Sequential Logic Model Deciphers Dynamic Transcriptional Control of Gene Expressions

Cellular signaling involves a sequence of events from ligand binding to membrane receptors through transcription factors activation and the induction of mRNA expression. The transcriptional-regulatory system plays a pivotal role in the control of gene expression. A novel computational approach to the study of gene regulation circuits is presented here.Based on the concept of finite state machine, which provides a discrete view of gene regulation, a novel sequential logic model (SLM) is developed to decipher control mechanisms of dynamic transcriptional regulation of gene expressions. The SLM technique is also used to systematically analyze the dynamic function of transcriptional inputs, the dependency and cooperativity, such as synergy effect, among the binding sites with respect to when, how much and how fast the gene of interest is expressed. expression and additional activities of binding sites are required. Further analyses suggest detailed mechanism of R switch activity where indirect dependency occurs in between UI activity and R switch during specification to differentiation stage. is a promising step for further application of the proposed method

Ecosystem-Driven Design of In-Home Terminals Based on Open Platform for the

Abstract—In-home healthcare services based on the Internet-of-Things (IoT) have great business potentials. To turn it into reality, a business ecosystem should be established first. Technical solutions should therefore aim for a cooperative ecosystem by meeting the interoperability, security, and system integration requirements. In this paper, we propose an ecosystem-driven design strategy and apply it in the design of an open-platform-based in-home healthcare terminal. A cooperative business ecosystem is formulated by merging the traditiona

African-Caribbean cancer consortium for the study of viral, genetic and environmental cancer risk factors

This is a short summary of a meeting of the "African-Caribbean Cancer Consortium", jointly organized by the University of Pittsburgh, Department of Epidemiology and the University of Pittsburgh Cancer Institute, held in Montego Bay, Jamaica as a satellite meeting at the Caribbean Health Research Council, 52nd Annual Council and Scientific meeting on May 4, 2007

Threshold-dominated regulation hides genetic variation in gene expression networks

<p>Abstract</p> <p>Background</p> <p>In dynamical models with feedback and sigmoidal response functions, some or all variables have thresholds around which they regulate themselves or other variables. A mathematical analysis has shown that when the dose-response functions approach binary or on/off responses, any variable with an equilibrium value close to one of its thresholds is very robust to parameter perturbations of a homeostatic state. We denote this threshold robustness. To check the empirical relevance of this phenomenon with response function steepnesses ranging from a near on/off response down to Michaelis-Menten conditions, we have performed a simulation study to investigate the degree of threshold robustness in models for a three-gene system with one downstream gene, using several logical input gates, but excluding models with positive feedback to avoid multistationarity. Varying parameter values representing functional genetic variation, we have analysed the coefficient of variation (<it>CV</it>) of the gene product concentrations in the stable state for the regulating genes in absolute terms and compared to the <it>CV </it>for the unregulating downstream gene. The sigmoidal or binary dose-response functions in these models can be considered as phenomenological models of the aggregated effects on protein or mRNA expression rates of all cellular reactions involved in gene expression.</p> <p>Results</p> <p>For all the models, threshold robustness increases with increasing response steepness. The <it>CV</it>s of the regulating genes are significantly smaller than for the unregulating gene, in particular for steep responses. The effect becomes less prominent as steepnesses approach Michaelis-Menten conditions. If the parameter perturbation shifts the equilibrium value too far away from threshold, the gene product is no longer an effective regulator and robustness is lost. Threshold robustness arises when a variable is an active regulator around its threshold, and this function is maintained by the feedback loop that the regulator necessarily takes part in and also is regulated by. In the present study all feedback loops are negative, and our results suggest that threshold robustness is maintained by negative feedback which necessarily exists in the homeostatic state.</p> <p>Conclusion</p> <p>Threshold robustness of a variable can be seen as its ability to maintain an active regulation around its threshold in a homeostatic state despite external perturbations. The feedback loop that the system necessarily possesses in this state, ensures that the robust variable is itself regulated and kept close to its threshold. Our results suggest that threshold regulation is a generic phenomenon in feedback-regulated networks with sigmoidal response functions, at least when there is no positive feedback. Threshold robustness in gene regulatory networks illustrates that hidden genetic variation can be explained by systemic properties of the genotype-phenotype map.</p

Beyond the Gene

This paper is a response to the increasing difficulty biologists find in agreeing upon a definition of the gene, and indeed, the increasing disarray in which that concept finds itself. After briefly reviewing these problems, we propose an alternative to both the concept and the word gene—an alternative that, like the gene, is intended to capture the essence of inheritance, but which is both richer and more expressive. It is also clearer in its separation of what the organism statically is (what it tangibly inherits) and what it dynamically does (its functionality and behavior). Our proposal of a genetic functor, or genitor, is a sweeping extension of the classical genotype/phenotype paradigm, yet it appears to be faithful to the findings of contemporary biology, encompassing many of the recently emerging—and surprisingly complex—links between structure and functionality

DNA topoisomerases participate in fragility of the oncogene RET

Fragile site breakage was previously shown to result in rearrangement of the RET oncogene, resembling the rearrangements found in thyroid cancer. Common fragile sites are specific regions of the genome with a high susceptibility to DNA breakage under conditions that partially inhibit DNA replication, and often coincide with genes deleted, amplified, or rearranged in cancer. While a substantial amount of work has been performed investigating DNA repair and cell cycle checkpoint proteins vital for maintaining stability at fragile sites, little is known about the initial events leading to DNA breakage at these sites. The purpose of this study was to investigate these initial events through the detection of aphidicolin (APH)-induced DNA breakage within the RET oncogene, in which 144 APHinduced DNA breakpoints were mapped on the nucleotide level in human thyroid cells within intron 11 of RET, the breakpoint cluster region found in patients. These breakpoints were located at or near DNA topoisomerase I and/or II predicted cleavage sites, as well as at DNA secondary structural features recognized and preferentially cleaved by DNA topoisomerases I and II. Co-treatment of thyroid cells with APH and the topoisomerase catalytic inhibitors, betulinic acid and merbarone, significantly decreased APH-induced fragile site breakage within RET intron 11 and within the common fragile site FRA3B. These data demonstrate that DNA topoisomerases I and II are involved in initiating APH-induced common fragile site breakage at RET, and may engage the recognition of DNA secondary structures formed during perturbed DNA replication

Indeterminacy of Reverse Engineering of Gene Regulatory Networks: The Curse of Gene Elasticity

Gene Regulatory Networks (GRNs) have become a major focus of interest in recent years. A number of reverse engineering approaches have been developed to help uncover the regulatory networks giving rise to the observed gene expression profiles. However, this is an overspecified problem due to the fact that more than one genotype (network wiring) can give rise to the same phenotype. We refer to this phenomenon as “gene elasticity.” In this work, we study the effect of this particular problem on the pure, data-driven inference of gene regulatory networks.We simulated a four-gene network in order to produce “data” (protein levels) that we use in lieu of real experimental data. We then optimized the network connections between the four genes with a view to obtain the original network that gave rise to the data. We did this for two different cases: one in which only the network connections were optimized and the other in which both the network connections as well as the kinetic parameters (given as reaction probabilities in our case) were estimated. We observed that multiple genotypes gave rise to very similar protein levels. Statistical experimentation indicates that it is impossible to differentiate between the different networks on the basis of both equilibrium as well as dynamic data.We show explicitly that reverse engineering of GRNs from pure expression data is an indeterminate problem. Our results suggest the unsuitability of an inferential, purely data-driven approach for the reverse engineering transcriptional networks in the case of gene regulatory networks displaying a certain level of complexity

The Taiwan Birth Panel Study: a prospective cohort study for environmentally- related child health

<p>Abstract</p> <p>Background</p> <p>The Taiwan Birth Panel Study (TBPS) is a prospective follow-up study to investigate the development of child health and disease in relation to in-utero and/or early childhood environmental exposures. The rationale behind the establishment of such a cohort includes the magnitude of potential environmental exposures, the timing of exposure window, fatal and children's susceptibility to toxicants, early exposure delayed effects, and low-level or unknown neurodevelopmental toxicants.</p> <p>Methods</p> <p>A total of 486 mother-infant paired was enrolled from April 2004 to January 2005 in this study. Maternal blood before delivery, placenta and umbilical cord blood at birth, and mothers' urine after delivery were collected. The follow-up was scheduled at birth, 4, 6 months, and 1, 2, 3 and 5 years. The children's blood, urine, hair, and saliva were collected at 2 years of age and children's urine was collected at 5 years of age as well. The study has been approved by the ethical committee of National Taiwan University Hospital. All the subjects signed the inform consent on entering the study and each of the follow up.</p> <p>Results</p> <p>Through this prospective birth cohort, the main health outcomes were focused on child growth, neurodevelopment, behaviour problem and atopic diseases. We investigated the main prenatal and postnatal factors including smoking, heavy metals, perfluorinated chemicals, and non-persistent pesticides under the consideration of interaction of the environment and genes.</p> <p>Conclusions</p> <p>This cohort study bridges knowledge gaps and answers unsolved issues in the low-level, prenatal or postnatal, and multiple exposures, genetic effect modification, and the initiation and progression of "environmentally-related childhood diseases."</p

Uncovering cis Regulatory Codes Using Synthetic Promoter Shuffling

Revealing the spectrum of combinatorial regulation of transcription at individual promoters is essential for understanding the complex structure of biological networks. However, the computations represented by the integration of various molecular signals at complex promoters are difficult to decipher in the absence of simple cis regulatory codes. Here we synthetically shuffle the regulatory architecture — operator sequences binding activators and repressors — of a canonical bacterial promoter. The resulting library of complex promoters allows for rapid exploration of promoter encoded logic regulation. Among all possible logic functions, NOR and ANDN promoter encoded logics predominate. A simple transcriptional cis regulatory code determines both logics, establishing a straightforward map between promoter structure and logic phenotype. The regulatory code is determined solely by the type of transcriptional regulation combinations: two repressors generate a NOR: NOT (a OR b) whereas a repressor and an activator generate an ANDN: a AND NOT b. Three-input versions of both logics, having an additional repressor as an input, are also present in the library. The resulting complex promoters cover a wide dynamic range of transcriptional strengths. Synthetic promoter shuffling represents a fast and efficient method for exploring the spectrum of complex regulatory functions that can be encoded by complex promoters. From an engineering point of view, synthetic promoter shuffling enables the experimental testing of the functional properties of complex promoters that cannot necessarily be inferred ab initio from the known properties of the individual genetic components. Synthetic promoter shuffling may provide a useful experimental tool for studying naturally occurring promoter shuffling