The Sum of the Parts: Large-Scale Modeling in Systems Biology
Gross et al. (2017): The Sum of the Parts: Large-Scale Modeling in Systems Biology
Some researchers have made attempts to reduce biological system to its parts:
Part-whole reductionism assumes that there is a "higher level" of the system as a whole and a “lower” level of the parts of the system, and it is often summarized by saying that the system is "nothing but the sum of its parts."
However, the authors of this paper argue why they think that a biological system cannot be understood "bottom-up":
Whole-cell models can be used to discover properties arising at the interfaces of dynamically coupled processes within a biological system, thereby making more apparent what is lost through decomposition. Similarly, multi-scale modeling highlights the relevance of macroscale parameters and models and challenges the view that living systems can be understood "bottom-up."
As the editor wrote:
This contribution [...] focuses on the promises and possible pitfalls of large-scale modelling in systems biology, from both a practical and a theoretical point of view. [...] Gross and Green distinguish two types of reductionisms: "modular reductionism" (the claim that living systems consist of individual functional sub-units) and "bottom-up reductionism" (the claim that biological phenomena can and should be studied at the molecular level, understood as the "fundamental" biological level). They show how systems biology questions both forms of reductionism. To this end, they use two examples: whole-cell modelling of a very "simple" bacterium (from a piece published in 2012) [M. genitalium], and multi-scale computer modelling of the human heart.
The authors examine two kinds of "heuristic strategies":
Important heuristics in molecular and cell biology are what Bechtel and Richardson ([1993] 2010) identified as the twin strategies of decomposition and localization. Decomposition starts from the idea that the activity of a system is a product of component functions. Localization then consists in mapping those component functions on structural parts of the system.
Moreover, they write about a strategy named "recomposition":
Recomposition refers to the investigation of whether the postulated component operations, given appropriate information about organizational features and environmental conditions, will yield the systemic behavior. In this context they highlight the increasing application of network models and computational simulations that are central methods in systems biology.
The authors further explain why computer models of biological systems have to be simplified:
If one simply reproduces everything that happens inside the cell on a computer, then the model will be as intractable as the system itself. Such projects have been criticized also by scientists, including systems biologists themselves. They argue that it is doubtful whether one could incorporate, in practice or even in principle, the "astronomical" number of individual interactions, given the computational demands this would entail (Noble 2012). To get a rough idea, Bassingthwaighte et al. (2009) estimate that if determined via quantum mechanical calculations, the process of protein folding inside a cell would by itself require months of computation on the fastest parallel computers currently available.
A paper by Karr et al. from 2012 described a model of a whole cell. This model was composed of several submodels:
The submodels correspond to processes that describe six areas of cell biology: transport and metabolism, replication and maintenance of DNA, synthesis and maturation of RNA molecules, synthesis and maturation of proteins, cytokinesis (the physical division of the cytoplasm at the end of the cell cycle), and interaction with the host organism. Essentially, each process can be represented as a set of chemical reactions that convert chemical substrates (inputs) into products (outputs) using enzymatic catalysts. [...] In the process of model integration, Karr et al. drew on the assumption that the processes by which different functional modules interact can be described on a longer time scale than the processes occurring within each module. Thus, the modeling strategy is based on a temporal decomposition of functional modules but only for short time-scales[.]
The model basically works by solving several differential equations. Almost a thousand databases were consulted to design the model. And yet it is not complete:
Many processes in the whole-cell model are simply "black-boxed" or represented in very coarse-grained ways.
The model in fact made some predictions which had not been observed in vivo before:
With regard to integrated behavior, the model makes some interesting predictions that the authors refer to as model-driven discoveries. They noticed, for instance, that the overall length of the cell cycle in the simulation showed considerably less variability than the single stages of the cycle alone. Thus cell cycle length appears to be regulated in some way, even though no regulation has explicitly been incorporated in the model. By analyzing the output of their simulations, Karr et al. found that the availability of single DNA nucleotides seemed to be responsible for the phenomenon. They observed that the lengths of two stages of the cell cycle, replication initiation and replication, are inversely related to each other. If replication initiation is slow, a large pool of nucleotides builds up which in turn speeds up the subsequent replication process. [...] While predictions of this kind will have to be further investigated experimentally, they point to a way in which whole-cell modeling might not only correct scientists' ideas about specific mechanisms, but lead to a revised picture of biological organization.
The authors conclude their analysis of Karr's publication with the words:
Karr et al.'s model provides one of the few examples of an attempt to completely recompose the information generated in molecular biology, biochemistry and systems biology. Such models carry the potential for uncovering aspects of systems that are hidden by partial representations, or what some systems biologists call "dynamic interfaces" between processes. Important aspects of such interfaces are often discovered through model failure, i.e. through failed attempts to directly integrate different processes. It is thus important to consider the value of such models not as a representational end product but as an epistemic tool with which biologists can arrive at new discoveries and probe their ideas of biological organization.
In the next chapter the authors write about cardiac modelling and first describe the "tyranny of scales" problem:
The problem refers to the observation that no single mathematical model is sufficient to capture behaviors at all spatial scales. Many physical properties and the concepts used to describe them vary with scale. For instance, whereas surface properties are typically negligible when developing macroscale chemical models due to their minor impact at this scale, they dominate the dynamics of materials and particles at the nanoscale (Bursten 2015). Because the significance and conceptual stability of aspects such as surface tension is multi-valued across scales (Wilson 2012), the modeler must combine mathematical models that rely on different theoretical assumptions about the target system.
Furthermore, they write about continuum and structural models and that:
Bridging the gap between continuum and structural models, and between deterministic and stochastic models, is a hard challenge. [...] Continuum models and discrete models, as well as stochastic and deterministic models, must be combined through careful attention and decision-making on which aspects are dominant (or can be neglected) at different scales. Boundary conditions play a crucial role for this purpose. [...] Ultimately, the aim is to develop an integrated system of models into a whole heart simulation.
They argue why reductionism does not work in cardiac modelling:
In the case examined here, the heart rhythm is generated by electrical potential in nerve cells and is constituted by the so-called Hodgkin cycle of oscillating ionic current across a membrane. Oscillations occur via gating of protein channels. However, the gating of ion channels is also determined by the cell voltage (a cell-level parameter) that is influenced by intercellular coupling and the dynamics of other processes across the membrane. Accordingly, the behavior of the system cannot be understood from an analysis of the constituents in isolation or at the lowest scale.
The next chapter is about large-scale modelling. The authors mention a couple of challenges and conclude that:
Despite these challenges, large-scale modeling may be worthwhile because there may be no other way to gain insight into the integrated nature of living systems and to understand complex diseases. [...] We conclude from our analysis that large modeling projects should be seen as specific tools that hold the potential to assess and test our assumptions about biological organization and complexity. They can be used to detect reductionistic biases even though they are not without bias themselves. They do not obviate the need for small scale experiments and partial models, but they can complement more traditional techniques in order to get closer to the way in which biological systems function as wholes.