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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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Fellermann H, Cardelli L. Programming chemistry in DNA-addressable bioreactors. J R Soc Interface 2014; 11:rsif.2013.0987. [PMID: 25121647 DOI: 10.1098/rsif.2013.0987] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
We present a formal calculus, termed the chemtainer calculus, able to capture the complexity of compartmentalized reaction systems such as populations of possibly nested vesicular compartments. Compartments contain molecular cargo as well as surface markers in the form of DNA single strands. These markers serve as compartment addresses and allow for their targeted transport and fusion, thereby enabling reactions of previously separated chemicals. The overall system organization allows for the set-up of programmable chemistry in microfluidic or other automated environments. We introduce a simple sequential programming language whose instructions are motivated by state-of-the-art microfluidic technology. Our approach integrates electronic control, chemical computing and material production in a unified formal framework that is able to mimic the integrated computational and constructive capabilities of the subcellular matrix. We provide a non-deterministic semantics of our programming language that enables us to analytically derive the computational and constructive power of our machinery. This semantics is used to derive the sets of all constructable chemicals and supermolecular structures that emerge from different underlying instruction sets. Because our proofs are constructive, they can be used to automatically infer control programs for the construction of target structures from a limited set of resource molecules. Finally, we present an example of our framework from the area of oligosaccharide synthesis.
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Affiliation(s)
- Harold Fellermann
- School of Computing Science, Newcastle University, King's Gate, Newcastle upon Tyne NE1 7RU, UK Center for Fundamental Living Technology, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark
| | - Luca Cardelli
- Microsoft Research Cambridge, 21 Station Road, Cambridge CB1 2FB, UK
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Maus C, Rybacki S, Uhrmacher AM. Rule-based multi-level modeling of cell biological systems. BMC SYSTEMS BIOLOGY 2011; 5:166. [PMID: 22005019 PMCID: PMC3306009 DOI: 10.1186/1752-0509-5-166] [Citation(s) in RCA: 103] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2011] [Accepted: 10/17/2011] [Indexed: 11/10/2022]
Abstract
BACKGROUND Proteins, individual cells, and cell populations denote different levels of an organizational hierarchy, each of which with its own dynamics. Multi-level modeling is concerned with describing a system at these different levels and relating their dynamics. Rule-based modeling has increasingly attracted attention due to enabling a concise and compact description of biochemical systems. In addition, it allows different methods for model analysis, since more than one semantics can be defined for the same syntax. RESULTS Multi-level modeling implies the hierarchical nesting of model entities and explicit support for downward and upward causation between different levels. Concepts to support multi-level modeling in a rule-based language are identified. To those belong rule schemata, hierarchical nesting of species, assigning attributes and solutions to species at each level and preserving content of nested species while applying rules. Further necessities are the ability to apply rules and flexibly define reaction rate kinetics and constraints on nested species as well as species that are nested within others. An example model is presented that analyses the interplay of an intracellular control circuit with states at cell level, its relation to cell division, and connections to intercellular communication within a population of cells. The example is described in ML-Rules - a rule-based multi-level approach that has been realized within the plug-in-based modeling and simulation framework JAMES II. CONCLUSIONS Rule-based languages are a suitable starting point for developing a concise and compact language for multi-level modeling of cell biological systems. The combination of nesting species, assigning attributes, and constraining reactions according to these attributes is crucial in achieving the desired expressiveness. Rule schemata allow a concise and compact description of complex models. As a result, the presented approach facilitates developing and maintaining multi-level models that, for instance, interrelate intracellular and intercellular dynamics.
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Affiliation(s)
- Carsten Maus
- University of Rostock, Institute of Computer Science, Albert-Einstein-Str. 22, 18059 Rostock, Germany
| | - Stefan Rybacki
- University of Rostock, Institute of Computer Science, Albert-Einstein-Str. 22, 18059 Rostock, Germany
| | - Adelinde M Uhrmacher
- University of Rostock, Institute of Computer Science, Albert-Einstein-Str. 22, 18059 Rostock, Germany
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The Attributed Pi-Calculus with Priorities. TRANSACTIONS ON COMPUTATIONAL SYSTEMS BIOLOGY XII 2010. [DOI: 10.1007/978-3-642-11712-1_2] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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A Language for Biochemical Systems: Design and Formal Specification. TRANSACTIONS ON COMPUTATIONAL SYSTEMS BIOLOGY XII 2010. [DOI: 10.1007/978-3-642-11712-1_3] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Hunt CA, Ropella GEP, Lam TN, Tang J, Kim SHJ, Engelberg JA, Sheikh-Bahaei S. At the biological modeling and simulation frontier. Pharm Res 2009; 26:2369-400. [PMID: 19756975 PMCID: PMC2763179 DOI: 10.1007/s11095-009-9958-3] [Citation(s) in RCA: 66] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2009] [Accepted: 08/13/2009] [Indexed: 01/03/2023]
Abstract
We provide a rationale for and describe examples of synthetic modeling and simulation (M&S) of biological systems. We explain how synthetic methods are distinct from familiar inductive methods. Synthetic M&S is a means to better understand the mechanisms that generate normal and disease-related phenomena observed in research, and how compounds of interest interact with them to alter phenomena. An objective is to build better, working hypotheses of plausible mechanisms. A synthetic model is an extant hypothesis: execution produces an observable mechanism and phenomena. Mobile objects representing compounds carry information enabling components to distinguish between them and react accordingly when different compounds are studied simultaneously. We argue that the familiar inductive approaches contribute to the general inefficiencies being experienced by pharmaceutical R&D, and that use of synthetic approaches accelerates and improves R&D decision-making and thus the drug development process. A reason is that synthetic models encourage and facilitate abductive scientific reasoning, a primary means of knowledge creation and creative cognition. When synthetic models are executed, we observe different aspects of knowledge in action from different perspectives. These models can be tuned to reflect differences in experimental conditions and individuals, making translational research more concrete while moving us closer to personalized medicine.
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Affiliation(s)
- C Anthony Hunt
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA.
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Bentley PJ. Methods for improving simulations of biological systems: systemic computation and fractal proteins. J R Soc Interface 2009; 6 Suppl 4:S451-66. [PMID: 19324681 DOI: 10.1098/rsif.2008.0505.focus] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Modelling and simulation are becoming essential for new fields such as synthetic biology. Perhaps the most important aspect of modelling is to follow a clear design methodology that will help to highlight unwanted deficiencies. The use of tools designed to aid the modelling process can be of benefit in many situations. In this paper, the modelling approach called systemic computation (SC) is introduced. SC is an interaction-based language, which enables individual-based expression and modelling of biological systems, and the interactions between them. SC permits a precise description of a hypothetical mechanism to be written using an intuitive graph-based or a calculus-based notation. The same description can then be directly run as a simulation, merging the hypothetical mechanism and the simulation into the same entity. However, even when using well-designed modelling tools to produce good models, the best model is not always the most accurate one. Frequently, computational constraints or lack of data make it infeasible to model an aspect of biology. Simplification may provide one way forward, but with inevitable consequences of decreased accuracy. Instead of attempting to replace an element with a simpler approximation, it is sometimes possible to substitute the element with a different but functionally similar component. In the second part of this paper, this modelling approach is described and its advantages are summarized using an exemplar: the fractal protein model. Finally, the paper ends with a discussion of good biological modelling practice by presenting lessons learned from the use of SC and the fractal protein model.
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Affiliation(s)
- Peter J Bentley
- Department of Computer Science, University College London, London, UK.
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Danos V, Feret J, Fontana W, Harmer R, Krivine J. Rule-Based Modelling and Model Perturbation. LECTURE NOTES IN COMPUTER SCIENCE 2009. [DOI: 10.1007/978-3-642-04186-0_6] [Citation(s) in RCA: 28] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Calder M, Hillston J. Process Algebra Modelling Styles for Biomolecular Processes. LECTURE NOTES IN COMPUTER SCIENCE 2009. [DOI: 10.1007/978-3-642-04186-0_1] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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Guerriero ML. Qualitative and Quantitative Analysis of a Bio-PEPA Model of the Gp130/JAK/STAT Signalling Pathway. LECTURE NOTES IN COMPUTER SCIENCE 2009. [DOI: 10.1007/978-3-642-04186-0_5] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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Guerriero ML, Prandi D, Priami C, Quaglia P. Process Calculi Abstractions for Biology. ALGORITHMIC BIOPROCESSES 2009. [DOI: 10.1007/978-3-540-88869-7_23] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Poudret M, Arnould A, Comet JP, Le Gall P, Meseure P, Képès F. Topology-based abstraction of complex biological systems: application to the Golgi apparatus. Theory Biosci 2008; 127:79-88. [DOI: 10.1007/s12064-008-0030-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2007] [Accepted: 12/31/2007] [Indexed: 12/01/2022]
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Dematté L, Priami C, Romanel A. The BlenX Language: A Tutorial. FORMAL METHODS FOR COMPUTATIONAL SYSTEMS BIOLOGY 2008. [DOI: 10.1007/978-3-540-68894-5_9] [Citation(s) in RCA: 30] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Fages F, Rizk A. On the Analysis of Numerical Data Time Series in Temporal Logic. COMPUTATIONAL METHODS IN SYSTEMS BIOLOGY 2007. [DOI: 10.1007/978-3-540-75140-3_4] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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Modelling Cellular Processes Using Membrane Systems with Peripheral and Integral Proteins. COMPUTATIONAL METHODS IN SYSTEMS BIOLOGY 2006. [DOI: 10.1007/11885191_8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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Calzone L, Chabrier-Rivier N, Fages F, Soliman S. Machine Learning Biochemical Networks from Temporal Logic Properties. TRANSACTIONS ON COMPUTATIONAL SYSTEMS BIOLOGY VI 2006. [DOI: 10.1007/11880646_4] [Citation(s) in RCA: 53] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Rosselló F, Valiente G. Graph Transformation in Molecular Biology. FORMAL METHODS IN SOFTWARE AND SYSTEMS MODELING 2005. [DOI: 10.1007/978-3-540-31847-7_7] [Citation(s) in RCA: 16] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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