling efforts require scalable and zoomable approaches to visualization. It is currently difficult to compare models in the literature or even those downloaded from databases with each other. Rule-based encoding can facilitates management and comparative analysis of models using tools we developed and we will apply in Aim 2.
Increased accessibility of model analysis and parameter estimation tools for complex models The computational demands of many parameter estimation tasks require HPC resources that many end users lack expertise to utilize. We will develop novel parameter estimation interfaces in Aim 2 that wil provide users with transparent access to HPC resources available through the XSEDE platform in Aim 3.
Unique infrastructure for rule-based modeling capabilities In Aim 3 we will harden the software libraries and interfaces that are critical to the continued availability of RBM capabilities for developers and end users.
Related work/software Other RBM software tools have been developed, but none appears to have the strengths of the BioNetGen platform. Indeed, some major packages use BioNetGen as an underlying RBM engine, including VCell and Smoldyn.
There is little overlap between current VCell capabilities and the aims proposed here, and in fact the proposed simulator development in Aim 1, the visualization capabilities in Aim 2, and the software infrastructure in Aim 3 may all be used to expand VCell functionality in the future (see Blinov letter of support). Besides BioNetGen, the other major RBM language based on citations, use by non-affiliated groups, and integration into other software frameworks is Kappa, which is open source and written in the OCaml language. Kappa is well-known in the computer science community, but has comparatively few biological applications. Another notable effort is Simmune, which combines graphical, RBM specification and PDE and ODE simulations. Currently, Simmune does not support stochastic simulations and source code is not available, which limits its use for outside development. A number of other software tools have been developed for RBM, especially in recent years, but most of these were developed for relatively specialized applications and/or are not being actively developed.
Specific Aims
- Advance rule-based modeling technology for cell-scale simulations
Network-free simulation is a key technology for the largest scale simulations because it avoids explicit generation of the full reaction network without sacrificing accuracy. The computational cost of these simulations remains a significant limitation to their application, particularly for computationally intensive tasks such as parameter estimation, which motivates Subaim 1.1: Adapt and further develop methods to accelerate stochastic simulations of rule-based models. The integration of rule-based modeling capabilities into MCell in TR&D2 enables spatially-resolved simulations of large networks exhibiting combinatorial complexity, but simulating cell-sized molecular populations remains prohibitive, which motivates Subaim 1.2: Develop a compartmental network-free simulator; and Subaim 1.3: Develop a network-free subvolume-based simulator to complement TR&D2 and enable simulation-intensive parameter estimation tasks required by multiple DBPs. Both coarse-grained simulators will initially be developed to handle static geometries and subsequently extended to handle dynamic geometries..
- Further develop advanced visual interfaces for building, managing and analyzing rule-based models
Visual interfaces are essential to the modern collaborative modeling endeavor: they make modeling accessible to non-specialists and enable collaborative development and re-use of models developed by other groups, motivating Subaim 2.1: Extend RuleBender, the graphical front end to BioNetGen, to enable visual model building without detailed knowledge of the BioNetGen language and provide additional model visualization capabilities that we have developed as prototypes in the initial funding period. Facilitating model annotation, comparison, and re-use motivates Subaim 2.2: Develop a database of molecules, rules, and models that can be used for comparative analysis of existing models and development of new models. This aim will take advantage of the Atomizer tool we have developed that enables any reaction network model to be recast as a rule-based one. Finally, facilitating analysis of model dynamics to enable mechanistic discovery from data motivates Subaim 2.3: Develop RuleBender interfaces for parameter sweeps, sensitivity analysis, and parameter estimation.
- Extend and refine software infrastructure for rule-based modeling
Although the BioNetGen framework already provides rule-based modeling capabilities for a number of major software tools including the Virtual Cell, pySB, rxncon, BioUML, MCell and WESTPA, the core capabilities provided under the BioNetGen umbrella are currently distributed among multiple codebases in different languages, motivating Subaim 3.1: Develop a hardened C/C++ library and application programming interface (API) for BioNetGen that exposes both high- and low-level functionality. The computationally intensive nature of parameter estimation and other model analysis tasks motivates Subaim 3.2: Develop a web portal that will provide end users direct access to rule-based modeling capabilities and HPC resources.
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Research Highlights
The Bahar (TR&D1) and Sorkin (DBP3) labs published an article in the Journal of Biological Chemistry, selected as one of JBC's "Editors' Picks. Our results demonstrate a direct coupling between conformational dynamics of DAT, functional activity of the transporter and its oligomerization leading to endocytosis. The high specificity of such coupling for DAT makes the TM4-9 hub a new target for pharmacological modulation of DAT activity and subcellular localization. (Read more)
Differences in the intrinsic spatial dynamics of the chromatin contribute to cell differentiation
Comparison with RNA-seq expression data reveals a strong overlap between highly expressed genes and those distinguished by high mobilities in the present study, in support of the role of the intrinsic spatial dynamics of chromatin as a determinant of cell differentiation. (Read more)
Nanoscale co-organization and coactivation of AMPAR, NMDAR, and mGluR at excitatory synapses
Work by TR&D2 Investigators and collaborators provide insights into the nanometer scale organization of postsynaptic glutamate receptors using a combination of dual-color superresolution imaging, electrophysiology, and computational modeling. (Read more)
Parallel Tempering with Lasso for model reduction in systems biology
TR&D3 Investigators and collaborators develop PTLasso, a Bayesian model reduction approach that combines Parallel Tempering with Lasso regularization, to automatically extract minimal subsets of detailed models that are sufficient to explain experimental data. On both synthetic and real biological data, PTLasso is an effective method to isolate distinct parts of a larger signaling model that are sufficient for specific data. (Read more)
Image-derived models of cell organization changes during differentiation and drug treatments
Our work on modeling PC12 cells undergoing differentiation into neuron-like morphologies (under C&SP11, completed) has been published in Molecular Biology of the Cell. We have also made the large dataset of 3D images collected in that study available through Dryad. (Read more)
Monoamine transporters: structure, intrinsic dynamics and allosteric regulation
T&RD1 investigators Mary Cheng and Ivet Bahar published an invited review article in Nature Structural & Molecular Biology, addressing recent progress in the elucidation of the structural dynamics of MATs and their conformational landscape and transitions, as well as allosteric regulation mechanisms. (Read more)
Trimerization of dopamine transporter triggered by AIM-100 binding
The Bahar (TR&D1) and Sorkin (DBP3) labs explored the trimerization of dopamine transporter (DAT) triggered by a furopyrimidine, AIM-100, using a combination of computational and biochemical methods, and single-molecule live-cell imaging assays. (Read more)
Pre-post synaptic alignment through neuroligin-1 tunes synaptic transmission efficiency
TR&D2 investigators and collaborators describe organizing role of neuroligin-1 to align post-synaptic AMPA Receptors with pre-synaptic release sites into trans-synaptic “nano-columns” to enhance signaling.(Read more)
Inferring the Assembly Network of Influenza Virus
In an article in PLoS Computational Biology, MMBioS TR&D4 members Xiongto Ruan and Bob Murphy collaborated with Seema Lakdawala to address this question of the assembly network of the Influenza virus.(Read more)
Our findings highlight an important mechanism by which proteins genetically implicated in Parkinson’s disease (PD; PINK1) and frontotemporal dementia (FTD; VCP) interact to support the health and maintenance of neuronal arbors.(Read more)
Improved methods for modeling cell shape
In a recent paper in Bioinformatics, Xiongtao Ruan and Bob Murphy of TR&D4 addressed the question of how best to model cell and nuclear shape.(Read more)
New tool to predict pathogenicity of missense variants based on structural dynamics: RHAPSODY
We demonstrated that the analysis of a protein’s intrinsic dynamics can be successfully used to improve the prediction of the effect of point mutations on a protein functionality. This method employs ANM/GNM tools (Read more)
New method for investigating chromatin structural dynamics.
By adapting the Gaussian Network Model (GNM) protein-modeling framework, we were able to model chromatin dynamics using Hi-C data, which led to the identification of novel cross-correlated distal domains (CCDDs) that were found to also be associated with increased gene co-expression. (Read more)
Structural elements coupling anion conductance and substrate transport identified
We identified an intermediate anion channeling state (iChS) during the global transition from the outward facing (OF) to inward facing state (IFS). Our prediction was tested and validated by experimental study conducted in the Amara lab (NIMH). Critical residues and interactions were analyzed by SCAM, electrophysiology and substrate uptake experiments (Read more)
Integrating MMBioS technologies for multiscale discovery
TR&D teams driven by individual DBPs are naturally joining forces, integrating their tools to respond to the needs of the DBP, and creating integrative frameworks for combining structural and kinetic data and computing technologies at multiple scales. (Read more)
Large scale visualization of rule-based models.
Signaling in living cells is mediated through a complex network of chemical interactions. Current predictive models of signal pathways have hundreds of reaction rules that specify chemical interactions, and a comprehensive model of a stem cell or cancer cell would be expected to have many more. Visualizations of rules and their interactions are needed to navigate, organize, communicate and analyze large signaling models. (Read more)
Integration of MCellR into MCell/CellBlender
Using spatial biochemical models of SynGAP/PSD95, MMBioS investigators were able to merge the MCellR code-base with the MCell code-base and validate its utility and correctness of this sophisticated technology now easily accessible through the MCell/CellBlender GUI. (Read more)
Causal relationships of spatial distributions of T cell signaling proteins
The idea is to identify a relationship in which a change in the concentration of one protein in one cell region consistently is associated with a change in the concentration of another protein in the same or a different region. We used the data from our Science Signaling paper reported last year to construct a model for T cells undergoing stimulation by both the T cell receptor and the costimulatory receptor. (Read more...)
BioNetGen modeling helps reveal immune system response decision
To attack or to let be is an important decision that our immune systems must make to protect our bodies from foreign invaders or protect bodily tissues from an immune attack. Using modeling and experiments, we have painted a sharper picture of how T cells make these critical decisions. (Read more)
Tools for determining the spatial relationships between different cell components
An important task for understanding how cells are organized is determining which components have spatial patterns that are related to each other.Read more
Pipeline for creation of spatiotemporal maps
Using a combination of diffeomorphic methods and improved cell segmentation, we developed a CellOrganizer pipeline for use in DPB4 to construct models of the 4D distributions of actin and 8 of its regulators during the response of T cells to antigen presentation. Read more
Multi-scale Hybrid Methodology
The hybrid methodology, coMD, that we have recently developed [1] has been recently extended to construct the energy landscape near the functional states of LeuT (Fig 1) [2]. This is the first energy landscape constructed for this NSS family member. Read more
Insights into the cooperative dynamics of AMPAR
Comparative analysis of AMPAR and NMDAR dynamics reveals striking similarities, opening the way to designing new modulators of allosteric interactions. Read more
Improved Sampling of Cell-Scale Models using the WE Strategy
The WE strategy for orchestrating a large set of parallel simulations has now been extended to spatially resolved cell-scale systems. The WESTPA implementation of WE has been used to control MCell simulations, including models built using a BioNetGen-CellOrganizer pipeline for situating complex biochemistry within spatially realistic cell models. Read more
Anatomy and Function of an Excitatory Network in the Visual Cortex
MMBioS researcher Greg Hood’s collaboration with Wei-Chung Allen Lee of Harvard University and R. Clay Reid of the Allen Institute for Brain Science concerning the reconstruction of an excitatory nerve-cell network in the mouse brain cortex at a subcellular level using the AlignTK software has been published in Nature. Read more
Molecular Mechanism of Dopamine Transport by hDAT
Dopamine transporters (DATs) control neurotransmitter dopamine (DA) homeostasis by reuptake of excess DA, assisted by sodium and chloride ions. The recent resolution of DAT structure (dDAT) from Drosophila permits us for the first time to directly view the sequence of events involved in DA reuptake in human DAT (hDAT). Read more
Synaptic Facilitation Revealed
An investigation of several mechanisms of short-term facilitation at the frog neuromuscular junction concludes that the presence of a second class of calcium sensor proteins distinct from synaptotagmin can explain known properties of facilitation. Read more
Sparse Graphical Models of Protein:Protein Interactions
DgSpi is a new method for learning and using graphical models that explicitly represent the amino acid basis for interaction specificity and extend earlier classification-oriented approaches to predict ΔG of binding. Read more
Advancing Parallel Bio-simulations
A new non-Markovian analysis can eliminate bias in estimates of long-timescale behavior, such as the mean first-passage time for the dissociation of methane molecules in explicit solvent. Read more