One of the grand goals of synthetic biology is to become able to design and construct cells ab initio, bottom up, from their basic building blocks (as opposed to just tweaking the already existing cells). For this, a good understanding is needed about how the cells function. Studying the biological cells in a lab is, certainly, one key aspect of building that understanding, but it has also serious limitations: experiments tend to be slow, costly, difficult, and usually constrained by practicalities into exploring only a small region of the large space of theoretically possible cells. Therefore, it is highly desirable to complement the experiments with a theoretical framework of how the cells function, as well as with computational modelling and design tools for exploring the fundamental possibilities and limits of cells.
The creation of such a set of theoretical and computational tools is exactly the goal that we have set out to achieve. It is certainly possible to approach this goal in different ways, and different scientists have indeed taken different routes towards it. Some build cell models that rely heavily on detailed physical modelling — this can give a high level of realism, but is also computationally very difficult and can be too detailed for seeing the higher-level general functional principles of a cell. Others build models that include an abstraction of some of the key parts of the cell, such as metabolic networks, but leave out some other fundamental aspects, such as cell geometry. Our preference has been to try to include all fundamentally important subsystems of a cell, but at the same time to keep the level of abstraction sufficiently high to maintain the comprehensibility of the models as well as make the models easily computable on a personal computer, and, additionally, to make the framework sufficiently flexible so that the level of detail, abstractness and complexity can be changed as necessary.
So, over many (tens of) years we have been developing such a theoretical framework of the functioning of bacterial cells, complemented with computational models based on that theory, and now we are in the process of publishing a series of papers about this work. The modelling framework itself is flexible enough to allow reaching the complexity of full genome-scale cell models and even bacterial consortia, but in the interest of clarity and soft introduction, the models in our first papers are highly simplified and easily graspable.
The first paper, published a year ago, focused on the self-reproduction processes in proto-cells (doubling of their components) composed of different combinations of cellular subsystems.
In the current paper, we extend the detailed analysis of structural and functional peculiarities of self-reproduction processes to unit cells of the Cooper-Helmstetter-Donachie cell cycle theory. The unit cell is an important concept for cell design. We show that:
Our modelling framework allows to calculate physiological parameters (numbers of cell components, flux patterns, cellular composition, etc.) of unit cells, including also unit cell mass that determines the DNA replication initiation conditions.
Unit cells might have additional cell (cushioning) components that are responsible not only for carrying out various special functions, but also for regulating cell size and stabilizing the growth of cells.
The optimal productivity of the synthesis of cushioning components (useful cellular load) is observed at doubling time approximately two times longer than the minimal doubling time of the unit cells.
The productivity of the synthesis of cushioning components can be considered to characterize also cell's capacity to synthesize biotechnologically useful products, and, therefore, our modelling framework allows to find optimal parameter regions also for biotechnologically useful synthesis. The following figure shows the productivity optima in two different types of unit cells (a more detailed description can be found in the published article):
3D and 2D graphs showing cushioning protein synthesis productivity values for two cell models.
Overall, we hope that the modelling framework we have developed helps to uncover and illuminate the fundamental principles of cells, and, after we present in upcoming publications also the more advanced model families of this framework, provides also a solid foundation and tools for computational cell design.