Near-optimal experimental design for model selection in systems biology

Motivation: Biological systems are understood through iterations of modeling and experimentation. Not all experiments, however, are equally valuable for predictive modeling. This study introduces an efficient method for experimental design aimed at selecting dynamical models from data. Motivated by biological applications, the method enables the design of crucial experiments: it determines a highly informative selection of measurement readouts and time points. Results: We demonstrate formal guarantees of design efficiency on the basis of previous results. By reducing our task to the setting of graphical models, we prove that the method finds a near-optimal design selection with a polynomial number of evaluations. Moreover, the method exhibits the best polynomial-complexity constant approximation factor, unless P = NP. We measure the performance of the method in comparison with established alternatives, such as ensemble non-centrality, on example models of different complexity. Efficient design accelerates the loop between modeling and experimentation: it enables the inference of complex mechanisms, such as those controlling central metabolic operation. Availability: Toolbox ‘NearOED' available with source code under GPL on the Machine Learning Open Source Software Web site (mloss.org). Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Biological Networks

Networks of coordinated interactions among biological entities govern a myriad of biological functions that span a wide range of both length and time scales—from ecosystems to individual cells and from years to milliseconds. For these networks, the concept “the whole is greater than the sum of its parts” applies as a norm rather than an exception. Meanwhile, continued advances in molecular biology and high-throughput technology have enabled a broad and systematic interrogation of whole-cell networks, allowing the investigation of biological processes and functions at unprecedented breadth and resolution—even down to the single-cell level. The explosion of biological data, especially molecular-level intracellular data, necessitates new paradigms for unraveling the complexity of biological networks and for understanding how biological functions emerge from such networks. These paradigms introduce new challenges related to the analysis of networks in which quantitative approaches such as machine learning and mathematical modeling play an indispensable role. The Special Issue on “Biological Networks” showcases advances in the development and application of in silico network modeling and analysis of biological systems

Scalable Front End Designs for Communication and Learning

In this work we provide three examples of estimation/detection problems, for which customizing the Front End to the specific application makes the system more efficient and scalable. The three problems we consider are all classical, but face new scalability challenges. This introduces additional constraints, accounting for which results in front end designs that are very distinct from the conventional approaches. The first two case studies pertain to the canonical problems of synchronization and equalization for communication links. As the system bandwidths scale, challenges arise due to the limiting resolution of analog-to-digital converters (ADCs). We discuss system designs that react to this bottleneck by drastically relaxing the precision requirements of the front end and correspondingly modifying the back end algorithms using Bayesian principles. The third problem we discuss belongs to the field of computer vision. Inspired by the research in neuroscience about the mammalian visual system, we redesign the front end of a machine vision system to be neuro-mimetic, followed by layers of unsupervised learning using simple k-means clustering. This results in a framework that is intuitive, more computationally efficient compared to the approach of supervised deep networks, and amenable to the increasing availability of large amounts of unlabeled data. We first consider the problem of blind carrier phase and frequency synchronization in order to obtain insight into the performance limitations imposed by severe quantization constraints. We adopt a mixed signal analog front end that coarsely quantizes the phase and employs a digitally controlled feedback that applies a phase shift prior to the ADC, this acts as a controllable dither signal and aids in the estimation process. We propose a control policy for the feedback and show that combined with blind Bayesian algorithms, it results in excellent performance, close to that of an unquantized system.Next, we take up the problem of channel equalization with severe limits on the number of slicers available for the ADC. We find that the standard flash ADC architecture can be highly sub-optimal in the presence of such constraints. Hence we explore a ``space-time'' generalization of the flash architecture by allowing a fixed numberof slicers to be dispersed in time (sampling phase) as well as space (i.e., amplitude). We show that optimizing the slicer locations, conditioned on the channel, results in significant gains in the bit error rate (BER) performance. Finally, we explore alternative ways of learning convolutionalnets for machine vision, making it easier to interpret and simpler to implement than currently used purely supervised nets. In particular, we investigate a framework that combines a neuro-mimetic front end (designed in collaboration with the neuroscientists from the psychology department at UCSB) together with unsupervised feature extraction based on clustering. Supervised classification, using a generic support vector machine (SVM), is applied at the end.We obtain competitive classification results on standard image databases, beating the state of the art for NORB (uniform-normalized) and approaching it for MNIST

Simplification de modèles mathématiques représentant des cultures cellulaires

L’utilisation de cellules vivantes dans un procédé industriel tire profit de la complexité inhérente au vivant pour accomplir des tâches complexes et dont la compréhension est parfois limitée. Que ce soit pour la production de biomasse, pour la production de molécules d’intérêt ou pour la décomposition de molécules indésirables, ces procédés font appel aux multiples réactions formant le métabolisme cellulaire. Afin de décrire l’évolution de ces systèmes, des modèles mathématiques composés d’un ensemble d’équations différentielles sont utilisés. Au fur et à mesure que les connaissances du métabolisme se sont développées, les modèles mathématiques le représentant se sont complexifiés. Le niveau de complexité requis pour expliquer les phénomènes en jeu lors d’un procédé spécifique est difficile à définir. Ainsi, lorsqu’on tente de modéliser un nouveau procédé, la sélection du modèle à utiliser peut être problématique. Une des options intéressantes est la sélection d’un modèle provenant de la littérature et adapté au procédé utilisé. L’information contenue dans le modèle doit alors être évaluée en fonction des phénomènes observables dans les conditions d’opération. Souvent, les modèles provenant de la littérature sont surparamétrés pour l’utilisation dans les conditions d’opération des procédés ciblées. Cela fait en sorte de causer des problèmes d’identifiabilité des paramètres. De plus, l’ensemble des variables d’état utilisées dans le modèle n’est pas nécessairement mesuré dans les conditions d’opération normales. L’objectif de ce projet est de cibler l’information utilisable contenue dans les modèles par la simplification méthodique de ceux-ci. En effet, la simplification des modèles permet une meilleure compréhension des dynamiques à l’oeuvre dans le procédé. Ce projet a permis de définir et d’évaluer trois méthodes de simplification de modèles mathématiques servant à décrire un procédé de culture cellulaire. La première méthode est basée sur l’application de critères sur les différents éléments du modèle, la deuxième est basée sur l’utilisation d’un critère d’information du type d’Akaike et la troisième considère la réduction d’ordre du modèle par retrait de variables d’état. Les résultats de ces méthodes de simplification sont présentés à l’aide de quatre modèles cellulaires provenant de la littérature