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16 projects in result set.
Whole-Cell Computational Model of Mycoplasma genitalium
- The goal of this project was to develop the first detailed, "whole-cell" computational model of the entire life cycle of living organism, <i>Mycoplasma genitalium</i>. The model describes the dynamics of every molecule over the entire life cycle and accounts for the specific function of every annotated gene product.
We anticipate that whole-cell models will be critical for synthetic biology and personalized medicine. Please see the project website <a href="http://wholecell.org">wholecell.org</a> and the Downloads page to explore the whole-cell knowledge base and simulations and obtain the model code. | |
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Registered: 2012-01-24 03:21 |
Integrated Flux Balance Analysis Model of Escherichia coli
- This project includes several MATLAB scripts that simulate E. coli central metabolism and the effects of single gene deletions on metabolism using 3 approaches -- iFBA, rFBA, and ODE. The project also includes several MATLAB scripts that simulate biochemical networks using 1) integrated flux balance analysis (iFBA) -- a combined FBA, boolean regulatory, and ODE approach; 2) regulatory flux balance analysis (rFBA); and 3) ordinary differential equations (ODE). Additionally, the project includes several MATLAB and php scripts for visualizing metabolic simulations. | |
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Registered: 2008-06-11 23:27 |
DireX - Conformational Sampling under Low Resolution Restraints
- This project provides a tool for efficient conformational sampling of protein structures under low resolution restraints. Low resolution electron density maps obtained from, e.g., electron microscopy, as well as distance restraints obtained from NMR or FRET experiments can be used to guide the sampling. | |
Activity Percentile: 67.94 Registered: 2007-03-07 20:47 |
MSMBuilder
- MSMBuilder is an open source software package for automating the construction and analysis of Markov state models (MSMs). It is primarily written in the python programming language with C extensions for the most time consuming routines.
MSMs are a powerful means of modeling the structure and dynamics of molecular systems, like proteins. An MSM is essentially a map of the conformational space a molecule explores. Such models consist of a set of states and a matrix of transition probabilities (or, equivalently, transition rates) between each pair of states. Intuitively, the states in an MSM can be thought of as corresponding to local minima in the free energy landscape that ultimately determines a molecule’s structure and dynamics.
MSMBuilder includes tools for
- Constructing an MSM from a set of computer simulations (typically molecular dynamics simulations in standard formats like xtc, dcd, and pdb)
- Validating statistical properties of MSMs
- Mimicking various experimental protocols to allow a quantitative comparison with experiments
- Driving efficient simulations via adaptive sampling (which decides where new simulations should be run to minimize statistical uncertainty in a model)
<p style="font-size:20px">For more information, including the latest releases, see our website at</p><p style="font-size:20px; text-align:center; font-weight:600;"><a href="http://msmbuilder.org">MSMBuilder.org</a></p> | |
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Registered: 2008-11-26 04:53 |
Computational Analysis of Kinase Selectivity using Structural Knowledge
- Here, we present a knowledge-based approach to profile kinase selectivity based on the similarity between drug binding microenvironments. To allow large-scale kinase site similarity profiling, we have created a kinome structure database consisting of 5000 inhibitor-binding pockets from 187 unique human kinase crystal structures. | |
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Registered: 2017-04-18 20:03 |
Multiscale Modeling of the Mammalian Circadian Clock: The Role of GABA Signaling
- The synchronization and entrainment of coupled biological oscillators is an emerging research area in complex network systems. The mammalian circadian clock located in the suprachiasmatic nucleus (SCN) of the hypothalamus consists of approximately 20,000 pacemaker neurons that are coupled together to produce a robust overall rhythm that drives other bodily functions such as sleep patterns. The SCN represents an ideal model system for studying biological network design and behavior due to accumulating data on individual SCN neurons and their interactions. Experimental studies have shown that SCN intercellular communication is primarily mediated by two neurotransmitters: vasoactive intestinal peptide (VIP) and gamma-aminobutyric acid (GABA). While VIP is well established as an essential synchronizing agent, the role of GABA with respect to its inhibitory/excitatory, day/night, synchronizing and entrainment effects remains controversial. Improved understanding of neurotransmitter mediated intercellular signaling in the SCN will have important clinical implications for prevention and treatment of circadian rhythm disruptions, including mood and sleep disorders and metabolic diseases.
The goal of this project is to develop a multiscale model of the SCN and to integrate this model with targeted experiments and novel computational tools to gain improved understanding of SCN connectivity, synchronization and entrainment properties. The research focuses on GABA signaling because its role in the SCN is prominent, not well understood, and recent advances by the three participating investigators will enable a complete and careful dissection of the role of this common neurotransmitter with synapse-level resolution across large arrays of circadian neurons. The multiscale model will establish a link between core clock genes and ion channels at the individual cell level and network synchronization and entrainment behavior at the SCN tissue level through cell-to-cell connectivity. Targeted experiments will be performed to inform the construction and validate the predictions of the network model. General computational techniques for model reduction and efficient simulation of heterogeneous cellular networks will be developed to facilitate analysis of model behavior over a wide range of environmental conditions. The research has the potential to be highly transformative by both advancing the multiscale modeling of coupled oscillators/complex networks and by fundamentally changing our understanding of GABA signaling in circadian timekeeping and potentially in other brain regions. | |
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Registered: 2017-03-28 00:27 |
Application for the simulation of the prosthetic gait
- This application has a dataset belonging to macha prosthetic patterns , in which the angle of the socket and prosthetic foot is changed.
It focuses on patients with transtibial amputation and uses opensim in MATLAB libraries to link and generate a model for opensim , based on data captured from a measuring TECHNAID brand. | |
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Registered: 2016-08-24 14:21 |
T cell calcium dynamics regulated by age-induced oxidation
- T cells reach a state of replicative senescence characterized by a decreased ability to proliferate and respond to foreign antigens. Calcium release associated with TCR engagement is widely used as a surrogate measure of T cell response. Using an ex vivo culture model that partially replicates features of organismal aging, we observe that while the amplitude of Ca2+ signaling does not change with time in culture, older T cells exhibit faster Ca2+ rise and a faster decay. Gene expression analysis of Ca2+ channels and pumps expressed in T cells by RT-qPCR identified overexpression of the plasma membrane CRAC channel subunit ORAI1 and PMCA in older T cells. To test whether overexpression of the plasma membrane Ca2+ channel is sufficient to explain the kinetic information, we adapted a previously published computational model by Maurya and Subramaniam to include additional details on the store-operated calcium entry (SOCE) process to recapitulate Ca2+ dynamics after T cell receptor stimulation. Simulations demonstrated that upregulation of ORAI1 and PMCA channels is not sufficient to explain the observed alterations in Ca2+ signaling. Instead, modeling analysis identified kinetic parameters associated with the IP3R and STIM1 channels as potential causes for alterations in Ca2+ dynamics associated with the long term ex vivo culturing protocol. Due to these proteins having known cysteine residues susceptible to oxidation, we subsequently investigated and observed transcriptional remodeling of metabolic enzymes, a shift to more oxidized redox couples, and post-translational thiol oxidation of STIM1. The model-directed findings from this study highlight changes in the cellular redox environment that may ultimately lead to altered T cell calcium dynamics during immunosenescence or organismal aging. | |
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Registered: 2016-07-01 17:00 |
Systematic Target Function Annotation of Human Transcription Factors
- Transcription factors (TFs), the key players in transcriptional regulation, have attracted great experimental attention, yet the functions of most human TFs remain poorly understood. Recent capabilities in genome-wide protein binding profiling have stimulated systematic studies of the hierarchical organization of human gene regulatory network and DNA-binding specificity of TFs, shedding light on combinatorial gene regulation. We show here that these data also enable a systematic annotation of the biological functions and functional diversity of TFs. We compiled a human gene regulatory network for 384 TFs covering the 146,096 TF-target gene relationships, extracted from over 850 ChIP-seq experiments as well as the literature. By integrating this network of TF-TF and TF-target gene relationships with 3,715 functional concepts from six sources of gene function annotations, we obtained over 9,000 confident functional annotations for 279 TFs. We observe extensive connectivity between transcription factors and Mendelian diseases, GWAS phenotypes, and pharmacogenetic pathways. Further, we show that transcription factors link apparently unrelated functions, even when the two functions do not share common genes. Finally, we analyze the pleiotropic functions of TFs and suggest that increased number of upstream regulators contributes to the functional pleiotropy of TFs. Our computational approach is complementary to focused experimental studies on TF functions, and the resulting knowledge can guide experimental design for discovering the unknown roles of TFs in human disease and drug response. | |
Activity Percentile: 0.00 Registered: 2015-11-17 06:49 |
STAMS: STRING-Assisted Module Search for Genome Wide Association Studies
- Analyzing GWAS data in the context of biological pathways helps us understand how genetic variation influences phenotype and increases power to find associations. However, the utility of pathway-based analysis tools is hampered by undercuration and reliance on a distribution of signal across all of the genes in a pathway. Methods that combine GWAS results with genetic networks to infer the key phenotype-modulating subnetworks combat these issues, but have primarily been limited to network definitions with yes/no labels for gene-gene interactions. A recent method (EW_dmGWAS) incorporates a biological network with weighted edge probability by requiring a secondary phenotype-specific expression dataset. In this paper, we combine an algorithm for weighted-edge module searching and a probabilistic gene interaction network (STRING) in order to develop a method, STAMS, for recovering modules of genes with strong associations to the phenotype and highly probable biologic coherence. Our method builds on EW_dmGWAS but does not require a secondary expression dataset and performs better in three test cases. | |
Activity Percentile: 0.00 Registered: 2015-11-23 20:57 |
Competing metabolic pathways control viral frameshifting and host resistance
- This project includes Matlab scripts that simulate the competition between the ISC and TUS pathways in E. coli and link this competition to lambda phage infection. A simulation of non-competitive interactions between the ISC and TUS pathways is provided as a control. | |
Activity Percentile: 0.00 Registered: 2011-12-09 20:28 |
Allosteric Transitions of Supramolecular Systems Explored by Network Models
- Most proteins are biomolecular machines. They perform their function by undergoing changes between different structures. Understanding the mechanism of transition between these structures is of major importance to design methods for controlling such transitions, and thereby modulating protein function.
However, exploring the transition between conformations is difficult, both experimentally and computationally, due to the transient nature of the intermediate, high energy conformers crossed over as the molecule undergoes structural changes. In many cases, only the two ending structures are known from experiments. Furthermore, the passage between the two end points does not necessarily involve a single pathway, but multiple pathways in the multidimensional energy landscape associated with the macromolecular structures. To bridge between structure and function, a molecular understanding of the most probable transition pathways between the two end structures is required.
While there are many computational methods for exploring the transitions of small proteins, the task of exploring the transition pathways becomes prohibitively expensive in the case of supramolecular systems. Coarse-grained models that lend themselves to analytical solutions appear to be the only possible means of approaching such cases. Motivated by the utility of elastic network models for describing the collective dynamics of biomolecular systems, and by the growing theoretical and experimental evidence in support of the intrinsic accessibility of functional substates, we introduce a new method, adaptive anisotropic network model (aANM) for exploring functional transitions.
As described by aANM, a series of intermediate conformations along the transition pathways between the initial and final conformations were generated by successive deformations of both end structures that were iteratively updated. The directions of deformations were determined by implementing the deformations along the directions of dominant ANM modes accessible to the intermediate states. The recruitment of the particular subsets of modes results from a tradeoff between minimizing the path length and selecting the direction of the lowest increase in internal energy. To calculate the ANM modes, please visit our related websites.
ANM: http://ignmtest.ccbb.pitt.edu/cgi-bin/anm/anm1.cgi
GNM: http://ignm.ccbb.pitt.edu/GNM_Online_Calculation.htm
PCA_NEST: http://ignm.ccbb.pitt.edu/oPCA_Online.htm
The bacterial chaperonin GroEL is a supramolecular machine that has been broadly studied in recent years using both experimental and theoretical or computational methods. Yet, a structure-based analysis of the transition of the intact chaperonin between its functional forms has been held back by the large size of the chaperonin. The aANM method is proposed as a first approximation toward approaching this challenging task.
The application of aANM to GroEL, not only elucidated the highly probable pathways and the hierarchic contribution of modes to achieve the transition; but also provided us with biologically significant information on critical interactions and sequence of events occurring during the chaperonin machinery and key contacts that make and break at the transition.
On a practical side, the major utility of the method lies in its application to the transitions of supramolecular systems beyond the range of exploration of other computational methods. The computing time in the present method is several orders of magnitude shorter than that required in regular molecular dynamics or Brownian dynamics simulations.
*Figure above: Snapshots of the protein chaperonin GroEL in its transition pathway, evolving from open (upper left) to closed form (lower right). The color scheme was inspired by Wassily Kandinsky and his artwork “Squares and Concentric Rings”.
For more information, please visit:
http://www.ccbb.pitt.edu/Faculty/bahar/index.php
http://www.ccbb.pitt.edu/Faculty/bahar/publications/YZResearch/Coupling.html
http://www.ccbb.pitt.edu/Faculty/bahar/publications/YZResearch/Transitions.html | |
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Activity Percentile: 0.00 Registered: 2010-06-22 01:53 |
EMMA - Markov Model Algorithms
- This project is no longer supported. Please switch to pyEMMA: <a href="http://pyemma.org">http://pyemma.org</a>
Markov models, often called Markov state models (MSMs) or kinetic transition networks, are concise and easy-to-analyze discrete models of continuous stochastic processes. They have been extensively used in the analysis of molecular dynamics (MD).
The EMMA software provides a number of command line tools that provide the basics of construction, validation and analysis of Markov models. EMMA is Java-based. | |
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Registered: 2010-05-25 14:59 |
Bionet biological cell network pathway simulation
- The methodology in bionet is distinguished from previous qualitative modeling approaches in several ways. The goal was to develop a model that would allow experimental biologists to use the kind of qualitative data found in typical journal articles to describe the interaction of genes, proteins, and other cellular components to create computer models of large numbers of interacting parts. This arose from a practical need in our research to keep track of myriad components in pathway models that were built from data extracted from dozens of journal articles. Biologists already do this kind of mental modeling every time they make a new hypothesis; a tool was needed to aid in this reasoning. Secondly, with new sources of data becoming available, it was important to design a methodology that could be expanded in the future to integrate new data sources to refine models.
Finally, biological processes span many scales. A kind of heuristic modeling is common in the literature, where molecular interactions are analyzed and used to create new hypotheses about cellular events, tissue processes or disease progression. For example, specific gene mutations accelerate tumor growth in specific tissues. This is a semi-quantitative relationship between two very different scales. Fuzzy network modeling can be used as a tool for aiding human reasoning when many interacting variables participate in complex interaction networks on several scales. Though the interactions can sometimes only be described approximately, the logic of the interactions is rigorous.
Pathway models can be constructed manually by biologists and manipulated to study the dynamics of alternative pathways. However, the power of this method is that it provides a framework for using various soft computing technologies to integrate diverse data sources to improve and refine models. Rule-based or fuzzy logic models are appropriate for manipulation by genetic or other evolutionary algorithms, which may be useful for drug target discovery. This process will be discussed in future papers that expand the basic model presented here. Details about methods for integration of high-throughput data with expert knowledge will also be reserved for future publications. Because the soft computing paradigm has been widely adopted for many engineering tasks, it is hoped that the framework presented here can be adopted and rapidly expanded by many researchers with expertise in these methods. Input files and code for all examples presented are available at the Bionet website.
Continued development of Bionet is funded in part by the Stowers Institute for Medical Research (http://www.stowers-institute.org). | |
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Activity Percentile: 0.00 Registered: 2009-08-12 20:28 |
Flexible (flexFBA) and time-linked (tFBA) Flux Balance Analysis methods
- (provided for computational biologists to reproduce publication results, and a small utility written for example use with the Cobra toolbox) The associated publication describes two complimentary methods that remove the inherent long-time assumptions of the biomass reaction used in FBA. Implementing a flexible objective flexFBA, enables a metabolic network to produce biological process reactants independently from one another. This flexibility is in contrast to the rigid proportion held by the traditional biomass reaction of FBA. Also, time-linked simulation (tFBA) can represent transitions between metabolic steady states by returning cell process byproducts at subsequent time-steps. | |
Activity Percentile: 0.00 Registered: 2013-08-19 17:12 |
In Silico Liver (ISL) - Multi-level, multi-attribute model mechanisms
- The ISL project consists of a body of Java code used and reused for exploring hypothetical liver mechanisms. The codebase is designed to allow for multiple distinct use cases and experimental designs, some of which we've published already. The scale/resolution of focus changes depending on the use case/experiment being considered. Typically, the hypothetical mechanism is finer grained than the falsifcation/validation data available. Much of the published work focuses on intracellular mechanisms, which requires the cellular and tissue contexts to be defined according to the literature. | |
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Registered: 2014-03-17 18:37 |