In-Progress Collaboration & Service Projects


7) Interfacing image-derived generative models with cell simulation engines

PIs: R. Murphy and G. Rohde, Carnegie Mellon University; M. Dittrich, Pittsburgh Supercomputing Center; J. Faeder, University of PittsburghCollaborators: T. Bartol, The Salk Institute; S. Andrews and R. Brent, Fred Hutchison Cancer Research Center; I. Moraru, J. Schaff and L. Loew, University of Connecticut

The primary goal of this collaborative project is to develop methods to exchange generative models of the spatiotemporal organization of cells between various cell simulation engines. This will enable those engines to have a greatly expanded source of cell geometries, to take full advantage of dynamic spatial models, and to combine models constructed by various means. The vision is for CellOrganizer to be able to work synergistically with Virtual Cell, Smoldyn, MCell, and any other cell simulator. A secondary, but important, goal will be to establish a de facto standard by which future systems for constructing generative cell models can interface with simulation engines. The interfacing would go well beyond the SBML spatial package, consisting of modules to generate various types of models from different image sources, to save them in exchangeable files, and most importantly, plugins for simulators to generate instances from the models to use in simulations and specify how those instances change over time.

In progress


15) Reconstructing zebrafish neural circuits controlling visually induced behaviors

PI: Art Wetzel and Greg Hood, Pittsburgh Supercomputing Center Collaborators: Florian Engert and David Hildebrand, Harvard University

The general goal of this new C&SP is the development of the larval zebrafish as a model system for the comprehensive identification and examination of neural circuits controlling visually induced behaviors. To that end our collaborators plan to establish and quantify a series of visually induced behaviours and analyze the individual resulting motor components. Using these assays they will monitor neuronal activity throughout the fish brain in an awake and intact preparation.

The underlying data are highly accurate zebrafish reconstructions from serial section SEM. The primary data is an 18,000 section series prepared by our collaborator David Hildebrand of a 6 day post fertilization specimen with full body cross section coverage extending from the front of the animal through the entire brain and into the initial spinal cord region. This SEM data consists of 3 resolutions 1) a very low resolution overview, ~1 micron/pixel, of the full18,000 section series that was used for ROI selection and targeting 2) a moderate resolution 60nm per pixel scan of full body cross sections over a 16,000 section ROI used for tracing myelinated pathways and 3) a 20 nm per pixel scan of a 12,000 section sub-region covering the entire brain that is used for detailed circuit tracing. As part of this collaboration we have applied our SWIFT approach to help produce highly accurate alignments of these image stacks. This required a number of significant improvements to the underlying core algorithms in our SWIFT software. Using SWIFT, we were already able to produce a very well aligned stack of the 60 nm image data shown in Figure 1 as a greatly reduced cut plane view. We aren currently using the SWIFT approach align the higher resolution 20nm stack, which is needed for accurate segmentation of the zebrafish brain area, into a geometry that is consistent with the lower resolution volumes.

The longer-term goal of correlating in-vivo neural activity with brain structure requires multi-modal alignment of EM data with 2-photon imaging as the animal reacts to visual stimuli. A preliminary example of the 2-photon data, acquired from a different specimen, is shown in Figure 2 with the primary forebrain ROI for in-vivo study indicated by the green square. After detailed reconstruction of pathways in the current dataset we will prepare a series of dual SEM plus 2-photo datasets for this forebrain region that will require accurate and efficient registration of the EM data for structural tracing along with alignment of the 2-photon data onto the EM data for correlation to in-vivo data recordings.

Publications Resulting from This Work

      • Hildebrand DGC, Torres RM, Choi W, Tran Minh Quan, Arthur Willis Wetzel, George ScottPlummer, Ruben Portugues, Isaac Henry Bianco, Owen Randlett, Stephan Saalfeld, Alex Baden, Kunal Lillaney, Randal Burns,Joshua Tzvi Vogelstein, Won-Ki Jeong, Jeff William Lichtman, Florian Engert (2016) Whole-brain serial-section electron microscopy in larval zebrafish Nature, revised version in preparation.

In progress


16) Latent Factor Models for Identification of Novel Neuroprotectives for Huntington's Disease

PI: Ivet Bahar and Bing Liu, University of Pittsburgh;  Collaborators: Robert Friedlander, Mark Shurdak, Andrew Stern, D. Lansing Taylor, University of Pittsburgh

This C&SP is supported by NIH award 5R01NS077748-04 (PI: Friedlander) entitled “Functional role of microRNAs in Huntington's disease pathogenesis”. Our focus is the identification of novel neuroprotectives using latent factor modeling (LFM). Firstly, we built a set of known neuroprotectives based on the data in the literature by collecting compounds that were identified in a large scale screen for neuroprotectives in Huntington’s disease (HD) as well as compounds that have been tested in clinical trials for their neuroprotective effect. Then, the LFM-based algorithm learns the most successful latent factor model of the drug-target interactions in the STITCH dataset after assessing the performance of multiple LFM learning algorithms. The LFM of STITCH allowed us to quantitatively assess the interaction profiles of the known neuroprotectives, which we then used to find new compounds toward (i) the discovery of new neuroprotectives by target-based diversification of known neuroprotectives, (ii) the discovery of mechanism of action hypotheses by identifying targets of interest. Specifically, for each target of known neuroprotectives, we identified other chemicals known to be interacting with high confidence and sorted them for maximal dissimilarity to the known neuroprotectives interacting with that target. This method identifies compounds that share the target of interest, but are otherwise as diverse in their interaction profile as possible. Compounds selected will be active if the target of interest is important for the HD etiology, therefore they represent new potential treatment candidates. Furthermore if one or more targets yield many neuroprotectives, that target is implicated as being central to the disease pathophysiology; therefore this method allows us to infer about the proteins regulating the disease phenotype by testing compounds alone.

Publications Resulting from This Work

      • Stern, A. M.; Schurdak, M. E.; Bahar, I.; Berg, J. M.; Taylor, D. L.(2016) A Perspective on Implementing a Quantitative Systems Pharmacology Platform for Drug Discovery and the Advancement of Personalized Medicine. J Biomol. Screen., in press. PMID: 26962875
      • Taylor DL, Gough A, Schurdak ME, Vernetti L, Chennubhotla CS, Lefever D, Pei F, Faeder JR, Lezon TR, Stern AM, Bahar I. (2019) Harnessing Human Microphysiology Systems as Key Experimental Models for Quantitative Systems Pharmacology. Handb Exp Pharmacol260:327-367. doi: 10.1007/164_2019_239.PMID: 31201557

In progress


17) Structure and Function of Voltage-Gated Calcium Channels

PI: Ivet Bahar and Mary Cheng, University of Pittsburgh; Tom Bartol, Salk Institute; Collaborators: Peter Wipf and Steve Meriney, University of Pittsburgh

This C&SP is a joint Charles E. Kaufman Foundation Grant proposal titled “Structure and Function of Voltage-Gated Calcium Channels (VGCCs)”, submitted on June 30 2015. The goal is to establish a chemistry - computational biology - neuroscience collaboration and launch a novel combination of strategic chemical synthesis (Wipf), VGCC mutagenesis and biophysical measurements of VGCC function (Meriney), and computational modeling (Bahar) toward the development a model of Cav2 VGCC gating. This work will fill a critical gap in our understanding of VGCC structure and function and significantly impact future studies in the field. VGCC are critical regulators of cellular function. We are interested in an important family of VGCCs (Cav2) that regulate communication between neurons in the brain. Despite its critical importance in cellular function, surprisingly little is known about VGCC structure and function, primarily because a crystal structure has proven very difficult to generate for this large membrane protein. In preliminary studies, the Wipf and Meriney labs have developed new tools targeted selectively to voltage-gated calcium channels. With the expertise of a new member of our team, Dr. Bahar, we propose to deploy novel chemical synthesis, calcium channel mutagenesis, biophysical measurements of calcium channel function, and computational modeling of channel movement to significantly increase our understanding of the structure-function relationships of voltage-gated calcium channels. This work will be transformative since it will impact the study of communication between cells, especially in the brain, and likely lead to the development of novel drugs that may be useful for a variety of diseases. Bahar lab will focus on structural modeling and simulations of VGCC gating and drug binding: a) modeling the structures and dynamics of the Cav2 VGCC in different conformational states, with a focus on key residues and interactions that control gating; and b) determination of the mechanism of action of the agonist gating modifiers of Cav2 VGCCs .

In progress


19) Modeling Dendritic Sparks at Dendritic Branch Points

PI: Terry Sejnowski and Tom Bartol, Salk Institute  Collaborators: Mark Ellisman, University of California at San Diego

The purpose of this C&SP is to collaborate with Mark Ellisman's lab to create realistic MCell models of calcium sparks that occur at dendritic branch points.

Neuronal calcium events similar to calcium sparks in cardiac myocytes have been observed to occur at dendritic branch points (Manita and Ross, J Neurosci, 2009). The origin of these events has been attributed to ryanodine receptors (RyRs) and IP3 receptors (IP3Rs). The Ellisman lab is working to reconstruct the subcellular ultrastructure of dendritic branch points and to identify the localization and relative concentrations of channels and molecules involved in calcium signaling and processing, including: ryanodine receptors (RyR1, RyR2, RyR3), IP3R), metabotropic glutamate receptors (mGluR), various voltage-gated calcium channels (T-Type, L-Type, and N-Type), various calcium-activated potassium channels (BK, SK, Kv4.2), and sodium channels. From these data we will help the Ellisman lab create a realistic model of dendritic branch points that incorporates both voltage dynamics at the neuronal level and calcium and other molecular dynamics at single branch points with via a combined NEURON and MCell system where both are using realistic morphologies from the same sample.

Publications Resulting from This Work

  • Garcia GC, Bartol TM, Phan S, Bushong EA, Perkins G, Sejnowski TJ, Ellisman MH, Skupin A. Mitochondrial morphology provides a mechanism for energy buffering at synapses. Sci Rep. 2019 Dec 4;9(1):18306. doi: 10.1038/s41598-019-54159-1.

In progress


20) Structural plasticity of chaperonins determined by cryo-electron microscopy: Modeling the machinery of GroEL and TRiC

Collaborating Investigators: Ivet Bahar, University of Pittsburgh, Wah Chiu, Baylor College of Medicine

Chaperonins are essential mediators of many functions in the cell, including protein folding. These are oligomeric machines, of ~ 1 megadalton, with two back-to-back rings enclosing a central cavity that accommodates polypeptide substrates. The Chiu lab has solved near-atomic resolution cryoEM structures of GroEL from bacteria, Mm-Cpn from archaea, and TRiC from eukaryotes. In addition, they have studied them in different nucleotide-binding states and with a variety of substrates. These studies show that chaperonins assume various conformations under different conditions, i.e. structural plasticity is an inherent property of chaperonins.

Our ongoing activity is to determine GroEL cryoEM structure with GroES and carboxylase substrate. In our newly funded program project, we will also pursue the cryoEM characterization of TRiC complexed with mutant huntingtin exon extracted from Huntington's disease (HD) cell model and mouse neuron model. These experiments will provide a mechanistic understanding of how TRiC prevents the progression of aggregation in polyQ, and mutant huntingtin exon as part of our effort to develop a TRiC-like reagent for HD therapeutics. The Bahar lab has experience on the machinery and conformational variability of GroEL-GroES, which will be further analyzed in the light of the new data generated by the Chiu lab, and with the help of novel extensions of elastic network models (ENMs). The computationally predicted dynamics will be compared to the structural variability observed in cryoEM, and the interactions that modulate GroEL allosteric machinery will be identified. Similar methods will be used for TRiC, toward elucidating its spectrum of motions and how its dynamics relates to its chaperoning activities. Target sites whose perturbation may alter/control TRiC machinery will be identified using ProDy tools, toward assisting in the design of new therapeutic strategies against HD. We anticipate a productive interaction between this project and C&SP16 on the identification of neuroprotectives for HD with Dr. Friedlander (UPMC), an expert in the etiology of HD as well as discovery of neuroprotectives against HD.

In progress


22) Modeling T cell fate decisions

Collaborating Investigators: Penelope Morel, Robin E. C. Lee, James R. Faeder, University of Pittsburgh

The T cell receptor (TCR) is an exquisitely sensitive molecular machine that can translate small differences in peptide/MHC ligands into profoundly different outcomes. Our long-term goal is to understand how T cells perceive signals of differing strengths in order to elucidate how this determines T helper (Th) cell fate. Antigen (Ag) dose affects Th differentiation; and several in vivo models have shown that low Ag dose favors T regulatory (Treg) and Th2 differentiation, whereas high Ag dose induces inflammatory Th1 cells. On the one hand, Treg cells are critical for the maintenance of self-tolerance and the prevention of autoimmune diseases such as diabetes; on the other, tumors induce Treg cells to suppress immune surveillance. Despite the powerful effect on T cell outcome, how these signals contribute to determining Th cell fate remains poorly understood.

By coupling mathematical modeling with detailed in vitro activation profiling we have identified several important feedback loops that control the degree of Akt/mTOR activation. Based on these and other biochemical mechanisms we have developed a rule-based model in BioNetGen and calibrated it to biochemical data for low and high dose stimulation using Bayesian parameter estimation with the ptempest software. This model predicts a sharp threshold for Akt activation with respect to antigen dose that depends on several key variables, most notably PTEN expression level, which exhibit considerable cell-to-cell variation. We are currently conducting experiments in mice that express a fluorescent reporter of TCR activation to determine whether there is a sharp activation threshold as predicted by the model, and in mice heterozygous for PTEN expression to examine how PTEN levels affect this threshold. We will use these data to refine the model and identify additional factors that can affect antigen dose thresholds. In addition, we will use the Amnis ImageStream technology available to the Morel lab and the single-cell live imaging pipeline being developed in the Lee lab to spatially resolve key signaling components, including Akt, PIP3, and NFκB. We will use these data to confirm temporal oscillations observed in preliminary data and to calibrate a coarse-grained spatial model.

Publications Resulting from This Work

    • Hawse WF, Sheehan RP, Miskov-Zinanov N, Menk AV, Kane LP, Faeder JR, Morel PA (2015) Cutting edge: Differential regulation of PTEN by TCR, Akt, and Fox01 Controls CD4+ T Cell fate decisions J Immunol 194: 4615-9 PMID: 25855357, PMC4418530
    • Morel PA, Faeder JR, Hawse WF, Miskov-Zivanov N (2014) Modeling the T cell immune response: a fascinating challenge J Pharmacokinet Pharmacodyn 41: 401-13 PMID: 25155903, PMC4210366
    • Miskov-Zivanov N, Turner MS, Kane LP, Morel PA, Faeder JR (2013) The duration of T cell stimulation is a critical determinant of cell fate and plasticity Sci Signal 6: ra97 PMID: 24194584, PMC4074924
    • Gupta S, Lee REC, Faeder JR. Parallel Tempering with Lasso for model reduction in systems biology. PLoS Comput Biol. 2020 Mar 9;16(3):e1007669. doi: 10.1371/journal.pcbi.1007669. eCollection 2020 Mar.PMID: 32150537 

In progress


23) CaMKII Structural Dynamics

Collaborating Investigators: John Kuriyan, University of California-Berkeley, Mary Kennedy, Caltech, Ivet Bahar, James R. Faeder, University of Pittsburgh, Tom M. Bartol, Terry J. Sejnowski, Salk Institute

This C&SP will focus on studying mechanisms of function of the complex biomolecular assembly of CaMKII and its dynamics. We recently showed that the human alpha isoform, CaMKIIalpha, literally acts as a device for propagating the activation by calcium signaling. This is achieved through subunit exchanges between activated CaMKII and holoenzyme assemblies that have not been activated. The spread of activation through these exchanges now suggests that activation can be maintained long after the disappearance of the initial signal. This is remarkable because it reveals a possible mechanism by which CaMKII assists in the induction of long-term memory in the brain. It is clear that the phosphorylation of the calmodulin-recognition element triggers the exchange. But in addition to localized interactions, there is something unique about the overall architecture of CaMKII so that it accommodates different oligomerization states. Starting from early crystal structures and EM images, we have seen that the holoenzyme could assume both dodecameric and tetradecameric forms. Our new observations now show that they coexist. An intriguing observation was: why does the vertical dimer dissociate, rather than a single subunit, or a horizontal dimer? ANM results unambiguously show that the dodecamer structure indeed favors the vertical dimers to be released. The type of analysis performed by ProDy can open the way to a better understanding of CaMKII dynamics. Additionally, coevolution analysis may give us additional information on interfacial interactions that modulate subunit exchange. These studies will help further understanding the dynamics and interactions of CaMKII.

In progress


24) Spatio-temporal cell biology

Collaborating Investigators: Alan Horwitz, Gregory Johnson, Allen Institute, Robert Murphy, Carnegie Mellon University

The goal of the Allen Institute for Cell Science (AICS) is to develop predictive models of cell behavior. The initial goal is to assemble a map of the shapes and positions of all major molecular machines and signaling complexes in human induced Pluripotent Stem (hiPS) cells and capture how they change during their differentiation into cardiomyocytes. hiPS cells will be genome-edited to express multiple fluorescent-tagged markers, and 3D images and short movies will be collected for 1000s of cells at various time points after initiation of differentiation, using a large fluorescence microscopy pipeline. We will combine images with various combinations of markers into a comprehensive 3D model of the undifferentiated stem cell and its changes during differentiation. To achieve this goal, several computational tools must be developed: 1) Models of causal spatio-temporal relationships of subcellular structures. 2) Integrated multi-protein subcellular organization models from images where only a small subset of markers are visible, and 3) Cellular population models.

This project takes advantage of the new CellOrganizer capabilities being developed for point process models and causal inference. It will also draw on the existing capabilities for learning point process models and for constructing models for a full differentiation process from short movies of parts of that process that were previously developed in conjunction with completed C&SP11 and similar to those in our recent study. The collaboration will involve both large-scale testing of CellOrganizer in the context of the AICS images and joint development and refinement of methods.

In progress


26) GPCR signaling and interactions. Learning from SILAC/proteomics

Collaborating Investigators: Kunhong (Kevin) Xiao, Ivet Bahar, James R. Faeder, University of Pittsburgh

This focus of this C&SP will focus on the development and application of quantitative proteomics and systems biology approaches to study GPCR (G protein couple receptor) structures, their signaling networks, and regulation. The approach will be to use mass spectrometry-based quantitative proteomics, in combination with systems, chemical, and structural biology to study the receptor structure-function relationship, macromolecular interactions, and the expression and posttranslational modifications of signaling molecules downstream of GPCR. The ultimate goal of this research is to provide a better understanding of structure-function relationship of GPCRs and their related proteins and provide tools and resources for structure-based drug design.

In progress


27) Circuit reconstruction of association cortex

Collaborating Investigators: Wei-Chung Allen Lee, Harvard, Art Wetzel, Greg Hood, Pittsburgh Supercomputing Center

This C&SP with Wei Chung Allen Lee at Harvard Medical School builds upon the successful DBP5 with Clay Reid of Harvard and the Allen Brain Institute during the 2012-2017 funding period. Lee is a former member of Reid's lab, and has continued this line of connectomics research as an independent researcher at HMS using both the original TEMCA microscope, and an improved successor, TEMCA-GT. Our previous collaborative work with Reid and Lee has focused on studying the pattern of anatomical connections among functionally characterized neurons within mouse visual cortex. Lee plans to study next a new cortical area in the mouse. This region of the cortex is hypothesized to be involved in key cognitive functions including decision-making, movement planning, attention, and reward. Recent work has demonstrated that groups of neurons in this brain region are activated sequentially. Neurons fire selectively during discrete epochs of a navigational task, and the resulting sequences of neuronal activity accurately predict the behavioral choice of the animal. Two of the unique aspects of this project are that we will, for the first time, be able to compare an association area with a sensory area and identify conserved and distinguishing features of connectivity motifs underlying their different network dynamics.

Publications Resulting from This Work

    • Lee, W-C. A, Vincent B, Reed M, Graham BJ, Hood G, Glattfelder K, Reid RC (2016) Anatomy and function of an excitatory network in the visual cortex Nature PMID:27018655, PMC4844839

In progress


29)  Spatiotemporal models of autophagy

PI: Joern Dengjel, University of Frieberg Collaborator: R. Murphy, Carnegie Mellon University

Macroautophagy is thought to be mainly cytoprotective, although it also has been linked to autophagic cell death (Feng et al., 2015). Autophagosomes are de-novo formed double membrane vesicles, which enwrap cytoplasm and target it for lysosomal degradation. Their biogenesis is complex and still holds many open questions (Lamb et al., 2013). We will generate wide-field as well as confocal fluorescent images of forming autophagosomes using different stress conditions such as amino acid and growth factor starvation. We will use cells stably expressing the autophagosomal marker MAP1LC3B (LC3) coupled to mRFP-GFP which allows the analysis of autophagosome turnover (Kimura et al., 2007). Also, we will generate images of all six fluorescently tagged isoforms of LC3 to determine if different isoforms have an influence on site of formation of autophagosomes and size/dynamics of vesicles. We will use these to construct image-derived models to enable comparison of different conditions.

Literature Cited

    • Feng, Y., Z. Yao, and D.J. Klionsky. (2015) How to control self-digestion: transcriptional, post-transcriptional, and post-translational regulation of autophagy. Trends Cell Biol.25:354-363.
    • Kimura, S., T. Noda, and T. Yoshimori. (2007) Dissection of the autophagosome maturation process by a novel reporter protein, tandem fluorescent-tagged LC3. Autophagy3:452-460.
    • Lamb, C.A., T. Yoshimori, and S.A. Tooze. 2013. The autophagosome: origins unknown, biogenesis complex. Nat Rev Mol Cell Biol.14:759-774.

Publications Resulting from This Work

    • C. Gretzmeier, S. Eiselein, G. R. Johnson, R. Engelke, H. Nowag, M. Zarei, V. Küttner, A. C. Becker, K. T. G. Rigbolt, M. Høyer-Hansen, J. S. Andersen, C. Münz, R. F. Murphy, and J. Dengjel (2017) Degradation of protein translation machinery by amino acid starvation-induced macroautophagy. Autophagy, in press.

In progress


30) Modeling of Tetrahymena basal body dynamics

Collaborating Investigators: Chad G. Pearson, University of Colorado, Robert Murphy, Carnegie Mellon University

Mapping basal body assembly in Tetrahymena cells.
The Pearson laboratory works on understanding how basal bodies assemble through the cell cycle in Tetrahymena cells. While most of this is directed at specific molecules in the assembly process, the Pearson lab is also interested in mapping where, spatially within Tetrahymena cells, basal bodies assemble and to understand where they go / separate upon assembly. This will allow us to explore the pattern that basal bodies organize into for normal ciliary beating and hydrodynamic flow. Therefore, the goals of this collaboration are the following: 1) Analyze the data previously acquired by the Pearson lab to map the stereotypical organization of basal bodies relative to a) the cell cycle and b) the cell size, 2) Modeling localized basal body separation to establish the temporal dynamics of new, daughter basal body separation from old, mother basal body separation, and 3) Live imaging of basal body separation to visualize “parts of” basal body separation during assembly and maturation and use CellOrganizer (TR&D4) to reassemble our “parts” to the full view.

Mapping the Tetrahymena ciliary beat pattern.
We want to measure/reconstruct the ciliary beat pattern in Tetrahymena cells. Using DIC imaging and high temporal resolution imaging (~500fps), we have low resolution imaging of the Tetrahymena ciliary beat pattern. We will marry these data with fixed timepoint fluorescence data to reconstruct a generative model of the Tetrahymena cilia, basal body and associated structures during the ciliary beat stroke.

There are two basic methods in CellOrganizer that will be used for this project. The first is to automatically assemble collections of static images of fixed cells that are at various points in the cell cycle into a model of the changes in basal body arrangement through the cycle. This will be done by measuring pairwise distances between images and assembling them into a shape space as we have done previously for cell and nuclear shape analysis. The second is to take short movies of living cells and assemble them into a dynamic shape space as has been done previously for the dynamics of H1299 cell shape changes. The main difference is that the distance measure will be based on basal body positions rather than overall cell shape.

In progress


31) Receptor Clustering and the Kinetics of T Cell-Mediated Killing

Collaborating Investigators: Yuri Sykulev, Thomas Jefferson University, James R. Faeder, University of Pittsburgh

The goal of the project is to develop the model Membrane systems that can recapitulate clustering of immune receptors in a controlled manner. We propose to utilize biodegradable nanolipoprotein particles (NLPs) as a universal platform to mimic molecular Membrane clustering. NLPs are self-assembled in solution to form discoidal Nanostructures containing Lipid Bilayers stabilized at the perimeter by apolipoprotein molecules. The size of the NLP ranges from 8 to 30 nm that allow capturing up to 50 molecules of soluble Ligands and enable us to achieve model cluster size and density close to physiological. We will use the NLPs to assemble pMHC and other MembraneLigands into model Membrane patches. We will study how changes in the Ligands density and composition of these patches Affect Binding of the model Membrane patches to live T cells and the kinetics and magnitude of TCR-mediated Signaling. This will provide a basis for the engineering of aAPC bearing the model Membrane patches incorporated into Lipid Bilayers covering the surface of glass beads. Such aAPCs will allow us to calibrate the strength of T cell stimulation. We will utilize these novel aAPCs to vary the strength of stimulation of naïve CD8+ T cells derived from OT-1 TCR Transgenic Mice in order to induce different Subsets of activated T cells with the same Specificity. Building of aAPCs is expected to enable us to expand T cells with instructional programs that allow T cells to persist, function, and migrate in a desired fashion after Adoptive Transfer. The experimental Data will also provide the basis for building a mathematical model to characterize how clustering of Ligands on an APC surface determines speed, sensitivity and Discrimination of the pMHC I ligand by activated and naïve CD8 T cells.

In progress


33) Integration of rule-based modeling capabilities with pySB modeling platform

Collaborating Investigators: Carlos Lopez, Vanderbilt, James R. Faeder, University of Pittsburgh

This C&SP is a collaboration that will integrate rule-base modeling into the PySB modeling platform. PySB is a framework for building mathematical models of biochemical systems as Python programs. PySB abstracts the complex process of creating equations describing interactions among multiple proteins or other biomolecules into a simple and intuitive domain specific programming language, which is internally translated into BioNetGen or Kappa rules and from there into systems of equations. PySB makes it straightforward to divide models into modules and to call libraries of reusable elements (macros) that encode standard biochemical actions. These features promote model transparency, reuse and accuracy. PySB also interoperates with standard scientific Python libraries such as NumPy, SciPy and SymPy, enabling model simulation and analysis.

In progress


34) Development of a high-level, rule-based whole-cell modeling language

Collaborating Investigators: Jonathan Karr, Mt. Sinai, James R. Faeder, University of Pittsburgh

Despite decades of research and the growing wealth of data, we do not have a unified understanding of cell biology. Whole-cell (WC) models that represent every gene function and that predict the cell cycle dynamics of every molecular species have the potential to unify our understanding of cell biology. Recently, Karr and others developed the first WC model. This was achieved by combining multiple mathematically distinct submodels of individual cellular pathways, and by developing custom software to simulate the model. However, this ad hoc approach is not easily reusable, expandable, or reproducible. New WC modeling tools are needed to build better models that can guide bioengineering and medicine.

This project will develop a high-level rule-based WC modeling language and a rule-based multi-algorithm simulator. The language will enable researchers to compactly describe biological processes, such as the translation of each amino acid, using high-level rule patterns and sequence, genomic, and biochemical data. WC-Lang will provide high-level data-based rules to succinctly describe large reactions networks in terms of reaction patterns and sequence, genomic, and other data, that will be encoded in BioNetGen. This high-level language will make it easier for researchers to develop and communicate WC models, and the BioNetGen implementation will make it possible to rapidly develop a reusable and efficient WC model simulator that will take advantage of BioNetGen’s network generation as well as the network-free simulation capabilities of NFsim. WC-Lang will be implemented as a Python library which encodes the high-level rules into lower-level BioNetGen rules. Use of rules to encode molecular interactions will open many possibilities for the visualization of WC models using contact maps and regulatory graphs. The new language along with an accompanying expanded simulator will enable researchers to build much larger and more accurate WC models that can guide bioengineering and precision medicine.

In progress


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