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Complex networks: the key to systems biology

COSTA, Luciano da Fontoura; RODRIGUES, Francisco A.; CRISTINO, Alexandre S.
Fonte: Sociedade Brasileira de Genética Publicador: Sociedade Brasileira de Genética
Tipo: Artigo de Revista Científica
ENG
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Though introduced recently, complex networks research has grown steadily because of its potential to represent, characterize and model a wide range of intricate natural systems and phenomena. Because of the intrinsic complexity and systemic organization of life, complex networks provide a specially promising framework for systems biology investigation. The current article is an up-to-date review of the major developments related to the application of complex networks in biology, with special attention focused on the more recent literature. The main concepts and models of complex networks are presented and illustrated in an accessible fashion. Three main types of networks are covered: transcriptional regulatory networks, protein-protein interaction networks and metabolic networks. The key role of complex networks for systems biology is extensively illustrated by several of the papers reviewed.; FAPESP; CNPq

Complex networks: the key to systems biology

Costa,Luciano da F.; Rodrigues,Francisco A.; Cristino,Alexandre S.
Fonte: Sociedade Brasileira de Genética Publicador: Sociedade Brasileira de Genética
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/01/2008 EN
Relevância na Pesquisa
460.30766%
Though introduced recently, complex networks research has grown steadily because of its potential to represent, characterize and model a wide range of intricate natural systems and phenomena. Because of the intrinsic complexity and systemic organization of life, complex networks provide a specially promising framework for systems biology investigation. The current article is an up-to-date review of the major developments related to the application of complex networks in biology, with special attention focused on the more recent literature. The main concepts and models of complex networks are presented and illustrated in an accessible fashion. Three main types of networks are covered: transcriptional regulatory networks, protein-protein interaction networks and metabolic networks. The key role of complex networks for systems biology is extensively illustrated by several of the papers reviewed.

Degree dependence in rates of transcription factor evolution explains the unusual structure of transcription networks

Stewart, Alexander J.; Seymour, Robert M.; Pomiankowski, Andrew
Fonte: The Royal Society Publicador: The Royal Society
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
472.96742%
Transcription networks have an unusual structure. In both prokaryotes and eukaryotes, the number of target genes regulated by each transcription factor, its out-degree, follows a broad tailed distribution. By contrast, the number of transcription factors regulating a target gene, its in-degree, follows a much narrower distribution, which has no broad tail. We constructed a model of transcription network evolution through trans- and cis-mutations, gene duplication and deletion. The effects of these different evolutionary processes on the network structure are enough to produce an asymmetrical in- and out-degree distribution. However, the parameter values required to replicate known in- and out-degree distributions are unrealistic. We then considered variation in the rate of evolution of a gene dependent upon its position in the network. When transcription factors with many regulatory interactions are constrained to evolve more slowly than those with few interactions, the details of the in- and out-degree distributions of transcription networks can be fully reproduced over a range of plausible parameter values. The networks produced by our model depend on the relative rates of the different evolutionary processes. By determining the circumstances under which the networks with the correct degree distributions are produced...

Towards an Evolutionary Model of Transcription Networks

Xie, Dan; Chen, Chieh-Chun; He, Xin; Cao, Xiaoyi; Zhong, Sheng
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
EN
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DNA evolution models made invaluable contributions to comparative genomics, although it seemed formidable to include non-genomic features into these models. In order to build an evolutionary model of transcription networks (TNs), we had to forfeit the substitution model used in DNA evolution and to start from modeling the evolution of the regulatory relationships. We present a quantitative evolutionary model of TNs, subjecting the phylogenetic distance and the evolutionary changes of cis-regulatory sequence, gene expression and network structure to one probabilistic framework. Using the genome sequences and gene expression data from multiple species, this model can predict regulatory relationships between a transcription factor (TF) and its target genes in all species, and thus identify TN re-wiring events. Applying this model to analyze the pre-implantation development of three mammalian species, we identified the conserved and re-wired components of the TNs downstream to a set of TFs including Oct4, Gata3/4/6, cMyc and nMyc. Evolutionary events on the DNA sequence that led to turnover of TF binding sites were identified, including a birth of an Oct4 binding site by a 2nt deletion. In contrast to recent reports of large interspecies differences of TF binding sites and gene expression patterns...

Developmental Transcriptional Networks Are Required to Maintain Neuronal Subtype Identity in the Mature Nervous System

Eade, Kevin T.; Fancher, Hailey A.; Ridyard, Marc S.; Allan, Douglas W.
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
EN
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During neurogenesis, transcription factors combinatorially specify neuronal fates and then differentiate subtype identities by inducing subtype-specific gene expression profiles. But how is neuronal subtype identity maintained in mature neurons? Modeling this question in two Drosophila neuronal subtypes (Tv1 and Tv4), we test whether the subtype transcription factor networks that direct differentiation during development are required persistently for long-term maintenance of subtype identity. By conditional transcription factor knockdown in adult Tv neurons after normal development, we find that most transcription factors within the Tv1/Tv4 subtype transcription networks are indeed required to maintain Tv1/Tv4 subtype-specific gene expression in adults. Thus, gene expression profiles are not simply “locked-in,” but must be actively maintained by persistent developmental transcription factor networks. We also examined the cross-regulatory relationships between all transcription factors that persisted in adult Tv1/Tv4 neurons. We show that certain critical cross-regulatory relationships that had existed between these transcription factors during development were no longer present in the mature adult neuron. This points to key differences between developmental and maintenance transcriptional regulatory networks in individual neurons. Together...

Evolution of Transcription Networks — Lessons from Yeasts

Li, Hao; Johnson, Alexander D.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em 14/09/2010 EN
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458.276%
That regulatory evolution is important in generating phenotypic diversity was suggested soon after the discovery of gene regulation. In the past few decades, studies in animals have provided a number of examples in which phenotypic changes can be traced back to specific alterations in transcriptional regulation. Recent advances in DNA sequencing technology and functional genomics have stimulated a new wave of investigation in simple model organisms. In particular, several genome-wide comparative analyses of transcriptional circuits across different yeast species have been performed. These studies have revealed that transcription networks are remarkably plastic: large scale rewiring in which target genes move in and out of regulons through changes in cis-regulatory sequences appears to be a general phenomenon. Transcription factor substitution and the formation of new combinatorial interactions are also important contributors to the rewiring. In several cases, a transition through intermediates with redundant regulatory programs has been suggested as a mechanism through which rewiring can occur without a loss in fitness. Because the basic features of transcriptional regulation are deeply conserved, we speculate that large scale rewiring may underlie the evolution of complex phenotypes in multi-cellular organisms; if so...

Model Transcriptional Networks with Continuously Varying Expression Levels

Carneiro, Mauricio O; Taubes, Clifford H.; Hartl, Daniel L.
Fonte: BioMed Central Publicador: BioMed Central
Tipo: Artigo de Revista Científica
EN_US
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Background: At a time when genomes are being sequenced by the hundreds, much attention has shifted from identifying genes and phenotypes to understanding the networks of interactions among genes. We developed a gene network developmental model expanding on previous models of transcription regulatory networks. In our model, each network is described by a matrix representing the interactions between transcription factors, and a vector of continuous values representing the transcription factor expression in an individual. Results: In this work we used the gene network model to look at the impact of mating as well as insertions and deletions of genes in the evolution of complexity of these networks. We found that the natural process of diploid mating increases the likelihood of maintaining complexity, especially in higher order networks (more than 10 genes). We also show that gene insertion is a very efficient way to add more genes to a network as it provides a much higher chance of developmental stability. Conclusions: The continuous model affords a more complete view of the evolution of interacting genes. The notion of a continuous output vector also incorporates the reality of gene networks and graded concentrations of gene products.; Mathematics; Organismic and Evolutionary Biology

Modular Evolution of DNA-Binding Preference of a Tbrain Transcription Factor Provides a Mechanism for Modifying Gene Regulatory Networks

Cheatle Jarvela, Alys M.; Brubaker, Lisa; Vedenko, Anastasia; Gupta, Anisha; Armitage, Bruce A.; Bulyk, Martha L.; Hinman, Veronica F.
Fonte: Oxford University Press Publicador: Oxford University Press
Tipo: Artigo de Revista Científica
EN_US
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Gene regulatory networks (GRNs) describe the progression of transcriptional states that take a single-celled zygote to a multicellular organism. It is well documented that GRNs can evolve extensively through mutations to cis-regulatory modules (CRMs). Transcription factor proteins that bind these CRMs may also evolve to produce novelty. Coding changes are considered to be rarer, however, because transcription factors are multifunctional and hence are more constrained to evolve in ways that will not produce widespread detrimental effects. Recent technological advances have unearthed a surprising variation in DNA-binding abilities, such that individual transcription factors may recognize both a preferred primary motif and an additional secondary motif. This provides a source of modularity in function. Here, we demonstrate that orthologous transcription factors can also evolve a changed preference for a secondary binding motif, thereby offering an unexplored mechanism for GRN evolution. Using protein-binding microarray, surface plasmon resonance, and in vivo reporter assays, we demonstrate an important difference in DNA-binding preference between Tbrain protein orthologs in two species of echinoderms, the sea star, Patiria miniata, and the sea urchin...

Inferring microRNA and transcription factor regulatory networks in heterogeneous data

Le, T.; Liu, L.; Liu, B.; Tsykin, A.; Goodall, G.; Satou, K.; Li, J.
Fonte: BioMed Central Ltd. Publicador: BioMed Central Ltd.
Tipo: Artigo de Revista Científica
Publicado em //2013 EN
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368.4229%
Background: Transcription factors (TFs) and microRNAs (miRNAs) are primary metazoan gene regulators. Regulatory mechanisms of the two main regulators are of great interest to biologists and may provide insights into the causes of diseases. However, the interplay between miRNAs and TFs in a regulatory network still remains unearthed. Currently, it is very difficult to study the regulatory mechanisms that involve both miRNAs and TFs in a biological lab. Even at data level, a network involving miRNAs, TFs and genes will be too complicated to achieve. Previous research has been mostly directed at inferring either miRNA or TF regulatory networks from data. However, networks involving a single type of regulator may not fully reveal the complex gene regulatory mechanisms, for instance, the way in which a TF indirectly regulates a gene via a miRNA. Results: We propose a framework to learn from heterogeneous data the three-component regulatory networks, with the presence of miRNAs, TFs, and mRNAs. This method firstly utilises Bayesian network structure learning to construct a regulatory network from multiple sources of data: gene expression profiles of miRNAs, TFs and mRNAs, target information based on sequence data, and sample categories. Then...

A systematic approach to reconstructing transcription networks in Saccharomyces cerevisiae

Wang, Wei; Cherry, J. Michael; Botstein, David; Li, Hao
Fonte: National Academy of Sciences Publicador: National Academy of Sciences
Tipo: Artigo de Revista Científica
EN
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Decomposing regulatory networks into functional modules is a first step toward deciphering the logical structure of complex networks. We propose a systematic approach to reconstructing transcription modules (defined by a transcription factor and its target genes) and identifying conditions/perturbations under which a particular transcription module is activated/deactivated. Our approach integrates information from regulatory sequences, genome-wide mRNA expression data, and functional annotation. We systematically analyzed gene expression profiling experiments in which the yeast cell was subjected to various environmental or genetic perturbations. We were able to construct transcription modules with high specificity and sensitivity for many transcription factors, and predict the activation of these modules under anticipated as well as unexpected conditions. These findings generate testable hypotheses when combined with existing knowledge on signaling pathways and protein–protein interactions. Correlating the activation of a module to a specific perturbation predicts links in the cell's regulatory networks, and examining coactivated modules suggests specific instances of crosstalk between regulatory pathways.

Modular Composition of Gene Transcription Networks

Gyorgy, Andras; Del Vecchio, Domitilla
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
Publicado em 13/03/2014 EN
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Predicting the dynamic behavior of a large network from that of the composing modules is a central problem in systems and synthetic biology. Yet, this predictive ability is still largely missing because modules display context-dependent behavior. One cause of context-dependence is retroactivity, a phenomenon similar to loading that influences in non-trivial ways the dynamic performance of a module upon connection to other modules. Here, we establish an analysis framework for gene transcription networks that explicitly accounts for retroactivity. Specifically, a module's key properties are encoded by three retroactivity matrices: internal, scaling, and mixing retroactivity. All of them have a physical interpretation and can be computed from macroscopic parameters (dissociation constants and promoter concentrations) and from the modules' topology. The internal retroactivity quantifies the effect of intramodular connections on an isolated module's dynamics. The scaling and mixing retroactivity establish how intermodular connections change the dynamics of connected modules. Based on these matrices and on the dynamics of modules in isolation, we can accurately predict how loading will affect the behavior of an arbitrary interconnection of modules. We illustrate implications of internal...

Integrating transcriptional and protein interaction networks to prioritize condition-specific master regulators

Padi, Megha; Quackenbush, John
Fonte: BioMed Central Publicador: BioMed Central
Tipo: Artigo de Revista Científica
EN_US
Relevância na Pesquisa
370.16234%
Background: Genome-wide libraries of yeast deletion strains have been used to screen for genes that drive phenotypes such as stress response. A surprising observation emerging from these studies is that the genes with the largest changes in mRNA expression during a state transition are not those that drive that transition. Here, we show that integrating gene expression data with context-independent protein interaction networks can help prioritize master regulators that drive biological phenotypes. Results: Genes essential for survival had previously been shown to exhibit high centrality in protein interaction networks. However, the set of genes that drive growth in any specific condition is highly context-dependent. We inferred regulatory networks from gene expression data and transcription factor binding motifs in Saccharomyces cerevisiae, and found that high-degree nodes in regulatory networks are enriched for transcription factors that drive the corresponding phenotypes. We then found that using a metric combining protein interaction and transcriptional networks improved the enrichment for drivers in many of the contexts we examined. We applied this principle to a dataset of gene expression in normal human fibroblasts expressing a panel of viral oncogenes. We integrated regulatory interactions inferred from this data with a database of yeast two-hybrid protein interactions and ranked 571 human transcription factors by their combined network score. The ranked list was significantly enriched in known cancer genes that could not be found by standard differential expression or enrichment analyses. Conclusions: There has been increasing recognition that network-based approaches can provide insight into critical cellular elements that help define phenotypic state. Our analysis suggests that no one network...

Fundamental Dynamic Units: Feedforward Networks and Adjustable Gates

Sauro, Herbert; Yang, Song
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 12/07/2009
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The activation/repression of a given gene is typically regulated by multiple transcription factors (TFs) that bind at the gene regulatory region and recruit RNA polymerase (RNAP). The interactions between the promoter region and TFs and between different TFs specify the dynamic responses of the gene under different physiological conditions. By choosing specific regulatory interactions with up to three transcription factors, we designed several functional motifs, each of which is shown to perform a certain function and can be integrated into larger networks. We analyzed three kinds of networks: (i) Motifs derived from incoherent feedforward motifs, which behave as `amplitude filters', or `concentration detectors'. These motifs respond maximally to input transcription factors with concentrations within a certain range. From these motifs homeostatic and pulse generating networks are derived. (ii) Tunable network motifs, which can behave as oscillators or switches for low and high concentrations of an input transcription factor, respectively. (iii) Transcription factor controlled adjustable gates, which switch between AND/OR gate characteristics, depending on the concentration of the input transcription factor. This study has demonstrated the utility of feedforward networks and the flexibility of specific transcriptional binding kinetics in generating new novel behaviors. The flexibility of feedforward networks as dynamic units may explain the apparent frequency that such motifs are found in real biological networks.; Comment: 26 pages...

The Logic Backbone of a Transcription Network

Lagomarsino, M. Cosentino; Jona, P.; Bassetti, B.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
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A great part of the effort in the study of coarse grained models of transcription networks is directed to the analysis of their dynamical features. In this letter, we consider the \emph{equilibrium} properties of such systems, showing that the logic backbone underlying all dynamic descriptions has the structure of a computational optimization problem. It involves variables, which correspond to gene expression levels, and constraints, which describe the effect of \emph{cis-}regulatory signal integration functions. In the simple paradigmatic case of Boolean variables and signal integration functions, we derive and discuss phase diagrams. Notably, the model exhibits a connectivity transition between a regime of simple, but uncertain, gene control, to a regime of complex combinatorial control.; Comment: 11 pages, 4 figures final

What Transcription Factors Can't Do: On the Combinatorial Limits of Gene Regulatory Networks

Werner, Eric
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
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A proof is presented that gene regulatory networks (GRNs) based solely on transcription factors cannot control the development of complex multicellular life. GRNs alone cannot explain the evolution of multicellular life in the Cambrian Explosion. Networks are based on addressing systems which are used to construct network links. The more complex the network the greater the number of links and the larger the required address space. It has been assumed that combinations of transcription factors generate a large enough address space to form GRNs that are complex enough to control the development of complex multicellular life. However, it is shown in this article that transcription factors do not have sufficient combinatorial power to serve as the basis of an addressing system for regulatory control of genomes in the development of complex organisms. It is proven that given $n$ transcription factor genes in a genome and address combinations of length $k$ then there are at most $n/k$ k-length transcription factor addresses in the address space. The complexity of embryonic development requires a corresponding complexity of control information in the cell and its genome. Therefore, a different addressing system must exist to form the complex control networks required for complex control systems. It is postulated that a new type of network evolved based on an RNA-DNA addressing system that utilized and subsumed the extant GRNs. These new developmental control networks are called CENES (for Control genes). The evolution of these new higher networks would explain how the Cambrian Explosion was possible. The architecture of these higher level networks may in fact be universal (modulo syntax) in the genomes of all multicellular life.; Comment: 12 pages...

DIA-MCIS. An Importance Sampling Network Randomizer for Network Motif Discovery and Other Topological Observables in Transcription Networks

Fusco, D.; Bassetti, B.; Jona, P.; Lagomarsino, M. Cosentino
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 01/06/2007
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Transcription networks, and other directed networks can be characterized by some topological observables such as for example subgraph occurrence (network motifs). In order to perform such kind of analysis, it is necessary to be able to generate suitable randomized network ensembles. Typically, one considers null networks with the same degree sequences of the original ones. The commonly used algorithms sometimes have long convergence times, and sampling problems. We present here an alternative, based on a variant of the importance sampling Montecarlo developed by Chen et al. [1].; Comment: 6 pages and 1 figure, included supplementary mathematical notes

A comparative evolutionary study of transcription networks

Sellerio, A. L.; Bassetti, B.; Isambert, H.; Lagomarsino, M. Cosentino
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 15/05/2008
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568.61715%
We present a comparative analysis of large-scale topological and evolutionary properties of transcription networks in three species, the two distant bacteria E. coli and B. subtilis, and the yeast S. cerevisiae. The study focuses on the global aspects of feedback and hierarchy in transcriptional regulatory pathways. While confirming that gene duplication has a significant impact on the shaping of all the analyzed transcription networks, our results point to distinct trends between the bacteria, where time constraints in the transcription of downstream genes might be important in shaping the hierarchical structure of the network, and yeast, which seems able to sustain a higher wiring complexity, that includes the more feedback, intricate hierarchy, and the combinatorial use of heterodimers made of duplicate transcription factors.

Combinatorial Limits of Transcription Factors and Gene Regulatory Networks in Development and Evolution

Werner, Eric
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 31/07/2015
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368.6774%
Gene Regulatory Networks (GRNs) consisting of combinations of transcription factors (TFs) and their cis promoters are assumed to be sufficient to direct the development of organisms. Mutations in GRNs are assumed to be the primary drivers for the evolution of multicellular life. Here it is proven that neither of these assumptions is correct. They are inconsistent with fundamental principles of combinatorics of bounded encoded networks. It is shown there are inherent complexity and control capacity limits for any gene regulatory network that is based solely on protein coding genes such as transcription factors. This result has significant practical consequences for understanding development, evolution, the Cambrian Explosion, as well as multi-cellular diseases such as cancer. If the arguments are sound, then genes cannot explain the development of complex multicellular organisms and genes cannot explain the evolution of complex multicellular life.; Comment: 11 pages. This paper gives a more formal proof of the informal proof given in (Werner, E., "What Transcription Factors Can't Do: On the Combinatorial Limits of Gene Regulatory Networks" arXiv:1312.5565 [q-bio.MN], 2013.) I put this out there for feedback from the life science...

Engineering Transcription Factors to Program Cell Fate Decisions

Kabadi, Ami Meda
Fonte: Universidade Duke Publicador: Universidade Duke
Tipo: Dissertação
Publicado em //2015
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371.079%

Technologies for engineering new functions into proteins are advancing biological research, biotechnology, and medicine at an astounding rate. Building on fundamental research of natural protein structure and function, scientists are identifying new protein domains with previously undescribed properties and engineering new proteins with expanded functionalities. Such tools are enabling the precise study of fundamental aspects of cellular behavior and the development of a new class of gene therapies that manipulate the expression of endogenous genes. The applications of these gene regulation technologies include but are not limited to controlling cell fate decisions, reprogramming cell lineage commitment, monitoring cellular states, and stimulating expression of therapeutic factors.

While the field has come a long way in the past 20 years, there are still many limitations. Historically, gene therapy and gene replacement therapies have relied on over-expression of natural transcription factors that activate specific endogenous gene networks. However, natural transcription factors are often inadequate for generating efficient, fast, and homogenous cellular responses. Furthermore, most natural transcription factors have complex structures and functions that are difficult to improve or alter by rational design. This thesis presents three novel and widely applicable methods for engineering transcription factors for programming cell fate decisions in primary human cells. MyoD is the master transcription factor defining the myogenic lineage. Expression of MyoD in certain non-myogenic lineages induces a coordinated change in differentiation state. We use MyoD as a model for developing our protein engineering techniques because myogenesis is a well-studied pathway that is characterized by an easily detected change in phenotype from mono-nucleated to multinucleated cells. Furthermore...

Dynamical Principles in Switching Networks

Jenista, Michael Joseph
Fonte: Universidade Duke Publicador: Universidade Duke
Tipo: Dissertação
Publicado em //2010
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464.60523%

Switching networks are a common model for biological systems, especially

for genetic transcription networks. Stuart Kaufman originally proposed

the usefulness of the Boolean framework, but much of the dynamical

features there are not realizable in a continuous analogue. We introduce the notion

of braid-like dynamics as a bridge between Boolean and continuous dynamics and

study its importance in the local dynamics of ring and ring-like networks. We discuss

a near-theorem on the global dynamics of general feedback networks, and in the final

chapter study the main ideas of this thesis in models of a yeast cell transcription network.

; Dissertation