Mark Burkitt

Using computational modeling to investigate sperm navigation and behavior in the female reproductive tract

The processes by which individual sperm cells navigate the length and complexity of the female reproductive tract and then reach and fertilize the oocyte is fascinating. Numerous complex processes potentially influence the transport of spermatozoa within the tract, resulting in a regulated supply of spermatozoa to the oocytes at the site of fertilization. Despite significant differences between species, breeds, and individuals, these processes converge to ensure that a sufficient number of high quality spermatozoa reach the oocytes, resulting in successful fertilization without a significant risk of polyspermy. Different factors, such as the physical complexity of the oviductal environment, changing swimming patterns, capacitation, chemotactic and thermotactic attraction, attachment and detachment from the oviductal epithelium, interactions with local oviductal secretions, individual variations in spermatozoa and subpopulations, peristaltic contractions, and the movement of fluid have all been theorized to influence the transport of spermatozoa to the site of fertilization. However, the predominance of each factor is not fully understood. Computational modeling provides a useful method for combining knowledge about the individual processes in complex systems to help understand the relative significance of each factor. The process of constructing and validating an agent-based computational model of sperm movement and transport within the oviductal environment is described in this report. Spermatozoa are modeled as individual cells with a set of behavioral rules defining how they interact with their local environment and regulate their internal state. The inclusion or potential exclusion of each factor is discussed, along with problems identifying parameters and defining behavioral rules from available literature. Finally, the benefits and limitations of the model are described.

Constructing Complex 3D Biological Environments from Medical Imaging Using High Performance Computing

Extracting information about the structure of biological tissue from static image data is a complex task requiring computationally intensive operations. Here, we present how multicore CPUs and GPUs have been utilized to extract information about the shape, size, and path followed by the mammalian oviduct, called the fallopian tube in humans, from histology images, to create a unique but realistic 3D virtual organ. Histology images were processed to identify the individual cross sections and determine the 3D path that the tube follows through the tissue. This information was then related back to the histology images, linking the 2D cross sections with their corresponding 3D position along the oviduct. A series of linear 2D spline cross sections, which were computationally generated for the length of the oviduct, were bound to the 3D path of the tube using a novel particle system technique that provides smooth resolution of self-intersections. This results in a unique 3D model of the oviduct, which is grounded in reality. The GPU is used for the processor intensive operations of image processing and particle physics based simulations, significantly reducing the time required to generate a complete model.

Computational modelling of maternal interactions with spermatozoa: potentials and prospects

Understanding the complex interactions between gametes, embryos and the maternal tract is required knowledge for combating infertility and developing new methods of contraception. Here we present some main aspects of spermatozoa interactions with the mammalian oviduct before fertilisation and discuss how computational modelling can be used as an invaluable aid to experimental investigation in this field. A complete predictive computational model of gamete and embryo interactions with the female reproductive tract is a long way off. However, the enormity of this task should not discourage us from working towards it. Computational modelling allows us to investigate aspects of maternal communication with gametes and embryos, which are financially, ethically or practically difficult to look at experimentally. In silico models of maternal communication with gametes and embryos can be used as tools to complement in vivo experiments, in the same way as in vitro and in situ models.

Modelling sperm behaviour in a 3D environment

The processes used by mammalian sperm to find the egg in the female reproductive tract are highly complex and poorly understood. Due to ethical and practical limitations, observing and measuring the behaviour of sperm within a live animal is difficult if not impossible without influencing their behaviour. One way to help understand the processes involved is through the use of computational modelling. The methodology used to construct the first agent based model of sperm movement within a 3D model of the mammalian oviduct is presented. The different processes represented within the model and the implementation of those processes is described. The simulation runtime is significantly reduced using collision detection optimisation and by harnessing the parallel processing power of the GPU to make concurrent simulation replicates. Validation of the model and the potential uses of the model once fully validated are described.

The Mood and Memory of Believable Adaptable Socially Intelligent Characters

In this paper a computational model for believable adaptable characters is presented, which takes into account several psychological theories: five factors model of personality [1], the pleasure arousal dominance[2] and the social cognitive factors [3] to create a computation model able to process emotionally coded events in input, alter the characters mood, the memory associate with the set of entities connected with the event, and in the long run the personality; and produce an immediate emotional reaction in the character, which might or might not be displayed according to the social cognitive factors, the goal of the character and the environment in which the event is taking place.

3D Modelling of Complex Biological Structures: The Oviduct

A novel technique using a particle system constrained by Newtonian forces is presented for the algorithmic construction of small scale, complex 3D biological structures based on real world biological data. This allows models of structures too small to be accurately recreated using medical imaging technologies such as Magnetic Resonance Imaging (MRI) to be created. The resulting model provides a geometrically realistic 3D environment which can be used to study the biological interactions which occur within. The technique is used to create a model of an oviduct, but could also be applied to similar organs such as the colon. The model is validated using measurements and visual comparisons from biological data. Finally, the technique is implemented using single-core and multi-core CPU techniques and using GPU acceleration. The performance of each implementation is then compared.

Agent-Based High-Performance Simulation of Biological Systems on the GPU

Simulation of biological systems are computationally demanding due to the large scale reaction networks of bacterial cells. This scalability issue escalates, in particular, when bacterial colonies, formed by many individual cells, are simulated. Agent-based modelling environments on parallel architectures, such as the FLAME (Flexible Large-scale Modelling Environment) framework, are good candidates to simulate such systems, but due to the complex nature of cellular systems more advance technology is needed. In this paper, we utilise FLAME GPU, extending FLAME with a high performance graphics processing unit, to simulate a pulse generator, a typical multicellular synthetic biology system. This system is specified using a membrane computing model. We also illustrate the performance improvement of FLAME GPU over FLAME.