The Goodwin Model: Simulating the Effect of Light Pulses on the Circadian Sporulation Rhythm of Neurospora Crassa

Ruoff, Peter; Vinsjevik, Merete; Monnerjahn, Christian; Rensing, Ludger

doi:10.1006/jtbi.2000.2239

Cited by 129 publications

(119 citation statements)

References 46 publications

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“…Using Drosophila network terminology as an example (31), this negative feedback loop acting on transcriptional activation via complexation can be compared to the transcriptional deactivation of period (per) and timeless (tim) genes after PER͞TIM complexation with the Clock͞Cycle (dCLK͞CYC) transcriptional activating dimer. The circuit differs from that of any documented circadian network, but the analogy is close enough to suggest alternative models of circadian networks (P.F., unpublished data) which, contrary to some existing ones, do not make crucial use of high Hill coefficients (32) or delays coming from chains of phosphorylation (33).…”

Section: Resultsmentioning

confidence: 87%

Design of genetic networks with specified functions by evolution in silico

François¹,

Hakim²

2004

Proc. Natl. Acad. Sci. U.S.A.

262

231

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Recent studies have provided insights into the modular structure of genetic regulatory networks and emphasized the interest of quantitative functional descriptions. Here, to provide a priori knowledge of the structure of functional modules, we describe an evolutionary procedure in silico that creates small gene networks performing basic tasks. We used it to create networks functioning as bistable switches or oscillators. The obtained circuits provide a variety of functional designs, demonstrate the crucial role of posttranscriptional interactions, and highlight design principles also found in known biological networks. The procedure should prove helpful as a way to understand and create small functional modules with diverse functions as well as to analyze large networks.

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Section: Resultsmentioning

confidence: 87%

Design of genetic networks with specified functions by evolution in silico

François¹,

Hakim²

2004

Proc. Natl. Acad. Sci. U.S.A.

262

231

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“…The focus of this contribution is such an analysis for several hundred variants of a cellular circuit model that has been successfully used to describe circadian rhythms in different organisms (6,32,33).…”

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confidence: 99%

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

Wagner

2005

Proc. Natl. Acad. Sci. U.S.A.

141

122

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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. complexity ͉ gene networks

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Section: Modelling Circadian Clocksmentioning

confidence: 99%

“…Various ODE models of circadian clocks have been proposed since the first one proposed by Goodwin [115,116,236], one of the most robust and based on a physiological mechanism being the one by Leloup and Goldebeter [111,165,166], but others have been proposed, in particular by Forger [92,93,94], and by Tyson and Novak [259], reviewed in [95]. One of the most referenced ones, by Leloup and Goldbeter, writes:…”

Section: Modelling Circadian Clocksmentioning

confidence: 99%

Modelling Physiological and Pharmacological Control on Cell Proliferation to Optimise Cancer Treatments

Clairambault

2009

Math. Model. Nat. Phenom.

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Abstract. This review aims at presenting a synoptic, if not exhaustive, point of view on some of the problems encountered by biologists and physicians who deal with natural cell proliferation and disruptions of its physiological control in cancer disease. It also aims at suggesting how mathematicians are naturally challenged by these questions and how they might help, not only biologists to deal theoretically with biological complexity, but also physicians to optimise therapeutics, on which last point the focus will be set here. To this purpose, mathematical modelling should represent proliferating cell population dynamics with natural built-in control targets (which implies modelling the cell division cycle), together with the distribution of drugs in the organism and their molecular actions on different targets at the cell level on proliferation, i.e., molecular pharmacokinetics-pharmacodynamics of antiproliferative drugs. This should make possible optimal control of drug delivery with constraints to be determined according to the main pharmacological issues encountered in the clinic: unwanted toxic side-effects, occurrence of drug resistance. Mathematical modelling should also take into account physiological determinants of cell and tissue proliferation, such as intervention of the immune system, circadian control on cell cycle checkpoint proteins, and activity of intracellular drug processing enzymes together with individual variations in the activities of these proteins (genetic polymorphism). Taking these points into account will add to the rich scenery of normal or disrupted cell and tissue regulations, and their corrections by drugs, a natural environmental, whole body physiological, frame. It is necessary indeed to consider such a framework if one wants to eventually be actually helpful to clinicians who routinely treat by combinations of drugs living Humans with their complex whole body regulations, often dependent on genotypic variations, and not isolated cells or tissues.Key words: cell proliferation, population dynamics, physiologically structured partial differential equations, pharmacological control, pharmacokinetics-pharmacodynamics, physiological modelling, cancer treatments, therapeutic optimisation, individualised medicine AMS subject classification: 92-02, 92B05 Biological common knowledge on cancerThis section shortly presents the general scenery of cancer evolution and features that must be included in models designed to describe and control it, bearing in mind a more descriptive (physicist's) than a therapeutic (physician's) point of view, only to reverse this perspective in the following sections, in which will be stressed the interest of modelling from the point of view of control, at least if one wants to design models for therapeutic applications. This biological section will be only a short sketch, if one considers that on this subject many books have already been written, e.g. [24,78,96,97,223]. Cancer: a challenging public health problemCardiovascular diseases and cancer are by fa...

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The Goodwin Model: Simulating the Effect of Light Pulses on the Circadian Sporulation Rhythm of Neurospora Crassa

Cited by 129 publications

References 46 publications

Design of genetic networks with specified functions by evolution in silico

Design of genetic networks with specified functions by evolution in silico

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

Modelling Physiological and Pharmacological Control on Cell Proliferation to Optimise Cancer Treatments

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