Explore Biological Pathways from Noisy Array Data by Directed Acyclic Boolean Networks

We consider the structure of directed acyclic Boolean (DAB) networks as a tool for exploring biological pathways. In a DAB network, the basic objects are binary elements and their Boolean duals. A DAB is characterized by two kinds of pairwise relations: similarity and prerequisite. The latter is a partial order relation, namely, the on-status of one element is necessary for the on-status of another element. A DAB network is uniquely determined by the state space of its elements. We arrange samples from the state space of a DAB network in a binary array and introduce a random mechanism of measurement error. Our inference strategy consists of two stages. First, we consider each pair of elements and try to identify their most likely relation. In the meantime, we assign a score, s-p-score, to this relation. Second, we rank the s-p-scores obtained from the first stage. We expect that relations with smaller s-p-scores are more likely to be true, and those with larger s-p-scores are more likely to be false. The key idea is the definition of s-scores (referring to similarity), p-scores (referring to prerequisite), and s-p-scores. As with classical statistical tests, control of false negatives and false positives are our primary concerns. We illustrate the method by a simulated example, the classical arginine biosynthetic pathway, and show some exploratory results on a published microarray expression dataset of yeast Saccharomyces cerevisiae obtained from experiments with activation and genetic perturbation of the pheromone response MAPK pathway

Constructing Biological Pathways by a Two-Step Counting Approach

Networks are widely used in biology to represent the relationships between genes and gene functions. In Boolean biological models, it is mainly assumed that there are two states to represent a gene: on-state and off-state. It is typically assumed that the relationship between two genes can be characterized by two kinds of pairwise relationships: similarity and prerequisite. Many approaches have been proposed in the literature to reconstruct biological relationships. In this article, we propose a two-step method to reconstruct the biological pathway when the binary array data have measurement error. For a pair of genes in a sample, the first step of this approach is to assign counting numbers for every relationship and select the relationship with counting number greater than a threshold. The second step is to calculate the asymptotic p-values for hypotheses of possible relationships and select relationships with a large p-value. This new method has the advantages of easy calculation for the counting numbers and simple closed forms for the p-value. The simulation study and real data example show that the two-step counting method can accurately reconstruct the biological pathway and outperform the existing methods. Compared with the other existing methods, this two-step method can provide a more accurate and efficient alternative approach for reconstructing the biological network

Autocatalytic closure and the evolution of cellular information processing networks

Cellular Information Processing Networks (CIPNs) are chemical networks of interacting molecules occurring in living cells. Through complex molecular interactions, CIPNs are able to coordinate critical cellular activities in response to internal and external stimuli. We hypothesise that CIPNs may be abstractly regarded as subsets of collectively autocatalytic (i.e., organisationally closed) reaction networks. These closure properties would subsequently interact with the evolution and adaptation of CIPNs capable of distinct information processing abilities. This hypothesis is motivated by the fact that CIPNs may require a mechanism enabling the self-maintenance of core components of the network when subjected to internal and external perturbations and during cellular divisions. Indeed, partially replicated or defective CIPNs may lead to the malfunctioning and premature death of the cell. In this thesis, we evaluate different existing computational approaches to model and evolve chemical reaction networks in silico. Following this literature review, we propose an evolutionary simulation platform capable of evolving artificial CIPNs from a bottom-up perspective. This system is a novel agent-based Artificial Chemistry (AC) which employs a term rewriting system called the Molecular Classifier System (MCS.bl). The latter is derived from the Holland broadcast language formalism. Our first series of experiments focuses on the emergence and evolution of selfmaintaining molecular organisations in the MCS.bl. Such experiments naturally relate to similar studies conducted in ACs such as Tierra, Alchemy and α-universes. Our results demonstrate some counter-intuitive outcomes, not indicated in previous literature. We examine each of these “unexpected” evolutionary dynamics (including an elongation catastrophe phenomenon) which presented various degenerate evolutionary trajectories. To address these robustness and evolvability issues, we evaluate several model variants of the MCS.bl. This investigation illuminates the key properties required to allow the self-maintenance and stable evolution of closed reaction networks in ACs. We demonstrate how the elongation catastrophe phenomenon can be prevented using a multi-level selectional model of the MCS.bl (which acts both at the molecular and cellular level). Using this multi-level selectional MCS.bl which was implemented as a parallel system, we successfully evolve an artificial CIPN to perform a simple pre-specified information processing task. We also demonstrate how signalling crosstalk may enable the cooperation of distinct closed CIPNs when mixed together in the same reaction space. We finally present the evolution of closed crosstalking and multitasking CIPNs exhibiting a higher level of complexity