Master of Computational Biology

AIMS

Data processing is fundamental to computing and data science. This subject gives an introduction to various aspects of data processing including database management, representation and analysis of data, information retrieval, visualisation and reporting, and cloud computing. This subject introduces students to the area, with an emphasis on both tools and underlying foundations.

INDICATIVE CONTENT

The subject's focus is on the data pipeline, and activities known colloquially as 'data wrangling'. Indicative topics covered include:

• Capturing data (data ingress)
• Data representation and storage
• Cleaning, normalization and filling in missing data (imputation)
• Combing multiple sources of data (data integration)
• Query languages and processing
• Scripting to support the data pipeline
• Distributing a database over multiple nodes (sharding), cloud computing file systems

Visualisation and presentation

This subject introduces the concepts of mathematical and computational modelling of biological systems, and how they are applied to data in order to study the underlying drivers of observed behaviour. The subject emphasises the role of abstraction and simplification of biological systems and requires an understanding of the underlying biological mechanisms. Combined with an introduction to sampling-based methods for statistical inference, students will learn how to identify common patterns in the rich and diverse nature of biological phenomena and appreciate how the modelling process leads to new insight into biological phenomena.

• Modelling: Deterministic and stochastic population-level dynamic models; agent-based computational models; and geospatial statistical models will be introduced and studied. Indicative examples will be drawn from health (e.g. infectious diseases, cell tumour growth, developmental biology), ecology (e.g. predator-prey systems, sustainable harvesting, environmental decision making) and biotechnology (e.g. biochemical and metabolic models).
• Simulation: Sampling based methods (e.g Monte Carlo simulation, Approximate Bayesian Computation) for parameter estimation and hypothesis testing will be introduced, and their importance in modern computational biology discussed.

This subject introduces the biology and technology underlying modern genomics data, features of the resulting data types including the frequency and patterns of error and missingness, and the statistical methods used to analyse them. It will include hands-on data analysis using R software. The material covered will evolve as genomics technology and practice change, and will span the following four areas: introduction to genomics technology and the resulting data, population genetics, association analysis including tests of association and major sources of confounding, heritability and prediction both in human genetics and for animal and plant breeding, and analysis of expression quantitative trait loci.

The subject introduces students to the molecular and cellular aspects of biological systems with particular emphasis on human biology. The course is arranged for students to generate an understanding of the molecular aspects of biology at the biomolecular, sub-cellular and cellular level. The genetic inheritance of traits is considered at the level of the individual and populations. This multi-disciplinary subject is co-taught by staff in the departments of Biochemistry and Molecular Biology and Genetics. There is particular emphasis on integration of these disciplines with students receiving both theoretical and practical knowledge of fundamental and frontier research and development in these areas. Students in the course will be extended through their participation in problem classes. They will write a major essay integrating the learnings with contemporary literature in the fields of genetics, molecular and cellular biology. Students will be mentored in this task by the course coordinator.

AIMS

The aim of this subject is for students to develop familiarity and competence in assessing and designing computer programs for computational efficiency. Although computers manipulate data very quickly, to solve large-scale problems, we must design strategies so that the calculations combine effectively. Over the latter half of the 20th century, an elegant theory of computational efficiency developed. This subject introduces students to the fundamentals of this theory and to many of the classical algorithms and data structures that solve key computational questions. These questions include distance computations in networks, searching items in large collections, and sorting them in order.

INDICATIVE CONTENT

Topics covered include complexity classes and asymptotic notation; empirical analysis of algorithms; abstract data types including queues, trees, priority queues and graphs; algorithmic techniques including brute force, divide-and-conquer, dynamic programming and greedy approaches; space and time trade-offs; and the theoretical limits of algorithm power.

Randomness is inherent in biological data and the analysis of data arising in both Bioinformatics and Biostatistics requires knowledge of sophisticated probability models and statistical techniques. This subject develops the underlying probability theory that is necessary to understand these models and techniques. Computer packages are used for numerical and theoretical calculations but no programming skills are required. Elements of Probability will be co-taught with MAST20006 Probability for Statistics.

The analysis of data arising in Bioinformatics and Biostatistics requires the use of sophisticated statistical techniques and computing packages.  This subject introduces the basic elements of statistical modelling, computation and data analysis. Students will develop the ability to fit statistical models to data, estimate parameters of interest and test hypotheses.  Both classical and Bayesian approaches will be covered.  The importance of the underlying mathematical theory of statistics and the use of modern statistical software will be emphasised.

Concepts covered include: descriptive statistics, random sample, statistical inference, point estimation, interval estimation, properties of estimators, maximum likelihood, confidence intervals, hypothesis testing, Bayesian inference.  Applications covered include: exploratory data analysis, inference for samples from univariate distributions, simple linear regression, correlation, goodness-of-fit tests, analysis of variance.

The lectures in this subject are co-taught with MAST20005 Statistics; the practice classes are separate. 

AIMS

This subject introduces the fundamental concepts of computing programming, and how to solve simple problems using high-level procedural language, with a specific emphasis on data manipulation, transformation, and visualisation of data.

INDICATIVE CONTENT

Fundamental programming constructs; fundamental data structures; abstraction; basic program structures; algorithmic problem solving; use of modules.

The subject assumes no prior knowledge of computer programming.

The subject will cover statistical analysis of data arising from modern genomics, and their practical application using R and specialist software. RNA-seq, epigenomics and metagenomics assays will be introduced, together with properties of the resulting data, appropriate pre-analyses and advanced statistical methods and algorithms. Methods for biomarker discovery, including supervised learning and multivariate analysis techniques will also be covered, as will statistical models and techniques for phylogenetics.

This subject builds on your knowledge of how biological modelling provides insight into complex biological phenomena. With a focus on mechanistic modelling and viewing biological systems as dynamic in nature, you will learn how to develop and implement “real-world” models, applicable to current open problems in computational biology. Advanced approaches to model-based analysis of data will be introduced, including Bayesian hierarchical modelling. Software languages and packages for modelling and statistical analysis (e.g. SBML and STAN) will be introduced. Motivating problems will be drawn from across the spectrum of biology from genetics to ecology.

Fitting models to data is a fundamental component of computational biology. In this subject we teach statistical and machine learning approaches, including methods specifically developed for handling spatial data. The subject will give you understanding of, and practice in, a range of modern techniques, and show how these are used in real world problems with typically available data. Topics covered include statistical learning methods for regression and classification, spatio-temporal modelling (point processes, agent-based models, spatio-temporal population simulations), spatial analyses and geographic information systems, and spatial optimisation. Diverse applications from health and ecology will be discussed and use as case studies.

AIMS

Technological advances in obtaining high throughput data have stimulated the development of new computational approaches to bioinformatics. This subject will cover core computational challenges in analysing bioinformatics data. We cover important algorithmic approaches and data structures used in solving these problems, and the challenges that arise as these problems increase in scale.

The subject is a core subject in the MSc (Bioinformatics), and is an elective in the Master of Information Technology and the Master of Engineering. It can also be taken by PhD students and by undergraduate students, subject to the approval of the lecturer.

INDICATIVE CONTENT

The subject covers key algorithms used in bioinformatics, with a focus on genomics. Indicative topics are: sequence alignment (dynamic algorithms and seed-and-extend), genome assembly, variant detection, phylogenetic construction, genomic intervals, complexity and correctness of algorithms, clustering and classification of genomics data, data reduction and visualisation. The subject assumes you have experience in programming and familiarity with the foundations of genomics.

The research project option within the Master of Computational Biology will be available for students who have demonstrated a strong aptitude for research to perform a short research project under the direction of a supervisor. The research project will build on the skills obtained in the course. Students will determine the structure they follow in consultation with the project coordinator.

The following structures are possible:

Structure 1

• Research Project in Comp Biology pt1 (12.5 points)
• Research Project in Comp Biology pt2 (37.5 points)

Structure 2

• Research Project in Comp Biology pt1 (25 points)
• Research Project in Comp Biology pt2 (25 points)

The research project option within the Master of Computational Biology will be available for students who have demonstrated a strong aptitude for research to perform a short research project under the direction of a supervisor. The research project will build on the skills obtained in the course. Students will determine the structure they follow in consultation with the project coordinator.

The following structures are possible:

Structure 1

• Research Project in Comp Biology pt1 (12.5 points)
• Research Project in Comp Biology pt2 (37.5 points)

Structure 2

• Research Project in Comp Biology pt1 (25 points)
• Research Project in Comp Biology pt2 (25 points)

The research project option within the Master of Computational Biology will be available for students who have demonstrated a strong aptitude for research to perform a short research project under the direction of a supervisor. The research project will build on the skills obtained in the course. Students will determine the structure they follow in consultation with the project coordinator.

The following structures are possible:

Structure 1

• Research Project in Comp Biology pt1 (12.5 points)
• Research Project in Comp Biology pt2 (37.5 points)

Structure 2

• Research Project in Comp Biology pt1 (25 points)
• Research Project in Comp Biology pt2 (25 points)

The research project option within the Master of Computational Biology will be available for students who have demonstrated a strong aptitude for research to perform a short research project under the direction of a supervisor. The research project will build on the skills obtained in the course. Students will determine the structure they follow in consultation with the project coordinator.

The following structures are possible:

Structure 1

• Research Project in Comp Biology pt1 (12.5 points)
• Research Project in Comp Biology pt2 (37.5 points)

Structure 2

• Research Project in Comp Biology pt1 (25 points)
• Research Project in Comp Biology pt2 (25 points)

The research project option within the Master of Computational Biology will be available for students who have demonstrated a strong aptitude for research to perform a short research project under the direction of a supervisor. The research project will build on the skills obtained in the course. Students will determine the structure they follow in consultation with the project coordinator.

The following structures are possible:

Structure 1

• Research Project in Comp Biology pt1 (12.5 points)
• Research Project in Comp Biology pt2 (37.5 points)

Structure 2

• Research Project in Comp Biology pt1 (25 points)
• Research Project in Comp Biology pt2 (25 points)

The research project option within the Master of Computational Biology will be available for students who have demonstrated a strong aptitude for research to perform a short research project under the direction of a supervisor. The research project will build on the skills obtained in the course. Students will determine the structure they follow in consultation with the project coordinator. The following structures are possible.

Structure 1

• Research Project in Comp Biology pt1 (12.5pts)
• Research Project in Comp Biology pt2 (37.5pts)

Structure 2

• Research Project in Comp Biology pt1 (25pts)
• Research Project in Comp Biology pt2 (25pts)

In this subject, students will apply the skills developed during the practical training subjects to solve an industry-relevant problem, either in industry or research. Working in teams or individually under only general guidance from staff members, they will be required to design, implement, analyse and report on the project. Emphasis will be on providing advice to the client.

Application of the technical and analytical skills that they have learned during their degree will challenge them to develop proficiencies in both analysis and reporting that approach the quality of similar work expected in the workforce. An important focus will be the development of the ability to present results in ways that can be best adopted by industry-based clients.

Bioinformatics involves the analysis of biological data and randomness is inherent in both the biological processes themselves and the sampling mechanisms by which they are observed. This subject first introduces stochastic processes and their applications in Bioinformatics, including evolutionary models. It then considers the application of classical statistical methods including estimation, hypothesis testing, model selection, multiple comparisons, and multivariate statistical techniques in Bioinformatics.

This subject describes current technologies used to sequence genomes - the starting point for comparative analyses of genes and proteins. The field of informatics has evolved to analyse and interpret large amounts of data generated by the new biotechnologies. Advanced topics will include transcriptome technologies, genome evolution and sequence similarity analysis techniques to identify protein orthologues and paralogues. The subject will cover bioinformatic analysis of protein structure and motifs at the secondary and tertiary levels, and modelling studies aimed at drug design. This subject will explore the latest developments in bioinformatics and detail how systems biology is helping to model complex biological processes.

Computing techniques and data mining methods are indispensable in modern statistical research and data science applications, where “Big Data” problems are often involved. This subject will introduce a number of recently developed methods and applications in computational statistics and data science that are scalable to large datasets and high-performance computing. The data mining methods to be introduced include general model diagnostic and assessment techniques, kernel and local polynomial nonparametric regression, basis expansion and nonparametric spline regression, generalised additive models, classification and regression trees, forward stagewise and gradient boosting models. Important statistical computing algorithms and techniques used in data science will be explained in detail. These include the bootstrap resampling and inference, cross-validation, the EM algorithm and Louis method, and Markov chain Monte Carlo methods including adaptive rejection and squeeze sampling, sequential importance sampling, slice sampling, Gibbs sampler and Metropolis-Hastings algorithm.

Bioinformatics is a diverse discipline that draws on a range of technical areas and is applied to a range of biological problems. In this subject a series of case studies is used to illustrate the application of bioinformatics to biological,agricultural, and medical problems. These case studies will be directly based on current practical research and taught by the researchers.

This subject introduces the fundamental mathematical models used to study infectious diseases at both the epidemiological and within-host scale. The emphasis is on: 1) how models are developed, from conceptualisation through to implementation in software; and 2) how to apply models to questions of epidemiological, public health and biological importance. Statistical techniques for the model-based analysis of relevant data resources will be introduced.

• Epidemiology: epidemic/endemic behaviour and intervention strategies to reduce transmission, the SIR model, including demography, threshold behaviour, phase-plane analysis;
• Viral dynamics: host-pathogen interactions, the mediating influences of immunomodulatory agents and antimicrobials, the TIV model, including the immune response, pharmacokinetic-pharmacodynamic models;
• Model sensitivity and uncertainty analysis, scenario analysis, parameter estimation, model comparison

Modern techniques have revolutionised biology and medicine, but interpretative and predictive tools are needed. Mathematical modelling is such a tool, providing explanations for counter-intuitive results and predictions leading to new experimental directions. The broad flavour of the area and the modelling process will be discussed. Applications will be drawn from many areas including population growth, epidemic modelling, biological invasion, pattern formation, tumour modelling, developmental biology and tissue engineering. A large range of mathematical techniques will be discussed, for example discrete time models, ordinary differential equations, partial differential equations, stochastic models and cellular automata.

AIMS:

This subject introduces mathematical and computational modelling, simulation and analysis of biological systems. The emphasis is on developing models, with examples, using MATLAB.

INDICATIVE CONTENT:

Topics include:

Modelling biochemical reactions. Law of mass action. Enzymes and regulation of enzyme reactions. Thermodynamics of reversible biochemical reactions. Cellular homeostasis. Application of ordinary differential equations to these problems.

Modelling large reaction networks. Flux balance analysis and constraint-based methods. Genome-scale models. Regulation of gene expression. Gene regulatory networks in systems and synthetic biology. Network inference and statistical modelling of –omic data. Knowledge-based modelling in systems biology.

This subject focuses on statistical models of the distribution of species and ecophysiological models of species niches. These two areas of environmental modelling have grown substantially in the last decade or two, and have become core parts of ecology. They are closely related, but they differ philosophically and practically. They are both used for understanding and predicting the distributions of species. The statistical models (also known as habitat suitability models, bioclimatic envelopes or ecological niche models) use observed geographical distributions to characterise relationships between a species and its environment and can be considered ‘top-down’ in approach. Ecophysiological (or mechanistic) models take a ‘bottom-up’ approach by characterising the physiological processes influencing a species’ distribution and integrate models of microclimates, energy balance, heat balance, and water balance.

You will learn about both approaches from lecturers who are world experts in these topics. The subject will help you to understand the merits and drawbacks of the two approaches to species modelling and equip you with important skills that are in high demand in ecology and conservation. The subject includes the following topics: compilation, processing and management of data, fitting models by statistical estimation and empirical measurement, spatial prediction of distributions (mapping), and model evaluation.

Modelling is a fundamental component of Environmental Science, being used for prediction, monitoring, auditing, evaluation, and assessment. This subject introduces students to a wide range of models used by environmental scientists including models of climate change, population dynamics, pollution, hydrology, habitat and species distributions. Both deterministic and stochastic models are used as examples. The subject explains how to develop conceptual models that can then be quantified and analysed using mathematical and statistical methods. Topics covered include development of the basic model structure, estimation of parameters and calibration, methods of analysis, sensitivity analysis, model evaluation and model refinement. The subject teaches students how to simplify apparently complex problems.

This subject provides an in-depth look into the methods, algorithms and techniques behind biomolecular structure determination. In particular, students will be exposed to prominent techniques in the field for example: X-ray Crystallography, Nuclear Magnetic Resonance (NMR) and cryogenic-Electron Microscopy (cryo-EM). As part of a general introduction into measurement techniques students will visit state-of-the-art NMR, cryo-EM and X-ray facilities at the Bio21 Institute and the Australian Synchrotron. Using industry standard software packages, students will learn to input and handle data to reconstruct biomolecular structure and dynamics across the range of techniques. The computer based laboratories will be overseen by practitioners in the field. This multi-disciplinary subject is co-taught by staff in the School of Physics, Chemistry and Biomedical sciences. There is particular emphasis on integration of these disciplines with students receiving both theoretical and practical knowledge of fundamental and frontier research and development in biomolecular structure determination.

This subject provides an advanced introduction to non-equilibrium statistical mechanics. The subject focuses on collective phenomena in complex many-body systems with an emphasis on diffusive processes, stability and the emergence of long-range order, with examples drawn from physics, chemistry, biology and economics. Specific topics include diffusive stochastic processes (Fokker-Planck equations), birth-death processes (master equations), kinetic transport, and spatio-temporal pattern formation in unstable nonlinear systems (bifurcations, chaos, reaction-diffusion equations). 

This subject provides the foundation theory and methods needed for biostatisticians to apply and critically interpret statistical inference, the science of drawing conclusions from data that are subject to variability. Major topics include review of the key concepts of estimation including sampling variability and construction of confidence intervals; null hypothesis testing; methods of inference based on likelihood theory (Fisher and observed information, likelihood ratio, Wald and score tests); and an introduction to the Bayesian approach to inference. The approach will emphasise a critical understand¬ing of the role of statistical inference in health research.

This subject provides the foundation for regression modelling. Topics covered include: the method of least squares; regression models and related statistical inference; flexible nonparametric regression; analysis of covariance to adjust for confounding; multiple regression with matrix algebra; model construction and interpretation (use of indicator variables, parameterisation, interaction and transformations); model checking and diagnostics; regression to the mean; handling of baseline values; the analysis of variance; variance components and random effects.

Why is it essential that scientists learn to communicate effectively to a variety of audiences? What makes for engaging communication when it comes to science? How does the style of communication need to change for different audiences? What are the nuts and bolts of good science writing? What are the characteristics of effective public speaking?

Weekly seminars and tutorials will consider the important role science and technology plays in twenty-first century society and explore why it is vital that scientists learn to articulate their ideas to a variety of audiences in an effective and engaging manner. These audiences may include school students, agencies that fund research, the media, government, industry, and the broader public. Other topics include the philosophy of science communication, talking about science on the radio, effective public speaking, writing press releases and science feature articles, science performance, communicating science on the web and how science is reported in the media.

Students will develop skills in evaluating examples of science and technology communication to identify those that are most effective and engaging. Students will also be given multiple opportunities to receive feedback and improve their own written and oral communication skills.

Students will work in small teams on team projects to further the communication skills developed during the seminar programme. These projects will focus on communicating a given scientific topic to a particular audience using spoken, visual, written or web-based communication.

What is conflict of interest? What should a scientist do when he or she finds fraud is occurring on a scientific research team? How does a scientist write and defend an animal ethics submission and get it approved? What are the ethical issues associated with peer review? This subject is intended to give students a broad overview of research ethics in a scientific context. It will include topics on scientific integrity; conflicts of interest; data recording management; authorship and peer review; animal experimentation and regulations; privacy and confidentiality of records; and, finally, research in humans.

This subject examines the workplace environment and the range of competencies needed to operate effectively. Communication is central to success in the workplace, from proposing projects, consulting and influencing colleagues, through to reporting. Students will gain a range of communication skills in writing, oral and presentation skills, and using graphics and statistics, to communicate science to others with whom they work.

Excellent scientific leadership is not only required in academic research groups, but also in technological industries and many areas of government. This subject will examine the nature and styles and consequences of leadership and decision making in academia, industry and government.

Students will examine, through a series of lectures, seminars and workshops, the roles of leadership in: motivation, ethics, risk and the development of a productive organisational culture drawing upon case studies, personal accounts from scientific leaders and their own personal experiences.

In addition, students will learn strategies to deal with staff and clients, build teams, make decisions, think strategically, develop self awareness, identify and manage conflict of interest, identify opportunity and value diversity.

This subject will give an overview of the tools that businesses use to manage their external environment. The subject addresses three main areas: negotiation skills, marketing and competitivestrategy. Students will use case studies and simulations to practice negotiation skills. Topics in marketing will include an overview of brands, creating a marketing plan and understanding customers. Finally the competitive strategy component of the subject will focus on the topics of gains from trade, how to price and how to understand and change the competitive environment.

This subject will give an overview of the tools required to operate successfully in an organisational environment. The focus of the subject is the internal workings of an organisation and specifically addresses three main areas: working with people, managing budgets and understanding basic accounting, and managing processes and projects.

As a scientist, it is not only important to be able to experiment, research and discover, it is also vital that you can communicate your research effectively in a variety of ways. Even the most brilliant research is wasted if no one knows it has been done or if your target audience is unable to understand it.

In this subject you will develop your written and oral communication skills to ensure that you communicate your science as effectively as possible. We will cover effective science writing and oral presentations across a number of formats: writing a thesis; preparing, submitting and publishing journal papers; searching for, evaluating and citing appropriate references; peer review, making the most of conferences; applying for grants and jobs; and using social media to publicise your research.

You will have multiple opportunities to practice, receive feedback and improve both your oral and written communication skills.

Please note: students must be undertaking their own research in order to enrol in this subject.

This subject involves completion of an 80-100 hour science or technology work placement integrating academic learning in science areas of study, employability skills and attributes and an improved knowledge of science and technology organisations, workplace culture and career pathways. The placement is supplemented by pre- and post-placement classes designed to develop an understanding of science and technology professions, introduce skills for developing, identifying and articulating employability skills and attributes and linking them to employer requirements in the science and technology domains. The placement will draw on students’ specific discipline skills associated with the science core of their degree. Pre-placement seminars will also include consideration of career planning and professional skills.

Students will be responsible for identifying a suitable work placement prior to the semester, with support of the Subject Coordinator. In the semester prior to your placement you should attend Careers & Employment (C&E) employment preparation seminars and workshops as well as accessing other C&E resources to assist you in identifying potential host organisations http://careers.unimelb.edu.au .You will need to commence your approaches to organisations at least 4 weeks before the placement. More information is available in the Subject Guide. Placements must be approved by the Subject Coordinator. If you have problems finding a placement you should approach the Subject Coordinator.

On completion of the subject, students will have completed and reported on a course-related project in a science or technology workplace. They will also have enhanced employability skills including communication, interpersonal, analytical and problem-solving, organisational and time-management, and an understanding of career planning and professional development.