Biomedical subjects

Bor-Sen Chen

Publications and source records attributed to Bor-Sen Chen.

7 recordsLinked to original sources

PubMed2006

Computational reconstruction of transcriptional regulatory modules of the yeast cell cycle.

BACKGROUND: A transcriptional regulatory module (TRM) is a set of genes that is regulated by a common set of transcription factors (TFs). By organizing the genome into TRMs, a living cell can coordinate the activities of many genes and carry out complex functions. Therefore, identifying TRMs is helpful for understanding gene regulation. RESULTS: Integrating gene expression and ChIP-chip data, we develop a method, called MOdule Finding Algorithm (MOFA), for reconstructing TRMs of the yeast cell cycle. MOFA identified 87 TRMs, which together contain 336 distinct genes regulated by 40 TFs. Using various kinds of data, we validated the biological relevance of the identified TRMs. Our analysis shows that different combinations of a fairly small number of TFs are responsible for regulating a large number of genes involved in different cell cycle phases and that there may exist crosstalk between the cell cycle and other cellular processes. MOFA is capable of finding many novel TF-target gene relationships and can determine whether a TF is an activator or/and a repressor. Finally, MOFA refines some clusters proposed by previous studies and provides a better understanding of how the complex expression program of the cell cycle is regulated. CONCLUSION: MOFA was developed to reconstruct TRMs of the yeast cell cycle. Many of these TRMs are in agreement with previous studies. Further, MOFA inferred many interesting modules and novel TF combinations. We believe that computational analysis of multiple types of data will be a powerful approach to studying complex biological systems when more and more genomic resources such as genome-wide protein activity data and protein-protein interaction data become available.

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PubMed2006

Identification of transcription factor cooperativity via stochastic system model.

MOTIVATION: Transcription factor binding sites are known to co-occur in the same gene owing to cooperativity of the transcription factors (TFs) that bind to them. Genome-wide location data can help us understand how an individual TF regulates its target gene. Nevertheless, how TFs cooperate to regulate their target genes still needs further study. In this study, genome-wide location data and expression profiles are integrated to reveal how TFs cooperate to regulate their target genes from the stochastic system perspective. RESULTS: Based on a stochastic dynamic model, a new measurement of TF cooperativity is developed according to the regulatory abilities of cooperative TF pairs and the number of their occurrences. Our method is employed to the yeast cell cycle and reveals successfully many cooperative TF pairs confirmed by previous experiments, e.g. Swi4-Swi6 in G1/S phase and Ndd1-Fkh2 in G2/M phase. Other TF pairs with potential cooperativity mentioned in our results can provide new directions for future experiments. Finally, a cooperative TF network of cell cycle is constructed from significant cooperative TF pairs. CONTACT: bschen@ee.nthu.edu.tw SUPPLEMENTARY INFORMATION: http://www.ee.nthu.edu.tw/~bschen/cooperativity/

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PubMed2006

On the attenuation and amplification of molecular noise in genetic regulatory networks.

BACKGROUND: Noise has many important roles in cellular genetic regulatory functions at the nanomolar scale. At present, no good theory exists for identifying all possible mechanisms of genetic regulatory networks to attenuate the molecular noise to achieve regulatory ability or to amplify the molecular noise to randomize outcomes to the advantage of diversity. Therefore, the noise filtering of genetic regulatory network is an important topic for gene networks under intrinsic fluctuation and extrinsic noise. RESULTS: Based on stochastic dynamic regulation equation, the intrinsic fluctuation in reaction rates is modeled as a state-dependent stochastic process, which will influence the stability of gene regulatory network, especially, with low concentrations of reacting species. Then the mechanisms of genetic regulatory network to attenuate or amplify extrinsic fluctuation are revealed from the nonlinear stochastic filtering point of view. Furthermore, a simple measure of attenuation level or amplification level of extrinsic noise for genetic regulatory networks is also introduced by nonlinear robust filtering method. Based on the global linearization scheme, a convenient method is introduced to measure noise attenuation or amplification for each gene of the nonlinear stochastic regulatory network by solving a set of filtering problems, which correspond to a set of linearized stochastic regulatory networks. Finally, by the proposed methods, several simulation examples of genetic regulatory networks are given to measure their robust stability under intrinsic fluctuations, and to estimate the genes' attenuation and amplification levels under extrinsic noises. CONCLUSION: In this study, a stochastic nonlinear dynamic model is developed for genetic regulatory networks under intrinsic fluctuation and extrinsic noise. By the method we proposed, we could determine the robust stability under intrinsic fluctuations and identify the genes that are significantly affected by extrinsic noises, which we call the weak structure of the network. This method will be potential for robust gene circuit design in future, on which a drug design could be based.

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PubMed2005

Dynamic modeling of cis-regulatory circuits and gene expression prediction via cross-gene identification.

BACKGROUND: Gene expression programs depend on recognition of cis elements in promoter region of target genes by transcription factors (TFs), but how TFs regulate gene expression via recognition of cis elements is still not clear. To study this issue, we define the cis-regulatory circuit of a gene as a system that consists of its cis elements and the interactions among their recognizing TFs and develop a dynamic model to study the functional architecture and dynamics of the circuit. This is in contrast to traditional approaches where a cis-regulatory circuit is constructed by a mutagenesis or motif-deletion scheme. We estimate the regulatory functions of cis-regulatory circuits using microarray data. RESULTS: A novel cross-gene identification scheme is proposed to infer how multiple TFs coordinate to regulate gene transcription in the yeast cell cycle and to uncover hidden regulatory functions of a cis-regulatory circuit. Some advantages of this approach over most current methods are that it is based on data obtained from intact cis-regulatory circuits and that a dynamic model can quantitatively characterize the regulatory function of each TF and the interactions among the TFs. Our method may also be applicable to other genes if their expression profiles have been examined for a sufficiently long time. CONCLUSION: In this study, we have developed a dynamic model to reconstruct cis-regulatory circuits and a cross-gene identification scheme to estimate the regulatory functions of the TFs that control the regulation of the genes under study. We have applied this method to cell cycle genes because the available expression profiles for these genes are long enough. Our method not only can quantify the regulatory strengths and synergy of the TFs but also can predict the expression profile of any gene having a subset of the cis elements studied.

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PubMed2005

Quantitative inference of dynamic regulatory pathways via microarray data.

BACKGROUND: The cellular signaling pathway (network) is one of the main topics of organismic investigations. The intracellular interactions between genes in a signaling pathway are considered as the foundation of functional genomics. Thus, what genes and how much they influence each other through transcriptional binding or physical interactions are essential problems. Under the synchronous measures of gene expression via a microarray chip, an amount of dynamic information is embedded and remains to be discovered. Using a systematically dynamic modeling approach, we explore the causal relationship among genes in cellular signaling pathways from the system biology approach. RESULTS: In this study, a second-order dynamic model is developed to describe the regulatory mechanism of a target gene from the upstream causality point of view. From the expression profile and dynamic model of a target gene, we can estimate its upstream regulatory function. According to this upstream regulatory function, we would deduce the upstream regulatory genes with their regulatory abilities and activation delays, and then link up a regulatory pathway. Iteratively, these regulatory genes are considered as target genes to trace back their upstream regulatory genes. Then we could construct the regulatory pathway (or network) to the genome wide. In short, we can infer the genetic regulatory pathways from gene-expression profiles quantitatively, which can confirm some doubted paths or seek some unknown paths in a regulatory pathway (network). Finally, the proposed approach is validated by randomly reshuffling the time order of microarray data. CONCLUSION: We focus our algorithm on the inference of regulatory abilities of the identified causal genes, and how much delay before they regulate the downstream genes. With this information, a regulatory pathway would be built up using microarray data. In the present study, two signaling pathways, i.e. circadian regulatory pathway in Arabidopsis thaliana and metabolic shift pathway from fermentation to respiration in yeast Saccharomyces cerevisiae, are reconstructed using microarray data to evaluate the performance of our proposed method. In the circadian regulatory pathway, we identified mainly the interactions between the biological clock and the photoperiodic genes consistent with the known regulatory mechanisms. We also discovered the now less-known regulations between crytochrome and phytochrome. In the metabolic shift pathway, the casual relationship of enzymatic genes could be detected properly.

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PubMed2005

A new measure of the robustness of biochemical networks.

MOTIVATION: The robustness of a biochemical network is defined as the tolerance of variations in kinetic parameters with respect to the maintenance of steady state. Robustness also plays an important role in the fail-safe mechanism in the evolutionary process of biochemical networks. The purposes of this paper are to use the synergism and saturation system (S-system) representation to describe a biochemical network and to develop a robustness measure of a biochemical network subject to variations in kinetic parameters. Since most biochemical networks in nature operate close to the steady state, we consider only the robustness measurement of a biochemical network at the steady state. RESULTS: We show that the upper bound of the tolerated parameter variations is related to the system matrix of a biochemical network at the steady state. Using this upper bound, we can calculate the tolerance (robustness) of a biochemical network without testing many parametric perturbations. We find that a biochemical network with a large tolerance can also better attenuate the effects of variations in rate parameters and environments. Compensatory parameter variations and network redundancy are found to be important mechanisms for the robustness of biochemical networks. Finally, four biochemical networks, such as a cascaded biochemical network, the glycolytic-glycogenolytic pathway in a perfused rat liver, the tricarboxylic acid cycle in Dictyostelium discoideum and the cAMP oscillation network in bacterial chemotaxis, are used to illustrate the usefulness of the proposed robustness measure.

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PubMed2004

Quantitative characterization of the transcriptional regulatory network in the yeast cell cycle.

MOTIVATION: Genome-wide gene expression programs have been monitored and analyzed in the yeast Saccharomyces cerevisiae, but how cells regulate global gene expression programs in response to environmental changes is still far from being understood. We present a systematic approach to quantitatively characterize the transcriptional regulatory network of the yeast cell cycle. For the interpretative purpose, 20 target genes were selected because their expression patterns fluctuated in a periodic manner concurrent with the cell cycle and peaked at different phases. In addition to the most significant five possible regulators of each specific target gene, the expression pattern of each target gene affected by synergy of the regulators during the cell cycle was characterized. Our first step includes modeling the dynamics of gene expression and extracting the transcription rate from a time-course microarray data. The second step embraces finding the regulators that possess a high correlation with the transcription rate of the target gene, and quantifying the regulatory abilities of the identified regulators. RESULTS: Our network discerns not only the role of the activator or repressor for each specific regulator, but also the regulatory ability of the regulator to the transcription rate of the target gene. The highly coordinated regulatory network has identified a group of significant regulators responsible for the gene expression program through the cell cycle progress. This approach may be useful for computing the regulatory ability of the transcriptional regulatory networks in more diverse conditions and in more complex eukaryotes. SUPPLEMENTARY INFORMATION: Matlab code and test data are available at http://www.ee.nthu.edu.tw/~bschen/quantitative/regulatory_network.htm

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