Trichoplax Adhaerens

Gene Ontology Consortium

There are three separate aspects to the GO effort:

• GO project is a collaborative effort to address the need for consistent descriptions of gene products across databases

• development and maintenance of the ontologies themselves

The Regulatory Sequence Analysis Tools

• development of tools that facilitate the creation, maintenance and use of ontologies

Began as a collaboration between three model organism databases:

• Drosophila (FlyBase)
• Saccharomyces (SGD)
• Mouse (MGD)

• annotation of gene products

• grew to incorporate many databases, including several of the world's major repositories for plant, animal and microbial genomes

• GO project has three structured ontologies that describe gene products in terms of their associated biological processes, cellular components and molecular functions.

UniProt also has a proteome database that contains the proteomes of many organisms including Trichoplax adhaerens

The RSAT provides a series of computer programs designed to detect regulatory signals in non-coding sequences

Uniprot

UniProt can run BLAST analysis on sequences

The Basic Local Alignment Search Tool (BLAST) finds regions of local similarity between sequences,

which is used to:

• infer functional and evolutionary relationships between sequences
• identify members of gene families

Running a BLAST

Gene: FEN1

Protein: Flap endonuclease 1

UniProt identifier: B3RVF0

Results

UniProt is a central repository of protein data created by combining the Swiss-Prot, TrEMBL and PIR-PSD databases.

• comprehensive, high-quality and freely accessible database of protein sequence and functional information

• contains a large amount of information about biological functions of proteins derived from the research literature

Promoter 2.0 Prediction Server

Trichoplax Adhaerens

facts

Trichoplax Adhaerens

• Promoter 2.0 predicts transcription start sites of PolII promoters in DNA sequences

An introduction to the organism

• developed as an evolution of simulated transcription factors that interact with sequences in promoter regions

• Genome contains pseudogenes

(essential for neuron function and development)

• Marine amoeba-like organism, (~0.5 mm)

National Center for Biotechnology Information (NCBI): Finding a complete genome sequence

• builds on principles common to neural networks and genetic algorithms

Feeding

• absorbs nutrients

• external digestion

• forms a digestive cavity around algae

• T. adhaerens feeds on small algae

such as Chlorophyta (green algae)

• Possibility that neuron-like structures were lost

• Smallest known animal genome (105 Mbp)

• Only species in its phylum

• Only 6 cell types (no nervous system)

Introduction to the organism: Trichoplax adhearens

Run using T. adhaerens mitochondrial DNA

• A description of Trichoplax Adhaerens

• How T. adhaerens is being studied in the field of Synthetic Biology

• An introduction to various Bioinformatic tools

Shotgun sequencing

• method used for sequencing long DNA strands

• DNA is broken up randomly into numerous small segments

• lipophil cells

• segments are sequenced using chain termination method to obtain reads

• Multiple overlapping reads obtained by performing several rounds of sequencing

Crystal cells

• contain a birefringent crystal
• arrayed around the rim

• gland cells

• computer programs use overlapping ends to assemble reads into a continuous sequence

• ciliated epithelial cells

Gland cells

• express several proteins typical of neurosecretory cells

subset express FMRFamide-like neuropeptide

• composition suggests that they are neurosecretory cells

(control locomotive and feeding behavior)

Lipophil cells

• project deep into interior
• alternate with regularly spaced fiber cells

More about the cells

• most found within thick ventral plate, including:

• 6 somatic cell types

• allows cell types to be identified, counted and spatial arrangement to be determined

Bioinformatic tools

• cellular organisation investigated using microscopy techniques

• organised body plan

• different cell types arranged in distinct patterns

Cellular Organisation

Introduction to Synthetic Biology

The MecaGen Platform:

Modeling with MecaGen

• theoretical, yet realistic agent-based model and simulation platform of animal embryogenesis

• centered on the physico-chemical coupling of cell mechanics with gene expression and molecular signaling

• allows scientists to investigate multiscale dynamics of early stages of biological morphogenesis

• Embryogenesis is an emergent, self-organized phenomenon based on a multitude of cells and their genetically regulated/regulating behavior

• DNA synthesis and genome engineering tools

• Biosensors

• simulation tools

Discovering Bioinformatics

MecaGen will be used in the mechanogenetic modeling and simulation of T. adhaerens

• computer-aided-design

T. adhaerens displays emergent phenomena including

• pattern formation
• collective motion
• morphogenesis
• swarm intelligence

Within Synthetic Biology various technologies are used including:

The reconstruction and modeling of TA are relevant to understanding many processes in vertebrates and humans (particularly through analogies with embryogenesis)

• Designed proteins

To accommodate T. adhaerens specific requirements MecaGen will need to be extended to include cellular locomotion mechanisms based on cilia, fluid dynamics, and interactions with the environment

(visiting the University of Strasbourg)

• Cell transformation

• synthesis and optimisation of bio-fuels

• implementation of Boolean functions with biological material for cancer detection

Amy Gooch

• manufacturing new low-cost drugs

Aim of the MecaPlax Project

• to contribute to the characterisation of the processes underlying the development of the structural organisation, form, structural features (morphogenesis) and behaviour of T. adhaerens

• to use evolutionary algorithms to optimise the biological network
• to then simulate the network via an automated network conception step

Synthetic biology is an emerging interdisciplinary branch of biology and engineering, which has applications in:

Durham University

Natural Sciences Year 1

amy.gooch@durham.ac.uk

Synthetic Biology

Population size

Evolutionary Algorithms continued:

Settings that need to be specified

Initialiser

• algorithm optimising a biological system
• therefore, when assigning a value for a parameter, a random value is chosen between the two logarithmic values of the interval and the parameter is initialised to the power of this value

Crossover function

Simple replacement, barycentric crossover, blx-alpha crossover, SBX crossover

Mutator function

Random draw, relative mutation (addition of a Gaussian noise to aid the program in finding the global optimum), auto-adaptive mutation

Evaluator

SBOL Visual is an open-source graphical

notation that uses schematic “glyphs” to specify genetic parts, devices, modules, and systems

Synthetic Biology Open

Language Visual

• Each gene can be considered to be either activated or inhibited

• gene state depends on presence/absence of several regulatory proteins that act to activate or inhibit gene expression

• Gene regulatory networks can be described with Boolean relationships

• Regulatory proteins themselves are synthesised by other genes

• therefore whole gene regulatory network modelled as a Boolean network

(A Boolean data type is a data type with only two possible values: true or false)

GeNeDA (GEne NEtwork Design Automation) is a tool developed upon this principle

(difference between Boolean abstraction and actual behavior can be significant)

Artificial gene regulatory networks can be integrated into a living cell to implement a new functionality

Computer-aided design saves time and money

(compared with the equivalent trial-error design approach)

Evolutionary algorithms play an important role in:

• design automation
• system-level optimization
• optimisation of diverse biological networks

Use of genetic algorithms in genetic

regulatory networks design automation

Thank you

for listening