Catálogo de publicaciones - libros

During cytokinesis, the last step in cell division, cells must rearrange their shape so as to produce two daughter cells of equal size. To this end, the temporal and spatial distribution of a wide variety of proteins must be coordinated. In this paper we review some of the basic steps in the process. Moreover, we argue that a key step in the process is the feedback regulation of motor proteins. In doing so, we show that the study of cellular shape change can benefit from a systems-level approach.

Regulation of gene expression is the fundamental biological process through which a variety of cell functions are organized and executed ([9],[12]). Due to recent advance of molecular biology, a number of gene regulatory networks have been identified which are capable of executing important tasks such as signal transduction, switching, homeostasis, rhythm generation, developmental pattern formation and so on. They are surprizingly complex and highly sophisticated. Even a simple network of metabolic pathway in is complex enough to be comparable with the most advanced man-made control system in industry. Theoretical research to understand the underlying principle of gene regulation mechanism has been quite active in the last few decades, and successfully enhanced our understanding of the network operations through the notions of positive and negative feedbacks, robustness, time-delay, stability, bifurcation and so on. However, nonlinearity and complexity which represent fundamental characteristic features of life phenomena have been insurmountable obstacles for getting in-depth inderslanding of gene regulating networks beyond modelling/simulation paradigm.

We consider the problem of computing decentralized control policies for stochastic systems with finite state and action spaces. Synthesis of optimal decentralized policies for such problems is known to be NP-hard [1]. Here we focus on methods for efficiently computing meaningful suboptimal decentralized control policies. The algorithms we present here are based on approximation of optimal -functions. We show that the performance loss associated with choosing decentralized policies with respect to an approximate -function is related to the approximation error.

A fixed-parameter active steering compensation scheme that is designed to improve the dynamic behaviour of high-performance motorcycles is introduced. The design methodology is based on the Glover-McFarlane loop-shaping procedure. The steering compensator so designed, is seen as a possible replacement for a conventional steering damper, or as an alternative to the more recently introduced passive mechanical compensation networks. In comparison with these networks, active compensation has several potential advantages including: (i) the positive-reality of the compensator is no longer a requirement; (ii) it is no longer necessary for the device to be low-order; (iii) in a software implementation, it is easy to adjust the compensator parameters and (iv) an adaptive, or parameter varying version of this scheme is a routine extension. The study makes use of computer simulations that exploit a state-of-the-art motorcycle model whose parameter set is based on a Suzuki GSX-R1000 sports machine. The results extend further the significant improvements achieved in the dynamic properties of the primary oscillatory modes (‘wobble’ and ‘weave’) obtained previously by replacing the conventional steering damper with passive mechanical steering compensation schemes.

For some reason I now forget, Keith Glover and I were asked to prepare a “discussion paper” on frequency and time domain methods in system identification by the organizers of the IFAC symposium on System Identification in Darmstadt 1979, [8]. I visited Keith in Cambridge in April 1979 to prepare our paper. This was my first “scientific” visit to Cambridge, and I had some very productive and pleasant days with Keith there. After the symposium we were asked to produce an Automatica version of the discussion paper, and this was eventually published as [9]. Although I have met Keith and had interesting and enjoyable discussions with him many, many times since then, these two papers remain our only joint publications.

The problem of model based signal estimation is fundamental to control theory and signal processing, and several approaches have been developed in last decades, for instance, the Kalman filter ( optimal filtering) [1], optimal filtering [15] and set-membership approach [4]. The performance of these optimal filters degrades in the presence of model/plant mismatch. Robust filter techniques have been studied to relieve this situation, and numerous papers on this subject have appeared, namely, [9, 24] and references therein, and the text by I.R. Petersen and A.V. Savkin [21] is a comprehensive collection of Riccati based ( , and set-membership) approaches. Most of these results are characterized by first upper-bounding the performance objective, then selecting filter parameters to minimize the upper bound. There is little quantitative analysis on the conservativeness introduced by the use of upper bounds. Usually, these bounds guarantee performance not just over all fixed values of the uncertainty, but over time-varying uncertainty as well. Hence, if the actual uncertainty model is time-invariant, these design methods may be conservative. A recent paper by Geromel et al. ([11]) considers systems with time-invariant uncertainty, and the bound they optimize partially exploits the time-invariance of the uncertainty.

Given a nominal plant, a perturbed plant, an uncertainty structure and performance weights, we use robust model validation ideas to define and compute a measure of the distance between the nominal and perturbed plants. We also define a stability margin for a feedback system that is related to robust stability and nominal performance, and derive conditions for the stability and bounds for the performance degradation of the perturbed feedback system in terms of the distance measure. These robust stability and robust performance results give the distance measure a feedback interpretation. The simplicity and power of our procedure for computing the distance between two systems is illustrated using a normalized coprime factor uncertainty model to derive results that have already been published in the literature using different techniques. All systems considered in this paper are linear timeinvariant.

Robust stability, robust performance, distance measures, model validation, feedback systems, gap metric, -gap matric, control, stability margin

In this paper, we study the problem of constructing models for a stationary stochastic process { } assuming values in a finite set . It is assumed that only a path of the process is known, and not the full statistics of the process. Two kinds of problems are studied, namely: modelling for prediction, and modelling for classification. For the prediction problem, in a companion paper it is shown that a well-known approach of modelling the given process as a multi-step Markov process is in fact the solution satisfying certain nonnegativity constraints. In the present paper, accuracy and confidence bounds are derived for the parameters of this multi-step Markov model. So far as the author is aware, such bounds have not been published previously. For the classification problem, it is assumed that two distinct sets of sample paths of two separate stochastic processes are available – call them { , ... , } and { , ... , }. The objective here is to develop not one but models, called and respectively, such that the strings have much larger likelihoods with the model than with the model , and the opposite is true for the strings . Then a new string is classified into the set or according as its likelihood is larger from the model or the model . For the classification problem, we develop a new algorithm called the 4M (Mixed Memory Markov Model) algorithm, which is an improvement over variable length Markov models. We then apply the 4M algorithm to the problem of finding genes from the genome. The performance of the 4M algorithm is compared against that of the popular algorithm. In most of the test cases studied, the 4M algorithm correctly classifies both coding as well as non-coding regions more than 90% of the time. Moreover, the accuracy of the 4M algorithm compares well with that of . At the same time, the 4M algorithm is amenable to statistical analysis.

This paper presents a survey of some recent results which are primarily concerned with the action of relatively simple controllers working in complex networks. Feedback system design is all about getting the right amount of gain into the right places. In the design of a controller for a given linear multivariable system (with maybe an uncertain frequency response, but with a known topology of inputs and outputs), this means getting the right amount of gain into the right frequency ranges and in the right directions. theory, to which Keith Glover has of course contributed a great deal, tells us a lot about managing these tradeoffs (the loop shaping design method in particular [1]). Here though, we are not concerned with complex multivariable systems, but rather simple scalar systems which are perfectly well controlled by simple controllers – or rather they would be if only they weren’t also connected to other systems who are in turn connected with more systems in a complex web of feedback interactions. A further complication in the applications we have in mind is that we rarely have information about the whole network, we only know the local interactions. A natural question again is “how do we get the right amount of gain into the right places”.

It is a pleasure to dedicate this Festschrift article to Keith Glover on the occasion of his 60-th birthday. Keith came to MIT in 1969, armed with a degree from Imperial College, a couple of years of industrial experience, and a Kennedy scholarship, ready for some serious research. I, at that time a young assistant professor at MIT, had the good fortune to become Keith’s M.Sc. and Ph.D. supervisor, who he could choose freely because his scholarship made him, so to speak, ‘self-supporting’.