Research and training

Delivering a workforce trained in the development of transformative technologies that will rapidly expand the Australian pharmaceutical, diagnostic and defence sectors.

We aim to enable academic and industry partnership in the Australian life sciences sector, to expand and diversify our existing industrial pipelines and allow our small and medium-sized biotech enterprises to flourish. We will promote concurrent development of next-generation technologies and high-level training in the relevant skills for our emerging researchers.

The Centre will boost the Australian healthcare ecosystem by developing transformative technologies that increase manufacturing capability, fast-track production, and deliver optimal solutions for healthcare and wellbeing.

Research themes

High-throughput technologies for big data analysis

Integration of high-throughput 3D cell printing technology with big data acquisition platforms for drug repurposing, bioprospecting and biosecurity. To realise the full potential of automation, high-throughput screening 3D printing and big data analytics, the MTP-Connect and AMGC sector competitive plan recommends a focus on developing workforce skilled across these technologies. We have developed a strategic plan to enable training in these skill gaps.

High-content screening and machine learning

Develop high-content screening (HCS) approach that integrates automated multiparametric image analysis with machine learning methodologies. Automation in data acquisition approaches such as HCS demands the increasing use of Deep Learning (DL) approaches for the life sciences and medical industries. There is a crucial shortage of skills beyond the math/statistics and programming of machine learning. Data scientists require subject matter expertise with this combination of skills is rare. Herein we will bridge the gap by training across both data acquisition and data analysis technologies.

Single-cell sequencing and artificial intelligence

Single-cell RNA sequencing (scRNA-seq) technologies have enabled the large-scale whole-transcriptome profiling of each individual cell in a population. For scRNA-seq to be integrated with high-throughput techniques, it is pivotal to develop data clustering methods of single cells to reveal cell subtypes changes in the relation with other cell types. We will achieve this by developing advanced AI methods for scRNA-seq technologies. Solutions to wellbeing is becoming highly reliant on convergent technologies via integration methods as artificial intelligence (AI) and genomics. For example, this has enabled increasing number of repositioned medications led to the idea that a systematic (hypothesis-free) screen of all known drugs might uncover additional compatible targets. A recent high-profile example is Remdesivir, for treatment of COVID-19. Herein we adopt a convergent approach by enabling integration of AI and genomics with automation in cell screening technologies.

Sensing and on-chip learning

The Internet of Things (IoT) has the power to revolutionise pharmaceutical manufacturing from drug discovery to remote patient access and monitoring. Top pharmaceutical companies around the globe are increasingly adopting IoT technologies in their manufacturing plants in an effort to achieve optimisation and improve process efficiency. We aim to enable IoT as a platform in high-throughput and big data pipeline, by achieving the key first step: integration of sensors with on-chip learning platforms. Convergence of sensors and data analytics are pivotal devices to monitor, control and diagnose issues in real time, enabling increased predictability and reduced downtime processing. We aim to develop skills that cover On-Chip learning methodologies, along with sensor development, which are central for translation of sensing platforms.

Research themes

High-throughput technologies for big data analysis

High-content screening and machine learning

Single-cell sequencing and artificial intelligence

Sensing and on-chip learning

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