1
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Bouguéon M, Legagneux V, Hazard O, Bomo J, Siegel A, Feret J, Théret N. A rule-based multiscale model of hepatic stellate cell plasticity: Critical role of the inactivation loop in fibrosis progression. PLoS Comput Biol 2024; 20:e1011858. [PMID: 39074160 PMCID: PMC11309422 DOI: 10.1371/journal.pcbi.1011858] [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: 01/24/2024] [Revised: 08/08/2024] [Accepted: 07/05/2024] [Indexed: 07/31/2024] Open
Abstract
Hepatic stellate cells (HSC) are the source of extracellular matrix (ECM) whose overproduction leads to fibrosis, a condition that impairs liver functions in chronic liver diseases. Understanding the dynamics of HSCs will provide insights needed to develop new therapeutic approaches. Few models of hepatic fibrosis have been proposed, and none of them include the heterogeneity of HSC phenotypes recently highlighted by single-cell RNA sequencing analyses. Here, we developed rule-based models to study HSC dynamics during fibrosis progression and reversion. We used the Kappa graph rewriting language, for which we used tokens and counters to overcome temporal explosion. HSCs are modeled as agents that present seven physiological cellular states and that interact with (TGFβ1) molecules which regulate HSC activation and the secretion of type I collagen, the main component of the ECM. Simulation studies revealed the critical role of the HSC inactivation process during fibrosis progression and reversion. While inactivation allows elimination of activated HSCs during reversion steps, reactivation loops of inactivated HSCs (iHSCs) are required to sustain fibrosis. Furthermore, we demonstrated the model's sensitivity to (TGFβ1) parameters, suggesting its adaptability to a variety of pathophysiological conditions for which levels of (TGFβ1) production associated with the inflammatory response differ. Using new experimental data from a mouse model of CCl4-induced liver fibrosis, we validated the predicted ECM dynamics. Our model also predicts the accumulation of iHSCs during chronic liver disease. By analyzing RNA sequencing data from patients with non-alcoholic steatohepatitis (NASH) associated with liver fibrosis, we confirmed this accumulation, identifying iHSCs as novel markers of fibrosis progression. Overall, our study provides the first model of HSC dynamics in chronic liver disease that can be used to explore the regulatory role of iHSCs in liver homeostasis. Moreover, our model can also be generalized to fibroblasts during repair and fibrosis in other tissues.
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Affiliation(s)
- Matthieu Bouguéon
- Univ Rennes, Inria, CNRS, IRISA, UMR 6074, Rennes, France
- Univ Rennes, Inserm, EHESP, Irset, UMR S1085, Rennes, France
| | | | - Octave Hazard
- École Polytechnique, Palaiseau, France
- DI-ENS (Inria, ÉNS, CNRS, PSL University), École normale supérieure, Paris, France
| | - Jérémy Bomo
- Univ Rennes, Inria, CNRS, IRISA, UMR 6074, Rennes, France
- Univ Rennes, Inserm, EHESP, Irset, UMR S1085, Rennes, France
| | - Anne Siegel
- Univ Rennes, Inria, CNRS, IRISA, UMR 6074, Rennes, France
| | - Jérôme Feret
- DI-ENS (Inria, ÉNS, CNRS, PSL University), École normale supérieure, Paris, France
- Team Antique, Inria, Paris, France
| | - Nathalie Théret
- Univ Rennes, Inria, CNRS, IRISA, UMR 6074, Rennes, France
- Univ Rennes, Inserm, EHESP, Irset, UMR S1085, Rennes, France
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2
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Ershova A, Minev D, Corea-Dilbert FE, Yu D, Deng J, Fontana W, Shih WM. Enzyme-Free Exponential Amplification via Growth and Scission of Crisscross Ribbons from Single-Stranded DNA Components. J Am Chem Soc 2024; 146:218-227. [PMID: 38133996 PMCID: PMC10785819 DOI: 10.1021/jacs.3c08205] [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: 07/30/2023] [Revised: 12/03/2023] [Accepted: 12/04/2023] [Indexed: 12/24/2023]
Abstract
The self-assembly of DNA-based monomers into higher-order structures has significant potential for realizing various biomimetic behaviors including algorithmic assembly, ultrasensitive detection, and self-replication. For these behaviors, it is desirable to implement high energetic barriers to undesired spurious nucleation, where such barriers can be bypassed via seed-initiated assembly. Joint-neighbor capture is a mechanism enabling the construction of such barriers while allowing for algorithmic behaviors, such as bit-copying. Cycles of polymerization with division could accordingly be used for implementing exponential growth in self-replicating materials. Previously, we demonstrated crisscross polymerization, a strategy that attains robust seed-dependent self-assembly of single-stranded DNA and DNA-origami monomers via joint-neighbor capture. Here, we expand the crisscross assembly to achieve autonomous, isothermal exponential amplification of ribbons through their concurrent growth and scission via toehold-mediated strand displacement. We demonstrate how this crisscross chain reaction, or 3CR, can be used as a detection strategy through coupling to single- and double-stranded nucleic acid targets and introduce a rule-based stochastic modeling approach for simulating molecular self-assembly behaviors such as crisscross-ribbon scission.
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Affiliation(s)
- Anastasia Ershova
- Department
of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, United States
- Wyss
Institute for Biologically Inspired Engineering at Harvard University, Boston, Massachusetts 02115, United States
- Department
of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts 02115, United States
| | - Dionis Minev
- Department
of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, United States
- Wyss
Institute for Biologically Inspired Engineering at Harvard University, Boston, Massachusetts 02115, United States
- Department
of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts 02115, United States
| | - F. Eduardo Corea-Dilbert
- Department
of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, United States
| | - Devon Yu
- Department
of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, United States
| | - Jie Deng
- Department
of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, United States
- Wyss
Institute for Biologically Inspired Engineering at Harvard University, Boston, Massachusetts 02115, United States
- Department
of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts 02115, United States
| | - Walter Fontana
- Department
of Systems Biology, Harvard Medical School, Boston, Massachusetts 02115, United States
| | - William M. Shih
- Department
of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, United States
- Wyss
Institute for Biologically Inspired Engineering at Harvard University, Boston, Massachusetts 02115, United States
- Department
of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts 02115, United States
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3
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Liu F, Heiner M, Gilbert D. Hybrid modelling of biological systems: current progress and future prospects. Brief Bioinform 2022; 23:6555400. [PMID: 35352101 PMCID: PMC9116374 DOI: 10.1093/bib/bbac081] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 02/12/2022] [Accepted: 02/16/2022] [Indexed: 11/15/2022] Open
Abstract
Integrated modelling of biological systems is becoming a necessity for constructing models containing the major biochemical processes of such systems in order to obtain a holistic understanding of their dynamics and to elucidate emergent behaviours. Hybrid modelling methods are crucial to achieve integrated modelling of biological systems. This paper reviews currently popular hybrid modelling methods, developed for systems biology, mainly revealing why they are proposed, how they are formed from single modelling formalisms and how to simulate them. By doing this, we identify future research requirements regarding hybrid approaches for further promoting integrated modelling of biological systems.
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Affiliation(s)
- Fei Liu
- School of Software Engineering, South China University of Technology, Guangzhou 510006, P.R. China
- Corresponding author: Fei Liu, School of Software Engineering, South China University of Technology, Guangzhou 510006, P.R. China. E-mail:
| | - Monika Heiner
- Department of Computer Science, Brandenburg University of Technology Cottbus-Senftenberg, Cottbus 03046, Germany
| | - David Gilbert
- Department of Computer Science, Brunel University London, Middlesex UB8 3PH, UK
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4
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Vittadello ST, Stumpf MPH. Model comparison via simplicial complexes and persistent homology. ROYAL SOCIETY OPEN SCIENCE 2021; 8:211361. [PMID: 34659787 PMCID: PMC8511761 DOI: 10.1098/rsos.211361] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 09/16/2021] [Indexed: 05/21/2023]
Abstract
In many scientific and technological contexts, we have only a poor understanding of the structure and details of appropriate mathematical models. We often, therefore, need to compare different models. With available data we can use formal statistical model selection to compare and contrast the ability of different mathematical models to describe such data. There is, however, a lack of rigorous methods to compare different models a priori. Here, we develop and illustrate two such approaches that allow us to compare model structures in a systematic way by representing models as simplicial complexes. Using well-developed concepts from simplicial algebraic topology, we define a distance between models based on their simplicial representations. Employing persistent homology with a flat filtration provides for alternative representations of the models as persistence intervals, which represent model structure, from which the model distances are also obtained. We then expand on this measure of model distance to study the concept of model equivalence to determine the conceptual similarity of models. We apply our methodology for model comparison to demonstrate an equivalence between a positional-information model and a Turing-pattern model from developmental biology, constituting a novel observation for two classes of models that were previously regarded as unrelated.
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Affiliation(s)
- Sean T. Vittadello
- School of BioSciences and School of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Michael P. H. Stumpf
- School of BioSciences and School of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria 3010, Australia
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5
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Computational Phosphorylation Network Reconstruction: An Update on Methods and Resources. Methods Mol Biol 2021. [PMID: 34270057 DOI: 10.1007/978-1-0716-1625-3_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Abstract
Most proteins undergo some form of modification after translation, and phosphorylation is one of the most relevant and ubiquitous post-translational modifications. The succession of protein phosphorylation and dephosphorylation catalyzed by protein kinase and phosphatase, respectively, constitutes a key mechanism of molecular information flow in cellular systems. The protein interactions of kinases, phosphatases, and their regulatory subunits and substrates are the main part of phosphorylation networks. To elucidate the landscape of phosphorylation events has been a central goal pursued by both experimental and computational approaches. Substrate specificity (e.g., sequence, structure) or the phosphoproteome has been utilized in an array of different statistical learning methods to infer phosphorylation networks. In this chapter, different computational phosphorylation network inference-related methods and resources are summarized and discussed.
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6
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Romers J, Thieme S, Münzner U, Krantz M. A scalable method for parameter-free simulation and validation of mechanistic cellular signal transduction network models. NPJ Syst Biol Appl 2020; 6:2. [PMID: 31934349 PMCID: PMC6954118 DOI: 10.1038/s41540-019-0120-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2018] [Accepted: 11/20/2019] [Indexed: 11/09/2022] Open
Abstract
The metabolic modelling community has established the gold standard for bottom-up systems biology with reconstruction, validation and simulation of mechanistic genome-scale models. Similar methods have not been established for signal transduction networks, where the representation of complexes and internal states leads to scalability issues in both model formulation and execution. While rule- and agent-based methods allow efficient model definition and execution, respectively, model parametrisation introduces an additional layer of uncertainty due to the sparsity of reliably measured parameters. Here, we present a scalable method for parameter-free simulation of mechanistic signal transduction networks. It is based on rxncon and uses a bipartite Boolean logic with separate update rules for reactions and states. Using two generic update rules, we enable translation of any rxncon model into a unique Boolean model, which can be used for network validation and simulation-allowing the prediction of system-level function directly from molecular mechanistic data. Through scalable model definition and simulation, and the independence of quantitative parameters, it opens up for simulation and validation of mechanistic genome-scale models of signal transduction networks.
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Affiliation(s)
- Jesper Romers
- Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Sebastian Thieme
- Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Ulrike Münzner
- Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Japan
| | - Marcus Krantz
- Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany
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7
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Santibáñez R, Garrido D, Martin AJM. Pleione: A tool for statistical and multi-objective calibration of Rule-based models. Sci Rep 2019; 9:15104. [PMID: 31641245 PMCID: PMC6805871 DOI: 10.1038/s41598-019-51546-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Accepted: 09/24/2019] [Indexed: 11/17/2022] Open
Abstract
Mathematical models based on Ordinary Differential Equations (ODEs) are frequently used to describe and simulate biological systems. Nevertheless, such models are often difficult to understand. Unlike ODE models, Rule-Based Models (RBMs) utilise formal language to describe reactions as a cumulative number of statements that are easier to understand and correct. They are also gaining popularity because of their conciseness and simulation flexibility. However, RBMs generally lack tools to perform further analysis that requires simulation. This situation arises because exact and approximate simulations are computationally intensive. Translating RBMs into ODEs is commonly used to reduce simulation time, but this technique may be prohibitive due to combinatorial explosion. Here, we present the software called Pleione to calibrate RBMs. Parameter calibration is essential given the incomplete experimental determination of reaction rates and the goal of using models to reproduce experimental data. The software distributes stochastic simulations and calculations and incorporates equivalence tests to determine the fitness of RBMs compared with data. The primary features of Pleione were thoroughly tested on a model of gene regulation in Escherichia coli. Pleione yielded satisfactory results regarding calculation time and error reduction for multiple simulators, models, parameter search strategies, and computing infrastructures.
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Affiliation(s)
- Rodrigo Santibáñez
- Network Biology Lab, Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, Santiago, 8580745, Chile
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, 7820436, Chile
| | - Daniel Garrido
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, 7820436, Chile
| | - Alberto J M Martin
- Network Biology Lab, Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, Santiago, 8580745, Chile.
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8
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Harmer R, Le Cornec YS, Legare S, Oshurko E. Bio-Curation for Cellular Signalling: The KAMI Project. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:1562-1573. [PMID: 30908261 DOI: 10.1109/tcbb.2019.2906164] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The general question of what constitutes bio-curation for rule-based modelling of cellular signalling is posed. A general approach to the problem is presented, based on rewriting in hierarchies of graphs, together with a specific instantiation of the methodology that addresses our particular bio-curation problem. The current state of the ongoing development of the KAMI bio-curation tool, based on this approach, is outlined along with our plans for future development.
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9
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Spaccasassi C, Lakin MR, Phillips A. A Logic Programming Language for Computational Nucleic Acid Devices. ACS Synth Biol 2019; 8:1530-1547. [PMID: 30372611 DOI: 10.1021/acssynbio.8b00229] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Computational nucleic acid devices show great potential for enabling a broad range of biotechnology applications, including smart probes for molecular biology research, in vitro assembly of complex compounds, high-precision in vitro disease diagnosis and, ultimately, computational theranostics inside living cells. This diversity of applications is supported by a range of implementation strategies, including nucleic acid strand displacement, localization to substrates, and the use of enzymes with polymerase, nickase, and exonuclease functionality. However, existing computational design tools are unable to account for these strategies in a unified manner. This paper presents a logic programming language that allows a broad range of computational nucleic acid systems to be designed and analyzed. The language extends standard logic programming with a novel equational theory to express nucleic acid molecular motifs. It automatically identifies matching motifs present in the full system, in order to apply a specified transformation expressed as a logical rule. The language supports the definition of logic predicates, which provide constraints that need to be satisfied in order for a given rule to be applied. The language is sufficiently expressive to encode the semantics of nucleic strand displacement systems with complex topologies, together with computation performed by a broad range of enzymes, and is readily extensible to new implementation strategies. Our approach lays the foundation for a unifying framework for the design of computational nucleic acid devices.
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Affiliation(s)
| | - Matthew R. Lakin
- Department of Computer Science, University of New Mexico, Albuquerque, New Mexico 87131, United States
- Center for Biomedical Engineering, University of New Mexico, Albuquerque, New Mexico 87131, United States
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10
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Mjolsness E. Prospects for Declarative Mathematical Modeling of Complex Biological Systems. Bull Math Biol 2019; 81:3385-3420. [PMID: 31175549 PMCID: PMC6677696 DOI: 10.1007/s11538-019-00628-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2018] [Accepted: 05/30/2019] [Indexed: 01/06/2023]
Abstract
Declarative modeling uses symbolic expressions to represent models. With such expressions, one can formalize high-level mathematical computations on models that would be difficult or impossible to perform directly on a lower-level simulation program, in a general-purpose programming language. Examples of such computations on models include model analysis, relatively general-purpose model reduction maps, and the initial phases of model implementation, all of which should preserve or approximate the mathematical semantics of a complex biological model. The potential advantages are particularly relevant in the case of developmental modeling, wherein complex spatial structures exhibit dynamics at molecular, cellular, and organogenic levels to relate genotype to multicellular phenotype. Multiscale modeling can benefit from both the expressive power of declarative modeling languages and the application of model reduction methods to link models across scale. Based on previous work, here we define declarative modeling of complex biological systems by defining the operator algebra semantics of an increasingly powerful series of declarative modeling languages including reaction-like dynamics of parameterized and extended objects; we define semantics-preserving implementation and semantics-approximating model reduction transformations; and we outline a “meta-hierarchy” for organizing declarative models and the mathematical methods that can fruitfully manipulate them.
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Affiliation(s)
- Eric Mjolsness
- Department of Computer Science, University of California, Irvine, CA, 92697, USA.
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11
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Berenger F, Zhang KYJ, Yamanishi Y. Chemoinformatics and structural bioinformatics in OCaml. J Cheminform 2019; 11:10. [PMID: 30719579 PMCID: PMC6689879 DOI: 10.1186/s13321-019-0332-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Accepted: 01/22/2019] [Indexed: 11/10/2022] Open
Abstract
Background OCaml is a functional programming language with strong static types, Hindley–Milner type inference and garbage collection. In this article, we share our experience in prototyping chemoinformatics and structural bioinformatics software in OCaml. Results First, we introduce the language, list entry points for chemoinformaticians who would be interested in OCaml and give code examples. Then, we list some scientific open source software written in OCaml. We also present recent open source libraries useful in chemoinformatics. The parallelization of OCaml programs and their performance is also shown. Finally, tools and methods useful when prototyping scientific software in OCaml are given. Conclusions In our experience, OCaml is a programming language of choice for method development in chemoinformatics and structural bioinformatics.
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Affiliation(s)
- Francois Berenger
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan.
| | - Kam Y J Zhang
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, Yokohama, Kanagawa, Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan.,PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama, Japan
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12
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Abstract
We present a protocol for building, validating, and simulating models of signal transduction networks. These networks are challenging modeling targets due to the combinatorial complexity and sparse data, which have made it a major challenge even to formalize the current knowledge. To address this, the community has developed methods to model biomolecular reaction networks based on site dynamics. The strength of this approach is that reactions and states can be defined at variable resolution, which makes it possible to adapt the model resolution to the empirical data. This improves both scalability and accuracy, making it possible to formalize large models of signal transduction networks. Here, we present a method to build and validate large models of signal transduction networks. The workflow is based on rxncon, the reaction-contingency language. In a five-step process, we create a mechanistic network model, convert it into an executable Boolean model, use the Boolean model to evaluate and improve the network, and finally export the rxncon model into a rule-based format. We provide an introduction to the rxncon language and an annotated, step-by-step protocol for the workflow. Finally, we create a small model of the insulin signaling pathway to illustrate the protocol, together with some of the challenges-and some of their solutions-in modeling signal transduction.
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Affiliation(s)
- Jesper Romers
- Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Sebastian Thieme
- Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Ulrike Münzner
- Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany.,Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Japan
| | - Marcus Krantz
- Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany.
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13
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Henze R, Grünert G, Ibrahim B, Dittrich P. Spatial Rule-Based Simulations: The SRSim Software. Methods Mol Biol 2019; 1945:231-249. [PMID: 30945249 DOI: 10.1007/978-1-4939-9102-0_10] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
SRSim combines rule-based reaction network models with spatial particle simulations allowing to simulate the dynamics of large molecular complexes changing according to a set of chemical reaction rules. As the rule can contain patterns of molecular complexes and specific states of certain binding sites, a combinatorially complex or even infinitely sized reaction network can be defined. Particles move in a three-dimensional space according to molecular dynamics implemented by LAMMPS, while the BioNetGen language is used to formulate reaction rules. Geometric information is added in a specific XML format. The simulation protocol is exemplified by two different variants of polymerization as well as a toy model of DNA helix formation. SRSim is open source and available for download.
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Affiliation(s)
- Richard Henze
- Department of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany
| | - Gerd Grünert
- Department of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany
| | - Bashar Ibrahim
- Chair of Bioinformatics, Matthias-Schleiden-Institute, Friedrich Schiller University Jena, Jena, Germany
| | - Peter Dittrich
- Department of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany.
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14
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Andrei O, Fernández M, Kirchner H, Pinaud B. Strategy-Driven Exploration for Rule-Based Models of Biochemical Systems with PORGY. Methods Mol Biol 2019; 1945:43-70. [PMID: 30945242 DOI: 10.1007/978-1-4939-9102-0_3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
This chapter presents PORGY-an interactive visual environment for rule-based modelling of biochemical systems. We model molecules and molecule interactions as port graphs and port graph rewrite rules, respectively. We use rewriting strategies to control which rules to apply, and where and when to apply them. Our main contributions to rule-based modelling of biochemical systems lie in the strategy language and the associated visual and interactive features offered by PORGY. These features facilitate an exploratory approach to test different ways of applying the rules while recording the model evolution, and tracking and plotting parameters. We illustrate PORGY's features with a study of the role of a scaffold protein in RAF/MEK/ERK signalling.
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Affiliation(s)
- Oana Andrei
- School of Computing Science, University of Glasgow, Glasgow, UK
| | | | | | - Bruno Pinaud
- University of Bordeaux, CNRS UMR5800 LaBRI, Talence, France.
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15
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Waites W, Mısırlı G, Cavaliere M, Danos V, Wipat A. A Genetic Circuit Compiler: Generating Combinatorial Genetic Circuits with Web Semantics and Inference. ACS Synth Biol 2018; 7:2812-2823. [PMID: 30408409 PMCID: PMC6305556 DOI: 10.1021/acssynbio.8b00201] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
A central strategy of synthetic biology is to understand the basic processes of living creatures through engineering organisms using the same building blocks. Biological machines described in terms of parts can be studied by computer simulation in any of several languages or robotically assembled in vitro. In this paper we present a language, the Genetic Circuit Description Language (GCDL) and a compiler, the Genetic Circuit Compiler (GCC). This language describes genetic circuits at a level of granularity appropriate both for automated assembly in the laboratory and deriving simulation code. The GCDL follows Semantic Web practice, and the compiler makes novel use of the logical inference facilities that are therefore available. We present the GCDL and compiler structure as a study of a tool for generating κ-language simulations from semantic descriptions of genetic circuits.
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Affiliation(s)
- William Waites
- School
of Informatics, University of Edinburgh, Edinburgh EH8 9YL, U.K.,E-mail:
| | - Göksel Mısırlı
- School
of Computing and Mathematics, Keele University, Newcastle ST5 5BG, U.K.
| | - Matteo Cavaliere
- School
of Computing & Mathematics, Manchester
Metropolitan University, Manchester M15 6BH, U.K.
| | - Vincent Danos
- School
of Informatics, University of Edinburgh, Edinburgh EH8 9YL, U.K.,École
Normale Supérieure, Paris, CNRS, 75005 Paris, France
| | - Anil Wipat
- School
of Computing Science, Newcastle University, Newcastle upon Tyne NE1
7RU, U.K.
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16
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Shahinuzzaman M, Khetan J, Barua D. A spatio-temporal model reveals self-limiting Fc ɛRI cross-linking by multivalent antigens. ROYAL SOCIETY OPEN SCIENCE 2018; 5:180190. [PMID: 30839725 PMCID: PMC6170560 DOI: 10.1098/rsos.180190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2018] [Accepted: 08/23/2018] [Indexed: 06/09/2023]
Abstract
Aggregation of cell surface receptor proteins by multivalent antigens is an essential early step for immune cell signalling. A number of experimental and modelling studies in the past have investigated multivalent ligand-mediated aggregation of IgE receptors (FcɛRI) in the plasma membrane of mast cells. However, understanding of the mechanisms of FcɛRI aggregation remains incomplete. Experimental reports indicate that FcɛRI forms relatively small and finite-sized clusters when stimulated by a multivalent ligand. By contrast, modelling studies have shown that receptor cross-linking by a trivalent ligand may lead to the formation of large receptor superaggregates that may potentially give rise to hyperactive cellular responses. In this work, we have developed a Brownian dynamics-based spatio-temporal model to analyse FcɛRI aggregation by a trivalent antigen. Unlike the existing models, which implemented non-spatial simulation approaches, our model explicitly accounts for the coarse-grained site-specific features of the multivalent species (molecules and complexes). The model incorporates membrane diffusion, steric collisions and sub-nanometre-scale site-specific interaction of the time-evolving species of arbitrary structures. Using the model, we investigated temporal evolution of the species and their diffusivities. Consistent with a recent experimental report, our model predicted sharp decay in species mobility in the plasma membrane in response receptor cross-linking by a multivalent antigen. We show that, due to such decay in the species mobility, post-stimulation receptor aggregation may become self-limiting. Our analysis reveals a potential regulatory mechanism suppressing hyperactivation of immune cells in response to multivalent antigens.
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Affiliation(s)
| | | | - Dipak Barua
- Author for correspondence: Dipak Barua e-mail:
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17
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Generalizing Gillespie's Direct Method to Enable Network-Free Simulations. Bull Math Biol 2018; 81:2822-2848. [PMID: 29594824 DOI: 10.1007/s11538-018-0418-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Accepted: 03/19/2018] [Indexed: 12/22/2022]
Abstract
Gillespie's direct method for stochastic simulation of chemical kinetics is a staple of computational systems biology research. However, the algorithm requires explicit enumeration of all reactions and all chemical species that may arise in the system. In many cases, this is not feasible due to the combinatorial explosion of reactions and species in biological networks. Rule-based modeling frameworks provide a way to exactly represent networks containing such combinatorial complexity, and generalizations of Gillespie's direct method have been developed as simulation engines for rule-based modeling languages. Here, we provide both a high-level description of the algorithms underlying the simulation engines, termed network-free simulation algorithms, and how they have been applied in systems biology research. We also define a generic rule-based modeling framework and describe a number of technical details required for adapting Gillespie's direct method for network-free simulation. Finally, we briefly discuss potential avenues for advancing network-free simulation and the role they continue to play in modeling dynamical systems in biology.
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18
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Gyori BM, Bachman JA, Subramanian K, Muhlich JL, Galescu L, Sorger PK. From word models to executable models of signaling networks using automated assembly. Mol Syst Biol 2017; 13:954. [PMID: 29175850 PMCID: PMC5731347 DOI: 10.15252/msb.20177651] [Citation(s) in RCA: 83] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Word models (natural language descriptions of molecular mechanisms) are a common currency in spoken and written communication in biomedicine but are of limited use in predicting the behavior of complex biological networks. We present an approach to building computational models directly from natural language using automated assembly. Molecular mechanisms described in simple English are read by natural language processing algorithms, converted into an intermediate representation, and assembled into executable or network models. We have implemented this approach in the Integrated Network and Dynamical Reasoning Assembler (INDRA), which draws on existing natural language processing systems as well as pathway information in Pathway Commons and other online resources. We demonstrate the use of INDRA and natural language to model three biological processes of increasing scope: (i) p53 dynamics in response to DNA damage, (ii) adaptive drug resistance in BRAF‐V600E‐mutant melanomas, and (iii) the RAS signaling pathway. The use of natural language makes the task of developing a model more efficient and it increases model transparency, thereby promoting collaboration with the broader biology community.
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Affiliation(s)
- Benjamin M Gyori
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - John A Bachman
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Kartik Subramanian
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Jeremy L Muhlich
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Lucian Galescu
- Institute for Human and Machine Cognition, Pensacola, FL, USA
| | - Peter K Sorger
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
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19
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Bittig AT, Uhrmacher AM. ML-Space: Hybrid Spatial Gillespie and Particle Simulation of Multi-Level Rule-Based Models in Cell Biology. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017; 14:1339-1349. [PMID: 27514063 DOI: 10.1109/tcbb.2016.2598162] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Spatio-temporal dynamics of cellular processes can be simulated at different levels of detail, from (deterministic) partial differential equations via the spatial Stochastic Simulation algorithm to tracking Brownian trajectories of individual particles. We present a spatial simulation approach for multi-level rule-based models, which includes dynamically hierarchically nested cellular compartments and entities. Our approach ML-Space combines discrete compartmental dynamics, stochastic spatial approaches in discrete space, and particles moving in continuous space. The rule-based specification language of ML-Space supports concise and compact descriptions of models and to adapt the spatial resolution of models easily.
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20
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Xie L, Smith GR, Schwartz R. Derivative-Free Optimization of Rate Parameters of Capsid Assembly Models from Bulk in Vitro Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017; 14:844-855. [PMID: 27168601 PMCID: PMC5581941 DOI: 10.1109/tcbb.2016.2563421] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
The assembly of virus capsids proceeds by a complicated cascade of association and dissociation steps, the great majority of which cannot be directly experimentally observed. This has made capsid assembly a rich field for computational models, but there are substantial obstacles to model inference for such systems. Here, we describe progress on fitting kinetic rate constants defining capsid assembly models to experimental data, a difficult data-fitting problem because of the high computational cost of simulating assembly trajectories, the stochastic noise inherent to the models, and the limited and noisy data available for fitting. We evaluate the merits of data-fitting methods based on derivative-free optimization (DFO) relative to gradient-based methods used in prior work. We further explore the advantages of alternative data sources through simulation of a model of time-resolved mass spectrometry data, a technology for monitoring bulk capsid assembly that can be expected to provide much richer data than previously used static light scattering approaches. The results show that advances in both the data and the algorithms can improve model inference. More informative data sources lead to high-quality fits for all methods, but DFO methods show substantial advantages on less informative data sources that better represent current experimental practice.
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Affiliation(s)
- Lu Xie
- Joint Carnegie Mellon/University of Pittsburgh Ph.D. Program in Computational Biology, Pittsburgh, PA USA and Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA USA 15213
| | - Gregory R. Smith
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA USA 15213
| | - Russell Schwartz
- Department of Biological Sciences and Computational Biology Department, Pittsburgh, PA USA 15213.
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21
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Mathematical Models for Immunology: Current State of the Art and Future Research Directions. Bull Math Biol 2016; 78:2091-2134. [PMID: 27714570 PMCID: PMC5069344 DOI: 10.1007/s11538-016-0214-9] [Citation(s) in RCA: 80] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2016] [Accepted: 09/26/2016] [Indexed: 01/01/2023]
Abstract
The advances in genetics and biochemistry that have taken place over the last 10 years led to significant advances in experimental and clinical immunology. In turn, this has led to the development of new mathematical models to investigate qualitatively and quantitatively various open questions in immunology. In this study we present a review of some research areas in mathematical immunology that evolved over the last 10 years. To this end, we take a step-by-step approach in discussing a range of models derived to study the dynamics of both the innate and immune responses at the molecular, cellular and tissue scales. To emphasise the use of mathematics in modelling in this area, we also review some of the mathematical tools used to investigate these models. Finally, we discuss some future trends in both experimental immunology and mathematical immunology for the upcoming years.
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22
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Harris LA, Hogg JS, Tapia JJ, Sekar JAP, Gupta S, Korsunsky I, Arora A, Barua D, Sheehan RP, Faeder JR. BioNetGen 2.2: advances in rule-based modeling. Bioinformatics 2016; 32:3366-3368. [PMID: 27402907 DOI: 10.1093/bioinformatics/btw469] [Citation(s) in RCA: 121] [Impact Index Per Article: 15.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2015] [Accepted: 06/27/2016] [Indexed: 12/18/2022] Open
Abstract
: BioNetGen is an open-source software package for rule-based modeling of complex biochemical systems. Version 2.2 of the software introduces numerous new features for both model specification and simulation. Here, we report on these additions, discussing how they facilitate the construction, simulation and analysis of larger and more complex models than previously possible. AVAILABILITY AND IMPLEMENTATION Stable BioNetGen releases (Linux, Mac OS/X and Windows), with documentation, are available at http://bionetgen.org Source code is available at http://github.com/RuleWorld/bionetgen CONTACT: bionetgen.help@gmail.comSupplementary information: Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Leonard A Harris
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Justin S Hogg
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - José-Juan Tapia
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - John A P Sekar
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Sanjana Gupta
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Ilya Korsunsky
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Arshi Arora
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Dipak Barua
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Robert P Sheehan
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - James R Faeder
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
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23
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Schaff JC, Vasilescu D, Moraru II, Loew LM, Blinov ML. Rule-based modeling with Virtual Cell. Bioinformatics 2016; 32:2880-2. [PMID: 27497444 DOI: 10.1093/bioinformatics/btw353] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2016] [Accepted: 05/30/2016] [Indexed: 01/09/2023] Open
Abstract
UNLABELLED Rule-based modeling is invaluable when the number of possible species and reactions in a model become too large to allow convenient manual specification. The popular rule-based software tools BioNetGen and NFSim provide powerful modeling and simulation capabilities at the cost of learning a complex scripting language which is used to specify these models. Here, we introduce a modeling tool that combines new graphical rule-based model specification with existing simulation engines in a seamless way within the familiar Virtual Cell (VCell) modeling environment. A mathematical model can be built integrating explicit reaction networks with reaction rules. In addition to offering a large choice of ODE and stochastic solvers, a model can be simulated using a network free approach through the NFSim simulation engine. AVAILABILITY AND IMPLEMENTATION Available as VCell (versions 6.0 and later) at the Virtual Cell web site (http://vcell.org/). The application installs and runs on all major platforms and does not require registration for use on the user's computer. Tutorials are available at the Virtual Cell website and Help is provided within the software. Source code is available at Sourceforge. CONTACT vcell_support@uchc.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- James C Schaff
- R. D. Berlin Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, CT 06030, USA
| | - Dan Vasilescu
- R. D. Berlin Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, CT 06030, USA
| | - Ion I Moraru
- R. D. Berlin Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, CT 06030, USA
| | - Leslie M Loew
- R. D. Berlin Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, CT 06030, USA
| | - Michael L Blinov
- R. D. Berlin Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, CT 06030, USA
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Di Camillo B, Carlon A, Eduati F, Toffolo GM. A rule-based model of insulin signalling pathway. BMC SYSTEMS BIOLOGY 2016; 10:38. [PMID: 27245161 PMCID: PMC4888568 DOI: 10.1186/s12918-016-0281-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/23/2015] [Accepted: 05/12/2016] [Indexed: 12/13/2022]
Abstract
BACKGROUND The insulin signalling pathway (ISP) is an important biochemical pathway, which regulates some fundamental biological functions such as glucose and lipid metabolism, protein synthesis, cell proliferation, cell differentiation and apoptosis. In the last years, different mathematical models based on ordinary differential equations have been proposed in the literature to describe specific features of the ISP, thus providing a description of the behaviour of the system and its emerging properties. However, protein-protein interactions potentially generate a multiplicity of distinct chemical species, an issue referred to as "combinatorial complexity", which results in defining a high number of state variables equal to the number of possible protein modifications. This often leads to complex, error prone and difficult to handle model definitions. RESULTS In this work, we present a comprehensive model of the ISP, which integrates three models previously available in the literature by using the rule-based modelling (RBM) approach. RBM allows for a simple description of a number of signalling pathway characteristics, such as the phosphorylation of signalling proteins at multiple sites with different effects, the simultaneous interaction of many molecules of the signalling pathways with several binding partners, and the information about subcellular localization where reactions take place. Thanks to its modularity, it also allows an easy integration of different pathways. After RBM specification, we simulated the dynamic behaviour of the ISP model and validated it using experimental data. We the examined the predicted profiles of all the active species and clustered them in four clusters according to their dynamic behaviour. Finally, we used parametric sensitivity analysis to show the role of negative feedback loops in controlling the robustness of the system. CONCLUSIONS The presented ISP model is a powerful tool for data simulation and can be used in combination with experimental approaches to guide the experimental design. The model is available at http://sysbiobig.dei.unipd.it/ was submitted to Biomodels Database ( https://www.ebi.ac.uk/biomodels-main/ # MODEL 1604100005).
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Affiliation(s)
- Barbara Di Camillo
- Department of Information Engineering, University of Padova, Via Gradenigo 6A, Padova, 35131, Italy
| | - Azzurra Carlon
- Department of Information Engineering, University of Padova, Via Gradenigo 6A, Padova, 35131, Italy.,Magnetic Resonance Center (CERM) and Department of Chemistry "Ugo Schiff", University of Florence, Florence, Italy
| | - Federica Eduati
- Department of Information Engineering, University of Padova, Via Gradenigo 6A, Padova, 35131, Italy.,European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge, UK
| | - Gianna Maria Toffolo
- Department of Information Engineering, University of Padova, Via Gradenigo 6A, Padova, 35131, Italy.
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25
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Abstract
As the amount of biological data in the public domain grows, so does the range of modeling and analysis techniques employed in systems biology. In recent years, a number of theoretical computer science developments have enabled modeling methodology to keep pace. The growing interest in systems biology in executable models and their analysis has necessitated the borrowing of terms and methods from computer science, such as formal analysis, model checking, static analysis, and runtime verification. Here, we discuss the most important and exciting computational methods and tools currently available to systems biologists. We believe that a deeper understanding of the concepts and theory highlighted in this review will produce better software practice, improved investigation of complex biological processes, and even new ideas and better feedback into computer science.
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Affiliation(s)
- Ezio Bartocci
- Faculty of Informatics, Technische Universität Wien, Vienna, Austria
| | - Pietro Lió
- Computer Laboratory, University of Cambridge, Cambridge, United Kingdom
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26
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Fey D, Halasz M, Dreidax D, Kennedy SP, Hastings JF, Rauch N, Munoz AG, Pilkington R, Fischer M, Westermann F, Kolch W, Kholodenko BN, Croucher DR. Signaling pathway models as biomarkers: Patient-specific simulations of JNK activity predict the survival of neuroblastoma patients. Sci Signal 2015; 8:ra130. [DOI: 10.1126/scisignal.aab0990] [Citation(s) in RCA: 118] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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27
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Chylek LA, Harris LA, Faeder JR, Hlavacek WS. Modeling for (physical) biologists: an introduction to the rule-based approach. Phys Biol 2015; 12:045007. [PMID: 26178138 PMCID: PMC4526164 DOI: 10.1088/1478-3975/12/4/045007] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Models that capture the chemical kinetics of cellular regulatory networks can be specified in terms of rules for biomolecular interactions. A rule defines a generalized reaction, meaning a reaction that permits multiple reactants, each capable of participating in a characteristic transformation and each possessing certain, specified properties, which may be local, such as the state of a particular site or domain of a protein. In other words, a rule defines a transformation and the properties that reactants must possess to participate in the transformation. A rule also provides a rate law. A rule-based approach to modeling enables consideration of mechanistic details at the level of functional sites of biomolecules and provides a facile and visual means for constructing computational models, which can be analyzed to study how system-level behaviors emerge from component interactions.
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Affiliation(s)
- Lily A Chylek
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853, USA
- Theoretical Biology and Biophysics Group, Theoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
| | - Leonard A Harris
- Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, TN 37212, USA
| | - James R Faeder
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, USA
| | - William S Hlavacek
- Theoretical Biology and Biophysics Group, Theoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
- New Mexico Consortium, Los Alamos, NM 87544, USA
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29
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Feng S, Ollivier JF, Swain PS, Soyer OS. BioJazz: in silico evolution of cellular networks with unbounded complexity using rule-based modeling. Nucleic Acids Res 2015; 43:e123. [PMID: 26101250 PMCID: PMC4627059 DOI: 10.1093/nar/gkv595] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2014] [Accepted: 05/26/2015] [Indexed: 11/13/2022] Open
Abstract
Systems biologists aim to decipher the structure and dynamics of signaling and regulatory networks underpinning cellular responses; synthetic biologists can use this insight to alter existing networks or engineer de novo ones. Both tasks will benefit from an understanding of which structural and dynamic features of networks can emerge from evolutionary processes, through which intermediary steps these arise, and whether they embody general design principles. As natural evolution at the level of network dynamics is difficult to study, in silico evolution of network models can provide important insights. However, current tools used for in silico evolution of network dynamics are limited to ad hoc computer simulations and models. Here we introduce BioJazz, an extendable, user-friendly tool for simulating the evolution of dynamic biochemical networks. Unlike previous tools for in silico evolution, BioJazz allows for the evolution of cellular networks with unbounded complexity by combining rule-based modeling with an encoding of networks that is akin to a genome. We show that BioJazz can be used to implement biologically realistic selective pressures and allows exploration of the space of network architectures and dynamics that implement prescribed physiological functions. BioJazz is provided as an open-source tool to facilitate its further development and use. Source code and user manuals are available at: http://oss-lab.github.io/biojazz and http://osslab.lifesci.warwick.ac.uk/BioJazz.aspx.
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Affiliation(s)
- Song Feng
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
| | | | - Peter S Swain
- SynthSys, The University of Edinburgh, Edinburgh, United Kingdom
| | - Orkun S Soyer
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
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30
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Chylek LA, Wilson BS, Hlavacek WS. Modeling biomolecular site dynamics in immunoreceptor signaling systems. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2015; 844:245-62. [PMID: 25480645 DOI: 10.1007/978-1-4939-2095-2_12] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The immune system plays a central role in human health. The activities of immune cells, whether defending an organism from disease or triggering a pathological condition such as autoimmunity, are driven by the molecular machinery of cellular signaling systems. Decades of experimentation have elucidated many of the biomolecules and interactions involved in immune signaling and regulation, and recently developed technologies have led to new types of quantitative, systems-level data. To integrate such information and develop nontrivial insights into the immune system, computational modeling is needed, and it is essential for modeling methods to keep pace with experimental advances. In this chapter, we focus on the dynamic, site-specific, and context-dependent nature of interactions in immunoreceptor signaling (i.e., the biomolecular site dynamics of immunoreceptor signaling), the challenges associated with capturing these details in computational models, and how these challenges have been met through use of rule-based modeling approaches.
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Affiliation(s)
- Lily A Chylek
- Department of Chemistry and Chemical Biology, Cornell University, 14853, Ithaca, NY, USA,
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31
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Abstract
Behaviours of complex biomolecular systems are often irreducible to the elementary properties of their individual components. Explanatory and predictive mathematical models are therefore useful for fully understanding and precisely engineering cellular functions. The development and analyses of these models require their adaptation to the problems that need to be solved and the type and amount of available genetic or molecular data. Quantitative and logic modelling are among the main methods currently used to model molecular and gene networks. Each approach comes with inherent advantages and weaknesses. Recent developments show that hybrid approaches will become essential for further progress in synthetic biology and in the development of virtual organisms.
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Affiliation(s)
- Nicolas Le Novère
- Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, UK
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32
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Abstract
The succession of protein activation and deactivation mediated by phosphorylation and dephosphorylation events constitutes a key mechanism of molecular information transfer in cellular systems. To deduce the details of those molecular information cascades and networks has been a central goal pursued by both experimental and computational approaches. Many computational network reconstruction methods employing an array of different statistical learning methods have been developed to infer phosphorylation networks based on different types of molecular data sets such as protein sequence, protein structure, or phosphoproteomics data. In this chapter, different computational network inference methods and resources for biological network reconstruction with a particular focus on phosphorylation networks are surveyed.
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33
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Pedersen M, Yordanov B. Programming languages for circuit design. Methods Mol Biol 2014; 1244:81-104. [PMID: 25487094 DOI: 10.1007/978-1-4939-1878-2_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
This chapter provides an overview of a programming language for Genetic Engineering of Cells (GEC). A GEC program specifies a genetic circuit at a high level of abstraction through constraints on otherwise unspecified DNA parts. The GEC compiler then selects parts which satisfy the constraints from a given parts database. GEC further provides more conventional programming language constructs for abstraction, e.g., through modularity. The GEC language and compiler is available through a Web tool which also provides functionality, e.g., for simulation of designed circuits.
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Affiliation(s)
- Michael Pedersen
- Department of Plant Sciences, Cambridge University, Downing Street, Cambridge, CB2 3EA, UK,
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Abstract
Multi-state modeling of biomolecules refers to a series of techniques used to represent and compute the behavior of biological molecules or complexes that can adopt a large number of possible functional states. Biological signaling systems often rely on complexes of biological macromolecules that can undergo several functionally significant modifications that are mutually compatible. Thus, they can exist in a very large number of functionally different states. Modeling such multi-state systems poses two problems: the problem of how to describe and specify a multi-state system (the “specification problem”) and the problem of how to use a computer to simulate the progress of the system over time (the “computation problem”). To address the specification problem, modelers have in recent years moved away from explicit specification of all possible states and towards rule-based formalisms that allow for implicit model specification, including the κ-calculus [1], BioNetGen [2]–[5], the Allosteric Network Compiler [6], and others [7], [8]. To tackle the computation problem, they have turned to particle-based methods that have in many cases proved more computationally efficient than population-based methods based on ordinary differential equations, partial differential equations, or the Gillespie stochastic simulation algorithm[9], [10]. Given current computing technology, particle-based methods are sometimes the only possible option. Particle-based simulators fall into two further categories: nonspatial simulators, such as StochSim [11], DYNSTOC [12], RuleMonkey [9], [13], and the Network-Free Stochastic Simulator (NFSim) [14], and spatial simulators, including Meredys [15], SRSim [16], [17], and MCell [18]–[20]. Modelers can thus choose from a variety of tools, the best choice depending on the particular problem. Development of faster and more powerful methods is ongoing, promising the ability to simulate ever more complex signaling processes in the future.
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Affiliation(s)
- Melanie I. Stefan
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America
- * E-mail: (MIS); (MBK)
| | - Thomas M. Bartol
- Salk Institute for Biological Studies, La Jolla, California, United States of America
| | - Terrence J. Sejnowski
- 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
- * E-mail: (MIS); (MBK)
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35
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Wenskovitch JE, Harris LA, Tapia JJ, Faeder JR, Marai GE. MOSBIE: a tool for comparison and analysis of rule-based biochemical models. BMC Bioinformatics 2014; 15:316. [PMID: 25253680 PMCID: PMC4261755 DOI: 10.1186/1471-2105-15-316] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2014] [Accepted: 09/16/2014] [Indexed: 12/31/2022] Open
Abstract
Background Mechanistic models that describe the dynamical behaviors of biochemical systems are common in computational systems biology, especially in the realm of cellular signaling. The development of families of such models, either by a single research group or by different groups working within the same area, presents significant challenges that range from identifying structural similarities and differences between models to understanding how these differences affect system dynamics. Results We present the development and features of an interactive model exploration system, MOSBIE, which provides utilities for identifying similarities and differences between models within a family. Models are clustered using a custom similarity metric, and a visual interface is provided that allows a researcher to interactively compare the structures of pairs of models as well as view simulation results. Conclusions We illustrate the usefulness of MOSBIE via two case studies in the cell signaling domain. We also present feedback provided by domain experts and discuss the benefits, as well as the limitations, of the approach. Electronic supplementary material The online version of this article (doi:10.1186/1471-2105-15-316) contains supplementary material, which is available to authorized users.
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Affiliation(s)
| | | | | | | | - G Elisabeta Marai
- Electronic Visualization Lab, Department of Computer Science, University of Illinois at Chicago, 60607 Chicago, USA.
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Clancy T, Hovig E. From proteomes to complexomes in the era of systems biology. Proteomics 2014; 14:24-41. [PMID: 24243660 DOI: 10.1002/pmic.201300230] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2013] [Revised: 10/22/2013] [Accepted: 11/06/2013] [Indexed: 01/16/2023]
Abstract
Protein complexes carry out almost the entire signaling and functional processes in the cell. The protein complex complement of a cell, and its network of complex-complex interactions, is referred to here as the complexome. Computational methods to predict protein complexes from proteomics data, resulting in network representations of complexomes, have recently being developed. In addition, key advances have been made toward understanding the network and structural organization of complexomes. We review these bioinformatics advances, and their discovery-potential, as well as the merits of integrating proteomics data with emerging methods in systems biology to study protein complex signaling. It is envisioned that improved integration of proteomics and systems biology, incorporating the dynamics of protein complexes in space and time, may lead to more predictive models of cell signaling networks for effective modulation.
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Affiliation(s)
- Trevor Clancy
- Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
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Chylek LA, Akimov V, Dengjel J, Rigbolt KTG, Hu B, Hlavacek WS, Blagoev B. Phosphorylation site dynamics of early T-cell receptor signaling. PLoS One 2014; 9:e104240. [PMID: 25147952 PMCID: PMC4141737 DOI: 10.1371/journal.pone.0104240] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2014] [Accepted: 07/07/2014] [Indexed: 11/18/2022] Open
Abstract
In adaptive immune responses, T-cell receptor (TCR) signaling impacts multiple cellular processes and results in T-cell differentiation, proliferation, and cytokine production. Although individual protein-protein interactions and phosphorylation events have been studied extensively, we lack a systems-level understanding of how these components cooperate to control signaling dynamics, especially during the crucial first seconds of stimulation. Here, we used quantitative proteomics to characterize reshaping of the T-cell phosphoproteome in response to TCR/CD28 co-stimulation, and found that diverse dynamic patterns emerge within seconds. We detected phosphorylation dynamics as early as 5 s and observed widespread regulation of key TCR signaling proteins by 30 s. Development of a computational model pointed to the presence of novel regulatory mechanisms controlling phosphorylation of sites with central roles in TCR signaling. The model was used to generate predictions suggesting unexpected roles for the phosphatase PTPN6 (SHP-1) and shortcut recruitment of the actin regulator WAS. Predictions were validated experimentally. This integration of proteomics and modeling illustrates a novel, generalizable framework for solidifying quantitative understanding of a signaling network and for elucidating missing links.
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Affiliation(s)
- Lily A. Chylek
- 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
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York, United States of America
| | - Vyacheslav Akimov
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense M, Denmark
| | - Jörn Dengjel
- Department of Dermatology, Medical Center; Freiburg Institute for Advanced Studies (FRIAS); BIOSS Centre for Biological Signalling Studies; ZBSA Center for Biological Systems Analysis, University of Freiburg, Freiburg, Germany
| | - Kristoffer T. G. Rigbolt
- Department of Dermatology, Medical Center; Freiburg Institute for Advanced Studies (FRIAS); BIOSS Centre for Biological Signalling Studies; ZBSA Center for Biological Systems Analysis, University of Freiburg, Freiburg, Germany
| | - Bin Hu
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
- Department of Biology, University of New Mexico, Albuquerque, New Mexico, United States of America
| | - William S. Hlavacek
- 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
- Department of Biology, University of New Mexico, Albuquerque, New Mexico, United States of America
| | - Blagoy Blagoev
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense M, Denmark
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Korsunsky I, McGovern K, LaGatta T, Olde Loohuis L, Grosso-Applewhite T, Griffeth N, Mishra B. Systems biology of cancer: a challenging expedition for clinical and quantitative biologists. Front Bioeng Biotechnol 2014; 2:27. [PMID: 25191654 PMCID: PMC4137540 DOI: 10.3389/fbioe.2014.00027] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2014] [Accepted: 07/18/2014] [Indexed: 11/25/2022] Open
Abstract
A systems-biology approach to complex disease (such as cancer) is now complementing traditional experience-based approaches, which have typically been invasive and expensive. The rapid progress in biomedical knowledge is enabling the targeting of disease with therapies that are precise, proactive, preventive, and personalized. In this paper, we summarize and classify models of systems biology and model checking tools, which have been used to great success in computational biology and related fields. We demonstrate how these models and tools have been used to study some of the twelve biochemical pathways implicated in but not unique to pancreatic cancer, and conclude that the resulting mechanistic models will need to be further enhanced by various abstraction techniques to interpret phenomenological models of cancer progression.
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Affiliation(s)
- Ilya Korsunsky
- Department of Computer Science, Courant Institute, New York University, New York, NY, USA
| | - Kathleen McGovern
- Department of Mathematics and Statistics, Hunter College, City University of New York, New York, NY, USA
| | - Tom LaGatta
- Department of Mathematics, Courant Institute, New York University, New York, NY, USA
| | - Loes Olde Loohuis
- Department of Computer Science, The Graduate Center, City University of New York, New York, NY, USA
| | - Terri Grosso-Applewhite
- Department of Computer Science, The Graduate Center, City University of New York, New York, NY, USA
| | - Nancy Griffeth
- Department of Mathematics and Computer Science, Lehman College, City University of New York, New York, NY, USA
| | - Bud Mishra
- Department of Computer Science, Courant Institute, New York University, New York, NY, USA
- Department of Mathematics, Courant Institute, New York University, New York, NY, USA
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Hogg JS, Harris LA, Stover LJ, Nair NS, Faeder JR. Exact hybrid particle/population simulation of rule-based models of biochemical systems. PLoS Comput Biol 2014; 10:e1003544. [PMID: 24699269 PMCID: PMC3974646 DOI: 10.1371/journal.pcbi.1003544] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2013] [Accepted: 02/03/2014] [Indexed: 11/19/2022] Open
Abstract
Detailed modeling and simulation of biochemical systems is complicated by the problem of combinatorial complexity, an explosion in the number of species and reactions due to myriad protein-protein interactions and post-translational modifications. Rule-based modeling overcomes this problem by representing molecules as structured objects and encoding their interactions as pattern-based rules. This greatly simplifies the process of model specification, avoiding the tedious and error prone task of manually enumerating all species and reactions that can potentially exist in a system. From a simulation perspective, rule-based models can be expanded algorithmically into fully-enumerated reaction networks and simulated using a variety of network-based simulation methods, such as ordinary differential equations or Gillespie's algorithm, provided that the network is not exceedingly large. Alternatively, rule-based models can be simulated directly using particle-based kinetic Monte Carlo methods. This “network-free” approach produces exact stochastic trajectories with a computational cost that is independent of network size. However, memory and run time costs increase with the number of particles, limiting the size of system that can be feasibly simulated. Here, we present a hybrid particle/population simulation method that combines the best attributes of both the network-based and network-free approaches. The method takes as input a rule-based model and a user-specified subset of species to treat as population variables rather than as particles. The model is then transformed by a process of “partial network expansion” into a dynamically equivalent form that can be simulated using a population-adapted network-free simulator. The transformation method has been implemented within the open-source rule-based modeling platform BioNetGen, and resulting hybrid models can be simulated using the particle-based simulator NFsim. Performance tests show that significant memory savings can be achieved using the new approach and a monetary cost analysis provides a practical measure of its utility. Rule-based modeling is a modeling paradigm that addresses the problem of combinatorial complexity in biochemical systems. The key idea is to specify only those components of a biological macromolecule that are directly involved in a biochemical transformation. Until recently, this “pattern-based” approach greatly simplified the process of model building but did nothing to improve the performance of model simulation. This changed with the introduction of “network-free” simulation methods, which operate directly on the compressed rule set of a rule-based model rather than on a fully-enumerated set of reactions and species. However, these methods represent every molecule in a system as a particle, limiting their use to systems containing less than a few million molecules. Here, we describe an extension to the network-free approach that treats rare, complex species as particles and plentiful, simple species as population variables, while retaining the exact dynamics of the model system. By making more efficient use of computational resources for species that do not require the level of detail of a particle representation, this hybrid particle/population approach can simulate systems much larger than is possible using network-free methods and is an important step towards realizing the practical simulation of detailed, mechanistic models of whole cells.
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Affiliation(s)
- Justin S. Hogg
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
| | - Leonard A. Harris
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
| | - Lori J. Stover
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
| | - Niketh S. Nair
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
| | - James R. Faeder
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
- * E-mail:
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Pedersen M, Oury N, Gravill C, Phillips A. Bio Simulators: a web UI for biological simulation. ACTA ACUST UNITED AC 2014; 30:1491-2. [PMID: 24470571 DOI: 10.1093/bioinformatics/btu050] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
SUMMARY A host of formal, textual languages for modeling cellular processes have recently emerged, but their simulation tools often require an installation process which can pose a barrier for use. Bio Simulators is a framework for easy online deployment of simulators, providing a uniform web-based user interface to a diverse pool of tools. The framework is demonstrated through two plugins based on the KaSim Kappa simulator, one running directly in the browser and another running in the cloud. AVAILABILITY Web tool: bsims.azurewebsites.net. KaSim client side simulator: github.com/NicolasOury/KaSimJS. KaSim cloud simulator: github.com/mdpedersen/KaSimCloud. CONTACT michael.d.pedersen@gmail.com or Andrew.Phillips@microsoft.com SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Michael Pedersen
- Department of Plant Sciences, Cambridge University, Cambridge CB2 3EA, UK, School of Informatics, Edinburgh University, Edinburgh EH8 9AB, Scotland and Microsoft Research, Cambridge CB1 2FB, UK
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Slater T. Recent advances in modeling languages for pathway maps and computable biological networks. Drug Discov Today 2014; 19:193-8. [PMID: 24444544 DOI: 10.1016/j.drudis.2013.12.011] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2013] [Revised: 12/06/2013] [Accepted: 12/16/2013] [Indexed: 10/25/2022]
Abstract
As our theories of systems biology grow more sophisticated, the models we use to represent them become larger and more complex. Languages necessarily have the expressivity and flexibility required to represent these models in ways that support high-resolution annotation, and provide for simulation and analysis that are sophisticated enough to allow researchers to master their data in the proper context. These languages also need to facilitate model sharing and collaboration, which is currently best done by using uniform data structures (such as graphs) and language standards. In this brief review, we discuss three of the most recent systems biology modeling languages to appear: BEL, PySB and BCML, and examine how they meet these needs.
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Affiliation(s)
- Ted Slater
- OpenBEL Consortium, One Alewife Center, Suite 100, Cambridge, MA 02140, USA.
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Chylek LA, Harris LA, Tung CS, Faeder JR, Lopez CF, Hlavacek WS. Rule-based modeling: a computational approach for studying biomolecular site dynamics in cell signaling systems. WILEY INTERDISCIPLINARY REVIEWS. SYSTEMS BIOLOGY AND MEDICINE 2014; 6:13-36. [PMID: 24123887 PMCID: PMC3947470 DOI: 10.1002/wsbm.1245] [Citation(s) in RCA: 71] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2013] [Revised: 08/20/2013] [Accepted: 08/21/2013] [Indexed: 01/04/2023]
Abstract
Rule-based modeling was developed to address the limitations of traditional approaches for modeling chemical kinetics in cell signaling systems. These systems consist of multiple interacting biomolecules (e.g., proteins), which themselves consist of multiple parts (e.g., domains, linear motifs, and sites of phosphorylation). Consequently, biomolecules that mediate information processing generally have the potential to interact in multiple ways, with the number of possible complexes and posttranslational modification states tending to grow exponentially with the number of binary interactions considered. As a result, only large reaction networks capture all possible consequences of the molecular interactions that occur in a cell signaling system, which is problematic because traditional modeling approaches for chemical kinetics (e.g., ordinary differential equations) require explicit network specification. This problem is circumvented through representation of interactions in terms of local rules. With this approach, network specification is implicit and model specification is concise. Concise representation results in a coarse graining of chemical kinetics, which is introduced because all reactions implied by a rule inherit the rate law associated with that rule. Coarse graining can be appropriate if interactions are modular, and the coarseness of a model can be adjusted as needed. Rules can be specified using specialized model-specification languages, and recently developed tools designed for specification of rule-based models allow one to leverage powerful software engineering capabilities. A rule-based model comprises a set of rules, which can be processed by general-purpose simulation and analysis tools to achieve different objectives (e.g., to perform either a deterministic or stochastic simulation).
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Affiliation(s)
- Lily A. Chylek
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, USA
| | - Leonard A. Harris
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15260, USA
| | - Chang-Shung Tung
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - James R. Faeder
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15260, USA
| | - Carlos F. Lopez
- Department of Cancer Biology and Center for Quantitative Sciences, Vanderbilt University School of Medicine, Nashville, Tennessee 37212, USA
| | - William S. Hlavacek
- Theoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
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Programming biological models in Python using PySB. Mol Syst Biol 2013; 9:646. [PMID: 23423320 PMCID: PMC3588907 DOI: 10.1038/msb.2013.1] [Citation(s) in RCA: 131] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2012] [Accepted: 01/07/2013] [Indexed: 12/19/2022] Open
Abstract
PySB is a framework for creating biological models as Python programs using a
high-level, action-oriented vocabulary that promotes transparency, extensibility and
reusability. PySB interoperates with many existing modeling tools and supports
distributed model development. ![]()
PySB models are programs and leverage existing programming tools for documentation, testing, and collaborative development. Reusable functions can encode common low-level biochemical processes as well as high-level modules, making models transparent and concise. Modeling workflow is accelerated through close integration with Python numerical tools and interoperability with existing modeling software. We demonstrate the use of PySB to encode 15 alternative hypotheses for the mitochondrial regulation of apoptosis, including a new ‘Embedded Together' model based on recent biochemical findings.
Mathematical equations are fundamental to modeling biological networks, but as
networks get large and revisions frequent, it becomes difficult to manage equations
directly or to combine previously developed models. Multiple simultaneous efforts to
create graphical standards, rule-based languages, and integrated software
workbenches aim to simplify biological modeling but none fully meets the need for
transparent, extensible, and reusable models. In this paper we describe PySB, an
approach in which models are not only created using programs, they are programs.
PySB draws on programmatic modeling concepts from little b and ProMot, the
rule-based languages BioNetGen and Kappa and the growing library of Python numerical
tools. Central to PySB is a library of macros encoding familiar biochemical actions
such as binding, catalysis, and polymerization, making it possible to use a
high-level, action-oriented vocabulary to construct detailed models. As Python
programs, PySB models leverage tools and practices from the open-source software
community, substantially advancing our ability to distribute and manage the work of
testing biochemical hypotheses. We illustrate these ideas using new and previously
published models of apoptosis.
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Klann M, Koeppl H. Reaction schemes, escape times and geminate recombinations in particle-based spatial simulations of biochemical reactions. Phys Biol 2013; 10:046005. [DOI: 10.1088/1478-3975/10/4/046005] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Aitken S, Alexander RD, Beggs JD. A rule-based kinetic model of RNA polymerase II C-terminal domain phosphorylation. J R Soc Interface 2013; 10:20130438. [PMID: 23804443 PMCID: PMC3730697 DOI: 10.1098/rsif.2013.0438] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The complexity of many RNA processing pathways is such that a conventional systems modelling approach is inadequate to represent all the molecular species involved. We demonstrate that rule-based modelling permits a detailed model of a complex RNA signalling pathway to be defined. Phosphorylation of the RNA polymerase II (RNAPII) C-terminal domain (CTD; a flexible tail-like extension of the largest subunit) couples pre-messenger RNA capping, splicing and 3' end maturation to transcriptional elongation and termination, and plays a central role in integrating these processes. The phosphorylation states of the serine residues of many heptapeptide repeats of the CTD alter along the coding region of genes as a function of distance from the promoter. From a mechanistic perspective, both the changes in phosphorylation and the location at which they take place on the genes are a function of the time spent by RNAPII in elongation as this interval provides the opportunity for the kinases and phosphatases to interact with the CTD. On this basis, we synthesize the available data to create a kinetic model of the action of the known kinases and phosphatases to resolve the phosphorylation pathways and their kinetics.
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Affiliation(s)
- Stuart Aitken
- MRC Human Genetics Unit, IGMM, University of Edinburgh, Edinburgh EH4 2XU, UK.
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Hunt CA, Kennedy RC, Kim SHJ, Ropella GEP. Agent-based modeling: a systematic assessment of use cases and requirements for enhancing pharmaceutical research and development productivity. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2013; 5:461-80. [PMID: 23737142 PMCID: PMC3739932 DOI: 10.1002/wsbm.1222] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
A crisis continues to brew within the pharmaceutical research and development (R&D) enterprise: productivity continues declining as costs rise, despite ongoing, often dramatic scientific and technical advances. To reverse this trend, we offer various suggestions for both the expansion and broader adoption of modeling and simulation (M&S) methods. We suggest strategies and scenarios intended to enable new M&S use cases that directly engage R&D knowledge generation and build actionable mechanistic insight, thereby opening the door to enhanced productivity. What M&S requirements must be satisfied to access and open the door, and begin reversing the productivity decline? Can current methods and tools fulfill the requirements, or are new methods necessary? We draw on the relevant, recent literature to provide and explore answers. In so doing, we identify essential, key roles for agent-based and other methods. We assemble a list of requirements necessary for M&S to meet the diverse needs distilled from a collection of research, review, and opinion articles. We argue that to realize its full potential, M&S should be actualized within a larger information technology framework—a dynamic knowledge repository—wherein models of various types execute, evolve, and increase in accuracy over time. We offer some details of the issues that must be addressed for such a repository to accrue the capabilities needed to reverse the productivity decline. © 2013 Wiley Periodicals, Inc.
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Affiliation(s)
- C Anthony Hunt
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA.
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Abstract
The time-ordered product framework of quantum field theory can also be used to understand salient phenomena in stochastic biochemical networks. It is used here to derive Gillespie's stochastic simulation algorithm (SSA) for chemical reaction networks; consequently, the SSA can be interpreted in terms of Feynman diagrams. It is also used here to derive other, more general simulation and parameter-learning algorithms including simulation algorithms for networks of stochastic reaction-like processes operating on parameterized objects, and also hybrid stochastic reaction/differential equation models in which systems of ordinary differential equations evolve the parameters of objects that can also undergo stochastic reactions. Thus, the time-ordered product expansion can be used systematically to derive simulation and parameter-fitting algorithms for stochastic systems.
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Affiliation(s)
- Eric Mjolsness
- Department of Computer Science, University of California, Irvine, CA 92697, USA.
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Hwang W, Hwang Y, Lee S, Lee D. Rule-based multi-scale simulation for drug effect pathway analysis. BMC Med Inform Decis Mak 2013; 13 Suppl 1:S4. [PMID: 23566173 PMCID: PMC3618249 DOI: 10.1186/1472-6947-13-s1-s4] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Background Biological systems are robust and complex to maintain stable phenotypes under various conditions. In these systems, drugs reported the limited efficacy and unexpected side-effects. To remedy this situation, many pharmaceutical laboratories have begun to research combination drugs and some of them have shown successful clinical results. Complementary action of multiple compounds could increase efficacy as well as reduce side-effects through pharmacological interactions. However, experimental approach requires vast cost of preclinical experiments and tests as the number of possible combinations of compound dosages increases exponentially. Computer model-based experiments have been emerging as one of the most promising solutions to cope with such complexity. Though there have been many efforts to model specific molecular pathways using qualitative and quantitative formalisms, they suffer from unexpected results caused by distant interactions beyond their localized models. Results In this work, we propose a rule-based multi-scale modelling platform. We have tested this platform with Type 2 diabetes (T2D) model, which involves the malfunction of numerous organs such as pancreas, circulation system, liver, and adipocyte. We have extracted T2D-related 190 rules by manual curation from literature, pathway databases and converting from different types of existing models. We have simulated twenty-two T2D drugs. The results of our simulation show drug effect pathways of T2D drugs and whether combination drugs have efficacy or not and how combination drugs work on the multi-scale model. Conclusions We believe that our simulation would help to understand drug mechanism for the drug development and provide a new way to effectively apply existing drugs for new target. It also would give insight for identifying effective combination drugs.
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Affiliation(s)
- Woochang Hwang
- Department of Bio and Brain Engineering, KAIST, Daejeon, South Korea
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Eydgahi H, Chen WW, Muhlich JL, Vitkup D, Tsitsiklis JN, Sorger PK. Properties of cell death models calibrated and compared using Bayesian approaches. Mol Syst Biol 2013; 9:644. [PMID: 23385484 PMCID: PMC3588908 DOI: 10.1038/msb.2012.69] [Citation(s) in RCA: 71] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2012] [Accepted: 12/17/2012] [Indexed: 01/18/2023] Open
Abstract
Using models to simulate and analyze biological networks requires principled approaches to parameter estimation and model discrimination. We use Bayesian and Monte Carlo methods to recover the full probability distributions of free parameters (initial protein concentrations and rate constants) for mass-action models of receptor-mediated cell death. The width of the individual parameter distributions is largely determined by non-identifiability but covariation among parameters, even those that are poorly determined, encodes essential information. Knowledge of joint parameter distributions makes it possible to compute the uncertainty of model-based predictions whereas ignoring it (e.g., by treating parameters as a simple list of values and variances) yields nonsensical predictions. Computing the Bayes factor from joint distributions yields the odds ratio (∼20-fold) for competing 'direct' and 'indirect' apoptosis models having different numbers of parameters. Our results illustrate how Bayesian approaches to model calibration and discrimination combined with single-cell data represent a generally useful and rigorous approach to discriminate between competing hypotheses in the face of parametric and topological uncertainty.
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Affiliation(s)
- Hoda Eydgahi
- Center for Cell Decision Processes, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - William W Chen
- Center for Cell Decision Processes, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Jeremy L Muhlich
- Center for Cell Decision Processes, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Dennis Vitkup
- Center for Computational Biology and Bioinformatics, Columbia University, New York, NY, USA
| | - John N Tsitsiklis
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Peter K Sorger
- Center for Cell Decision Processes, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Center for Cell Decision Processes, Department of Systems Biology, Harvard Medical School, WAB Room 438, 200 Longwood Avenue, Boston, MA 02115, USA. Tel.:+1 617 432 6901/6902; Fax:+1 617 432 5012;
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50
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Tropical Geometries and Dynamics of Biochemical Networks Application to Hybrid Cell Cycle Models. ACTA ACUST UNITED AC 2012. [DOI: 10.1016/j.entcs.2012.05.016] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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