Project Tree
Now limiting view to projects in the following categories:
All Topics :: Biocomputational Focus :: Network Modeling and Analysis [Remove This Filter]
All Topics > Primary Content > Data Sets |
Browse By: |
11 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. | |
|
Registered: 2012-01-24 03:21 |
Data-driven prediction of gait with ankle exoskeletons
- The datasets included on this page contain walking data from twelve unimpaired adults walking on a treadmill while wearing bilateral passive ankle exoskeletons. Datasets are four minutes long, and contain kinematic and ground reaction force data, and electromyography from seven leg muscles bilaterally.
The associated Python code can be used to generate data-driven predictive models of response to the ankle exoskeletons. The associated MATLAB code can be used to perform statistical analyses of the data. | |
Registered: 2020-05-29 23:55 |
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 | |
|
Activity Percentile: 0.00 Registered: 2010-06-22 01:53 |
Prioritizing disease-specific gene candidates through network analysis
- This project is computational tool for taking a set of genes known to be associated with a given disorder or phenotype, and a large network (including, for example, protein-protein interactions), and using network analysis to produce other genes likely to be associated with the disorder/phenotype. | |
Activity Percentile: 0.00 Registered: 2014-08-04 21:28 |
Relating Essential Proteins to Drug Side-effects Using CCA
- novel insights about the molecular mechanisms of drug side-effects | |
Activity Percentile: 0.00 Registered: 2015-01-11 21:10 |
Dynamic Simulation of Joints Using Multi-Scale Modeling
- This research is funded by the National Science Foundation, Grant Number 506297, under the IMAG program for Multiscale Modeling. It is a collaborative effort that capitalizes on a diversity of expertise in areas such as clinical, experimental and computational biomechanics, nano-micro scale material modeling, finite element modeling, and neural networks.
Grant Numer: 506297
Principle Investigator: Trent Guess
Co-Investigators: Ganesh Thiagarajan, Amil Misra, Reza Derakhshani (University of Missouri - Kanas City), Lorin Maletsky (University of Kansas), Terence McIff (University of Kansas Medical Center)
Abstract from grant proposal
Dynamic loading of the knee is believed to play a significant role in the development and progression of tissue wear disease and injury. Macro level rigid body joint models provide insight into joint loading, motion, and motor control. The computational efficiency of these models facilitates dynamic simulation of neuromusculoskeletal systems, but a major limitation is their simplistic (or non-existent) representation of the non-linear, rate dependent behavior of soft tissue structures. This limitation prevents holistic computational approaches to investigating the complex interactions of knee structures and tissues, a limitation that hinders our understanding of the underlying mechanisms of knee injury and disease.
The objective of this project is to develop validated neural network models that reproduce the dynamic behavior of menisci-tibio-femoral articulations and to demonstrate the utility of these models in a musculoskeletal model of the leg. The specific aims of this study are:
Aim 1: Develop finite element (FE) models from micro-structure based constitutive methods that bridge the nano-micro scale behavior at the tissue level
Aim 2: Develop neural network (NN) based models that learn from FE simulation of dynamic behavior of menisci-tibio-femoral articulations
Aim 3: Validate the NN models within a rigid body dynamic model of a natural knee placed within a dynamic knee simulator
Aim 4: Demonstrate the utility of the NN models by placing them within a dynamic musculoskeletal model of the leg to study the interdependencies of the menisci and other knee tissues
Aim 5: Distribute the validated NN models of menisci-tibio-femoral dynamic response and contact pressure for use in any rigid body model of the knee or leg
The final product will be Neural Network (NN) models that conform to a modular application programming interface (API) that can be exported to any commercial integrated development environment (IDE) or in-house multi-body model. The NN models will be built upon a multi-scale approach and describe the non linear, rate dependent, non-homogenous dynamic response of menisci-tibio-femoral articulations in a computationally efficient modular package. The multi-scale modeling approach will be validated using a dynamic knee loading machine and the utility of the approach demonstrated by studying the interdependencies of menisci properties, tibio-femoral contact, and anterior cruciate ligament strain during a dual limb squat. A synergistic interdisciplinary team has been assembled to address the objective and aims of the proposed project comprising experts in rigid body dynamics and knee modeling, FE modeling, nano-micro scale material modeling, neural networks, and clinical and experimental biomechanics.
The proposed research will benefit society at large as the results of this work have potential applications to orthopedics, tissue engineering, and biomaterials. The work will also be a valuable asset to the musculoskeletal research community providing computational tools that may aid research in broad areas such as human movement, prosthetics, tissue engineering, sport injury, and disease. | |
|
Registered: 2006-10-18 19:49 |
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. | |
|
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. | |
|
Registered: 2016-08-24 14:21 |
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 |
Dynamic Redox Regulation of IL-4 Signaling
- Incomplete reduction of oxygen during respiration results in the formation of highly reactive molecules known as reactive oxygen species (ROS) that react indiscriminately with cellular components and adversely affect cellular function. For a long time ROS were thought solely to be undesirable byproducts of respiration. Indeed, high levels of ROS are associated with a number of diseases. Despite these facts, antioxidants, agents that neutralize ROS, have not shown any clinical benefits when used as oral supplements. This paradox is partially explained by discoveries over the last two decades demonstrating that ROS are not always detrimental and may be essential for controlling physiological processes like cell signaling. However, the mechanisms by which ROS react with biomolecules are not well understood. In this work we combined biological experiments with novel computational methods to identify the most important mechanisms of ROS-mediated regulation in the IL-4 signaling pathway of the immune system. We developed a detailed computer model of the IL-4 pathway and its regulation by ROS dependent and independent methods. Our work enhances the understanding of principles underlying regulation of cell signaling by ROS and has potential implications in advancing therapeutic methods targeting ROS and their adverse effects. | |
|
Activity Percentile: 0.00 Registered: 2015-10-16 21:42 |
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. | |
|
Registered: 2017-04-18 20:03 |