This work describes the development and characterization of a modular synthetic expression system that provides a broad range of adjustable and predictable expression levels in S. cerevisiae. The system works as a fixed-gain transcription amplifier, where the input signal is transferred via a synthetic transcription factor (sTF) onto a synthetic promoter, containing a defined core promoter, generating a transcription output signal. The system activation is based on the bacterial LexA-DNA-binding domain, a set of modified, modular LexA-binding sites and a selection of transcription activation domains. We show both experimentally and computationally that the tuning of the system is achieved through the selection of three separate modules, each of which enables an adjustable output signal: 1) the transcription-activation domain of the sTF, 2) the binding-site modules in the output promoter, and 3) the core promoter modules which define the transcription initiation site in the output promoter. The system has a novel bidirectional architecture that enables generation of compact, yet versatile expression modules for multiple genes with highly diversified expression levels ranging from negligible to very strong using one synthetic transcription factor. In contrast to most existing modular gene expression regulation systems, the present system is independent from externally added compounds. Furthermore, the established system was minimally affected by the several tested growth conditions. These features suggest that it can be highly useful in large scale biotechnology applications.

DNA microarray technologies are used extensively to profile the expression levels of thousands of genes under various conditions, yielding extremely large data-matrices. Thus, analyzing this information and extracting biologically relevant knowledge becomes a considerable challenge. A classical approach for tackling this challenge is to use clustering (also known as one-way clustering) methods where genes (or respectively samples) are grouped together based on the similarity of their expression profiles across the set of all samples (or respectively genes). An alternative approach is to develop biclustering methods to identify local patterns in the data. These methods extract subgroups of genes that are co-expressed across only a subset of samples and may feature important biological or medical implications. In this study we evaluate 13 biclustering and 2 clustering (k-means and hierarchical) methods. We use several approaches to compare their performance on two real gene expression data sets. For this purpose we apply four evaluation measures in our analysis: (1) we examine how well the considered (bi)clustering methods differentiate various sample types; (2) we evaluate how well the groups of genes discovered by the (bi)clustering methods are annotated with similar Gene Ontology categories; (3) we evaluate the capability of the methods to differentiate genes that are known to be specific to the particular sample types we study and (4) we compare the running time of the algorithms. In the end, we conclude that as long as the samples are well defined and annotated, the contamination of the samples is limited, and the samples are well replicated, biclustering methods such as Plaid and SAMBA are useful for discovering relevant subsets of genes and samples.

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a b s t r a c tWatson-Crick automata are finite state automata working on double-stranded tapes, introduced to investigate the potential of DNA molecules for computing. In this paper, we continue the investigation of descriptional complexity of Watson-Crick automata initiated by Păun et al. [A. Păun, M. Păun, State and transition complexity of Watson-Crick finite automata, in: G. Ciobanu, G. Paun (Eds.), Fundamentals of Computation Theory, FCT'99, in: LNCS, vol. 1684, 1999. In particular, we show that any finite language, as well as any unary regular language, can be recognized by a Watson-Crick automaton with only two, and respectively three, states. Also, we formally define the notion of determinism for these systems. Contrary to the case of non-deterministic Watson-Crick automata, we show that, for deterministic ones, the complementarity relation plays a major role in the acceptance power of these systems.

We investigate the open question asking whether there exist independent systems of three equations over three unknowns admitting non-periodic solutions, formulated in 1983 by Culik II and Karhumäki. In particular, we give a negative answer to this question for a large class of systems. More specifically, the question remains open only for a well specified class of systems. We also investigate systems of two equations over three unknowns for which we give necessary and sufficient conditions for admitting at most quasi-periodic solutions, i.e., solutions where the images of two unknowns are powers of a common word. In doing so, we also give a number of examples showing that these conditions represent a boundary point between systems admitting purely non-periodic solutions and those admitting at most quasi-periodic ones.

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Comparing alternative models for a given biochemical system is in general a very difficult problem: the models may focus on different aspects of the same system and may consist of very different species and reactions. The numerical setups of the models also play a crucial role in the quantitative comparison. When the alternative designs are submodels of a reference model, for example, knockdown mutants of a model, the problem of comparing them becomes simpler: they all have very similar, although not identical, underlying reaction networks, and the biological constraints are given by those in the reference model. In the first part of our study, we review several known methods for model decomposition and for quantitative comparison of submodels. We describe knockdown mutants, elementary flux modes, control‐based decomposition, mathematically controlled comparison and its extension, local submodel comparison and a discrete approach for comparing continuous submodels. In the second part of the paper we present a new statistical method for comparing submodels, which complements the methods presented in the review. The main difference between our approach and the known methods is related to the important question of how to chose the numerical setup in which to perform the comparison. In the case of the reviewed methods, the comparison is made in the numerical context of the reference model, i.e., in each of the alternative models both the kinetics of the reactions and the initial values of all variables are chosen to be identical to those from the reference model. We propose in this paper a different approach, better suited for response networks, where each alternative model is assumed to start from its own steady state under basal conditions. We demonstrate our approach with a case study focusing on the heat shock response in eukaryotes.