scholarly journals Spatial localisation meets biomolecular networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Govind Menon ◽  
J. Krishnan
Keyword(s):  
Synthetic Biology ◽  
Pattern Formation ◽  
Circuit Design ◽  
Systematic Approach ◽  
Temporal Networks ◽  
Spatial Organisation ◽  
Biomolecular Networks ◽  
Systems Chemistry ◽  
Spatial Localisation ◽  
Systems Framework

AbstractSpatial organisation through localisation/compartmentalisation of species is a ubiquitous but poorly understood feature of cellular biomolecular networks. Current technologies in systems and synthetic biology (spatial proteomics, imaging, synthetic compartmentalisation) necessitate a systematic approach to elucidating the interplay of networks and spatial organisation. We develop a systems framework towards this end and focus on the effect of spatial localisation of network components revealing its multiple facets: (i) As a key distinct regulator of network behaviour, and an enabler of new network capabilities (ii) As a potent new regulator of pattern formation and self-organisation (iii) As an often hidden factor impacting inference of temporal networks from data (iv) As an engineering tool for rewiring networks and network/circuit design. These insights, transparently arising from the most basic considerations of networks and spatial organisation, have broad relevance in natural and engineered biology and in related areas such as cell-free systems, systems chemistry and bionanotechnology.

scholarly journals Investigating Prebiotic Protocells for a Comprehensive Understanding of the Origins of Life: A Prebiotic Systems Chemistry Perspective

Life ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 49 ◽  
Author(s):  
Augustin Lopez ◽  
Michele Fiore
Keyword(s):  
Thin Films ◽  
Synthetic Biology ◽  
Chemical Compounds ◽  
Origins Of Life ◽  
Supramolecular Systems ◽  
Comprehensive Understanding ◽  
Giant Vesicles ◽  
Relevant Concept ◽  
Systems Chemistry ◽  
Emergence Of Life

Protocells are supramolecular systems commonly used for numerous applications, such as the formation of self-evolvable systems, in systems chemistry and synthetic biology. Certain types of protocells imitate plausible prebiotic compartments, such as giant vesicles, that are formed with the hydration of thin films of amphiphiles. These constructs can be studied to address the emergence of life from a non-living chemical network. They are useful tools since they offer the possibility to understand the mechanisms underlying any living cellular system: Its formation, its metabolism, its replication and its evolution. Protocells allow the investigation of the synergies occurring in a web of chemical compounds. This cooperation can explain the transition between chemical (inanimate) and biological systems (living) due to the discoveries of emerging properties. The aim of this review is to provide an overview of relevant concept in prebiotic protocell research.

Systems Chemistry: Pattern Formation in Random Dynamic Combinatorial Libraries

2007 ◽  
Vol 119 (46) ◽  
pp. 9014-9017 ◽  
Author(s):  
Peter T. Corbett ◽  
Jeremy K. M. Sanders ◽  
Sijbren Otto
Keyword(s):  
Pattern Formation ◽  
Combinatorial Libraries ◽  
Systems Chemistry

scholarly journals Biomedically relevant circuit‐design strategies in mammalian synthetic biology

2013 ◽  
Vol 9 (1) ◽  
pp. 691 ◽  
Author(s):  
William Bacchus ◽  
Dominique Aubel ◽  
Martin Fussenegger
Keyword(s):  
Synthetic Biology ◽  
Circuit Design ◽  
Design Strategies

scholarly journals Mathematical Modeling: Bridging the Gap between Concept and Realization in Synthetic Biology

2010 ◽  
Vol 2010 ◽  
pp. 1-16 ◽  
Author(s):  
Yuting Zheng ◽  
Ganesh Sriram
Keyword(s):  
Mathematical Modeling ◽  
Synthetic Biology ◽  
Circuit Design ◽  
Central Component ◽  
Transcription Network ◽  
Phenotype Analysis ◽  
Synthetic Circuit ◽  
Gene Regulatory ◽  
Mathematical Techniques

Mathematical modeling plays an important and often indispensable role in synthetic biology because it serves as a crucial link between the concept and realization of a biological circuit. We review mathematical modeling concepts and methodologies as relevant to synthetic biology, including assumptions that underlie a model, types of modeling frameworks (deterministic and stochastic), and the importance of parameter estimation and optimization in modeling. Additionally we expound mathematical techniques used to analyze a model such as sensitivity analysis and bifurcation analysis, which enable the identification of the conditions that cause a synthetic circuit to behave in a desired manner. We also discuss the role of modeling in phenotype analysis such as metabolic and transcription network analysis and point out some available modeling standards and software. Following this, we present three case studies—a metabolic oscillator, a synthetic counter, and a bottom-up gene regulatory network—which have incorporated mathematical modeling as a central component of synthetic circuit design.

scholarly journals Engineering pattern formation and morphogenesis

2020 ◽  
Vol 48 (3) ◽  
pp. 1177-1185
Author(s):  
Jamie A. Davies ◽  
Fokion Glykofrydis
Keyword(s):  
Tissue Engineering ◽  
Synthetic Biology ◽  
Pattern Formation ◽  
Research And Development ◽  
Reaction Diffusion ◽  
Future Research ◽  
Self Organized ◽  
Physical Form ◽  
Basic Understanding ◽  
Self Organizing

The development of natural tissues, organs and bodies depends on mechanisms of patterning and of morphogenesis, typically (but not invariably) in that order, and often several times at different final scales. Using synthetic biology to engineer patterning and morphogenesis will both enhance our basic understanding of how development works, and provide important technologies for advanced tissue engineering. Focusing on mammalian systems built to date, this review describes patterning systems, both contact-mediated and reaction-diffusion, and morphogenetic effectors. It also describes early attempts to connect the two to create self-organizing physical form. The review goes on to consider how these self-organized systems might be modified to increase the complexity and scale of the order they produce, and outlines some possible directions for future research and development.

scholarly journals Computational Methods Enabling Next-Generation Bioprocesses

Processes ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 214 ◽  
Author(s):  
Julio R. Banga ◽  
Filippo Menolascina
Keyword(s):  
Synthetic Biology ◽  
Computational Methods ◽  
Next Generation ◽  
Biomolecular Networks

Synthetic biology—the engineering of cells to rewire the biomolecular networks inside them—has witnessed phenomenal progress [...]

A theory of pattern formation for reaction–diffusion systems on temporal networks

2021 ◽  
Vol 477 (2247) ◽  
Author(s):  
Robert A. Van Gorder
Keyword(s):  
Pattern Formation ◽  
Selection Process ◽  
Reaction Diffusion ◽  
Time Interval ◽  
Temporal Networks ◽  
Reaction Diffusion Systems ◽  
Diffusion Systems ◽  
Underlying Network ◽  
Active Interest ◽  
Spatio Temporal

Networks have become ubiquitous in the modern scientific literature, with recent work directed at understanding ‘temporal networks’—those networks having structure or topology which evolves over time. One area of active interest is pattern formation from reaction–diffusion systems, which themselves evolve over temporal networks. We derive analytical conditions for the onset of diffusive spatial and spatio-temporal pattern formation on undirected temporal networks through the Turing and Benjamin–Feir mechanisms, with the resulting pattern selection process depending strongly on the evolution of both global diffusion rates and the local structure of the underlying network. Both instability criteria are then extended to the case where the reaction–diffusion system is non-autonomous, which allows us to study pattern formation from time-varying base states. The theory we present is illustrated through a variety of numerical simulations which highlight the role of the time evolution of network topology, diffusion mechanisms and non-autonomous reaction kinetics on pattern formation or suppression. A fundamental finding is that Turing and Benjamin–Feir instabilities are generically transient rather than eternal, with dynamics on temporal networks able to transition between distinct patterns or spatio-temporal states. One may exploit this feature to generate new patterns, or even suppress undesirable patterns, over a given time interval.

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