An abstract cell model that describes the self-organization of cell function in living systems

The principal aim of systems biology is to search for general principles that govern living systems. We develop an abstract dynamic model of a cell, rooted in Mesarovi? and Takahara's general systems theory. In this conceptual framework the function of the cell is delineated by the dynamic processes it can realize. We abstract basic cellular processes, i.e., metabolism, signalling, gene expression, into a mapping and consider cell functions, i.e., cell differentiation, proliferation, etc. as processes that determine the basic cellular processes that realize a particular cell function. We then postulate the existence of a 'coordination principle' that determines cell function. These ideas are condensed into a theorem: If basic cellular processes for the control and regulation of cell functions are present, then the coordination of cell functions is realized autonomously from within the system. Inspired by Robert Rosen's notion of closure to efficient causation, introduced as a necessary condition for a natural system to be an organism, we show that for a mathematical model of a self-organizing cell the associated category must be cartesian closed. Although the semantics of our cell model differ from Rosen's (M,R)-systems, the proof of our theorem supports (in parts) Rosen's argument that living cells have non-simulable properties. Whereas models that form cartesian closed categories can capture self-organization (which is a, if not the, fundamental property of living systems), conventional computer simulations of these models (such as virtual cells) cannot. Simulations can mimic living systems, but they are not like living systems.     

A relational theory of biological systems II

The general Theory of Categories is applied to the study of the (M, R)-systems previously defined. A set of axioms is provided which characterize “abstract (M, R)-systems”, defined in terms of the Theory of Categories. It is shown that the replication of the repair components of these systems may be accounted for in a natural way within this framework, thereby obviating the need for anad hoc postulation of a replication mechanism. A time-lag structure is introduced into these abstract (M, R)-systems. In order to apply this structure to a discussion of the “morphology” of these systems, it is necessary to make certain assumptions which relate the morphology to the time lags. By so doing, a system of abstract biology is in effect constructed. In particular, a formulation of a general Principle of Optimal Design is proposed for these systems. It is shown under what conditions the repair mechanism of the system will be localized into a spherical region, suggestive of the nuclear arrangements in cells. The possibility of placing an abstract (M, R)-system into optimal form in more than one way is then investigated, and a necessary and sufficient condition for this occurrence is obtained. Some further implications of the above assumptions are then discussed.     

Universally valid reduction of multiscale stochastic biochemical systems using simple non-elementary propensities

Biochemical systems consist of numerous elementary reactions governed by the law of mass action. However, experimentally characterizing all the elementary reactions is nearly impossible. Thus, over a century, their deterministic models that typically contain rapid reversible bindings have been simplified with non-elementary reaction functions (e.g., Michaelis-Menten and Morrison equations). Although the non-elementary reaction functions are derived by applying the quasi-steady-state approximation (QSSA) to deterministic systems, they have also been widely used to derive propensities for stochastic simulations due to computational efficiency and simplicity. However, the validity condition for this heuristic approach has not been identified even for the reversible binding between molecules, such as protein-DNA, enzyme-substrate, and receptor-ligand, which is the basis for living cells. Here, we find that the non-elementary propensities based on the deterministic total QSSA can accurately capture the stochastic dynamics of the reversible binding in general. However, serious errors occur when reactant molecules with similar levels tightly bind, unlike deterministic systems. In that case, the non-elementary propensities distort the stochastic dynamics of a bistable switch in the cell cycle and an oscillator in the circadian clock. Accordingly, we derive alternative non-elementary propensities with the stochastic low-state QSSA, developed in this study. This provides a universally valid framework for simplifying multiscale stochastic biochemical systems with rapid reversible bindings, critical for efficient stochastic simulations of cell signaling and gene regulation. To facilitate the framework, we provide a user-friendly open-source computational package, ASSISTER, that automatically performs the present framework.     

Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons

The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there exists a powerful computational framework for stochastic computations, probabilistic inference by sampling, which can explain a large number of macroscopic experimental data in neuroscience and cognitive science. But it has turned out to be surprisingly difficult to create a link between these abstract models for stochastic computations and more detailed models of the dynamics of networks of spiking neurons. Here we create such a link and show that under some conditions the stochastic firing activity of networks of spiking neurons can be interpreted as probabilistic inference via Markov chain Monte Carlo (MCMC) sampling. Since common methods for MCMC sampling in distributed systems, such as Gibbs sampling, are inconsistent with the dynamics of spiking neurons, we introduce a different approach based on non-reversible Markov chains that is able to reflect inherent temporal processes of spiking neuronal activity through a suitable choice of random variables. We propose a neural network model and show by a rigorous theoretical analysis that its neural activity implements MCMC sampling of a given distribution, both for the case of discrete and continuous time. This provides a step towards closing the gap between abstract functional models of cortical computation and more detailed models of networks of spiking neurons.     

Memory in microbes: quantifying history-dependent behavior in a bacterium

Memory is usually associated with higher organisms rather than bacteria. However, evidence is mounting that many regulatory networks within bacteria are capable of complex dynamics and multi-stable behaviors that have been linked to memory in other systems. Moreover, it is recognized that bacteria that have experienced different environmental histories may respond differently to current conditions. These "memory" effects may be more than incidental to the regulatory mechanisms controlling acclimation or to the status of the metabolic stores. Rather, they may be regulated by the cell and confer fitness to the organism in the evolutionary game it participates in. Here, we propose that history-dependent behavior is a potentially important manifestation of memory, worth classifying and quantifying. To this end, we develop an information-theory based conceptual framework for measuring both the persistence of memory in microbes and the amount of information about the past encoded in history-dependent dynamics. This method produces a phenomenological measure of cellular memory without regard to the specific cellular mechanisms encoding it. We then apply this framework to a strain of Bacillus subtilis engineered to report on commitment to sporulation and degradative enzyme (AprE) synthesis and estimate the capacity of these systems and growth dynamics to 'remember' 10 distinct cell histories prior to application of a common stressor. The analysis suggests that B. subtilis remembers, both in short and long term, aspects of its cell history, and that this memory is distributed differently among the observables. While this study does not examine the mechanistic bases for memory, it presents a framework for quantifying memory in cellular behaviors and is thus a starting point for studying new questions about cellular regulation and evolutionary strategy.     

Autopoietic and (M,R) systems

From the many attempts to produce a conceptual framework for the organization of living systems, the notions of (M,R) systems and Autopoiesis stand out for their rigor, their presupposition of the circularity of metabolism, and the new epistemologies that they imply. From their inceptions, these two notions have been essentially disconnected because each has defined its own language and tools. Here we demonstrate the existence of a deep conceptual link between (M,R) systems and Autopoietic systems. This relationship permits us to posit that Autopoietic systems, which have been advanced as capturing the central aspects of living systems, are a subset of (M,R) systems. This result, in conjunction with previous theorems proved by Rosen, can be used to outline a demonstration that the operation of Autopoietic systems cannot be simulated by Turing machines. This powerful result shows the potential of linking these two models. Finally, we suggest that the formalism of (M,R) systems could be used to model the circularity of metabolism.     

Mathematical modelling in the post-genome era: understanding genome expression and regulation--a system theoretic approach

This paper introduces a mathematical framework for modelling genome expression and regulation. Starting with a philosophical foundation, causation is identified as the principle of explanation of change in the realm of matter. Causation is, therefore, a relationship, not between components, but between changes of states of a system. We subsequently view genome expression (formerly known as 'gene expression') as a dynamic process and model aspects of it as dynamic systems using methodologies developed within the areas of systems and control theory. We begin with the possibly most abstract but general formulation in the setting of category theory. The class of models realised are state-space models, input--output models, autoregressive models or automata. We find that a number of proposed 'gene network' models are, therefore, included in the framework presented here. The conceptual framework that integrates all of these models defines a dynamic system as a family of expression profiles. It becomes apparent that the concept of a 'gene' is less appropriate when considering mathematical models of genome expression and regulation. The main claim of this paper is that we should treat (model) the organisation and regulation of genetic pathways as what they are: dynamic systems. Microarray technology allows us to generate large sets of time series data and is, therefore, discussed with regard to its use in mathematical modelling of gene expression and regulation.     

Murine B cell hybridomas bearing ligand-inducible Fc receptors for IgE

Interest in the regulation of IgE synthesis has generated investigation of low-affinity Fc receptors for IgE (Fc epsilon R) and the related immunoregulatory IgE-binding factors. In an effort to facilitate biochemical analysis of the B lymphocyte Fc epsilon R, hybridoma technology has been used to create stable cell lines that maintain Fc epsilon R in high numbers. Fusion of the HAT-sensitive B lymphoma, M12.4.5, with murine B cells from Nippostrongylus brasiliensis infected BALB/c mice led to the formation of hybrid cells of B cell phenotype, all of which were Fc epsilon R+, including several that had greater than 50,000 Fc epsilon R/cell. The Fc epsilon R on these cells were biochemically identical to the Fc epsilon R on normal B cells with respect to binding affinity (approximately equal to 10(8) M-1), m.w. (49,000), and tryptic peptides. Each hybridoma cell line specifically increased its Fc epsilon R level between twofold and fourfold when cultured with rat or mouse IgE. Additional studies demonstrated that the increased IgE binding ability was due to an increase in receptor number rather than an affinity change, and the Fc epsilon R increase was seen on the entire cell population. Dose studies indicated that oligomeric IgE was 10-fold more effective than monomeric IgE in causing upregulation, and the effective concentrations required indicated that induction occurred only if IgE was present in saturating concentrations. Upon addition of IgE, peak Fc epsilon R levels were reached after 15 to 20 hr of culture; blocking protein synthesis with cycloheximide largely blocked the increase in Fc epsilon R levels. Additionally, the inductive signal IgE must constantly be present to maintain upregulated Fc epsilon R levels in that its removal from the culture resulted in a rapid decline of Fc epsilon R from induced to normal levels. Because Fc receptor upregulation is important to several systems describing Ig isotype-specific regulation, the ability to examine such receptor upregulation at a clonal level should aid in discerning the role of the Fc epsilon R in the regulation of IgE antibody synthesis.     

The effects of EGF-receptor density on multiscale tumor growth patterns

We studied the effects of epidermal growth factor receptor (EGFR) density on tumor growth dynamics, both on the sub- and the multi-cellular level using our previously developed model. This algorithm simulates the growth of a brain tumor using a multi-scale two-dimensional agent-based approach with an integrated transforming growth factor alpha (TGFalpha) induced EGFR-gene-protein interaction network. The results confirm that increasing cell receptor density correlates with an acceleration of the tumor system's spatio-temporal expansion dynamics. This multicellular behavior cannot be explained solely on the basis of spatial sub-cellular dynamics, which remain qualitatively similar amongst the three glioma cell lines investigated here in silico. Rather, we find that cells with higher EGFR density show an early increase in the phenotypic switching activity between proliferative and migratory traits, linked to a higher level of initial auto-stimulation by the PLCgamma-mediated TGFalpha-EGFR autocrine network. This indicates a more active protein level interaction in these chemotactically acting tumor systems and supports the role of post-translational regulation for the implemented EGFR pathway. Implications of these results for experimental cancer research are discussed.     

Modelling evolutionary cell behaviour using neural networks: Application to tumour growth

In this paper, we present a modelling framework for cellular evolution that is based on the notion that a cell’s behaviour is driven by interactions with other cells and its immediate environment. We equip each cell with a phenotype that determines its behaviour and implement a decision mechanism to allow evolution of this phenotype. This decision mechanism is modelled using feed-forward neural networks, which have been suggested as suitable models of cell signalling pathways. The environmental variables are presented as inputs to the network and result in a response that corresponds to the phenotype of the cell. The response of the network is determined by the network parameters, which are subject to mutations when the cells divide. This approach is versatile as there are no restrictions on what the input or output nodes represent, they can be chosen to represent any environmental variables and behaviours that are of importance to the cell population under consideration. This framework was implemented in an individual-based model of solid tumour growth in order to investigate the impact of the tissue oxygen concentration on the growth and evolutionary dynamics of the tumour. Our results show that the oxygen concentration affects the tumour at the morphological level, but more importantly has a direct impact on the evolutionary dynamics. When the supply of oxygen is limited we observe a faster divergence away from the initial genotype, a higher population diversity and faster evolution towards aggressive phenotypes. The implementation of this framework suggests that this approach is well suited for modelling systems where evolution plays an important role and where a changing environment exerts selection pressure on the evolving population.     

Overall and internal dynamics of DNA as monitored by five-atom-tethered spin labels.

Electron paramagnetic resonance (EPR) spectra of the two-atom-tethered six-membered ring thymidylate spin label (DUMTA) incorporated into duplexes of different sizes were found to display a helix length dependence and a local-order parameter S = 0.32 +/- 0.01 for B-DNA based on the dynamic cylinder model (Keyes, R. S., and A. M. Bobst. 1995. Detection of internal and overall dynamics of a two-atom-tethered spin-labeled DNA. Biochemistry. 34:9265-9276). This sensitivity to size, which reflects global tumbling, is now reported for the more flexible five-atom-tethered five-membered ring thymidylate spin label (DUAP) that can be readily incorporated enzymatically and sequence specifically into nucleic acids of different sizes. The DUAPs containing B-DNA systems were simulated with the same dynamic cylinder model, giving S = 0.20 +/- 0.01 for the more flexibly tethered spin label. This shows that S is dependent on tether length but not on global motion. An analysis with the same motional model of the B-Z transition in a (dG-dC)n polymer containing the five-atom-tethered six-membered ring cytidylate spin label (DCAT) (Strobel, O. K., R. S. Keyes, and A. M. Bobst. 1990b. Base dynamics of local Z-DNA conformations as detected by electron paramagnetic resonance with spin-labeled deoxycytidine analogues. Biochemistry. 29:8522-8528) revealed an increase in S from 0.15 +/- 0.01 to 0.26 +/- 0.01 in response to the B- to Z-DNA transition. This indicates that S is not only sensitive to tether length, but also to conformational changes in DNA. Both the DUAP- and the DCAT-labeled systems were also simulated with a base disk model. From the DUAP spectral series, the perpendicular component of the correlation time tau perpendicular describing the spin-labeled base diffusion was found to be sensitive to global tumbling, confirming earlier results obtained with DUMTA. The DCAT polymer results demonstrated that tau perpendicular monitors a conformational change from B- to Z-DNA, indicating that tau perpendicular is also sensitive to local base dynamics. These results confirm that the dynamics of five-atom-tethered nitroxides are coupled to the nucleic acid dynamics and, as with two-atom-tethered spin labels, can be characterized by S and tau perpendicular. The analyses of both spin-labeled systems provide good evidence for spin-labeled base motions within double-stranded DNA occurring on the nanosecond time scale, and establish that both labels can be used to monitor changes in global tumbling and local order parameter due to variations in DNA conformation and protein-DNA interactions.     

Theory of robustness of irreversible differentiation in a stem cell system: chaos hypothesis

Based on an extensive study of a dynamical systems model of the development of a cell society, a novel theory for stem cell differentiation and its regulation is proposed as the "chaos hypothesis". Two fundamental features of stem cell systems-stochastic differentiation of stem cells and the robustness of a system due to regulation of this differentiation-are found to be general properties of a system of interacting cells exhibiting chaotic intra-cellular reaction dynamics and cell division, whose presence does not depend on the detail of the model. It is found that stem cells differentiate into other cell types stochastically due to a dynamical instability caused by cell-cell interactions, in a manner described by the Isologous Diversification theory. This developmental process is shown to be stable not only with respect to molecular fluctuations but also with respect to the removal of cells. With this developmental process, the irreversible loss of multipotency accompanying the change from a stem cell to a differentiated cell is shown to be characterized by a decrease in the chemical diversity in the cell and of the complexity of the cellular dynamics. The relationship between the division speed and the loss of multipotency is also discussed. Using our model, some predictions that can be tested experimentally are made for a stem cell system.     

Cell cycle arrest and apoptosis induced by human Polo-like kinase 3 is mediated through perturbation of microtubule integrity

Human Polo-like kinase 3 (Plk3, previously termed Prk or Fnk) is involved in regulation of cell cycle progression through the M phase (B. Ouyang, H. Pan, L. Lu, J. Li, P. Stambrook, B. Li, and W. Dai, J. Biol. Chem. 272:28646-28651, 1997). Here we report that in most interphase cells endogenous Plk3 was predominantly localized around the nuclear membrane. Double labeling with Plk3 and gamma-tubulin, the latter a major component of pericentriole materials, revealed that Plk3 was closely associated with centrosomes and that its localization to centrosomes was dependent on the integrity of microtubules. Throughout mitosis, Plk3 appeared to be localized to mitotic apparatus such as spindle poles and mitotic spindles. During telophase, a significant amount of Plk3 was also detected in the midbody. Ectopic expression of Plk3 mutants dramatically changed cell morphology primarily due to their effects on microtubule dynamics. Expression of a constitutively active Plk3 (Plk3-A) resulted in rapid cell shrinkage, which led to formation of cells with an elongated, unsevered, and taxol-sensitive midbody. In contrast, cells expressing a kinase-defective Plk3 (Plk3(K52R)) mutant exhibited extended, deformed cytoplasmic structures, the phenotype of which was somewhat refractory to taxol treatment. Expression of both Plk3-A and Plk3(K52R) induced apparent G(2)/M arrest followed by apoptosis, although the kinase-defective mutant was less effective. Taken together, our studies strongly suggest that Plk3 plays an important role in the regulation of microtubule dynamics and centrosomal function in the cell and that deregulated expression of Plk3 results in cell cycle arrest and apoptosis.     

Non-steroidal inhibition of granulosa cell aromatase activity in vitro

Treatment of cyclic rats with the substituted triazole R151885 (1,1-di (4-fluorophenyl)-2-(1,2,4-triazol-1-yl)-ethanol causes delayed ovulation with suppressed blood oestradiol levels. To determine if R151885 can exert a direct action on ovarian oestrogen biosynthesis, we studied its effect on steroidogenesis in granulosa cell cultures from prepubertal rat ovaries. The cells were incubated for 48 h in medium containing 100 ng human FSH/ml and 10(-7) M testosterone to induce steroidogenic enzymes. When R151885 was also present in the culture medium, there was a marked and concentration-dependent reduction in granulosa cell oestradiol production. Inhibition was half-maximal at approx 3 X 10(-7) M and almost complete at 10(-5) M R151885. Progesterone and 20 alpha-hydroxy-4-pregnen-3-one production were unaffected except by the highest concentration of the substituted triazole (36% inhibition at 10(-5) M). Direct assessment of aromatase activity in the 48-h cultured monolayers (oestradiol formation during a 3-h incubation with 10(-7) M testosterone) was made to determine if the inhibitory effect of R151885 was due to reduced aromatase induction/activation. This was not the case, since cells cultured in the presence of 10(-6) or 10(-5) M R151885 had levels of aromatase up to 60% higher than those cultured in its absence. To determine acute effects of R151885 on testosterone (10(-7) M) aromatization, 3-h incubations were carried out using granulosa cell suspensions with high extant aromatase activity due to stimulation by ovine FSH (100 micrograms sc, twice daily for 2 days) in vivo. The triazole acted as an apparent competitive aromatase inhibitor (apparent Km for testosterone 2.5 X 10(-8) M in the absence of R151885 rising to 4.4 X 10(-8) M in the presence of 10(-7) M R151885). Its potency as an aromatase inhibitor was approximately 10 times greater than that of the naturally occurring steroidal aromatase inhibitor 5 alpha-dihydrotestosterone. Various structurally related substances proved to be even more potent aromatase inhibitors than R151885. The most active were also substituted 4,4'-difluorophenyl derivatives containing an imidazolyl or pyridyl moiety instead of the 1,2,4-triazolyl substituent in R151885. This study has identified a novel series of nonsteroidal substances which have the characteristics of potent and specific inhibitors of testosterone aromatization by rat granulosa cells in vitro.     

Cell dynamics and gene expression control in tissue homeostasis and development

During tissue and organ development and maintenance, the dynamic regulation of cellular proliferation and differentiation allows cells to build highly elaborate structures. The development of the vertebrate retina or the maintenance of adult intestinal crypts, for instance, involves the arrangement of newly created cells with different phenotypes, the proportions of which need to be tightly controlled. While some of the basic principles underlying these processes developing and maintaining these organs are known, much remains to be learnt from how cells encode the necessary information and use it to attain those complex but reproducible arrangements. Here, we review the current knowledge on the principles underlying cell population dynamics during tissue development and homeostasis. In particular, we discuss how stochastic fate assignment, cell division, feedback control and cellular transition states interact during organ and tissue development and maintenance in multicellular organisms. We propose a framework, involving the existence of a transition state in which cells are more susceptible to signals that can affect their gene expression state and influence their cell fate decisions. This framework, which also applies to systems much more amenable to quantitative analysis like differentiating embryonic stem cells, links gene expression programmes with cell population dynamics.     

Towards Predictive Computational Models of Oncolytic Virus Therapy: Basis for Experimental Validation and Model Selection

Oncolytic viruses are viruses that specifically infect cancer cells and kill them, while leaving healthy cells largely intact. Their ability to spread through the tumor makes them an attractive therapy approach. While promising results have been observed in clinical trials, solid success remains elusive since we lack understanding of the basic principles that govern the dynamical interactions between the virus and the cancer. In this respect, computational models can help experimental research at optimizing treatment regimes. Although preliminary mathematical work has been performed, this suffers from the fact that individual models are largely arbitrary and based on biologically uncertain assumptions. Here, we present a general framework to study the dynamics of oncolytic viruses that is independent of uncertain and arbitrary mathematical formulations. We find two categories of dynamics, depending on the assumptions about spatial constraints that govern that spread of the virus from cell to cell. If infected cells are mixed among uninfected cells, there exists a viral replication rate threshold beyond which tumor control is the only outcome. On the other hand, if infected cells are clustered together (e.g. in a solid tumor), then we observe more complicated dynamics in which the outcome of therapy might go either way, depending on the initial number of cells and viruses. We fit our models to previously published experimental data and discuss aspects of model validation, selection, and experimental design. This framework can be used as a basis for model selection and validation in the context of future, more detailed experimental studies. It can further serve as the basis for future, more complex models that take into account other clinically relevant factors such as immune responses.     

Regulation of mitochondrial bioenergetics by the non-canonical roles of mitochondrial dynamics proteins in the heart

Recent advancement in mitochondrial research has significantly extended our knowledge on the role and regulation of mitochondria in health and disease. One important breakthrough is the delineation of how mitochondrial morphological changes, termed mitochondrial dynamics, are coupled to the bioenergetics and signaling functions of mitochondria. In general, it is believed that fusion leads to an increased mitochondrial respiration efficiency and resistance to stress-induced dysfunction while fission does the contrary. This concept seems not applicable to adult cardiomyocytes. The mitochondria in adult cardiomyocytes exhibit fragmented morphology (tilted towards fission) and show less networking and movement as compared to other cell types. However, being the most energy-demanding cells, cardiomyocytes in the adult heart possess vast number of mitochondria, high level of energy flow, and abundant mitochondrial dynamics proteins. This apparent discrepancy could be explained by recently identified new functions of the mitochondrial dynamics proteins. These “non-canonical” roles of mitochondrial dynamics proteins range from controlling inter-organelle communication to regulating cell viability and survival under metabolic stresses. Here, we summarize the newly identified non-canonical roles of mitochondrial dynamics proteins. We focus on how these fission and fusion independent roles of dynamics proteins regulate mitochondrial bioenergetics. We also discuss potential molecular mechanisms, unique intracellular location, and the cardiovascular disease relevance of these non-canonical roles of the dynamics proteins. We propose that future studies are warranted to differentiate the canonical and non-canonical roles of dynamics proteins and to identify new approaches for the treatment of heart diseases. This article is part of a Special issue entitled Cardiac adaptations to obesity, diabetes and insulin resistance, edited by Professors Jan F.C. Glatz, Jason R.B. Dyck and Christine Des Rosiers.     

Major Changes in the Ecology of the Wadden Sea: Human Impacts, Ecosystem Engineering and Sediment Dynamics

Shallow soft-sediment systems are mostly dominated by species that, by strongly affecting sediment dynamics, modify their local environment. Such ecosystem engineering species can have either sediment-stabilizing or sediment-destabilizing effects on tidal flats. They interplay with abiotic forcing conditions (wind, tide, nutrient inputs) in driving the community structure and generating spatial heterogeneity, determining the composition of different communities of associated species, and thereby affecting the channelling of energy through different compartments in the food web. This suggests that, depending on local species composition, tidal flats may have conspicuously different geomorphology and biological functions under similar external conditions. Here we use a historical reconstruction of benthic production in the Wadden Sea to construct a framework for the relationships between human impacts, ecosystem engineering and sediment dynamics. We propose that increased sediment disturbances by human exploitation interfere with biological controls of sediment dynamics, and thereby have shifted the dominant compartments of both primary and secondary production in the Wadden Sea, transforming the intertidal from an internally regulated and spatially heterogeneous, to an externally regulated and spatially homogenous system. This framework contributes to the general understanding of the interaction between biological and environmental control of ecosystem functioning, and suggests a general framework for predicting effects of human impacts on soft-bottom ecosystems.