CiteULike: monod's library [83 articles]

Efficiency, Robustness and Stochasticity of Gene Regulatory Networks in Systems Biology: lambda Switch as a Working Example

X Zhu — 2005-12-06T14:39:44-00:00

(2 Dec 2005)

Phage lambda is one of the most studied biological models in modern molecular biology. Over the past 50 years quantitative experimental knowledge on this biological model has been accumulated at all levels: physics, chemistry, genomics, proteomics, functions, and more. All its components have been known to a great detail. The theoretical task has been to integrate its components to make the organism working quantitatively in a harmonic manner. This would test our biological understanding and would lay a solid fundamental for further explorations and applications, an obvious goal of systems biology. One of the outstanding challenges in doing so has been the so-called stability puzzle of lambda switch: the biologically observed robustness and its difficult mathematical reconstruction based on known experimental values. In this chapter we review the recent theoretical and experimental efforts on tackling this problem. An emphasis is put on the minimum quantitative modeling where a successful numerical agreement between experiments and modeling has been achieved. A novel method tentatively named stochastic dynamical structure analysis emerged from such study is also discussed within a broad modeling perspective.

Robustness and Evolvability in Living Systems (Princeton Studies in Complexity)

Andreas Wagner — 2005-10-17T18:38:38-00:00

(01 August 2005)

All living things are remarkably complex, yet their DNA is unstable, undergoing countless random mutations over generations. Despite this instability, most animals do not grow two heads or die, plants continue to thrive, and bacteria continue to divide. Robustness and Evolvability in Living Systems tackles this perplexing paradox. The book explores why genetic changes do not cause organisms to fail catastrophically and how evolution shapes organisms' robustness. Andreas Wagner looks at this problem from the ground up, starting with the alphabet of DNA, the genetic code, RNA, and protein molecules, moving on to genetic networks and embryonic development, and working his way up to whole organisms. He then develops an evolutionary explanation for robustness. Wagner shows how evolution by natural selection preferentially finds and favors robust solutions to the problems organisms face in surviving and reproducing. Such robustness, he argues, also enhances the potential for future evolutionary innovation. Wagner also argues that robustness has less to do with organisms having plenty of spare parts (the redundancy theory that has been popular) and more to do with the reality that mutations can change organisms in ways that do not substantively affect their fitness. Unparalleled in its field, this book offers the most detailed analysis available of all facets of robustness within organisms. It will appeal not only to biologists but also to engineers interested in the design of robust systems and to social scientists concerned with robustness in human communities and populations.

Modern theories of metabolic control and their applications (review).

HV Westerhoff — 2005-12-03T00:08:06-00:00

Biosci Rep, Vol. 4, No. 1. (January 1984), pp. 1-22.

Existing, qualitative notions with respect to the way in which enzyme properties control metabolism are discussed in the light of the control analysis developed by H. Kacser and J. A. Burns ( (1973) in: Rate Control of Biological Processes, Davies DD, ed., Cambridge University Press, pp. 65-104) and R. Heinrich and T. A. Rapoport ( (1974) Eur. J. Biochem. 42, 89-95), and recent experimental data. Points at which the existing notions should be adjusted are: Metabolic control is shared by enzymes rather than confined to one rate-limiting enzyme per pathway. Whether an enzyme exercises strong control on a flux cannot be deduced solely from its own properties, nor is it directly related to its distance from equilibrium. With respect to metabolic control, enzymes should be classified into four groups, rather than two (reversible versus irreversible). The distribution of control among the enzymes depends on the metabolic conditions. Control structures of metabolic pathways probably differ with the function of that pathway.

Circuit topology and the evolution of robustness in two-gene circadian oscillators.

Andreas Wagner — 2005-08-16T18:57:34-00:00

Proc Natl Acad Sci U S A (8 August 2005)

Many parameters driving the behavior of biochemical circuits vary extensively and are thus not fine-tuned. Therefore, the topology of such circuits (the who-interacts-with-whom) is key to understanding their central properties. I here explore several hundred different topologies of a simple biochemical model of circadian oscillations to ask two questions: Do different circuits differ dramatically in their robustness to parameter change? If so, can a process of gradual molecular evolution find highly robust topologies when starting from less robust topologies? I find that the distribution of robustness among different circuit topologies is highly skewed: Most show low robustness, whereas very few topologies are highly robust. To address the second evolutionary question, I define a topology graph, each of whose nodes corresponds to one circuit topology that shows circadian oscillations. Two nodes in this graph are connected if they differ by only one regulatory interaction within the circuit. For the circadian oscillator I study, most topologies are connected in this graph, making evolutionary transitions from low to high robustness easy. A similar approach has been used to study the evolution of robustness in biological macromolecules, with similar results. This suggests that the same principles govern the evolution of robustness on different levels of biological organization. The regulatory interlocking of several oscillating gene products in biological circadian oscillators may exist because it provides robustness.

Robustness of cellular functions.

J Stelling — 2004-11-22T00:52:45-00:00

Cell, Vol. 118, No. 6. (17 September 2004), pp. 675-685.

Robustness, the ability to maintain performance in the face of perturbations and uncertainty, is a long-recognized key property of living systems. Owing to intimate links to cellular complexity, however, its molecular and cellular basis has only recently begun to be understood. Theoretical approaches to complex engineered systems can provide guidelines for investigating cellular robustness because biology and engineering employ a common set of basic mechanisms in different combinations. Robustness may be a key to understanding cellular complexity, elucidating design principles, and fostering closer interactions between experimentation and theory.

Biological robustness.

H Kitano — 2004-12-14T12:19:04-00:00

Nat Rev Genet, Vol. 5, No. 11. (November 2004), pp. 826-837.

Robustness is a ubiquitously observed property of biological systems. It is considered to be a fundamental feature of complex evolvable systems. It is attained by several underlying principles that are universal to both biological organisms and sophisticated engineering systems. Robustness facilitates evolvability and robust traits are often selected by evolution. Such a mutually beneficial process is made possible by specific architectural features observed in robust systems. But there are trade-offs between robustness, fragility, performance and resource demands, which explain system behaviour, including the patterns of failure. Insights into inherent properties of robust systems will provide us with a better understanding of complex diseases and a guiding principle for therapy design.

Stochasticity in transcriptional regulation: origins, consequences, and mathematical representations.

TB Kepler — 2005-12-03T00:05:39-00:00

Biophys J, Vol. 81, No. 6. (December 2001), pp. 3116-3136.

Transcriptional regulation is an inherently noisy process. The origins of this stochastic behavior can be traced to the random transitions among the discrete chemical states of operators that control the transcription rate and to finite number fluctuations in the biochemical reactions for the synthesis and degradation of transcripts. We develop stochastic models to which these random reactions are intrinsic and a series of simpler models derived explicitly from the first as approximations in different parameter regimes. This innate stochasticity can have both a quantitative and qualitative impact on the behavior of gene-regulatory networks. We introduce a natural generalization of deterministic bifurcations for classification of stochastic systems and show that simple noisy genetic switches have rich bifurcation structures; among them, bifurcations driven solely by changing the rate of operator fluctuations even as the underlying deterministic system remains unchanged. We find stochastic bistability where the deterministic equations predict monostability and vice-versa. We derive and solve equations for the mean waiting times for spontaneous transitions between quasistable states in these switches.

Integrative analysis of cell cycle control in budding yeast.

KC Chen — 2005-12-03T00:04:00-00:00

Mol Biol Cell, Vol. 15, No. 8. (August 2004), pp. 3841-3862.

The adaptive responses of a living cell to internal and external signals are controlled by networks of proteins whose interactions are so complex that the functional integration of the network cannot be comprehended by intuitive reasoning alone. Mathematical modeling, based on biochemical rate equations, provides a rigorous and reliable tool for unraveling the complexities of molecular regulatory networks. The budding yeast cell cycle is a challenging test case for this approach, because the control system is known in exquisite detail and its function is constrained by the phenotypic properties of >100 genetically engineered strains. We show that a mathematical model built on a consensus picture of this control system is largely successful in explaining the phenotypes of mutants described so far. A few inconsistencies between the model and experiments indicate aspects of the mechanism that require revision. In addition, the model allows one to frame and critique hypotheses about how the division cycle is regulated in wild-type and mutant cells, to predict the phenotypes of new mutant combinations, and to estimate the effective values of biochemical rate constants that are difficult to measure directly in vivo.

Biological networks: the tinkerer as an engineer.

U Alon — 2005-12-03T00:03:24-00:00

Science, Vol. 301, No. 5641. (26 September 2003), pp. 1866-1867.

This viewpoint comments on recent advances in understanding the design principles of biological networks. It highlights the surprising discovery of "good-engineering" principles in biochemical circuitry that evolved by random tinkering.

The driving force for life's emergence: kinetic and thermodynamic considerations.

A Pross — 2005-12-01T18:15:13-00:00

J Theor Biol, Vol. 220, No. 3. (7 February 2003), pp. 393-406.

The principles that govern the emergence of life from non-life remain a subject of intense debate. The evolutionary paradigm built up over the last 50 years, that argues that the evolutionary driving force is the Second Law of Thermodynamics, continues to be promoted by some, while severely criticized by others. If the thermodynamic drive toward ever-increasing entropy is not what drives the evolutionary process, then what does? In this paper, we analyse this long-standing question by building on Eigen's "replication first" model for life's emergence, and propose an alternative theoretical framework for understanding life's evolutionary driving force. Its essence is that life is a kinetic phenomenon that derives from the kinetic consequences of autocatalysis operating on specific biopolymeric systems, and this is demonstrably true at all stages of life's evolution--from primal to advanced life forms. Life's unique characteristics--its complexity, energy-gathering metabolic systems, teleonomic character, as well as its abundance and diversity, derive directly from the proposition that from a chemical perspective the replication reaction is an extreme expression of kinetic control, one in which thermodynamic requirements have evolved to play a supporting, rather than a directing, role. The analysis leads us to propose a new sub-division within chemistry--replicative chemistry. A striking consequence of this kinetic approach is that Darwin's principle of natural selection: that living things replicate, and therefore evolve, may be phrased more generally: that certain replicating things can evolve, and may therefore become living. This more general formulation appears to provide a simple conceptual link between animate and inanimate matter.

Jump-starting a cellular world: investigating the origin of life, from soup to networks.

R Robinson — 2005-11-15T14:58:21-00:00

PLoS Biol, Vol. 3, No. 11. (November 2005)

Surviving heat shock: control strategies for robustness and performance.

H El-Samad — 2005-11-30T19:02:14-00:00

Proc Natl Acad Sci U S A, Vol. 102, No. 8. (22 February 2005), pp. 2736-2741.

Molecular biology studies the cause-and-effect relationships among microscopic processes initiated by individual molecules within a cell and observes their macroscopic phenotypic effects on cells and organisms. These studies provide a wealth of information about the underlying networks and pathways responsible for the basic functionality and robustness of biological systems. At the same time, these studies create exciting opportunities for the development of quantitative and predictive models that connect the mechanism to its phenotype then examine various modular structures and the range of their dynamical behavior. The use of such models enables a deeper understanding of the design principles underlying biological organization and makes their reverse engineering and manipulation both possible and tractable The heat shock response presents an interesting mechanism where such an endeavor is possible. Using a model of heat shock, we extract the design motifs in the system and justify their existence in terms of various performance objectives. We also offer a modular decomposition that parallels that of traditional engineering control architectures.

Motifs, control, and stability.

J Doyle — 2005-11-29T02:40:34-00:00

PLoS Biol, Vol. 3, No. 11. (November 2005)

Artificial cell-cell communication in yeast Saccharomyces cerevisiae using signaling elements from Arabidopsis thaliana.

Ming-Tang Chen — 2005-11-28T04:17:03-00:00

Nat Biotechnol (20 November 2005)

The construction of synthetic cell-cell communication networks can improve our quantitative understanding of naturally occurring signaling pathways and enhance our capabilities to engineer coordinated cellular behavior in cell populations. Towards accomplishing these goals in eukaryotes, we developed and analyzed two artificial cell-cell communication systems in yeast. We integrated Arabidopsis thaliana signal synthesis and receptor components with yeast endogenous protein phosphorylation elements and new response promoters. In the first system, engineered yeast 'sender' cells synthesize the plant hormone cytokinin, which diffuses into the environment and activates a hybrid exogenous/endogenous phosphorylation signaling pathway in nearby engineered yeast 'receiver' cells. For the second system, the sender network was integrated into the receivers under positive-feedback regulation, resulting in population density-dependent gene expression (that is, quorum sensing). The combined experimental work and mathematical modeling of the systems presented here can benefit various biotechnology applications for yeast and higher level eukaryotes, including fermentation processes, biomaterial fabrication and tissue engineering.

Fundamental issues in systems biology.

Maureen A O'malley — 2005-11-27T22:59:37-00:00

Bioessays, Vol. 27, No. 12. (December 2005), pp. 1270-1276.

In the context of scientists' reflections on genomics, we examine some fundamental issues in the emerging postgenomic discipline of systems biology. Systems biology is best understood as consisting of two streams. One, which we shall call 'pragmatic systems biology', emphasises large-scale molecular interactions; the other, which we shall refer to as 'systems-theoretic biology', emphasises system principles. Both are committed to mathematical modelling, and both lack a clear account of what biological systems are. We discuss the underlying issues in identifying systems and how causality operates at different levels of organisation. We suggest that resolving such basic problems is a key task for successful systems biology, and that philosophers could contribute to its realisation. We conclude with an argument for more sociologically informed collaboration between scientists and philosophers. BioEssays 27:1270-1276, 2005. (c) 2005 Wiley Periodicals, Inc.

Foundations for engineering biology

Drew Endy — 2005-11-23T18:52:38-00:00

Nature, Vol. 438, No. 7067., pp. 449-453.

Design principles of a bacterial signalling network

Markus Kollmann — 2005-11-23T18:52:37-00:00

Nature, Vol. 438, No. 7067., pp. 504-507.

Reconstruction of genetic circuits

David Sprinzak — 2005-11-23T18:52:38-00:00

Nature, Vol. 438, No. 7067., pp. 443-448.

Robustness in bacterial chemotaxis.

U Alon — 2004-11-22T00:17:30-00:00

Nature, Vol. 397, No. 6715. (14 January 1999), pp. 168-171.

Networks of interacting proteins orchestrate the responses of living cells to a variety of external stimuli, but how sensitive is the functioning of these protein networks to variations in their biochemical parameters? One possibility is that to achieve appropriate function, the reaction rate constants and enzyme concentrations need to be adjusted in a precise manner, and any deviation from these 'fine-tuned' values ruins the network's performance. An alternative possibility is that key properties of biochemical networks are robust; that is, they are insensitive to the precise values of the biochemical parameters. Here we address this issue in experiments using chemotaxis of Escherichia coli, one of the best-characterized sensory systems. We focus on how response and adaptation to attractant signals vary with systematic changes in the intracellular concentration of the components of the chemotaxis network. We find that some properties, such as steady-state behaviour and adaptation time, show strong variations in response to varying protein concentrations. In contrast, the precision of adaptation is robust and does not vary with the protein concentrations. This is consistent with a recently proposed molecular mechanism for exact adaptation, where robustness is a direct consequence of the network's architecture.

Robustness in simple biochemical networks.

N Barkai — 2004-12-01T19:13:20-00:00

Nature, Vol. 387, No. 6636. (26 June 1997), pp. 913-917.

Cells use complex networks of interacting molecular components to transfer and process information. These "computational devices of living cells" are responsible for many important cellular processes, including cell-cycle regulation and signal transduction. Here we address the issue of the sensitivity of the networks to variations in their biochemical parameters. We propose a mechanism for robust adaptation in simple signal transduction networks. We show that this mechanism applies in particular to bacterial chemotaxis. This is demonstrated within a quantitative model which explains, in a unified way, many aspects of chemotaxis, including proper responses to chemical gradients. The adaptation property is a consequence of the network's connectivity and does not require the 'fine-tuning' of parameters. We argue that the key properties of biochemical networks should be robust in order to ensure their proper functioning.

Redundancy, antiredundancy, and the robustness of genomes.

DC Krakauer — 2005-11-12T16:34:59-00:00

Proc Natl Acad Sci U S A, Vol. 99, No. 3. (5 February 2002), pp. 1405-1409.

Genetic mutations that lead to undetectable or minimal changes in phenotypes are said to reveal redundant functions. Redundancy is common among phenotypes of higher organisms that experience low mutation rates and small population sizes. Redundancy is less common among organisms with high mutation rates and large populations, or among the rapidly dividing cells of multicellular organisms. In these cases, one even observes the opposite tendency: a hypersensitivity to mutation, which we refer to as antiredundancy. In this paper we analyze the evolutionary dynamics of redundancy and antiredundancy. Assuming a cost of redundancy, we find that large populations will evolve antiredundant mechanisms for removing mutants and thereby bolster the robustness of wild-type genomes; whereas small populations will evolve redundancy to ensure that all individuals have a high chance of survival. We propose that antiredundancy is as important for developmental robustness as redundancy, and is an essential mechanism for ensuring tissue-level stability in complex multicellular organisms. We suggest that antiredundancy deserves greater attention in relation to cancer, mitochondrial disease, and virus infection.

A function-based framework for understanding biological systems.

JD Thomas — 2005-11-12T15:11:17-00:00

Annu Rev Biophys Biomol Struct, Vol. 33 (2004), pp. 75-93.

Systems biology research is currently dominated by integrative, multidisciplinary approaches. Although important, these strategies lack an overarching systems perspective such as those used in engineering. We describe here the Axiomatic Design approach to system analysis and illustrate its utility in the study of biological systems. Axiomatic Design relates functions at all levels to the behavior of biological molecules and uses a Design Matrix to understand these relationships. Such an analysis reveals that robustness in many biological systems is achieved through the maintenance of functional independence of numerous subsystems. When the interlinking (coupling) of systems is required, biological systems impose a functional period in order to maximize successful operation of the system. Ultimately, the application of Axiomatic Design methods to the study of biological systems will aid in handling cross-scale models, identifying control points, and predicting system-wide effects of pharmacological agents.

Dynamic Properties of Network Motifs Contribute to Biological Network Organization.

Robert J Prill — 2005-10-07T19:43:29-00:00

PLoS Biol, Vol. 3, No. 11. (4 October 2005)

Biological networks, such as those describing gene regulation, signal transduction, and neural synapses, are representations of large-scale dynamic systems. Discovery of organizing principles of biological networks can be enhanced by embracing the notion that there is a deep interplay between network structure and system dynamics. Recently, many structural characteristics of these non-random networks have been identified, but dynamical implications of the features have not been explored comprehensively. We demonstrate by exhaustive computational analysis that a dynamical property-stability or robustness to small perturbations-is highly correlated with the relative abundance of small subnetworks (network motifs) in several previously determined biological networks. We propose that robust dynamical stability is an influential property that can determine the non-random structure of biological networks.

Multiple feedback loops are key to a robust dynamic performance of tryptophan regulation in Escherichia coli.

KV Venkatesh — 2005-11-11T03:21:07-00:00

FEBS Lett, Vol. 563, No. 1-3. (9 April 2004), pp. 234-240.

Living systems must adapt quickly and stably to uncertain environments. A common theme in cellular regulation is the presence of multiple feedback loops in the network. An example of such a feedback structure is regulation of tryptophan concentration in Escherichia coli. Here, three distinct feedback mechanisms, namely genetic regulation, mRNA attenuation and enzyme inhibition, regulate tryptophan synthesis. A pertinent question is whether such multiple feedback loops are "a case of regulatory overkill, or do these different feedback regulators have distinct functions?" Another moot question is how robustness to uncertainties can be achieved structurally through biological interactions. Correlation between the feedback structure and robustness can be systematically studied by tools commonly employed in feedback theory. An analysis of feedback strategies in the tryptophan system in E. coli reveals that the network complexity arising due to the distributed feedback structure is responsible for the rapid and stable response observed even in the presence of system uncertainties.

Design and Diversity in Bacterial Chemotaxis: A Comparative Study in Escherichia coli and Bacillus subtilis

Christopher Rao — 2005-11-08T14:58:57-00:00

PLoS Biology, Vol. 2, No. 2. (February 2004), pp. E49-E49.

Comparable processes in different species often involve homologous genes. One question is whether the network structure, in particular the feedback control structure, is also conserved. The bacterial chemotaxis pathways in E. coli and B. subtilis both regulate the same task, namely, excitation and adaptation to environmental signals. Both pathways employ many orthologous genes. Yet how these orthologs contribute to network function in each organism is different. To investigate this problem, we propose what is to our knowledge the first computational model for B. subtilis chemotaxis and compare it to previously published models for chemotaxis in E. coli. The models reveal that the core control strategy for signal processing is the same in both organisms, though in B. subtilis there are two additional feedback loops that provide an additional layer of regulation and robustness. Furthermore, the network structures are different despite the similarity of the proteins in each organism. These results demonstrate the limitations of pathway inferences based solely on homology and suggest that the control strategy is an evolutionarily conserved property. [Journal Article; In English; United States]

Modeling network dynamics: the lac operon, a case study

Jose Vilar — 2005-11-08T14:34:58-00:00

The Journal Of Cell Biology, Vol. 161, No. 3. (12 May 2003), pp. 471-476.

We use the lac operon in Escherichia coli as a prototype system to illustrate the current state, applicability, and limitations of modeling the dynamics of cellular networks. We integrate three different levels of description (molecular, cellular, and that of cell population) into a single model, which seems to capture many experimental aspects of the system. [Journal Article, Review, Review, Tutorial; 26 Refs; In English; United States]

Analysis of optimality in natural and perturbed metabolic networks

Daniel Segre — 2005-11-08T14:34:09-00:00

Proceedings Of The National Academy Of Sciences Of The United States Of America, Vol. 99, No. 23. (12 November 2002), pp. 15112-15117.

An important goal of whole-cell computational modeling is to integrate detailed biochemical information with biological intuition to produce testable predictions. Based on the premise that prokaryotes such as Escherichia coli have maximized their growth performance along evolution, flux balance analysis (FBA) predicts metabolic flux distributions at steady state by using linear programming. Corroborating earlier results, we show that recent intracellular flux data for wild-type E. coli JM101 display excellent agreement with FBA predictions. Although the assumption of optimality for a wild-type bacterium is justifiable, the same argument may not be valid for genetically engineered knockouts or other bacterial strains that were not exposed to long-term evolutionary pressure. We address this point by introducing the method of minimization of metabolic adjustment (MOMA), whereby we test the hypothesis that knockout metabolic fluxes undergo a minimal redistribution with respect to the flux configuration of the wild type. MOMA employs quadratic programming to identify a point in flux space, which is closest to the wild-type point, compatibly with the gene deletion constraint. Comparing MOMA and FBA predictions to experimental flux data for E. coli pyruvate kinase mutant PB25, we find that MOMA displays a significantly higher correlation than FBA. Our method is further supported by experimental data for E. coli knockout growth rates. It can therefore be used for predicting the behavior of perturbed metabolic networks, whose growth performance is in general suboptimal. MOMA and its possible future extensions may be useful in understanding the evolutionary optimization of metabolism. [Journal Article; In English; United States]

Metabolic network structure determines key aspects of functionality and regulation

Jorg Stelling — 2005-11-08T14:33:49-00:00

Nature, Vol. 420, No. 6912. (14 November 2002), pp. 190-193.

The relationship between structure, function and regulation in complex cellular networks is a still largely open question. Systems biology aims to explain this relationship by combining experimental and theoretical approaches. Current theories have various strengths and shortcomings in providing an integrated, predictive description of cellular networks. Specifically, dynamic mathematical modelling of large-scale networks meets difficulties because the necessary mechanistic detail and kinetic parameters are rarely available. In contrast, structure-oriented analyses only require network topology, which is well known in many cases. Previous approaches of this type focus on network robustness or metabolic phenotype, but do not give predictions on cellular regulation. Here, we devise a theoretical method for simultaneously predicting key aspects of network functionality, robustness and gene regulation from network structure alone. This is achieved by determining and analysing the non-decomposable pathways able to operate coherently at steady state (elementary flux modes). We use the example of Escherichia coli central metabolism to illustrate the method. [Journal Article; In English; England]

Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth

Rafael Ibarra — 2005-11-08T14:32:50-00:00

Nature, Vol. 420, No. 6912. (14 November 2002), pp. 186-189.

Annotated genome sequences can be used to reconstruct whole-cell metabolic networks. These metabolic networks can be modelled and analysed (computed) to study complex biological functions. In particular, constraints-based in silico models have been used to calculate optimal growth rates on common carbon substrates, and the results were found to be consistent with experimental data under many but not all conditions. Optimal biological functions are acquired through an evolutionary process. Thus, incorrect predictions of in silico models based on optimal performance criteria may be due to incomplete adaptive evolution under the conditions examined. Escherichia coli K-12 MG1655 grows sub-optimally on glycerol as the sole carbon source. Here we show that when placed under growth selection pressure, the growth rate of E. coli on glycerol reproducibly evolved over 40 days, or about 700 generations, from a sub-optimal value to the optimal growth rate predicted from a whole-cell in silico model. These results open the possibility of using adaptive evolution of entire metabolic networks to realize metabolic states that have been determined a priori based on in silico analysis. [Journal Article; In English; England]

An amplified sensitivity arising from covalent modification in biological systems.

A Goldbeter — 2005-10-31T04:18:32-00:00

Proc Natl Acad Sci U S A, Vol. 78, No. 11. (November 1981), pp. 6840-6844.

The transient and steady-state behavior of a reversible covalent modification system is examined. When the modifying enzymes operate outside the region of first-order kinetics, small percentage changes in the concentration of the effector controlling either of the modifying enzymes can give much larger percentage changes in the amount of modified protein. This amplification of the response to a stimulus can provide additional sensitivity in biological control, equivalent to that of allosteric proteins with high Hill coefficients.

The selective values of alleles in a molecular network model are context dependent.

J Peccoud — 2005-11-01T00:38:14-00:00

Genetics, Vol. 166, No. 4. (April 2004), pp. 1715-1725.

Classical quantitative genetics has applied linear modeling to the problem of mapping genotypic to phenotypic variation. Much of this theory was developed prior to the availability of molecular biology. The current understanding of the mechanisms of gene expression indicates the importance of nonlinear effects resulting from gene interactions. We provide a bridge between genetics and gene network theories by relating key concepts from quantitative genetics to the parameters, variables, and performance functions of genetic networks. We illustrate this methodology by simulating the genetic switch controlling galactose metabolism in yeast and its response to selection for a population of individuals. Results indicate that genes have heterogeneous contributions to phenotypes and that additive and nonadditive effects are context dependent. Early cycles of selection suggest strong additive effects attributed to some genes. Later cycles suggest the presence of strong context-dependent nonadditive effects that are conditional on the outcomes of earlier selection cycles. A single favorable allele cannot be consistently identified for most loci. These results highlight the complications that can arise with the presence of nonlinear effects associated with genes acting in networks when selection is conducted on a population of individuals segregating for the genes contributing to the network.

Reverse engineering of biological complexity.

ME Csete — 2004-11-22T00:17:30-00:00

Science, Vol. 295, No. 5560. (1 March 2002), pp. 1664-1669.

Advanced technologies and biology have extremely different physical implementations, but they are far more alike in systems-level organization than is widely appreciated. Convergent evolution in both domains produces modular architectures that are composed of elaborate hierarchies of protocols and layers of feedback regulation, are driven by demand for robustness to uncertain environments, and use often imprecise components. This complexity may be largely hidden in idealized laboratory settings and in normal operation, becoming conspicuous only when contributing to rare cascading failures. These puzzling and paradoxical features are neither accidental nor artificial, but derive from a deep and necessary interplay between complexity and robustness, modularity, feedback, and fragility. This review describes insights from engineering theory and practice that can shed some light on biological complexity.

Can a biologist fix a radio?--Or, what I learned while studying apoptosis.

Y Lazebnik — 2004-11-22T00:17:30-00:00

Cancer Cell, Vol. 2, No. 3. (September 2002), pp. 179-182.

5th International Conference on Systems Biology (ICSB 2004), Heidelberg, October 9-13, 2004.

Stefan Schuster — 2005-10-31T17:28:01-00:00

Biosystems (18 October 2005)

A switch from high-fidelity to error-prone DNA double-strand break repair underlies stress-induced mutation.

RG Ponder — 2005-10-26T03:46:27-00:00

Mol Cell, Vol. 19, No. 6. (16 September 2005), pp. 791-804.

Special mechanisms of mutation are induced in microbes under growth-limiting stress causing genetic instability, including occasional adaptive mutations that may speed evolution. Both the mutation mechanisms and their control by stress have remained elusive. We provide evidence that the molecular basis for stress-induced mutagenesis in an E. coli model is error-prone DNA double-strand break repair (DSBR). I-SceI-endonuclease-induced DSBs strongly activate stress-induced mutations near the DSB, but not globally. The same proteins are required as for cells without induced DSBs: DSBR proteins, DinB-error-prone polymerase, and the RpoS starvation-stress-response regulator. Mutation is promoted by homology between cut and uncut DNA molecules, supporting a homology-mediated DSBR mechanism. DSBs also promote gene amplification. Finally, DSBs activate mutation only during stationary phase/starvation but will during exponential growth if RpoS is expressed. Our findings reveal an RpoS-controlled switch from high-fidelity to mutagenic DSBR under stress. This limits genetic instability both in time and to localized genome regions, potentially important evolutionary strategies.

Core genetic module: The mixed feedback loop.

P François — 2005-10-28T05:47:59-00:00

Phys Rev E Stat Nonlin Soft Matter Phys, Vol. 72, No. 3 Pt 1. (September 2005)

The so-called mixed feedback loop (MFL) is a small two-gene network where protein A regulates the transcription of protein B and the two proteins form a heterodimer. It has been found to be statistically over-represented in statistical analyses of gene and protein interaction databases and to lie at the core of several computer-generated genetic networks. Here, we propose and mathematically study a model of the MFL and show that, by itself, it can serve both as a bistable switch and as a clock (an oscillator) depending on kinetic parameters. The MFL phase diagram as well as a detailed description of the nonlinear oscillation regime are presented and some biological examples are discussed. The results emphasize the role of protein interactions in the function of genetic modules and the usefulness of modeling RNA dynamics explicitly.

Interlinked Fast and Slow Positive Feedback Loops Drive Reliable Cell Decisions

Onn Brandman — 2005-10-21T15:34:27-00:00

Science, Vol. 310, No. 5747. (21 October 2005), pp. 496-498.

Positive feedback is a ubiquitous signal transduction motif that allows systems to convert graded inputs into decisive, all-or-none outputs. Here we investigate why the positive feedback switches that regulate polarization of budding yeast, calcium signaling, Xenopus oocyte maturation, and various other processes use multiple interlinked loops rather than single positive feedback loops. Mathematical simulations revealed that linking fast and slow positive feedback loops creates a "dual-time" switch that is both rapidly inducible and resistant to noise in the upstream signaling system.

Multisite protein phosphorylation makes a good threshold but can be a poor switch.

Jeremy Gunawardena — 2005-10-03T17:50:52-00:00

Proc Natl Acad Sci U S A (29 September 2005)

Phosphorylation and dephosphorylation play a fundamental role in eukaryotic signaling. Some 30% of proteins are phosphorylated at any time, many on multiple sites, raising the question of how the cellular phosphorylation state is regulated. Previous work for one and two phosphorylation sites has revealed mechanisms, such as distributive phosphorylation, for switch-like regulation of maximally phosphorylated phosphoforms. These insights have led to the influential view that more phosphorylation sites leads to steeper switching, as proposed for substrates like cyclin E and the cyclin-dependent kinase inhibitor Sic1. An analytical study of the ordered distributive case reveals a more complex story. Multisite phosphorylation creates an efficient threshold: The proportion of maximally phosphorylated substrate is maintained close to 0 when the ratio of kinase to phosphatase activity lies below a suitable threshold, and this threshold increases with increasing numbers of sites, n. However, above the threshold, the response may not always abruptly switch between 0 and 1, as would be the case for an efficient switch, but may increase in a gradual manner, which becomes more hyperbolic with increasing n. Abrupt switching cannot be attributed merely to n being large. We point out that conventional measures of ultrasensitivity must be modified to discriminate between thresholding and switching; we discuss additional factors that influence switching efficiency and suggest new directions for experimental investigation.

Perspective: Evolution and detection of genetic robustness.

JA de Visser — 2005-10-01T02:59:25-00:00

Evolution Int J Org Evolution, Vol. 57, No. 9. (September 2003), pp. 1959-1972.

Robustness is the invariance of phenotypes in the face of perturbation. The robustness of phenotypes appears at various levels of biological organization, including gene expression, protein folding, metabolic flux, physiological homeostasis, development, and even organismal fitness. The mechanisms underlying robustness are diverse, ranging from thermodynamic stability at the RNA and protein level to behavior at the organismal level. Phenotypes can be robust either against heritable perturbations (e.g., mutations) or nonheritable perturbations (e.g., the weather). Here we primarily focus on the first kind of robustness--genetic robustness--and survey three growing avenues of research: (1) measuring genetic robustness in nature and in the laboratory; (2) understanding the evolution of genetic robustness: and (3) exploring the implications of genetic robustness for future evolution.

Robustness as a measure of plausibility in models of biochemical networks.

M Morohashi — 2004-11-22T00:17:30-00:00

J Theor Biol, Vol. 216, No. 1. (7 May 2002), pp. 19-30.

Theory, experiment, and observation suggest that biochemical networks which are conserved across species are robust to variations in concentrations and kinetic parameters. Here, we exploit this expectation to propose an approach to model building and selection. We represent a model as a mapping from parameter space to behavior space, and utilize bifurcation analysis to study the robustness of each region of steady-state behavior to parameter variations. The hypothesis that potential errors in models will result in parameter sensitivities is tested by analysis of two models of the biochemical oscillator underlying the Xenopus cell cycle. Our analysis successfully identifies known weaknesses in the older model and suggests areas for further investigation in the more recent, more plausible model. It also correctly highlights why the more recent model is more plausible.

Noise in Gene Expression: Origins, Consequences, and Control

Jonathan Raser — 2005-09-23T14:46:50-00:00

Science, Vol. 309, No. 5743. (23 September 2005), pp. 2010-2013.

Genetically identical cells and organisms exhibit remarkable diversity even when they have identical histories of environmental exposure. Noise, or variation, in the process of gene expression may contribute to this phenotypic variability. Recent studies suggest that this noise has multiple sources, including the stochastic or inherently random nature of the biochemical reactions of gene expression. In this review, we summarize noise terminology and comment on recent investigations into the sources, consequences, and control of noise in gene expression.

Systems biologyDeviations in mating

Avigdor Eldar — 2005-09-28T18:18:38-00:00

Nature, Vol. 437, No. 7059. (28 September 2005), pp. 631-632.

Regulated cell-to-cell variation in a cell-fate decision system

Alejandro Colman-Lerner — 2005-09-18T17:48:28-00:00

Nature (18 September 2005)

Spontaneous evolution of modularity and network motifs.

Nadav Kashtan — 2005-09-27T18:51:05-00:00

Proc Natl Acad Sci U S A (20 September 2005)

Biological networks have an inherent simplicity: they are modular with a design that can be separated into units that perform almost independently. Furthermore, they show reuse of recurring patterns termed network motifs. Little is known about the evolutionary origin of these properties. Current models of biological evolution typically produce networks that are highly nonmodular and lack understandable motifs. Here, we suggest a possible explanation for the origin of modularity and network motifs in biology. We use standard evolutionary algorithms to evolve networks. A key feature in this study is evolution under an environment (evolutionary goal) that changes in a modular fashion. That is, we repeatedly switch between several goals, each made of a different combination of subgoals. We find that such "modularly varying goals" lead to the spontaneous evolution of modular network structure and network motifs. The resulting networks rapidly evolve to satisfy each of the different goals. Such switching between related goals may represent biological evolution in a changing environment that requires different combinations of a set of basic biological functions. The present study may shed light on the evolutionary forces that promote structural simplicity in biological networks and offers ways to improve the evolutionary design of engineered systems.

Limitations of quantitative gene regulation models: a case study.

PM Kim — 2005-09-27T06:09:41-00:00

Genome Res, Vol. 13, No. 11. (November 2003), pp. 2391-2395.

Understanding the relationship between network structure and behavior is fundamental to the field of computational and systems biology. A particularly important distinction is the extent to which qualitative aspects of network performance are encoded in network topology as opposed to being determined through quantitative details, such as those of kinetics. Here, we develop a general and rigorous mathematical framework for the analysis of genetic networks and apply it to a family of synthetic gene networks. A key feature of our methodology involves determining network behavior that is insensitive to kinetic parameters such as rate constants and nonlinear functional dependencies of rates on molecular concentrations. Results indicate that behavior observed in some networks cannot be reconciled with standard gene expression and regulation models. We explore relaxing model assumptions to explain the observed behavior, allowing for both dynamic and stochastic phenomena, and propose an alternative model. Our alternative model includes the suggestion of a new mechanism by which the counterintuitive behavior could be achieved; central to the model is the assumption that the Clp protein degradation system, which is responsible for the regulatory proteins used in this study, becomes saturated.

Phenotypic Diversity, Population Growth, and Information in Fluctuating Environments.

Edo Kussell — 2005-09-23T21:32:25-00:00

Science (25 August 2005)

Organisms in fluctuating environments must adapt their behavior to survive. In clonal populations, this may be achieved through sensing followed by response, or through generation of diversity by stochastic phenotype switching. We show that stochastic switching can be favored over sensing when the environment changes infrequently. The optimal switching rates then mimic the statistics of environmental changes. We derive a relation between the long-term growth rate of the organism and the information available about its fluctuating environment.

Bio-switches: what makes them robust?

BM Slepchenko — 2005-09-26T06:22:38-00:00

Curr Opin Genet Dev, Vol. 14, No. 4. (August 2004), pp. 428-434.

Ideas of how a system of interacting enzymes can act as a switch are based on the concept of bistability of a biochemical network. This means that, because of the very structure of a signaling pathway, the system can be in one of two stable steady states: active or inactive. Switching from one state to another may then occur in response to external stimuli or as a result of internal development. However, the bistability of a biochemical network might not be robust enough to be the sole mechanism behind bio-switching. On the basis of recent experimental data on the cell-cycle G2/M transition during starfish oocyte meiotic maturation, it is shown that cooperative phenomena--such as phase changes associated with clustering, dissolution of aggregates and so on--may play central roles in providing a decisive and irreversible transition.

Bistability in cell signaling: How to make continuous processes discontinuous, and reversible processes irreversible.

James E. Ferrell — 2005-05-05T14:39:02-00:00

Chaos, Vol. 11, No. 1. (March 2001), pp. 227-236.

Xenopus oocyte maturation is an example of an all-or-none, irreversible cell fate induction process. In response to a submaximal concentration of the steroid hormone progesterone, a given oocyte may either mature or not mature, but it can exist in intermediate states only transiently. Moreover, once an oocyte has matured, it will remain arrested in the mature state even after the progesterone is removed. It has been hypothesized that the all-or-none character of oocyte maturation, and some aspects of the irreversibility of maturation, arise out of the bistability of the signal transduction system that triggers maturation. The bistability, in turn, is hypothesized to arise from the way the signal transducers are organized into a signaling circuit that includes positive feedback (which makes it so that the system cannot rest in intermediate states) and ultrasensitivity (which filters small stimuli out of the feedback loop, allowing the system to have a stable off-state). Here we review two simple graphical methods that are commonly used to analyze bistable systems, discuss the experimental evidence for bistability in oocyte maturation, and suggest that bistability may be a common means of producing all-or-none responses and a type of biochemical memory. (c) 2001 American Institute of Physics.

Missense meanderings in sequence space: a biophysical view of protein evolution

Mark Depristo — 2005-09-01T17:01:42-00:00

Nature Reviews Genetics, Vol. 6, No. 9. (01 September 2005), pp. 678-687.

Bifurcation discovery tool.

V Chickarmane — 2005-09-17T14:48:22-00:00

Bioinformatics, Vol. 21, No. 18. (15 September 2005), pp. 3688-3690.

MOTIVATION: Biochemical networks often yield interesting behavior such as switching, oscillation and chaotic dynamics. This article describes a tool that is capable of searching for bifurcation points in arbitrary ODE-based reaction networks by directing the user to regions in the parameter space, where such interesting dynamical behavior can be observed. RESULTS: We have implemented a genetic algorithm that searches for Hopf bifurcations, turning points and bistable switches. The software is implemented as a Systems Biology Workbench (SBW) enabled module and accepts the standard SBML model format. The interface permits a user to choose the parameters to be searched, admissible parameter ranges, and the nature of the bifurcation to be sought. The tool will return the parameter values for the model for which the particular behavior is observed. AVAILABILITY: The software, tutorial manual and test models are available for download at the following website: http:/www.sys-bio.org/ under the bifurcation link. The software is an open source and licensed under BSD. CONTACT: vijay_chickarmane@kgi.edu.