Cardiac Development Stages

1. Cardiac Development Stages • Carnegie Stages are commonly used to outline discrete points during embryogenesis. • Define physical features of the embryo at specific times during its development • Cardiac development begins at Stage 9 and continues through to Stage 23

2. Carnegie Stage 9 • Cardiac primordium forms • This cluster of cells eventually forms the heart. • Primary heart field, secondary heart field, myocardium, endocardium and neural tube

3. Carnegie Stages - Continued

4. Carnegie – Stage 23 • By stage 23, the heart is structurally developed • Shunts still exist in the body, mostly bypassing the liver and lungs • These close at birth, restoring normal circulation

5. 'Normal' Foetal Heart • At the end of normal development, the heart will have all major vessels and chambers. • Growth continues through the remainder of the pregnancy.

6. Heart – After the Birth • The foramen ovale closes, separating the atria • The ductus arteriosus closes, stopping the bypass of deoxygenated blood from the lungs.

7. Plan • Model development process for a normal heart, compare with a similar model for Tetralogy of Fallot. • Use differences between results from model to highlight the time periods during which structural differences begin occurring • Increase the detail of the model around these areas, repeating the comparisons at each stage.

8. Problems • Although development is well catalogued for the normal heart, there remains little definite information about the Tetralogy development • Specifics about gene involvement still unclear for either process, and there is an incredible amount of complexity behind morphogenesis • Existing information obtained from homologues such as mice and chicks, with different genetic information

9. Multiscale Modelling • There are a number of possible approaches to modelling a complex system. • Multiscale modelling is the process of creating a simulation to accurately represent the important features of a system at different spatial or temporal levels • The challenge lies in relating the different scales together to form a complete, working model

10. Multiscale Modelling Time Scales • Complete Pregnancy • Cardiac Development • Carnegie Stages • Cellular Growth and Differentiation (Mitosis) • Genetic and Chemical Signalling • Model will start spanning the entire cardiac development period and have details filled in at the shorter time-scales as needed

11. Multiscale Modelling – Time Domain • It is possible to break the cardiac development process into discrete stages • Using the Carnegie stages as a starting point it should be possible to model the processes behind the cardiac development and utilise said model for identifying potential causes for congenital heart defects.

12. Multiscale Modelling – Time Domain • Although not usually associated with biological process modelling, a finite state machine may be suitable for organising the processes driving cardiac development • As suggested by the name, FSMs are discrete models, with clearly defined conditions and pathways between them – they only have a finite number of states that they can be in at any point

13. Multiscale Modelling – Finite State Machine

14. Finite State Machines • Any FSM is comprised of three elements • States (reflects changes in the system from the initial conditions) • Transitions (shows the changes in states and defines the conditions for any change) • Actions (describes an activity to be undertaken)

15. Finite State Machines • All possible conditions must be defined else the model will fail to represent reality • All possible transitions must be made possible, which can lead to an immensely complex state machine • Discrete modelling process, no references to the actual time needed for a stage

16. Use of UML for State Machines • Expands on the traditional state machine architecture • Allows the use of hierarchical states within states, and extended global states which may be independent of the current location in the FSM • Processes can be orthogonalised if they have nothing in common, reducing the complexity of the transitions

17. Use of UML for State Machines • Example of a simple UML state machine • The boxes are states, the top half contains the actual state and the bottom half the processes within the state

18. UML State Machines • Extended States • Allows the usage of variables within the machine (for example a counter monitoring number of cycles performed) • Guard Conditions • Enable transitions only when specific criteria are met. • Needed for extended state usage, for example one option may become available only after 1000 steps but the actual steps don't matter • Hierarchically nestable states • Allows extremely complex systems to be built up at varying levels of detail • Therefore can create an general overview of a system, outlining the most general details, and use the ability to nest states to provide more detail if needed

19. UML State Machines • Orthogonal regions • If a particular state contains a number of sub-states which have no relationship it is possible to separate them. • This increases the simplicity of the final model as there are fewer needed transitions to model. • States are only truly orthogonal if there is no overlap at all. For example, it is possible to separate the number pad and the letter pad on a keyboard into orthogonal sections due to their separate special lock functions (“caps lock” forces capital letters, “num lock” enables the alternative functions and arrows on the number pad) • In the above example both sections belong to the “keyboard” state machine yet due to having no processes in common can be considered and evaluated separately

20. Standard State Machine • For comparison's sake, above is the same process shown using a standard Finite State Machine • There are an increased number of transitions for every stage to permit the global clear and off functions to be used

21. UML State Machine • Above is a UML-based state machine showing the effects of the two global commands (clear and power off) • Note how they are outside of the internal states and can be triggered at any time the calculator is in the 'on' state, regardless of what else is happening

22. State Machines for Biological Modelling? • Often, there are known stages in biological processes, with fairly well defined transitions between them. • In addition, these stages can often be broken down into having more detailed elements or can be considered to contain a number of sub-processes • One example that illustrates this rather well is the cell cycle

23. Cell Cycle Finite State Machine • At the most basic level, the cell cycle consists of two main stages, the Interphase (where individual cells grow and prepare for multiplication) and the Mitotic Phase (where the cell divides into two new cells)

24. Interphase Sub-State Machine • However, each of the two main stages can be considered to contain their own processes • Here, the interphase consists of two growth phases and a chromosome synthesis stage • The top level model would not be able to move on until the sub-level state machine reached its natural end

25. Mitosis Sub-State Machine • Similarly, the Mitotic Phase of the cell cycle can be broken down into sub-states which must occur in order before the cell cycle can proceed. • Each of the sub-states can be further broken down into more finely detailed state machines if required.

26. Using FSM for Cardiac Development • Finite state machines do not depend on a real timer, they are discrete systems whose states change depending on conditions • However, by controlling the available transitions between states and the processes during them it should be possible to model the process of cardiac development

27. Cardiac Development FSM • May be possible to use the Carnegie Stages as known states in a potential FSM-type model • Difficulties may occur when adding in detail at finer levels, as some processes may have cascading effects that are non-trivial to model. • In addition to this, not all of the effects of certain genes are known, nor are all of the processes controlling expression.