RuleMonkey: software for stochastic simulation of rule-based models

<p>Abstract</p> <p>Background</p> <p>The system-level dynamics of many molecular interactions, particularly protein-protein interactions, can be conveniently represented using reaction rules, which can be specified using model-specification languages, such as the BioNetGen language (BNGL). A set of rules implicitly defines a (bio)chemical reaction network. The reaction network implied by a set of rules is often very large, and as a result, generation of the network implied by rules tends to be computationally expensive. Moreover, the cost of many commonly used methods for simulating network dynamics is a function of network size. Together these factors have limited application of the rule-based modeling approach. Recently, several methods for simulating rule-based models have been developed that avoid the expensive step of network generation. The cost of these "network-free" simulation methods is independent of the number of reactions implied by rules. Software implementing such methods is now needed for the simulation and analysis of rule-based models of biochemical systems.</p> <p>Results</p> <p>Here, we present a software tool called RuleMonkey, which implements a network-free method for simulation of rule-based models that is similar to Gillespie's method. The method is suitable for rule-based models that can be encoded in BNGL, including models with rules that have global application conditions, such as rules for intramolecular association reactions. In addition, the method is rejection free, unlike other network-free methods that introduce null events, i.e., steps in the simulation procedure that do not change the state of the reaction system being simulated. We verify that RuleMonkey produces correct simulation results, and we compare its performance against DYNSTOC, another BNGL-compliant tool for network-free simulation of rule-based models. We also compare RuleMonkey against problem-specific codes implementing network-free simulation methods.</p> <p>Conclusions</p> <p>RuleMonkey enables the simulation of rule-based models for which the underlying reaction networks are large. It is typically faster than DYNSTOC for benchmark problems that we have examined. RuleMonkey is freely available as a stand-alone application <url>http://public.tgen.org/rulemonkey</url>. It is also available as a simulation engine within GetBonNie, a web-based environment for building, analyzing and sharing rule-based models.</p

Greater than recommended stiffness and power setting of a stance-phase powered leg prosthesis can improve step-to-step transition work and effective foot length ratio during walking in people with transtibial amputation

People with unilateral transtibial amputation (TTA) using a passive-elastic prosthesis exhibit lower positive affected leg trailing work (ALtrail Wpos) and a greater magnitude of negative unaffected leg leading work (ULlead Wneg) during walking than non-amputees, which may increase joint pain and osteoarthritis risk in the unaffected leg. People with TTA using a stance-phase powered prosthesis (e.g., BiOM, Ottobock, Duderstadt, Germany) walk with increased ALtrail Wpos and potentially decreased magnitude of ULlead Wneg compared to a passive-elastic prosthesis. The BiOM includes a passive-elastic prosthesis with a manufacturer-recommended stiffness category and can be tuned to different power settings, which may change ALtrail Wpos, ULlead Wneg, and the prosthesis effective foot length ratio (EFLR). Thirteen people with TTA walked using 16 different prosthetic stiffness category and power settings on a level treadmill at 0.75–1.75 m/s. We constructed linear mixed effects models to determine the effects of stiffness category and power settings on ALtrail Wpos, ULlead Wneg, and EFLR and hypothesized that decreased stiffness and increased power would increase ALtrail Wpos, not change and decrease ULlead Wneg magnitude, and decrease and not change prosthesis EFLR, respectively. We found there was no significant effect of stiffness category on ALtrail Wpos but increased stiffness reduced ULlead Wneg magnitude, perhaps due to a 0.02 increase in prosthesis EFLR compared to the least stiff category. Furthermore, we found that use of the BiOM with 10% and 20% greater than recommended power increased ALtrail Wpos and decreased ULlead Wneg magnitude at 0.75–1.00 m/s. However, prosthetic power setting depended on walking speed so that use of the BiOM increased ULlead Wneg magnitude at 1.50–1.75 m/s compared to a passive-elastic prosthesis. Ultimately, our results suggest that at 0.75–1.00 m/s, prosthetists should utilize the BiOM attached to a passive-elastic prosthesis with an increased stiffness category and power settings up to 20% greater than recommended based on biological ankle values. This prosthetic configuration can allow people with unilateral transtibial amputation to increase ALtrail Wpos and minimize ULlead Wneg magnitude, which could reduce joint pain and osteoarthritis risk in the unaffected leg and potentially lower the metabolic cost of walking

Transient compartmentalization of simian immunodeficiency virus variants in the breast milk of african green monkeys

Natural hosts of simian immunodeficiency virus (SIV), African green monkeys (AGMs), rarely transmit SIV via breast-feeding. In order to examine the genetic diversity of breast milk SIV variants in this limited-transmission setting, we performed phylogenetic analysis on envelope sequences of milk and plasma SIV variants of AGMs. Low-diversity milk virus populations were compartmentalized from that in plasma. However, this compartmentalization was transient, as the milk virus lineages did not persist longitudinally

Sulfotyrosine Recognition as Marker for Druggable Sites in the Extracellular Space

Chemokine signaling is a well-known agent of autoimmune disease, HIV infection, and cancer. Drug discovery efforts for these signaling molecules have focused on developing inhibitors targeting their associated G protein-coupled receptors. Recently, we used a structure-based approach directed at the sulfotyrosine-binding pocket of the chemokine CXCL12, and thereby demonstrated that small molecule inhibitors acting upon the chemokine ligand form an alternative therapeutic avenue. Although the 50 members of the chemokine family share varying degrees of sequence homology (some as little as 20%), all members retain the canonical chemokine fold. Here we show that an equivalent sulfotyrosine-binding pocket appears to be conserved across the chemokine superfamily. We monitored sulfotyrosine binding to four representative chemokines by NMR. The results suggest that most chemokines harbor a sulfotyrosine recognition site analogous to the cleft on CXCL12 that binds sulfotyrosine 21 of the receptor CXCR4. Rational drug discovery efforts targeting these sites may be useful in the development of specific as well as broad-spectrum chemokine inhibitors

Next-Generation Sequencing of Coccidioides immitis Isolated during Cluster Investigation

Next-generation sequencing enables use of whole-genome sequence typing (WGST) as a viable and discriminatory tool for genotyping and molecular epidemiologic analysis. We used WGST to confirm the linkage of a cluster of Coccidioides immitis isolates from 3 patients who received organ transplants from a single donor who later had positive test results for coccidioidomycosis. Isolates from the 3 patients were nearly genetically identical (a total of 3 single-nucleotide polymorphisms identified among them), thereby demonstrating direct descent of the 3 isolates from an original isolate. We used WGST to demonstrate the genotypic relatedness of C. immitis isolates that were also epidemiologically linked. Thus, WGST offers unique benefits to public health for investigation of clusters considered to be linked to a single source

Sensitization in Transplantation: Assessment of Risk (STAR) 2017 Working Group Meeting Report

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/144684/1/ajt14752_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/144684/2/ajt14752.pd

Validating Clustering of Molecular Dynamics Simulations Using Polymer Models

Abstract Background Molecular dynamics (MD) simulation is a powerful technique for sampling the meta-stable and transitional conformations of proteins and other biomolecules. Computational data clustering has emerged as a useful, automated technique for extracting conformational states from MD simulation data. Despite extensive application, relatively little work has been done to determine if the clustering algorithms are actually extracting useful information. A primary goal of this paper therefore is to provide such an understanding through a detailed analysis of data clustering applied to a series of increasingly complex biopolymer models. Results We develop a novel series of models using basic polymer theory that have intuitive, clearly-defined dynamics and exhibit the essential properties that we are seeking to identify in MD simulations of real biomolecules. We then apply spectral clustering, an algorithm particularly well-suited for clustering polymer structures, to our models and MD simulations of several intrinsically disordered proteins. Clustering results for the polymer models provide clear evidence that the meta-stable and transitional conformations are detected by the algorithm. The results for the polymer models also help guide the analysis of the disordered protein simulations by comparing and contrasting the statistical properties of the extracted clusters. Conclusions We have developed a framework for validating the performance and utility of clustering algorithms for studying molecular biopolymer simulations that utilizes several analytic and dynamic polymer models which exhibit well-behaved dynamics including: meta-stable states, transition states, helical structures, and stochastic dynamics. We show that spectral clustering is robust to anomalies introduced by structural alignment and that different structural classes of intrinsically disordered proteins can be reliably discriminated from the clustering results. To our knowledge, our framework is the first to utilize model polymers to rigorously test the utility of clustering algorithms for studying biopolymers