Interaction with IQGAP1 links APC to Rac1, Cdc42, and actin filaments during cell polarization and migration

Rho family GTPases, particularly Rac1 and Cdc42, are key regulators of cell polarization and directional migration. Adenomatous polyposis coli (APC) is also thought to play a pivotal role in polarized cell migration. We have found that IQGAP1, an effector of Rac1 and Cdc42, interacts directly with APC. IQGAP1 and APC localize interdependently to the leading edge in migrating Vero cells, and activated Rac1/Cdc42 form a ternary complex with IQGAP1 and APC. Depletion of either IQGAP1 or APC inhibits actin meshwork formation and polarized migration. Depletion of IQGAP1 or APC also disrupts localization of CLIP-170, a microtubule-stabilizing protein that interacts with IQGAP1. Taken together, these results suggest a model in which activation of Rac1 and Cdc42 in response to migration signals leads to recruitment of IQGAP1 and APC which, together with CLIP-170, form a complex that links the actin cytoskeleton and microtubule dynamics during cell polarization and directional migration.     

Neural computing in discovering RNA interactions.

High-order RNA structures are involved in regulating many biological processes; various algorithms have been designed to predict them. Experimental methods to probe such structures and to decipher the results are tedious. Artificial intelligence and the neural network approach can support the process of discovering RNA structures. Secondary structures of RNA molecules are probed by autoradiographing gels, separating end-labeled fragments generated by base-specific RNases. This process is performed in both conditions, denaturing (for sequencing purposes) and native. The resultant autoradiograms are scanned using line-detection techniques to identify the fragments by comparing the lines with those obtained by 'alkaline ladders'. The identified paired bases are treated by either one of two methods to find the foldings which are consistent with the RNases' 'cutting' rules. One exploits the maximum independent set algorithm; the other, the planarization algorithm. They require, respectively, n and n2 processing elements, where n is the number of base pairs. The state of the system usually converges to the near-optimum solution within about 500 iteration steps, where each processing element implements the McCulloch-Pitts binary neuron. Our simulator, based on the proposed algorithm, discovered a new structure in a sequence of 38 bases, which is more stable than that formerly proposed.     

Rho-kinase modulates the function of STEF, a Rac GEF, through its phosphorylation

Rho family GTPases are key regulators of various physiological processes. Several recent studies indicated that the antagonistic relationship between Rho and Rac is essential for cell polarity and that the Rac activity is negatively regulated by Rho. In this study, we found that Rho-kinase, an effector of Rho, counteracted the Rac GEF STEF-induced Rac1 activation in COS7 cells. Rho-kinase phosphorylated STEF at Thr1662 in vitro, and Y-27632, a Rho-kinase inhibitor, suppressed lysophosphatidic acid-induced phosphorylation of STEF in PC12D cells. STEF interacted with specific molecules such as microtubule-associated protein 1B, and the phosphorylation of STEF by Rho-kinase diminished its interaction with these molecules. STEF promoted nerve growth factor-induced neurite outgrowth in PC12D cells, while the phosphomimic mutant of STEF had a weakened ability to enhance neurite outgrowth. Taken together, these results suggest that the phosphorylation of STEF by Rho-kinase exerts the inhibitory effect on the function of STEF.     

Quantitative measurement of two-component pH-sensitive colorimetric spectra using multilayer neural networks

The purpose of this research was to develop a noise tolerant and faster processing approach for in vivo and in vitro spectrophotometric applications where distorted spectra are difficult to interpret quantitatively. A PC based multilayer neural network with a sigmoid activation function and a generalized delta learning rule was trained with a two component (protonated and unprotonated form) pH-dependent spectrum generated from microspectrophotometry of the vital dye neutral red (NR). The network makes use of the digitized absorption spectrum between 375 and 675 nm. The number of nodes in the input layer was determined by the required resolution. The number of output nodes determined the step size of the quantization value used to distinguish the input spectra (i.e. defined the number of distinct output steps). Mathematic analysis provided the conditions for which this network is guaranteed to converge. Simulation results showed that features of the input spectrum were successfully identified and stored in the weight matrix of the input and hidden layers. After convergent training with typical spectra, a calibration curve was constructed to interpret the output layer activity and therefore, predict interpolated pH values of unknown spectra. With its built-in redundant presentation, this approach needed no preprocessing procedures (baseline correction or intensive signal averaging) normally used in multicomponent analyses. The identification of unknown spectra with the activities of the output layer is a one step process using the convergent weight matrix. After learning from examples, real time applications can be accomplished without solving multiple linear equations as in the multiple linear regression method. This method can be generalized to pattern oriented sensory information processing and multi-sensor data fusion for quantitative measurement purposes.     

An artificial maximum neural network: a winner-take-all neuron model forcing the state of the system in a solution domain

A maximum neuron model is proposed in order to force the state of the system to converge to the solution in neural dynamics. The state of the system is always forced in a solution domain. The artificial maximum neural network is used for the module orientation problem and the bipartite subgraph problem. The usefulness of the maximum neural network is empirically demonstrated by simulating randomly generated massive nstances (examples) in both problems. In randomly generated more than one thousand instances our system always converges to the solution within one hundred iteration steps regardless of the problem size. Our simulation results show the effectiveness of our algorithms and support our claim that one class of NP-complete problems may be solvable in a polynomial time.     

A parallel algorithm for estimating the secondary structure in ribonucleic acids

A parallel algorithm for estimating the secondary structure of an RNA molecule is presented in this paper. The mathematical problem to compute an optimal folding based on free-energy minimization is mapped onto a graph planarization problem. In the planarization problem we want to maximize the number of edges in a plane with no two edges crossing each other. To solve a sequence of n bases, n(n — 1)/2 processing elements are used in our algorithm.     

An artificial hysteresis binary neuron: a model suppressing the oscillatory behaviors of neural dynamics

A hysteresis binary McCulloch-Pitts neuron model is proposed in order to suppress the complicated oscillatory behaviors of neural dynamics. The artificial hysteresis binary neural network is used for scheduling time-multiplex crossbar switches in order to demonstrate the effects of hysteresis. Time-multiplex crossbar switching systems must control traffic on demand such that packet blocking probability and packet waiting time are minimized. The system using n×n processing elements solves an n×n crossbar-control problem with O(1) time, while the best existing parallel algorithm requires O(n) time. The hysteresis binary neural network maximizes the throughput of packets through a crossbar switch. The solution quality of our system does not degrade with the problem size.