Modeling biological systems using Dynetica—a simulator of dynamic networks

Li, You; Hoonlor, Apirak; Yin, John

doi:10.1093/bioinformatics/btg009

Cited by 34 publications

(19 citation statements)

References 59 publications

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“…You et al, 2003, Tan et al, 2009, Hallen et al, 2011. Generating detailed experimental time course data is still in its infancy; although aided by advances in single-cell imaging and data extraction (e.g.…”

Section: Dynamic Network Modelsmentioning

confidence: 99%

Bayesian Learning from Marginal Data in Bionetwork Models

Bonassi¹,

Li²,

West³

2011

Statistical Applications in Genetics and Molecular Biology

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In studies of dynamic molecular networks in systems biology, experiments are increasingly exploiting technologies such as flow cytometry to generate data on marginal distributions of a few network nodes at snapshots in time. For example, levels of intracellular expression of a few genes, or cell surface protein markers, can be assayed at a series of interim time points and assumed steady-states under experimentally stimulated growth conditions in small cellular systems. Such marginal data on a small number of cellular markers will typically carry very limited information on the parameters and structure of dynamic network models, though experiments will typically be designed to expose variation in cellular phenotypes that are inherently related to some aspects of model parametrization and structure. Our work addresses statistical questions of how to integrate such data with dynamic stochastic models in order to properly quantify the information-or lack of information-it carries relative to models assumed. We present a Bayesian computational strategy coupled with a novel approach to summarizing and numerically characterizing biological phenotypes that are represented in terms of the resulting sample distributions of cellular markers. We build on Bayesian simulation methods and mixture modeling to define the approach to linking mechanistic mathematical models of network dynamics to snapshot data, using a toggle switch example integrating simulated and real data as context.

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Section: Dynamic Network Modelsmentioning

confidence: 99%

Bayesian Learning from Marginal Data in Bionetwork Models

Bonassi¹,

Li²,

West³

2011

Statistical Applications in Genetics and Molecular Biology

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“…Text S1 † lists the set of ODEs for modeling the mass-action kinetics of associated A-TMSD reactions using Dynetica. 92 The equations were evaluated with initial conditions listed in Table S3, † taking Boolean input combination ON-ON as an example.…”

Section: Methodsmentioning

confidence: 99%

Renewable DNA seesaw logic circuits enabled by photoregulation of toehold-mediated strand displacement

et al. 2017

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An important achievement in the field of DNA-based computation has been the development of experimental protocols for evaluation of Boolean logic circuits. These protocols for DNA circuits generally take as inputs single-stranded DNA molecules that encode Boolean values, and via a series of DNA hybridization reactions then release ssDNA strands to indicate Boolean output values. However, most of these DNA circuit protocols are use-once only, and there remains the major challenge of designing DNA circuits to be renewable for use with multiple sets of inputs. Prior proposed schemes to make DNA gates renewable suffered from multiple problems, including waste accumulation, signal restoration, noise tolerance, and limited scalable complexity. In this work, we propose a scalable design and in silico verifications for photoregulated renewable DNA seesaw logic circuits, which after processing a given set of inputs, can be repeatedly reset to reliably process other distinct inputs. To achieve renewability, specific toeholds in the system are labeled with photoresponsive molecules such as azobenzene to modulate the effective rate constants of toehold-mediated strand displacement (TMSD) reactions. Our proposed design strategy of leveraging the collective effect of TMSD and azobenzene-mediated dehybridization may provide new perspectives on achieving synchronized and localized control of DNA hybridizations in complex and scalable reaction networks efficiently and economically. Various devices such as molecular walkers and motors could potentially be engineered reusable, be simulated and subsequently implemented using our simplified design strategy.

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“…A number of computational tech- (D) A typical simulation result using the Gillespie algorithm. Results in (C) and (D) were generated using Dynetica (i.e., a simulator of dynamic networks) (143). Levels of prey and predator are expressed in numbers.…”

Section: Quantitative Modelsmentioning

confidence: 99%

“…But the situation is changing. To address this issue, a number of programs that aim to facilitate the model construction and analysis have been developed in the last several 121,143). Although different implementation strategies are adopted, all these programs strive to provide an intuitive interface and versatile simulation capabilities for the user.…”

Section: Software Toolsmentioning

confidence: 99%

“…Dynetica integrates model construction, analysis, and network visualization into a unified modeling framework; it is distinct from others in that (1) it allows time-course simulations using both deterministic algorithm (ODE-based) and the Gillespie algorithm (e.g., see Fig. 3), and (2) it facilitates the construction of genetic networks, where the majority of reactions revolve around gene expression (121,143).…”

Section: Software Toolsmentioning

confidence: 99%

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Toward Computational Systems Biology

2004

CBB

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The development and successful application of high-throughput technologies are transforming biological research. The large quantities of data being generated by these technologies have led to the emergence of systems biology, which emphasizes large-scale, parallel characterization of biological systems and integration of fragmentary information into a coherent whole. Complementing the reductionist approach that has dominated biology for the last century, mathematical modeling is becoming a powerful tool to achieve an integrated understanding of complex biological systems and to guide experimental efforts of engineering biological systems for practical applications. Here I give an overview of current mainstream approaches in modeling biological systems, highlight specific applications of modeling in various settings, and point out future research opportunities and challenges.

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Modeling biological systems using Dynetica—a simulator of dynamic networks

Abstract: Dynetica 1.0, example models, and the user's guide are available at http://www.its.caltech.edu/~you/Dynetica/Dynetica_page.htm

Cited by 34 publications

References 59 publications

Bayesian Learning from Marginal Data in Bionetwork Models

Bayesian Learning from Marginal Data in Bionetwork Models

Renewable DNA seesaw logic circuits enabled by photoregulation of toehold-mediated strand displacement

Toward Computational Systems Biology

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