Computational limits to binary genes

Zabet, Nicolae Radu; Chu, Dominique

doi:10.1098/rsif.2009.0474

Cited by 21 publications

(23 citation statements)

References 27 publications

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“…These organs bear no physical resemblance to the precursor entities . The emergence and stability of these biological patterns can be influenced by stochastic, random perturbations of highly non‐linear systems; noise in a system may contribute to a temporal pattern of gene expression during development . Such weak emergence arises from interactions at the elemental level and can be determined by stimulating or observing the system …”

Section: Characteristics Of a Developmental Complex Adaptive Systemmentioning

confidence: 99%

General and craniofacial development are complex adaptive processes influenced by diversity

O’Donnell

Hone

Hart

et al. 2014

Australian Dental Journal

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Complex systems are present in such diverse areas as social systems, economies, ecosystems and biology and, therefore, are highly relevant to dental research, education and practice. A Complex Adaptive System in biological development is a dynamic process in which, from interacting components at a lower level, higher level phenomena and structures emerge. Diversity makes substantial contributions to the performance of complex adaptive systems. It enhances the robustness of the process, allowing multiple responses to external stimuli as well as internal changes. From diversity comes variation in outcome and the possibility of major change; outliers in the distribution enhance the tipping points. The development of the dentition is a valuable, accessible model with extensive and reliable databases for investigating the role of complex adaptive systems in craniofacial and general development. The general characteristics of such systems are seen during tooth development: self-organization; bottom-up emergence; multitasking; self-adaptation; variation; tipping points; critical phases; and robustness. Dental findings are compatible with the Random Network Model, the Threshold Model and also with the Scale Free Network Model which has a Power Law distribution. In addition, dental development shows the characteristics of Modularity and Clustering to form Hierarchical Networks. The interactions between the genes (nodes) demonstrate Small World phenomena, Subgraph Motifs and Gene Regulatory Networks. Genetic mechanisms are involved in the creation and evolution of variation during development. The genetic factors interact with epigenetic and environmental factors at the molecular level and form complex networks within the cells. From these interactions emerge the higher level tissues, tooth germs and mineralized teeth. Approaching development in this way allows investigation of why there can be variations in phenotypes from identical genotypes; the phenotype is the outcome of perturbations in the cellular systems and networks, as well as of the genotype. Understanding and applying complexity theory will bring about substantial advances not only in dental research and education but also in the organization and delivery of oral health care.

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Section: Characteristics Of a Developmental Complex Adaptive Systemmentioning

confidence: 99%

General and craniofacial development are complex adaptive processes influenced by diversity

O’Donnell

Hone

Hart

et al. 2014

Australian Dental Journal

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“…The first case (all the binding sites must be bound for the gene to be regulated) results in a switch-like behavior of transcription [30,31] and consequently reduces leaky gene expression and noise in mRNA levels [32] . In this scenario, the cluster is acting as a buffer that prevents spurious transcription until the concentration of TF is high enough such that all binding sites are occupied.…”

Section: Mechanisms By Which Homotypic Clusters Could Influence Gene mentioning

confidence: 99%

Homotypic clusters of transcription factor binding sites: A model system for understanding the physical mechanics of gene expression

Ezer¹,

Zabet²,

Adryan³

2014

Computational and Structural Biotechnology Journal

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The organization of binding sites in cis-regulatory elements (CREs) can influence gene expression through a combination of physical mechanisms, ranging from direct interactions between TF molecules to DNA looping and transient chromatin interactions. The study of simple and common building blocks in promoters and other CREs allows us to dissect how all of these mechanisms work together. Many adjacent TF binding sites for the same TF species form homotypic clusters, and these CRE architecture building blocks serve as a prime candidate for understanding interacting transcriptional mechanisms. Homotypic clusters are prevalent in both bacterial and eukaryotic genomes, and are present in both promoters as well as more distal enhancer/silencer elements. Here, we review previous theoretical and experimental studies that show how the complexity (number of binding sites) and spatial organization (distance between sites and overall distance from transcription start sites) of homotypic clusters influence gene expression. In particular, we describe how homotypic clusters modulate the temporal dynamics of TF binding, a mechanism that can affect gene expression, but which has not yet been sufficiently characterized. We propose further experiments on homotypic clusters that would be useful in developing mechanistic models of gene expression.

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“…Similar relationships have been found in a number of contexts now including gene networks (Zabet and Chu, 2010;Chu et al, 2011) or bacterial adaptation systems (Lan et al, 2012). Ultimately, these trade-offs are a consequence of the limitations of Brownian computers (Bennett, 1982).…”

Section: Resultsmentioning

confidence: 77%

Limited by sensing - A minimal stochastic model of the lag-phase during diauxic growth

Chu

2017

Journal of Theoretical Biology

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Many microbes when grown on a mixture of two carbon sources utilise first and exclusively the preferred sugar, before switching to the less preferred carbon source. This results in two distinct exponential growth phases, often interrupted by a lag-phase of reduced growth termed the lag-phase. While the lag-phase appears to be an evolved feature, it is not clear what drives its evolution, as it comes with a substantial up-front fitness penalty due to lost growth. In this article a minimal mathematical model based on a master-equation approach is proposed. This model can explain many empirically observed phenomena. It suggests that the lag-phase can be understood as a manifestation of the trade-off between switching speed and switching efficiency. Moreover, the model predicts heterogeneity of the population during the lag-phase. Finally, it is shown that the switch from one carbon source to another one is a sensing problem and the lag-phase is a manifestation of known fundamental limitations of biological sensors.

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Computational limits to binary genes

Cited by 21 publications

References 27 publications

General and craniofacial development are complex adaptive processes influenced by diversity

General and craniofacial development are complex adaptive processes influenced by diversity

Homotypic clusters of transcription factor binding sites: A model system for understanding the physical mechanics of gene expression

Limited by sensing - A minimal stochastic model of the lag-phase during diauxic growth

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