The Interlacing of Upward and Downward Causation in Complex Living Systems: On Interactions, Self-Organization, Emergence and Wholeness

Luciano Boi

There is therefore no computer into which we could insert the DNA sequences to generate life, other than life itself. Far from being just a transient vehicle, the organism itself contains the key to interpreting its DNA, and so to give it meaning.

(Noble 2010: 1128)

The Challenge of Biological Complexity: Self-Organization, Emergence of Novelties and the Integration of the Parts into a Whole

The major challenge facing systems biology is complexity. Systems biology defines and analyzes the interrelationships of all the elements in a functioning system in order to understand how the systems work. At the core of the challenge is the need for a new approach, a shift from reduc- tionism to an integrative perspective. More precisely, what is needed is a conceptual framework for systems biology research. The concept of a complex system, i.e., a system of subsystems each belonging to a certain category of living entities such as proteins, tissues, organs, etc., needs first to be defined in general mathematical terms. It is rather clear, however, that, for a deeper understanding, in systems biology investigations should go beyond building numerical mathematical or computer models— important as they are. Biological phenomena cannot be predicted with the same degree of numerical precision as in classical physics. Explanations in terms of how the categories of systems are organized to function in ever-changing conditions are more revealing. Non-numerical mathematical tools are appropriate for the task. Such a categorical perspective led us to propose that the core of understanding in systems biology depends on the search for organizing principles, rather than just on the construction of predictive descriptions (i.e., models) that exactly outline the evolution of systems in space and time.

Biological systems are difficult to study because they are complex in several ways. One of the most important aspects of biological complexity is multi-levelness: the structural and functional organization of the human body into tissues and organs systems composed of cells. From molecules to organs, levels are interrelated and interdependent, so that the organism is able to conserve and adopt the integrity of its structural and functional organization against a setting of continuous changes within the organism and its environment. This capacity, usually described as “robustness”, is a consequence of nonlinear1 spatio-temporal intra- and inter-cellular interactions.

To understand disease-relevant processes, we therefore require methodologies that allow us to study nonlinear spatio-temporal systems with multiple levels of structural and functional organization. Nonlinear dynamics plays an important role for the explanation of highly nonlinear biological behaviors, such as biochemical and cellular rhythms or oscillations. According to biodynamics, biological systems are open systems of nonlinearly interacting elements. Consequently, the field of biodynamics might be defined as the study of the complex net of nonlinear dynamical interactions between and among molecules, cells and tissues, which give rise to the emergent functions of a biological system as a whole. The work of nonlinear dynamical interactions favor the self-organization of emergent macroscopic patterns, including temporal oscillations and spatio-temporal wave patterns, especially in chemical and biological systems. Numerous examples are now known at all levels of biological organization. The formation of biological rhythms and oscillatory dynamical states of different periodicities plays a fundamental role in living organisms.

The processes that underlie cellular oscillators are organized in complexly coupled biochemical networks, wherein feed-forward and feedback information flows provide the links between the different levels in the hierarchy of cell biochemical network organization. Such networks are also central components of the cellular machinery that controls biological signaling. Recently scientists were able to investigate the properties of biological signaling networks, such as their capacity to detect, transduce, process and store information. It was found that cellular signaling pathways may also exhibit properties of emergent complexity. Such findings demonstrate the impossibility of predicting the dynamics of cellular signal transduction processes only on the basis of isolated signaling molecules and their individual microscopic actions. In order to develop an integrative, dynamical picture of biological signaling processes, therefore, it will be necessary to characterize the nonlinear relationships among the different molecular species making up the biochemical reaction networks, which control all aspects of cellular regulation as, for example, RNA transcriptional control and cellular division.

Self-organization, i.e., the capacity of any complex living organism to intrinsically produce new properties and behaviors of organization and regulation, cannot be addressed by purely reductionist approaches. Living organisms present the following two fundamental features. (1) They are thermodynamically open systems; that is, they are in a state of permanent flux, continuously exchanging energy and matter with their environment. (2) They are characterized by a complex organization, which results from a vast network of molecular and cellular interactions involving a high degree of nonlinearity. Under appropriate conditions, the combination of these two features, openness and nonlinearity, enables complex systems to exhibit properties that are emergent or self-organizing. In biological systems, such properties may express themselves through the spontaneous formation, from (almost) random molecular interactions, of long-range correlated, macroscopic dynamical patterns in space and time—the process of self-organization. The dynamical states that result from self-organizing processes may have features such as excitability, bi-stability, periodicity, chaos or spatio-temporal patterns formation, and all of these can be observed in biological systems.

An important scientific and philosophical point, concerning the relationship between the complex and dynamical notions of emergence and system, deserves here a first clarification. All emergent properties are systemic in the sense that they pertain to the specific higher-order overall level of the organization and regulation of some complex living systems. This defines, so to say, a weak meaning of the word “emergence”. Furthermore, we say that properties are emergent (in the strong sense of the word) if they provide the system with new causal powers, and, then, if the behaviors they produce at the systemic level cannot be predicted from lower-level properties. Finally, one can say that any living entity forms a system if it exhibits emergent properties, in the strong sense of the world we just defined.

Self-organizing processes may give rise to new, unexpected properties and behaviors in living systems, also called emergent properties. Emergent properties can be defined as properties that are possessed by a dynamical system as a whole but not by its constituent parts. Otherwise stated, emergent phenomena are phenomena that are expressed at higher levels of organization in the system but not at the lower levels. The concept of self-organization implies the existence of a dynamical interdependence between the molecular interactions at the microscopic level and the emerging global structure at the macroscopic level (see Misteli 2001; Karsenti 2008). In other words, there is an active combination of upward and downward processes. The upward processes show that, under non-equilibrium constraints, molecular interactions tend to spontaneously synchronize their behavior, which initiates a collective, macroscopically ordered state. At the same time, the downward process shows that the newly forming macroscopic state acts upon the microscopic interactions to force further synchronizations. Through the continuing, energy-driven interplay between microscopic and macroscopic processes, the emergent, self-organizing structure is then stabilized and actively maintained.

The above argument shows that the origins and dynamics of emergent, macroscopic patterns, particularly in biological systems, cannot be simply deduced from the sum of the individual actions of the system’s microscopic elements. What is needed is an analysis of the system’s collective, macroscopic dynamics, which result from the complex web of molecular interactions between elements.

In despite of these theoretical and epistemological advances in the attempt to reach a better understanding of biological systems, the reductionist approach remains dominant, and systems biology is often seen as no more than integration of diverse data into models of systems. Reductionism in biology, and especially in biochemistry, has consisted in separating cells into their components, which were then separated into smaller components, and then studied in isolation. The reductionist stage was certainly necessary, but the time has come for moving beyond this, even beyond studying the interactions of the components with one another, because all of them are parts of a whole, and their presence in the whole can be understood only by taking into account the need for the whole. As it was recently emphasized by many scientists (Bains 2001; and A. Cornish-Bowden and M. L. Cardenas 2005), this way of thinking needs to be changed if systems biology is to lead to an understanding of life and to provide the benefits that are expected from it. The emphasis ought to be on the need for the system as a whole for understanding the components, not the converse. For example, general properties of metabolic systems, such as feedback inhibition, can be properly understood by taking into account supply and demand, i.e., requirements of the system as a whole (Cornish-Bowden and Cardenas 2005).

For a long time, and especially in the last sixty years, biological science has privileged an analytical method, i.e., the splitting up of the living systems into ever-smaller units. Even systems biology has been recently characterized by most molecular biologists as the integration of knowledge from diverse biological components and data into models of the system as a whole. In fact, this sort of definition is entirely reductionist, and makes systems biology almost a euphemism for the type of approach that systems biology theorists criticized: instead of using a view of the whole system as a way to understand its components, it seeks to explain the whole in terms of a vast list of components.

To show the effective causal role played by wholeness and systemic properties in biology, let us consider the following three examples (here we follow closely Cornish-Bowden and Cardenas 2005). (1) The first example concerns the many cases of cooperative feedback inhibition of metabolic pathways, which are now well known, such as the inhibition of asparto- kinase in bacteria by lysine. This type of observation is often explained by supposing that the biosynthetic flux is regulated by this feedback inhibition, and it would be subject to uncontrolled variations if there were no feedback loop. However, as the previously mentioned authors pointed out, this explanation goes wrong, because fluxes can be controlled perfectly well without feedback inhibition, either cooperative or not. The need comes not from flux control, but from concentration control: without feedback inhibition in this pathway, the rate at which lysine would be syn- thetized would still match the rate at which it is used in protein synthesis, but there would be huge and potentially damaging variations in the concentration of lysine and the intermediates in the pathway from aspartate. This sensitivity of metabolite concentrations to perturbations has major implications for the regulatory design of metabolism in living organisms. To understand this, it is necessary to represent biosynthesis pathways in a way that allows for analysis in terms of supply and demand, namely, in a more complete way than the one that is usually provided in textbooks of biochemistry. These typically show, for example, the biosynthesis of lysine as a series of reactions that begin with aspartate and end with lysine. However, lysine is not in any meaningful sense the end-product: it is produced not as an end in itself, but as a starting material for other processes, principally, in this case, protein synthesis. As protein synthesis accounts for most of the metabolic demand for lysine, it determines the rate at which it needs to be synthesized from aspartate. Omitting the conversion of lysine into protein from the pathway means omitting the one step that explains the feedback inhibition of aspartokinase by lysine. This inhibition cannot be explained solely in terms of the components involved, aspartokinase and lysine, but requires consideration of the whole system, including protein synthesis.

(2) The second example concerns the failure of genome sequencing to provide an effective explanation of how living organisms develop and evolve. There are at least two fundamental reasons for this failure. (i) The first is related to the essential fact that the expression of a genome, i.e., its state of activity, stands beyond the gene sequences and depends much more upon the peculiar spatial organization of the genome into the chromatin and the chromosome. Moreover, the functional properties of genomes are strongly determined by their cellular organization. The functional relevance of spatial and temporal genome organization at three interdependent levels must be stressed: the organization of nuclear processes; the organization of chromatin into higher-order domains; and the spatial arrangement of chromosomes and genes within the nuclear space. Each of these levels has regulatory potential, and all are interdependent. There is increasing evidence that the higher-order, topological organization of the genomes exert a fundamental influence on their functional properties, and on many cellular processes, including expression and genome stability (for more details, see Cremer et al. 2006; Misteli 2007).

(3) The third example regards the relationship between genotype and phenotype. We know that for more than half a century, the prevalent “dogma” was to think that the genotype completely and unidirectionally determine sthe phenotype and hence the fate of any complex living organism. Now, to be more precise, the problem is not so much that genome sequences contain no phenotypic information, but that we do not have reliable methods for undertaking all of the steps involved in deducing a phenotype from them. “A list of putative gene products, or even a list of putative enzymes, is not a phenotype, and converting it into a phenotype requires the construction of plausible metabolic map, which then needs further work to convert it into a possible phenotype. Finally, the possible phenotype can only become a real phenotype when all relevant kinetic and regulatory properties are taken into account, together with information about how all the components are organized into a three-dimensional whole—even a four-dimensional whole, given that the times when different components are made may be just as important as where they are placed” (Cornish-Bowden 2006).

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