Data-driven network models for genetic circuits from time-series data with incomplete measurements

Yeung, Enoch; Kim, Jongmin; Yuan, Ye; Murray, Richard M.

doi:10.1098/rsif.2021.0413

Cited by 7 publications

(4 citation statements)

References 88 publications

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“…In this work, a DBTL loop called Design Assemble Round Trip (DART) is presented for the rational design of synthetic biology genetic logic circuits. In principle, the technology is generalizable to dynamically complex circuit functions beyond logic ( 20 ) that are of interest to the synthetic biology community ( 21–23 ). DART is composed of tools for (i) the prediction of robust circuit topologies, (ii) prediction of the most effective choice of parts to construct the topology, (iii) sequence construction for selected designs, (iv) step-by-step instructions for build assembly and (v) reproducible experimental submission, data and metadata consolidation, data standardization and automated data analysis using a previously published test–learn loop called the Round Trip (RT, ( 24 )).…”

Section: Introductionmentioning

confidence: 99%

Robustness and reproducibility of simple and complex synthetic logic circuit designs using a DBTL loop

Cummins

Vrana²,

Moseley

et al. 2023

Synthetic Biology

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Computational tools addressing various components of design–build–test–learn (DBTL) loops for the construction of synthetic genetic networks exist but do not generally cover the entire DBTL loop. This manuscript introduces an end-to-end sequence of tools that together form a DBTL loop called Design Assemble Round Trip (DART). DART provides rational selection and refinement of genetic parts to construct and test a circuit. Computational support for experimental process, metadata management, standardized data collection and reproducible data analysis is provided via the previously published Round Trip (RT) test–learn loop. The primary focus of this work is on the Design Assemble (DA) part of the tool chain, which improves on previous techniques by screening up to thousands of network topologies for robust performance using a novel robustness score derived from dynamical behavior based on circuit topology only. In addition, novel experimental support software is introduced for the assembly of genetic circuits. A complete design-through-analysis sequence is presented using several OR and NOR circuit designs, with and without structural redundancy, that are implemented in budding yeast. The execution of DART tested the predictions of the design tools, specifically with regard to robust and reproducible performance under different experimental conditions. The data analysis depended on a novel application of machine learning techniques to segment bimodal flow cytometry distributions. Evidence is presented that, in some cases, a more complex build may impart more robustness and reproducibility across experimental conditions.

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Section: Introductionmentioning

confidence: 99%

Robustness and reproducibility of simple and complex synthetic logic circuit designs using a DBTL loop

Cummins

Vrana²,

Moseley

et al. 2023

Synthetic Biology

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show abstract

“…In this work, we consider the more general problem of fully reconstructing interpretable (mechanistic and parameterized) nonlinear dynamic models from time-series data. Recently, several approaches using methods based on sparse regression, Bayesian identification or symbolic regression have appeared [18,[30][31][32][33][34][35][36]. In this context, SINDy-PI [37] is an especially interesting parallel implicit version of SINDy because it allows the incorporation of implicit dynamics and rational nonlinear terms, thus enabling the discovery of kinetic functions (such as Michaelis-Menten) common in biochemical networks.…”

Section: Introductionmentioning

confidence: 99%

Distilling identifiable and interpretable dynamic models from biological data

Banga

Massonis

Villaverde

2023

Preprint

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Mechanistic dynamical models allow us to study the behavior of complex biological systems. They can provide an objective and quantitative understanding that would be difficult to achieve through other means. However, the systematic development of these models is a non-trivial exercise and an open problem in computational biology. Currently, many research efforts are focused on model discovery, i.e. automating the development of interpretable models from data. One of the main frameworks is sparse regression, where the sparse identification of nonlinear dynamics (SINDy) algorithm and its variants have enjoyed great success. SINDy-PI is an extension which allows the discovery of rational nonlinear terms, thus enabling the identification of kinetic functions common in biochemical networks, such as Michaelis-Menten. SINDy-PI also pays special attention to the recovery of parsimonious models (Occam's razor). Here we focus on biological models composed of sets of deterministic nonlinear ordinary differential equations. We present a methodology that, combined with SINDy-PI, allows the automatic discovery of structurally identifiable and observable models which are also mechanistically interpretable. The lack of structural identifiability and observability makes it impossible to uniquely infer parameter and state variables, which can compromise the usefulness of a model by distorting its mechanistic significance and hampering its ability to produce biological insights. We illustrate the performance of our method with six case studies. We find that, despite enforcing sparsity, SINDy-PI sometimes yields models that are unidentifiable. In these cases we show how our method transforms their equations in order to obtain a structurally identifiable and observable model which is also interpretable.

show abstract

Section: Introductionmentioning

confidence: 99%

Distilling identifiable and interpretable dynamic models from biological data

Massonis,

Villaverde,

Banga

2023

PLoS Comput Biol

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Mechanistic dynamical models allow us to study the behavior of complex biological systems. They can provide an objective and quantitative understanding that would be difficult to achieve through other means. However, the systematic development of these models is a non-trivial exercise and an open problem in computational biology. Currently, many research efforts are focused on model discovery, i.e. automating the development of interpretable models from data. One of the main frameworks is sparse regression, where the sparse identification of nonlinear dynamics (SINDy) algorithm and its variants have enjoyed great success. SINDy-PI is an extension which allows the discovery of rational nonlinear terms, thus enabling the identification of kinetic functions common in biochemical networks, such as Michaelis-Menten. SINDy-PI also pays special attention to the recovery of parsimonious models (Occam’s razor). Here we focus on biological models composed of sets of deterministic nonlinear ordinary differential equations. We present a methodology that, combined with SINDy-PI, allows the automatic discovery of structurally identifiable and observable models which are also mechanistically interpretable. The lack of structural identifiability and observability makes it impossible to uniquely infer parameter and state variables, which can compromise the usefulness of a model by distorting its mechanistic significance and hampering its ability to produce biological insights. We illustrate the performance of our method with six case studies. We find that, despite enforcing sparsity, SINDy-PI sometimes yields models that are unidentifiable. In these cases we show how our method transforms their equations in order to obtain a structurally identifiable and observable model which is also interpretable.

show abstract

Data-driven network models for genetic circuits from time-series data with incomplete measurements

Cited by 7 publications

References 88 publications

Robustness and reproducibility of simple and complex synthetic logic circuit designs using a DBTL loop

Robustness and reproducibility of simple and complex synthetic logic circuit designs using a DBTL loop

Distilling identifiable and interpretable dynamic models from biological data

Distilling identifiable and interpretable dynamic models from biological data

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