Matthew Poole

A Swarm Intelligence Framework for Reconstructing Gene Networks: Searching for Biologically Plausible Architectures

In this paper, we investigate the problem of reverse engineering the topology of gene regulatory networks from temporal gene expression data. We adopt a computational intelligence approach comprising swarm intelligence techniques, namely particle swarm optimization (PSO) and ant colony optimization (ACO). In addition, the recurrent neural network (RNN) formalism is employed for modeling the dynamical behavior of gene regulatory systems. More specifically, ACO is used for searching the discrete space of network architectures and PSO for searching the corresponding continuous space of RNN model parameters. We propose a novel solution construction process in the context of ACO for generating biologically plausible candidate architectures. The objective is to concentrate the search effort into areas of the structure space that contain architectures which are feasible in terms of their topological resemblance to real-world networks. The proposed framework is initially applied to the reconstruction of a small artificial network that has previously been studied in the context of gene network reverse engineering. Subsequently, we consider an artificial data set with added noise for reconstructing a subnetwork of the genetic interaction network of S. cerevisiae (yeast). Finally, the framework is applied to a real-world data set for reverse engineering the SOS response system of the bacterium Escherichia coli. Results demonstrate the relative advantage of utilizing problem-specific knowledge regarding biologically plausible structural properties of gene networks over conducting a problem-agnostic search in the vast space of network architectures.

Particle swarm optimization with an oscillating inertia weight

In this paper, we propose an alternative strategy of adapting the inertia weight parameter during the course of particle swarm optimization, by means of a non-monotonic inertia weight function of time. Results demonstrate that an oscillating inertia weight function is competitive and in some cases better than established inertia weight functions, in terms of consistency and speed of convergence.

The molecular basis of the chemosensitivity of metastatic cutaneous melanoma to chemotherapy

Background Chemotherapy benefits relatively few patients with cutaneous melanoma. The assessment of tumour chemosensitivity by the ATP-based tumour chemosensitivity assay (ATP-TCA) has shown strong correlation with outcome in cutaneous melanoma, but requires fresh tissue and dedicated laboratory facilities. Aim To examine whether the results of the ATP-TCA correlate with the expression of genes known to be involved in resistance to chemotherapy, based on the hypothesis that the molecular basis of chemosensitivity lies within known drug resistance mechanisms. Method The chemosensitivity of 47 cutaneous melanomas was assessed using the ATP-TCA and correlated with quantitative expression of 93 resistance genes measured by quantitative reverse transcriptase PCR (qRT-PCR) in a Taqman Array after extraction of total RNA from formalin-fixed paraffin-embedded tissue. Results Drugs susceptible to particular resistance mechanisms showed good correlation with genes linked to these mechanisms using signatures of up to 17 genes. Comparison of these signatures for DTIC, treosulfan and cisplatin showed several genes in common. HSP70, at least one human epidermal growth factor receptor, genes involved in apoptosis (IAP2, PTEN) and DNA repair (ERCC1, XPA, XRCC1, XRCC6) were present for these agents, as well as genes involved in the regulation of proliferation (Ki67, p21, p27). The combinations tested included genes represented in the single agent signatures. Conclusions These data suggest that melanoma chemosensitivity is influenced by known resistance mechanisms, including susceptibility to apoptosis. Use of a candidate gene approach may increase understanding of the mechanisms underlying chemosensitivity to drugs active against melanoma and provide signatures with predictive value.

Incorporating Heuristics in a Swarm Intelligence Framework for Inferring Gene Regulatory Networks from Gene Expression Time Series

In this paper, we address the problem of reverse-engineering a gene regulatory network from gene expression time series. We approach the problem by implementing an ant system to generate candidate network structures. The quality of a candidate structure is evaluated using a particle swarm optimization algorithm that tunes the parameters of the corresponding model, by minimizing the error between the actual time series and the trained models output. We extend this approach by incorporating domain-specific heuristics to the ant system, as a mechanism that has the potential to bias the pheromone amplification effect towards biologically plausible relationships. We apply the method to a subset of genes from a real world data set and report on the results.

Design of a blocks-based environment for introductory programming in Python

This paper details the design of a visual blocks-based tool for editing Python programs. Its purpose is to close the gap between programming using a simplified blocks-based language and textual programming in a mainstream language. As well as helping to guarantee the syntactic validity of programs, the tool aims to reduce the occurrence of run-time errors, a source of learner frustration with dynamic languages, by ensuring that constructed programs will remain well-typed during execution. The design promotes understanding of how data types are used in the language by representing them using colors: each expression block is colored according to its type, and each unfilled hole contains colors which indicate valid argument types. Connected blocks preserve conventional use of whitespace, demonstrating good practice for novice programmers.

Gene network inference using a swarm intelligence framework

In this paper, we present a framework for inferring gene regulatory networks from gene expression time series. A model-based approach is adopted, according to which the quality of a candidate architecture is evaluated by assessing the ability of the corresponding trained model to reproduce the available dynamics. Candidate architectures are generated in the context of the ant colony optimization (ACO) meta-heuristic and model training is performed using particle swarm optimization (PSO). We propose a novel solution construction heuristic for artificial ants, based on growth and preferential attachment, in order to generate candidate structures that adhere to well-known gene network properties. Preliminary results using an artificial network demonstrate the potential of the framework to infer the underlying network architecture to a promising degree of success.

Extending the design of a blocks-based python environment to support complex types

We are currently developing PyBlocks, a blocks-based environment which allows novice programmers to construct and execute Python programs. In the initial design of PyBlocks [1], Pythons basic data types and lists are represented using colors, every expression block is colored according to its type, and each unfilled slot contains color indicating all valid argument types. In this paper we extend the design to include Pythons most common built-in composite types (lists, tuples, dictionaries and sets) and to allow nesting of these where appropriate. Using example types from a pedagogical media computation library, we also show how further types may be supported. Together, these extensions provide almost any type novice Python programmers are likely to use.