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Husar A, Ordyan M, Garcia GC, Yancey JG, Saglam AS, Faeder JR, Bartol TM, Kennedy MB, Sejnowski TJ. MCell4 with BioNetGen: A Monte Carlo simulator of rule-based reaction-diffusion systems with Python interface. PLoS Comput Biol 2024; 20:e1011800. [PMID: 38656994 PMCID: PMC11073787 DOI: 10.1371/journal.pcbi.1011800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 05/06/2024] [Accepted: 01/03/2024] [Indexed: 04/26/2024] Open
Abstract
Biochemical signaling pathways in living cells are often highly organized into spatially segregated volumes, membranes, scaffolds, subcellular compartments, and organelles comprising small numbers of interacting molecules. At this level of granularity stochastic behavior dominates, well-mixed continuum approximations based on concentrations break down and a particle-based approach is more accurate and more efficient. We describe and validate a new version of the open-source MCell simulation program (MCell4), which supports generalized 3D Monte Carlo modeling of diffusion and chemical reaction of discrete molecules and macromolecular complexes in solution, on surfaces representing membranes, and combinations thereof. The main improvements in MCell4 compared to the previous versions, MCell3 and MCell3-R, include a Python interface and native BioNetGen reaction language (BNGL) support. MCell4's Python interface opens up completely new possibilities for interfacing with external simulators to allow creation of sophisticated event-driven multiscale/multiphysics simulations. The native BNGL support, implemented through a new open-source library libBNG (also introduced in this paper), provides the capability to run a given BNGL model spatially resolved in MCell4 and, with appropriate simplifying assumptions, also in the BioNetGen simulation environment, greatly accelerating and simplifying model validation and comparison.
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Affiliation(s)
- Adam Husar
- Computational Neurobiology Lab, Salk Institute for Biological Studies, La Jolla, California, United States of America
| | - Mariam Ordyan
- Institute for Neural Computations, University of California, San Diego, La Jolla, California, United States of America
| | - Guadalupe C. Garcia
- Computational Neurobiology Lab, Salk Institute for Biological Studies, La Jolla, California, United States of America
| | - Joel G. Yancey
- Computational Neurobiology Lab, Salk Institute for Biological Studies, La Jolla, California, United States of America
| | - Ali S. Saglam
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - James R. Faeder
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Thomas M. Bartol
- Computational Neurobiology Lab, Salk Institute for Biological Studies, La Jolla, California, United States of America
| | - Mary B. Kennedy
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America
| | - Terrence J. Sejnowski
- Computational Neurobiology Lab, Salk Institute for Biological Studies, La Jolla, California, United States of America
- Institute for Neural Computations, University of California, San Diego, La Jolla, California, United States of America
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Hoogesteyn AL, Rivas AL, Smith SD, Fasina FO, Fair JM, Kosoy M. Assessing complexity and dynamics in epidemics: geographical barriers and facilitators of foot-and-mouth disease dissemination. Front Vet Sci 2023; 10:1149460. [PMID: 37252396 PMCID: PMC10213354 DOI: 10.3389/fvets.2023.1149460] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Accepted: 04/17/2023] [Indexed: 05/31/2023] Open
Abstract
Introduction Physical and non-physical processes that occur in nature may influence biological processes, such as dissemination of infectious diseases. However, such processes may be hard to detect when they are complex systems. Because complexity is a dynamic and non-linear interaction among numerous elements and structural levels in which specific effects are not necessarily linked to any one specific element, cause-effect connections are rarely or poorly observed. Methods To test this hypothesis, the complex and dynamic properties of geo-biological data were explored with high-resolution epidemiological data collected in the 2001 Uruguayan foot-and-mouth disease (FMD) epizootic that mainly affected cattle. County-level data on cases, farm density, road density, river density, and the ratio of road (or river) length/county perimeter were analyzed with an open-ended procedure that identified geographical clustering in the first 11 epidemic weeks. Two questions were asked: (i) do geo-referenced epidemiologic data display complex properties? and (ii) can such properties facilitate or prevent disease dissemination? Results Emergent patterns were detected when complex data structures were analyzed, which were not observed when variables were assessed individually. Complex properties-including data circularity-were demonstrated. The emergent patterns helped identify 11 counties as 'disseminators' or 'facilitators' (F) and 264 counties as 'barriers' (B) of epidemic spread. In the early epidemic phase, F and B counties differed in terms of road density and FMD case density. Focusing on non-biological, geographical data, a second analysis indicated that complex relationships may identify B-like counties even before epidemics occur. Discussion Geographical barriers and/or promoters of disease dispersal may precede the introduction of emerging pathogens. If corroborated, the analysis of geo-referenced complexity may support anticipatory epidemiological policies.
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Affiliation(s)
| | - A. L. Rivas
- Center for Global Health, Internal Medicine, School of Medicine, University of New Mexico, Albuquerque, NM, United States
| | - S. D. Smith
- Geospatial Research Services, Ithaca, NY, United States
| | - F. O. Fasina
- Department of Veterinary Tropical Diseases, Faculty of Veterinary Science, University of Pretoria, Pretoria, South Africa
- ECTAD Food and Agriculture Organization (FAO), Nairobi, Kenya
| | - J. M. Fair
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, United States
| | - M. Kosoy
- KB One Health LLC, Fort Collins, CO, United States
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Solís C, Solaro RJ. Novel insights into sarcomere regulatory systems control of cardiac thin filament activation. J Gen Physiol 2021; 153:211903. [PMID: 33740037 PMCID: PMC7988513 DOI: 10.1085/jgp.202012777] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 02/23/2021] [Indexed: 12/11/2022] Open
Abstract
Our review focuses on sarcomere regulatory mechanisms with a discussion of cardiac-specific modifications to the three-state model of thin filament activation from a blocked to closed to open state. We discuss modulation of these thin filament transitions by Ca2+, by crossbridge interactions, and by thick filament–associated proteins, cardiac myosin–binding protein C (cMyBP-C), cardiac regulatory light chain (cRLC), and titin. Emerging evidence supports the idea that the cooperative activation of the thin filaments despite a single Ca2+ triggering regulatory site on troponin C (cTnC) cannot be considered in isolation of other functional domains of the sarcomere. We discuss long- and short-range interactions among these domains with the regulatory units of thin filaments, including proteins at the barbed end at the Z-disc and the pointed end near the M-band. Important to these discussions is the ever-increasing understanding of the role of cMyBP-C, cRLC, and titin filaments. Detailed knowledge of these control processes is critical to the understanding of mechanisms sustaining physiological cardiac state with varying hemodynamic load, to better defining genetic and acquired cardiac disorders, and to developing targets for therapies at the level of the sarcomeres.
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Affiliation(s)
- Christopher Solís
- University of Illinois at Chicago, College of Medicine, Department of Physiology and Biophysics and Center for Cardiovascular Research, Chicago, IL
| | - R John Solaro
- University of Illinois at Chicago, College of Medicine, Department of Physiology and Biophysics and Center for Cardiovascular Research, Chicago, IL
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Ronan T, Garnett R, Naegle KM. New analysis pipeline for high-throughput domain-peptide affinity experiments improves SH2 interaction data. J Biol Chem 2020; 295:11346-11363. [PMID: 32540967 DOI: 10.1074/jbc.ra120.012503] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 06/11/2020] [Indexed: 11/06/2022] Open
Abstract
Protein domain interactions with short linear peptides, such as those of the Src homology 2 (SH2) domain with phosphotyrosine-containing peptide motifs (pTyr), are ubiquitous and important to many biochemical processes of the cell. The desire to map and quantify these interactions has resulted in the development of high-throughput (HTP) quantitative measurement techniques, such as microarray or fluorescence polarization assays. For example, in the last 15 years, experiments have progressed from measuring single interactions to covering 500,000 of the 5.5 million possible SH2-pTyr interactions in the human proteome. However, high variability in affinity measurements and disagreements about positive interactions between published data sets led us here to reevaluate the analysis methods and raw data of published SH2-pTyr HTP experiments. We identified several opportunities for improving the identification of positive and negative interactions and the accuracy of affinity measurements. We implemented model-fitting techniques that are more statistically appropriate for the nonlinear SH2-pTyr interaction data. We also developed a method to account for protein concentration errors due to impurities and degradation or protein inactivity and aggregation. Our revised analysis increases the reported affinity accuracy, reduces the false-negative rate, and increases the amount of useful data by adding reliable true-negative results. We demonstrate improvement in classification of binding versus nonbinding when using machine-learning techniques, suggesting improved coherence in the reanalyzed data sets. We present revised SH2-pTyr affinity results and propose a new analysis pipeline for future HTP measurements of domain-peptide interactions.
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Affiliation(s)
- Tom Ronan
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Roman Garnett
- Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Kristen M Naegle
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
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Erickson KE, Rukhlenko OS, Shahinuzzaman M, Slavkova KP, Lin YT, Suderman R, Stites EC, Anghel M, Posner RG, Barua D, Kholodenko BN, Hlavacek WS. Modeling cell line-specific recruitment of signaling proteins to the insulin-like growth factor 1 receptor. PLoS Comput Biol 2019; 15:e1006706. [PMID: 30653502 PMCID: PMC6353226 DOI: 10.1371/journal.pcbi.1006706] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Revised: 01/30/2019] [Accepted: 12/09/2018] [Indexed: 12/27/2022] Open
Abstract
Receptor tyrosine kinases (RTKs) typically contain multiple autophosphorylation sites in their cytoplasmic domains. Once activated, these autophosphorylation sites can recruit downstream signaling proteins containing Src homology 2 (SH2) and phosphotyrosine-binding (PTB) domains, which recognize phosphotyrosine-containing short linear motifs (SLiMs). These domains and SLiMs have polyspecific or promiscuous binding activities. Thus, multiple signaling proteins may compete for binding to a common SLiM and vice versa. To investigate the effects of competition on RTK signaling, we used a rule-based modeling approach to develop and analyze models for ligand-induced recruitment of SH2/PTB domain-containing proteins to autophosphorylation sites in the insulin-like growth factor 1 (IGF1) receptor (IGF1R). Models were parameterized using published datasets reporting protein copy numbers and site-specific binding affinities. Simulations were facilitated by a novel application of model restructuration, to reduce redundancy in rule-derived equations. We compare predictions obtained via numerical simulation of the model to those obtained through simple prediction methods, such as through an analytical approximation, or ranking by copy number and/or KD value, and find that the simple methods are unable to recapitulate the predictions of numerical simulations. We created 45 cell line-specific models that demonstrate how early events in IGF1R signaling depend on the protein abundance profile of a cell. Simulations, facilitated by model restructuration, identified pairs of IGF1R binding partners that are recruited in anti-correlated and correlated fashions, despite no inclusion of cooperativity in our models. This work shows that the outcome of competition depends on the physicochemical parameters that characterize pairwise interactions, as well as network properties, including network connectivity and the relative abundances of competitors. Cells rely on networks of interacting biomolecules to sense and respond to environmental perturbations and signals. However, it is unclear how information is processed to generate appropriate and specific responses to signals, especially given that these networks tend to share many components. For example, receptors that detect distinct ligands and regulate distinct cellular activities commonly interact with overlapping sets of downstream signaling proteins. Here, to investigate the downstream signaling of a well-studied receptor tyrosine kinase (RTK), the insulin-like growth factor 1 (IGF1) receptor (IGF1R), we formulated and analyzed 45 cell line-specific mathematical models, which account for recruitment of 18 different binding partners to six sites of receptor autophosphorylation in IGF1R. The models were parameterized using available protein copy number and site-specific affinity measurements, and restructured to allow for network generation. We find that recruitment is influenced by the protein abundance profile of a cell, with different patterns of recruitment in different cell lines. Furthermore, in a given cell line, we find that pairs of IGF1R binding partners may be recruited in a correlated or anti-correlated fashion. We demonstrate that the simulations of the model have greater predictive power than protein copy number and/or binding affinity data, and that even a simple analytical model cannot reproduce the predicted recruitment ranking obtained via simulations. These findings represent testable predictions and indicate that the outputs of IGF1R signaling depend on cell line-specific properties in addition to the properties that are intrinsic to the biomolecules involved.
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Affiliation(s)
- Keesha E. Erickson
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | | | - Md Shahinuzzaman
- Department of Chemical and Biochemical Engineering, University of Missouri Science and Technology, Rolla, Missouri, United States of America
| | - Kalina P. Slavkova
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Yen Ting Lin
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Ryan Suderman
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Edward C. Stites
- The Salk Institute for Biological Studies, La Jolla, California, United States of America
| | - Marian Anghel
- Information Sciences Group, Computer, Computational and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Richard G. Posner
- Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, United States of America
| | - Dipak Barua
- Department of Chemical and Biochemical Engineering, University of Missouri Science and Technology, Rolla, Missouri, United States of America
| | - Boris N. Kholodenko
- Systems Biology Ireland, University College Dublin, Belfield, Dublin, Ireland
- School of Medicine and Medical Science and Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin, Ireland
| | - William S. Hlavacek
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
- * E-mail:
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Bouhaddou M, Barrette AM, Stern AD, Koch RJ, DiStefano MS, Riesel EA, Santos LC, Tan AL, Mertz AE, Birtwistle MR. A mechanistic pan-cancer pathway model informed by multi-omics data interprets stochastic cell fate responses to drugs and mitogens. PLoS Comput Biol 2018; 14:e1005985. [PMID: 29579036 PMCID: PMC5886578 DOI: 10.1371/journal.pcbi.1005985] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2017] [Revised: 04/05/2018] [Accepted: 01/16/2018] [Indexed: 01/02/2023] Open
Abstract
Most cancer cells harbor multiple drivers whose epistasis and interactions with expression context clouds drug and drug combination sensitivity prediction. We constructed a mechanistic computational model that is context-tailored by omics data to capture regulation of stochastic proliferation and death by pan-cancer driver pathways. Simulations and experiments explore how the coordinated dynamics of RAF/MEK/ERK and PI-3K/AKT kinase activities in response to synergistic mitogen or drug combinations control cell fate in a specific cellular context. In this MCF10A cell context, simulations suggest that synergistic ERK and AKT inhibitor-induced death is likely mediated by BIM rather than BAD, which is supported by prior experimental studies. AKT dynamics explain S-phase entry synergy between EGF and insulin, but simulations suggest that stochastic ERK, and not AKT, dynamics seem to drive cell-to-cell proliferation variability, which in simulations is predictable from pre-stimulus fluctuations in C-Raf/B-Raf levels. Simulations suggest MEK alteration negligibly influences transformation, consistent with clinical data. Tailoring the model to an alternate cell expression and mutation context, a glioma cell line, allows prediction of increased sensitivity of cell death to AKT inhibition. Our model mechanistically interprets context-specific landscapes between driver pathways and cell fates, providing a framework for designing more rational cancer combination therapy. Cancer is a complex and diverse disease. Two people with the same cancer type often respond differently to the same treatment. These differences are primarily driven by the fact that two type-matched tumors can possess distinct sets of mutations and gene expression profiles, provoking differential sensitivity to drugs. Over the past few decades, we have seen a shift away from more broadly cytotoxic drugs to more targeted molecules therapies; but how to match a patient with a specific drug or drug cocktail remains a difficult problem. Here, we build a mechanistic ordinary differential equation model describing the interactions between commonly mutated pan-cancer signaling pathways—receptor tyrosine kinases, Ras/RAF/ERK, PI3K/AKT, mTOR, cell cycle, DNA damage, and apoptosis. We develop methods for how to tailor the model to multi-omics data from a specific biological context, devise a novel stochastic algorithm to induce non-genetic cell-to-cell fluctuations in mRNA and protein quantities over time, and train the model against a wealth of biochemical and cell fate data to gain insight into the systems-level, context-specific control of proliferation and death. One day, we hope models of this kind could be tailored to patient-derived tumor mRNA sequencing data and used to prioritize patient-specific drug regimens.
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Affiliation(s)
- Mehdi Bouhaddou
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Anne Marie Barrette
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Alan D. Stern
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Rick J. Koch
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Matthew S. DiStefano
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Eric A. Riesel
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Luis C. Santos
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Annie L. Tan
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Alex E. Mertz
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Marc R. Birtwistle
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC, United States of America
- * E-mail:
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