Reusable building blocks in biological systems

Mireles, Víctor; Conrad, Tim

doi:10.1098/rsif.2018.0595

Cited by 10 publications

(8 citation statements)

References 53 publications

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“…In other words, modules exhibit internal causal cohesion coupled with a certain degree of autonomy from their context [ 28 – 30 ]. This means that modules can be re-used, since they are able to operate robustly across a certain range of circumstances [ 31 ].…”

Section: Kinds Of Modulesmentioning

confidence: 99%

Dynamical modules in metabolism, cell and developmental biology

Jaeger

Monk

2021

Interface Focus.

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Modularity is an essential feature of any adaptive complex system. Phenotypic traits are modules in the sense that they have a distinguishable structure or function, which can vary (quasi-)independently from its context. Since all phenotypic traits are the product of some underlying regulatory dynamics, the generative processes that constitute the genotype–phenotype map must also be functionally modular. Traditionally, modular processes have been identified as structural modules in regulatory networks. However, structure only constrains, but does not determine, the dynamics of a process. Here, we propose an alternative approach that decomposes the behaviour of a complex regulatory system into elementary activity-functions. Modular activities can occur in networks that show no structural modularity, making dynamical modularity more widely applicable than structural decomposition. Furthermore, the behaviour of a regulatory system closely mirrors its functional contribution to the outcome of a process, which makes dynamical modularity particularly suited for functional decomposition. We illustrate our approach with numerous examples from the study of metabolism, cellular processes, as well as development and pattern formation. We argue that dynamical modules provide a shared conceptual foundation for developmental and evolutionary biology, and serve as the foundation for a new account of process homology, which is presented in a separate contribution by DiFrisco and Jaeger to this focus issue.

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Section: Kinds Of Modulesmentioning

confidence: 99%

Dynamical modules in metabolism, cell and developmental biology

Jaeger

Monk

2021

Interface Focus.

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“…This requires some sort of hierarchical decomposition of these networks into manageable and intelligible subsystems, whose properties and behaviour can be analysed and understood in relative isolation (Simon, 1962; Riedl, 1975; Lewontin, 1978; Bonner, 1988; Raff, 1996; West-Eberhard, 2003; Schlosser and Wagner, 2004; Callebaut et al, 2005). If each subsystem possesses a clearly delimited and discernible function, the network can be subdivided into functional modules (Raff, 1996; von Dassow and Munro, 1999; Hartwell et al, 1999; Wagner et al, 2007; Mireles and Conrad, 2018). In the Introduction of our paper, we provide a careful argument showing that the most common approach to identify functional modules has severe limitations, and propose an alternative method, which we then use to dissect and analyse a specific pattern-forming network, the gap gene system of the vinegar fly, Drosophila melanogaster .…”

Section: Introductionmentioning

confidence: 99%

Modularity, criticality, and evolvability of a developmental gene regulatory network

Verd

Monk²,

Jaeger

2019

eLife

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The existence of discrete phenotypic traits suggests that the complex regulatory processes which produce them are functionally modular. These processes are usually represented by networks. Only modular networks can be partitioned into intelligible subcircuits able to evolve relatively independently. Traditionally, functional modularity is approximated by detection of modularity in network structure. However, the correlation between structure and function is loose. Many regulatory networks exhibit modular behaviour without structural modularity. Here we partition an experimentally tractable regulatory network—the gap gene system of dipteran insects—using an alternative approach. We show that this system, although not structurally modular, is composed of dynamical modules driving different aspects of whole-network behaviour. All these subcircuits share the same regulatory structure, but differ in components and sensitivity to regulatory interactions. Some subcircuits are in a state of criticality, while others are not, which explains the observed differential evolvability of the various expression features in the system.

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“…Bioinformatics tools such as PartCrafter [131] and other metagenomics approaches [148] can aid in discovering biological parts from novel sources, such as phage [84] or fungi [72] genomes. Others have used dictionary learning methods to identify reusable building blocks from biological systems [102] (e.g., sets of genes useful in metabolic engineering). For instance, in protein engineering, recent work used machine learning to find protein modules with minimal epistatic effects one can freely recombine into novel enzymes [92].…”

Section: Discussionmentioning

confidence: 99%

Open-endedness in synthetic biology: a route to continual innovation for biological design

Stock¹,

Gorochowski²

2023

Preprint

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Design in synthetic biology is typically goal-oriented: aiming to repurpose and optimize existing biological functions for new contexts, augmenting biology with new-to-nature capabilities, or creating life-like systems from scratch. While the field has seen many advances over the past two decades, bottlenecks in the complexity of the systems built are emerging and designs that function in the lab often fail when used in real-world contexts. Here, we propose an open-ended approach to biological design that aims to continuously generate innovative solutions capable of overcoming such hurdles. The fundamental principle we put forward is that the novelty of designed biology, be it a protein, genetic construct or organism, is at least as important as how well it fulfils its goal. Designing with novelty in mind, rather than a sole focus on optimization towards a single ``best'' design, may allow us to move beyond the diminishing returns we see in performance for most engineered biology to date. Research from the artificial life and evolutionary computing communities has demonstrated that embracing novelty can allow for the automatic generation of innovative and surprising solutions to challenging problems that move us beyond local optima. Synthetic biology now offers the ideal playground to test these ideas in living systems and explore more creative approaches to biological design.

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Reusable building blocks in biological systems

Cited by 10 publications

References 53 publications

Dynamical modules in metabolism, cell and developmental biology

Dynamical modules in metabolism, cell and developmental biology

Modularity, criticality, and evolvability of a developmental gene regulatory network

Open-endedness in synthetic biology: a route to continual innovation for biological design

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