Biomedical subjects

Yuhai Tu

Publications and source records attributed to Yuhai Tu.

10 recordsLinked to original sources

PubMed2003

Quantitative modeling of sensitivity in bacterial chemotaxis: the role of coupling among different chemoreceptor species.

We propose a general theoretical framework for modeling receptor sensitivity in bacterial chemotaxis, taking into account receptor interactions, including those among different receptor species. We show that our model can quantitatively explain the recent in vivo measurements of receptor sensitivity at different ligand concentrations for both mutant and wild-type strains. For mutant strains, our model can fit the experimental data exactly. For the wild-type cell, our model is capable of achieving high gain while having modest values of Hill coefficient for the response curves. Furthermore, the high sensitivity of the wild-type cell in our model is maintained for a wide range of ambient ligand concentrations, facilitated by near-perfect adaptation and dependence of ligand binding on receptor activity. Our study reveals the importance of coupling among different chemoreceptor species, in particular strong interactions between the aspartate (Tar) and serine (Tsr) receptors, which is crucial in explaining both the mutant and wild-type data. Predictions for the sensitivity of other mutant strains and possible improvements of our model for the wild-type cell are also discussed.

Bacterial Proteins↗

PubMed2003

Transcriptional analysis of the B cell germinal center reaction.

The germinal center (GC) reaction is crucial for T cell-dependent immune responses and is targeted by B cell lymphomagenesis. Here we analyzed the transcriptional changes that occur in B cells during GC transit (naive B cells --> centroblasts --> centrocytes --> memory B cells) by gene expression profiling. Naive B cells, characterized by the expression of cell cycle-inhibitory and antiapoptotic genes, become centroblasts by inducing an atypical proliferation program lacking c-Myc expression, switching to a proapoptotic program, and down-regulating cytokine, chemokine, and adhesion receptors. The transition from GC to memory cells is characterized by a return to a phenotype similar to that of naive cells except for an apoptotic program primed for both death and survival and for changes in the expression of cell surface receptors including IL-2 receptor beta. These results provide insights into the dynamics of the GC reaction and represent the basis for the analysis of B cell malignancies.

Apoptosis↗

PubMed2003

Perfect and near-perfect adaptation in a model of bacterial chemotaxis.

The signaling apparatus mediating bacterial chemotaxis can adapt to a wide range of persistent external stimuli. In many cases, the bacterial activity returns to its prestimulus level exactly, and this perfect adaptability is robust against variations in various chemotaxis protein concentrations. We model the bacterial chemotaxis signaling pathway, from ligand binding to CheY phosphorylation. By solving the steady-state equations of the model analytically, we derive a full set of conditions for the system to achieve perfect adaptation. The conditions related to the phosphorylation part of the pathway are discovered for the first time, while other conditions are generalizations of the ones found in previous works. Sensitivity of the perfect adaptation is evaluated by perturbing these conditions. We find that, even in the absence of some of the perfect adaptation conditions, adaptation can be achieved with near-perfect precision as a result of the separation of scales in both chemotaxis protein concentrations and reaction rates, or specific properties of the receptor distribution in different methylation states. Since near-perfect adaptation can be found in much larger regions of the parameter space than that defined by the perfect adaptation conditions, their existence is essential to understand robustness in bacterial chemotaxis.

Adaptation, Physiological↗

PubMed2003

Ising model of cardiac thin filament activation with nearest-neighbor cooperative interactions.

We have developed a model of cardiac thin filament activation using an Ising model approach from equilibrium statistical physics. This model explicitly represents nearest-neighbor interactions between 26 troponin/tropomyosin units along a one-dimensional array that represents the cardiac thin filament. With transition rates chosen to match experimental data, the results show that the resulting force-pCa (F-pCa) relations are similar to Hill functions with asymmetries, as seen in experimental data. Specifically, Hill plots showing (log(F/(1-F)) vs. log [Ca]) reveal a steeper slope below the half activation point (Ca(50)) compared with above. Parameter variation studies show interplay of parameters that affect the apparent cooperativity and asymmetry in the F-pCa relations. The model also predicts that Ca binding is uncooperative for low [Ca], becomes steeper near Ca(50), and becomes uncooperative again at higher [Ca]. The steepness near Ca(50) mirrors the steep F-pCa as a result of thermodynamic considerations. The model also predicts that the correlation between troponin/tropomyosin units along the one-dimensional array quickly decays at high and low [Ca], but near Ca(50), high correlation occurs across the whole array. This work provides a simple model that can account for the steepness and shape of F-pCa relations that other models fail to reproduce.

Actin Cytoskeleton↗

PubMed2003

Gene expression dynamics during germinal center transit in B cells.

The germinal center (GC) reaction in T cell dependent antibody responses is crucial for the generation of B cell memory and plays a critical role in B cell lymphomagenesis. To gain insight into the physiology of this reaction, we identified the transcriptional changes that occur in B cells during the GC-transit (naïve B cells --> CD77(+) centroblasts (CBs) --> CD77(-) centrocytes (CCs) --> memory B cells) by DNA microarray experiments and the subsequent data analysis employing unsupervised and supervised hierarchical clustering. The naïve B cell is characterized by a nonproliferative, anti-apoptotic phenotype and the expression of various chemokine and cytokine receptors. The transition from naïve B cells to CBs is associated with (1) the up-regulation of genes associated with cellular proliferation, DNA-repair, and chromatin remodeling; (2) the acquisition of a pro-apoptotic phenotype; (3) the down-regulation of cytokine, chemokine, and adhesion receptors expressed in the naïve cells; and (4) the expression of a distinct adhesion repertoire. The CB and the CC revealed surprisingly few gene expression differences, suggesting that the CC is heterogeneous in its cellular composition. The CB/CC to memory B cell transition shows a general reversion to the profile characteristic for the naïve B cells, with the exception of the up-regulation of several surface receptors, including CD27, CD80, and IL-2Rbeta, and the simultaneous expression of both anti- and pro-apoptotic genes. These gene expression profiles of the normal B cell subpopulations are being used to identify the signals occurring during GC development, the cellular derivation of various types of B cell malignancies, and the genes deregulated in GC-derived tumors.

B-Lymphocytes↗

PubMed2003

Identification of Hodgkin and Reed-Sternberg cell-specific genes by gene expression profiling.

Hodgkin lymphoma (HL) is a malignancy of unknown pathogenesis. The malignant Hodgkin and Reed/Sternberg (HRS) cells derive from germinal center B cells (or rarely, T cells) but have a heterogeneous and largely uncharacterized phenotype. Using microarrays, we compared the gene expression profile of four HL cell lines with profiles of the main B cell subsets and B cell non-HLs to find out whether HRS cells, despite their described heterogeneity, show a distinct gene expression, to study their relationship to other normal and malignant B cells, and to identify genes aberrantly or overexpressed by HRS cells. The HL lines indeed clustered as a distinct entity, irrespective of their B or T cell derivation, and their gene expression was most similar to that of EBV-transformed B cells and cell lines derived from diffuse large cell lymphomas showing features of in vitro-activated B cells. Twenty-seven genes, most of which were previously unknown to be expressed by HRS cells, showed aberrant expression specifically in these cells, e.g., the transcription factors GATA-3, ABF1, EAR3, and Nrf3. For five genes, expression in primary HRS cells was confirmed. The newly identified HL-specific genes may play important roles in the pathogenesis of HL, potentially represent novel diagnostic markers, and can be considered for therapeutic targeting.

B-Lymphocytes↗

PubMed2003

Identification of a global gene expression signature of B-chronic lymphocytic leukemia.

B-chronic lymphocytic leukemia (B-CLL) is an adult-onset leukemia characterized by significant accumulation of apoptosis-resistant monoclonal B lymphocytes. In this study, we performed gene expression profiling on B cells obtained from 10 healthy age-matched individuals and CLL B cells from 38 B-CLL patients to identify key genetic differences between CLL and normal B cells. In addition, we leveraged recent independent studies to assess the reproducibility of our molecular B-CLL signature. We used a novel combination of several methods of data analysis including our own software and identified 70 previously unreported genes that differentiate leukemic cells from normal B cells, as well as confirmed recently reported B-CLL specific expression levels of an additional 10 genes. Importantly, many of these genes have previously been linked with other cancers, thus lending further support to their importance as candidate genes leading to B-CLL pathogenesis. We have also validated a subset of these genes using independent methodologies. Moreover, we show that our genes can be used to create a diagnostics signature that performs with perfect sensitivity and specificity in an independent cohort of 21 B-CLL and 20 normal subjects, thus strongly validating the informative nature of our set of genes. Finally, we identified a group of 31 genes that distinguish between low (Rai stage 0) and high (Rai stage 4) risk patients, suggesting that there may also be a gene expression signature that associates with disease progression.

ATP-Binding Cassette Transporters↗

PubMed2002

Microscopic dynamics of silicon oxidation.

We study the silicon oxidation process and the dynamic structure of the SiO2-Si (001) interface using a grand canonical Monte Carlo approach. We find that Si-O-Si bridge bonds are the main building blocks of the advancing interface, and we identify a kinetic pathway that continually creates new bridge bonds. Oxidation proceeds by local events, with little evidence of "step flow" in the simulation. Yet the interface remains remarkably smooth and abrupt as it advances.

Journal Article↗

PubMed2002

Systematic learning of gene functional classes from DNA array expression data by using multilayer perceptrons.

Recent advances in microarray technology have opened new ways for functional annotation of previously uncharacterised genes on a genomic scale. This has been demonstrated by unsupervised clustering of co-expressed genes and, more importantly, by supervised learning algorithms. Using prior knowledge, these algorithms can assign functional annotations based on more complex expression signatures found in existing functional classes. Previously, support vector machines (SVMs) and other machine-learning methods have been applied to a limited number of functional classes for this purpose. Here we present, for the first time, the comprehensive application of supervised neural networks (SNNs) for functional annotation. Our study is novel in that we report systematic results for ~100 classes in the Munich Information Center for Protein Sequences (MIPS) functional catalog. We found that only ~10% of these are learnable (based on the rate of false negatives). A closer analysis reveals that false positives (and negatives) in a machine-learning context are not necessarily "false" in a biological sense. We show that the high degree of interconnections among functional classes confounds the signatures that ought to be learned for a unique class. We term this the "Borges effect" and introduce two new numerical indices for its quantification. Our analysis indicates that classification systems with a lower Borges effect are better suitable for machine learning. Furthermore, we introduce a learning procedure for combining false positives with the original class. We show that in a few iterations this process converges to a gene set that is learnable with considerably low rates of false positives and negatives and contains genes that are biologically related to the original class, allowing for a coarse reconstruction of the interactions between associated biological pathways. We exemplify this methodology using the well-studied tricarboxylic acid cycle.

Algorithms↗

PubMed1994

Chaotic domains: A numerical investigation.

We study the chaotic domain state in rotating convection using a model equation that allows for a continuous range of roll orientations as in the experimental system. Methods are developed for extracting the domain configuration from the resulting patterns that should be applicable to a wide range of domain states. Comparison with the truncated three mode amplitude equation description is made. (c) 1994 American Institute of Physics.

Journal Article↗