Formalizing Phenotypes of Regeneration

Lobo, Daniel

doi:10.1007/978-1-0716-2172-1_36

Cited by 3 publications

(1 citation statement)

References 42 publications

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“…Indeed, it is a current challenge to produce mechanistic hypotheses in terms of GRMs that can recapitulate observed spatial phenotypes. The methodology presented here could be employed to automatically infer such hypotheses directly from datasets of curated experimental gene expression patterns (Lobo, 2022;Roy et al, 2020). Overall, the presented method to aid in the design of GRMs able to produce arbitrary gene expression patterns has the potential to both enable the understanding of complex developmental processes as well as the design of complex dynamic synthetic systems.…”

Section: Designing Grms For Arbitrary Shapesmentioning

confidence: 99%

Automatic design of gene regulatory mechanisms for spatial pattern formation

Mousavi

Lobo

2023

Preprint

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Gene regulatory mechanisms (GRMs) control the formation of spatial and temporal expression patterns that can serve as regulatory signals for the development of complex shapes. Synthetic developmental biology aims to engineer such mechanisms for understanding and producing desired multicellular patterns. However, the design of synthetic GRMs that can produce a target spatial gene expression pattern is a current challenge due to the nonlinear interactions and feedback loops present in genetic circuits. Here we present a methodology to automatically design GRMs that can produce any given spatial pattern. The proposed approach uses two orthogonal morphogen gradients acting as positional information signals in a multicellular tissue area or culture, which constitutes a continuous field of engineered cells implementing the same designed GRM. To efficiently design both the circuit network and the interaction mechanisms - including the number of genes required for the formation of the target pattern - we developed an automated algorithm based on high-performance evolutionary computation. The tolerance of the algorithm can be configured to design GRMs that are either simple to produce approximate patterns or complex to produce precise patterns. We demonstrate its performance by automatically designing GRMs for a diverse set of synthetic spatial expression patterns. The proposed method offers a versatile approach to systematically design and discover pattern-producing genetic circuits.

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Section: Designing Grms For Arbitrary Shapesmentioning

confidence: 99%

Automatic design of gene regulatory mechanisms for spatial pattern formation

Mousavi

Lobo

2023

Preprint

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Automatic design of gene regulatory mechanisms for spatial pattern formation

Mousavi,

Lobo

2024

npj Syst Biol Appl

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Gene regulatory mechanisms (GRMs) control the formation of spatial and temporal expression patterns that can serve as regulatory signals for the development of complex shapes. Synthetic developmental biology aims to engineer such genetic circuits for understanding and producing desired multicellular spatial patterns. However, designing synthetic GRMs for complex, multi-dimensional spatial patterns is a current challenge due to the nonlinear interactions and feedback loops in genetic circuits. Here we present a methodology to automatically design GRMs that can produce any given two-dimensional spatial pattern. The proposed approach uses two orthogonal morphogen gradients acting as positional information signals in a multicellular tissue area or culture, which constitutes a continuous field of engineered cells implementing the same designed GRM. To efficiently design both the circuit network and the interaction mechanisms—including the number of genes necessary for the formation of the target spatial pattern—we developed an automated algorithm based on high-performance evolutionary computation. The tolerance of the algorithm can be configured to design GRMs that are either simple to produce approximate patterns or complex to produce precise patterns. We demonstrate the approach by automatically designing GRMs that can produce a diverse set of synthetic spatial expression patterns by interpreting just two orthogonal morphogen gradients. The proposed framework offers a versatile approach to systematically design and discover complex genetic circuits producing spatial patterns.

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Automatic Generation of Interactive Multidimensional Phase Portraits

Ogunsan

Lobo

2022

Preprint

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Mathematical models formally and precisely represent biological mechanisms with complex dynamics. To understand the possible behaviors of such systems, phase portrait diagrams can be used to visualize their overall global dynamics across a domain. However, producing these phase portrait diagrams is a laborious process reserved to mathematical experts. Here, we developed a computational methodology to automatically generate phase portrait diagrams to study biological dynamical systems based on ordinary differential equations. The method only needs as input the variables and equations describing a multidimensional biological system and it automatically outputs for each pair of dependent variables a complete phase portrait diagram, including the critical points and their stability, the nullclines of the system, and a vector space of the trajectories. Crucially, the portraits generated are interactive, and the user can move the visualized planar slice, change parameters with sliders, and add trajectories in the phase and time domains, after which the diagrams are updated in real time. The method is available as a user-friendly graphical interface or can be accessed programmatically with a Mathematica package. The generated portraits and particular views can be saved as computable notebooks preserving the interactive functionality, an approach that can be adopted for reproducible science and interactive pedagogical materials. The method, code, and examples are freely-available at https://lobolab.umbc.edu/autoportrait .

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Formalizing Phenotypes of Regeneration

Cited by 3 publications

References 42 publications

Automatic design of gene regulatory mechanisms for spatial pattern formation

Automatic design of gene regulatory mechanisms for spatial pattern formation

Automatic design of gene regulatory mechanisms for spatial pattern formation

Automatic Generation of Interactive Multidimensional Phase Portraits

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