David A. Rosenblueth

Syntactic recognition of regulatory regions in Escherichia coli

MOTIVATION One of the most common methodologies to identify cis-regulatory sites in regulatory regions in the DNA is that of weight matrices, as testified by several articles in this issue. An alternative to strengthen the computational predictions in regulatory regions is to develop methods that incorporate more biological properties present in such DNA regions. The grammatical implementation presented in this paper provides a concrete example in this direction. RESULTS On the basis of the analysis of an exhaustive collection of regulatory regions in Escherichia coli, a grammatical model for the regulatory regions of sigma 70 promoters has been developed. The terminal symbols of the grammar represent individual sites for the binding of activator and repressor proteins, and include the precise position of sites in relation to transcription initiation. Combining these symbols, the grammar generates a large number of different sentences, each of which can be searched for matching against a collection of regulatory regions by means of weight matrices specific for each set of sites for individual proteins. On the basis of this grammatical model, a Prolog syntactic recognizer is presented here. Specific subgrammars for ArgR, LexA and TyrR were implemented. When parsing a collection of 128 sigma 70 promoter regions, the syntactic recognizer produces a much lower number of false-positive sites than the standard search using weight matrices.

Measuring the Complexity of Self-Organizing Traffic Lights

We apply measures of complexity, emergence and self-organization to an abstract city traffic model for comparing a traditional traffic coordination method with a self-organizing method in two scenarios: cyclic boundaries and non-orientable boundaries. We show that the measures are useful to identify and characterize different dynamical phases. It becomes clear that different operation regimes are required for different traffic demands. Thus, not only traffic is a non-stationary problem, which requires controllers to adapt constantly. Controllers must also change drastically the complexity of their behavior depending on the demand. Based on our measures, we can say that the self-organizing method achieves an adaptability level comparable to a living system.

Life as Thermodynamic Evidence of Algorithmic Structure in Natural Environments

In evolutionary biology, attention to the relationship between stochastic organisms and their stochastic environments has leaned towards the adaptability and learning capabilities of the organisms rather than toward the properties of the environment. This article is devoted to the algorithmic aspects of the environment and its interaction with living organisms. We ask whether one may use the fact of the existence of life to establish how far nature is removed from algorithmic randomness. The paper uses a novel approach to behavioral evolutionary questions, using tools drawn from information theory, algorithmic complexity and the thermodynamics of computation to support an intuitive assumption about the near optimal structure of a physical environment that would prove conducive to the evolution and survival of organisms, and sketches the potential of these tools, at present alien to biology, that could be used in the future to address different and deeper questions. We contribute to the discussion of the algorithmic structure of natural environments and provide statistical and computational arguments for the intuitive claim that living systems would not be able to survive in completely unpredictable environments, even if adaptable and equipped with storage and learning capabilities by natural selection (brain memory or DNA).

An overview of existing modeling tools making use of model checking in the analysis of biochemical networks.

Model checking is a well-established technique for automatically verifying complex systems. Recently, model checkers have appeared in computer tools for the analysis of biochemical (and gene regulatory) networks. We survey several such tools to assess the potential of model checking in computational biology. Next, our overview focuses on direct applications of existing model checkers, as well as on algorithms for biochemical network analysis influenced by model checking, such as those using binary decision diagrams (BDDs) or Boolean-satisfiability solvers. We conclude with advantages and drawbacks of model checking for the analysis of biochemical networks.

Efficient Modelling of Embedded Software Systems and their Formal Verification

We propose vectors of finite-state machines whose transitions are labeled by formulas of a common-sense logic as the modeling tool for embedded systems software. We have previously shown that this methodology is very efficient in producing succinct and clear models (e.g., in contrast to plain finite-state machines, Petri nets, or Behavior Trees). We show that we can capture requirements precisely and that we can simulate and validate the models. We can, therefore, directly apply Model-Driven Engineering and deploy the models into software for diverse platforms with full tractability of requirements. Moreover, the sequential semantics of our vector of finite-state machines enables model-checking, formally establishing the correctness of the model. Finally, our approach facilitates systematic Failure Modes and Effects Analysis (FMEA) for diverse target platforms. We demonstrate the effectiveness of our methodology with several examples widely discussed in the software engineering literature and compare this with other approaches, showing that we can prove more properties, and that some claims about verification in such approaches have been exaggerated or are incomplete.

Adaptive self‐organization vs static optimization

Purpose – The purpose of this paper is to compare qualitatively two methods for coordinating traffic lights: a static optimization “green wave” method and an adaptive self‐organizing method.Design/methodology/approach – Statistical results were obtained from implementing a recently proposed model of city traffic based on elementary cellular automata in a computer simulation.Findings – The self‐organizing method delivers considerable improvements over the green‐wave method. Seven dynamical regimes and six phase transitions are identified and analyzed for the self‐organizing method.Practical implications – The paper shows that traffic light coordination can be improved in cities by using self‐organizing methods.Social implications – This improvement can have a noticeable effect on the quality of life of citizens.Originality/value – Understanding how self‐organization obtains adaptive solutions for complex problems can contribute to building more efficient systems.

The combination of the functionalities of feedback circuits is determinant for the attractors’ number and size in pathway-like Boolean networks

Molecular regulation was initially assumed to follow both a unidirectional and a hierarchical organization forming pathways. Regulatory processes, however, form highly interlinked networks with non-hierarchical and non-unidirectional structures that contain statistically overrepresented circuits or motifs. Here, we analyze the behavior of pathways containing non-unidirectional (i.e. bidirectional) and non-hierarchical interactions that create motifs. In comparison with unidirectional and hierarchical pathways, our pathways have a high diversity of behaviors, characterized by the size and number of attractors. Motifs have been studied individually showing that feedback circuit motifs regulate the number and size of attractors. It is less clear what happens in molecular networks that usually contain multiple feedbacks. Here, we find that the way feedback circuits couple to each other (i.e., the combination of the functionalities of feedback circuits) regulate both the number and size of the attractors. We show that the different expected results of epistasis analysis (a method to infer regulatory interactions) are produced by many non-hierarchical and non-unidirectional structures. Thus, these structures cannot be correctly inferred by epistasis analysis. Finally, we show that the combinations of functionalities, combined with other network properties, allow for a better characterization of regulatory structures.

Inference of Boolean Networks from Gene Interaction Graphs Using a SAT Solver

Boolean networks are important models of gene regulatory networks. Such models are sometimes built from: (1) a gene interaction graph and (2) a set of biological constraints. A gene interaction graph is a directed graph representing positive and negative gene regulations. Depending on the biological problem being solved, the set of biological constraints can vary, and may include, for example, a desired set of stationary states. We present a symbolic, SAT-based, method for inferring synchronous Boolean networks from interaction graphs augmented with constraints. Our method first constructs Boolean formulas in such a way that each truth assignment satisfying these formulas corresponds to a Boolean network modeling the given information. Next, we employ a SAT solver to obtain desired Boolean networks. Through a prototype, we show results illustrating the use of our method in the analysis of Boolean gene regulatory networks of the Arabidopsis thaliana root stem cell niche.

CTL update of Kripke models through protections

We present a nondeterministic, recursive algorithm for updating a Kripke model so as to satisfy a given formula of computation-tree logic (CTL). Recursive algorithms for model update face two dual difficulties: (1) Removing transitions from a Kripke model to satisfy a universal subformula may dissatisfy some existential subformulas. Conversely, (2) adding transitions to satisfy an existential subformula may dissatisfy some universal subformulas. To overcome these difficulties, we employ protections of the form 〈E,A,L〉, recording information about the satisfaction of subformulas previously treated by the algorithm. Intuitively, (1) E is the set of transitions that we cannot remove without compromising the satisfaction of previously treated subformulas. Conversely, (2) A is the set of transitions that we can add. Hence, update proceeds without diminishing E and without augmenting A. Finally, (3) L is a set of literals protecting the model labels. We illustrate our algorithm through several examples: Emerson and Clarkes mutual-exclusion problem, Clarke et. al.s microwave-oven example, synchronous counters, and randomly generated models and formulas. In addition, we compare our method with other update approaches for either CTL or fragments of CTL. Lastly, we provide proofs of soundness and completeness and a complexity analysis.

Efficient Model Checking and FMEA Analysis with Deterministic Scheduling of Transition-Labeled Finite-State Machines

A very successful tool for model-driven engineering of embedded systems is finite-state machines whose transitions are labeled with expressions of a common-sense logic. The deployment of models to different platforms and different programming languages makes it more imperative to confirm that the models are correct. However, systems are usually composed of concurrent behaviours, which complicates the potential use of model-checking technology. We structure models composed of several finite-state machines into a vector whose execution is a round-robin sequential off-line schedule. This enables model-checking of the requirements. We illustrate this with two case studies widely discussed in the literature. The models can be executed on diverse platforms, and we utilise the same interpreter to generate the corresponding Kripke structure suitable for verification with tools such as NUSMV.