We will show that there is a strong form of emergence in cell biology. Beginning with C.D. Broad’s classic discussion of emergence, we distinguish two conditions sufficient for emergence. Emergence in biology must be compatible with the thought that all explanations of systemic properties are mechanistic explanations and with their sufficiency. Explanations of systemic properties are always in terms of the properties of the parts within the system. Nonetheless, systemic properties can still be emergent. If the properties of the components (...) within the system cannot be predicted, even in principle, from the behavior of the system’s parts within simpler wholes then there also will be systemic properties which cannot be predicted, even in principle, on basis of the behavior of these parts. We show in an explicit case study drawn from molecular cell physiology that biochemical networks display this kind of emergence, even though they deploy only mechanistic explanations. This illustrates emergence and its place in nature. (shrink)

Systems biology is a vigorous and expanding discipline, in many ways a successor to genomics and perhaps unprecendented in its combination of biology with a ...

Mechanistic models in molecular systems biology are generally mathematical models of the action of networks of biochemical reactions, involving metabolism, signal transduction, and/or gene expression. They can be either simulated numerically or analyzed analytically. Systems biology integrates quantitative molecular data acquisition with mathematical models to design new experiments, discriminate between alternative mechanisms and explain the molecular basis of cellular properties. At the heart of this approach are mechanistic models of molecular networks. We focus on the articulation and development of mechanistic (...) models, identifying five constraints which guide the articulation of models in molecular systems biology. These constraints are not independent of one another, with the result that modeling becomes an iterative process. We illustrate the use of these constraints in the modeling of the mechanism for bistability in the lac operon. (shrink)

The progress of the molecular biosciences has been so enormous that a discipline studying how cellular functioning emerges out of the behaviors of their molecular constituents has become reality. Systems biology studies cells as spatiotemporal networks of interacting molecules using an integrative approach of theory , experimental biology , and quantitative network-wide analytical measurement . Its aim is to understand how molecules jointly bring about life. Systems biology is rapidly discovering principles governing the functioning of molecular networks and methods to (...) model and measure them. The application of the conceptual strength of systems sciences such as physics and engineering are opening up new ways of experimentally studying the design and functioning of molecular networks. Typically, in such studies models are used to highlight principles, design experiments, or to act in a predictive mode to specifically modify cellular behavior. In this trend article, theoretical approaches being applied in systems biology will be discussed. A number of books have recently appeared that deal with theoretical aspects of systems biology. In this article they are highlighted to facilitate the communication of systems biology to a broader audience. (shrink)

Microbial systems biology has made enormous advances in relating microbial physiology to the underlying biochemistry and molecular biology. By meticulously studying model microorganisms, in particular Escherichia coli and Saccharomyces cerevisiae, increasingly comprehensive computational models predict metabolic fluxes, protein expression, and growth. The modeling rationale is that cells are constrained by a limited pool of resources that they allocate optimally to maximize fitness. As a consequence, the expression of particular proteins is at the expense of others, causing trade‐offs between cellular objectives (...) such as instantaneous growth, stress tolerance, and capacity to adapt to new environments. While current computational models are remarkably predictive for E. coli and S. cerevisiae when grown in laboratory environments, this may not hold for other growth conditions and other microorganisms. In this contribution, we therefore discuss the relationship between the instantaneous growth rate, limited resources, and long‐term fitness. We discuss uses and limitations of current computational models, in particular for rapidly changing and adverse environments, and propose to classify microbial growth strategies based on Grimes's CSR framework. (shrink)

After more than a century of research on glycolysis, we have detailed descriptions of its molecular organization, but despite this wealth of knowledge, linking the enzyme properties to metabolic pathway behavior remains challenging. These challenges arise from multi‐layered regulation and the context and time dependence of component functions. However, when viewed as a system that functions according to the principles of supply and demand, a simplifying theoretical framework can be applied to study its regulation logic and to assess the coherence (...) of experimental interpretations. These principles are universally applicable, as they emphasize the common metabolic tasks of glycolysis: the provision of free‐energy carriers, and precursors for biosynthesis and stress‐related compounds. Here we will review the regulation of multi‐tasking by glycolysis and consider how an understanding of this central metabolic pathway can be pursued using general principles, rather than focusing on the biochemical details of constituent components. (shrink)

Biochemistry and molecular biology have been focusing on the structural, catalytic, and regulatory proper- ties of individual macromolecules from the perspective of clarifying the mechanisms of metabolism and gene expression. Complete genomes of ‘primitive’ living organisms seem to be substantially larger than necessary for metabolism and gene expression alone. This is in line with the findings of silent phenotypes for supposedly important genes, apparent redundancy of functions, and variegated networks of signal transduction and transcription factors. Here we propose that evolutionary (...) optimization has been much more intensive than to lead to the bare minima necessary for autonomous life. Much more complex organisms prevail. Much of this complexity arises in the nonlinear interactions between cellular macromolecules and in subtle differences between paralogs (isoenzymes). The complexity can only be understood when analyzed quantitatively, for which quantitative experimentation is needed in living systems that are as simple and manipulatable as possible, yet complex in the above sense. We illustrate this for the glutamine synthetase cascade in Escherichia coli. By reviewing recent molecular findings, we show that this cascade is much more complex than necessary for simple regulation of ammonia assimilation. Simulations suggest that the function of this complexity may lie in quasi-intelligent behavior, including conditioning and learning. (shrink)