Systems Biology: Biological Networks, Network Motifs, Switches and Oscillators

Systems Biology: Biological Networks, Network Motifs, Switches and Oscillators

 

 

From Biological switches and clocks

The living cell receives signals from its environment and its own internal state, processes the information, and initiates appropriate responses in terms of changes in gene expression, cell movement, and cell growth or death. Like a digital computer, information processing within cells is carried out by a complex network of switches and oscillators, but instead of being fabricated from silicon transistors and quartz crystals, the cell’s computer is an evolved network of interacting genes and proteins. In the same way that computer design was made possible by a sophisticated theory of electronic circuitry, a basic understanding of cellular regulatory mechanisms will require a relevant theory of biomolecular circuitry. Although the ‘engineering mindset’ is sorely needed to make sense of the cell’s circuitry, the squishy, sloppy, massively parallel, analogue nature of biochemistry is so different from the solid-state, precise, sequential, digital nature of computers that the mathematical tools and intellectual biases of the solid-state physicist/electrical engineer are not entirely appropriate to unravelling the molecular logic of cell physiology. New modelling paradigms and software tools are evolving to meet the challenges of the new ‘systems biology’ of the living cell.

 

 

System Biology includes study of the following among other areas.

  • Biological Networks
  • Network Motifs
  • Switches
  • Oscillators

 

 

Biological Networks

  • Protein–protein interaction networks
  • Gene regulatory networks (DNA–protein interaction networks)
  • Gene co-expression networks (transcript–transcript association networks)
  • Metabolic networks
  • Signaling networks
  • Neuronal networks
  • Between-species interaction networks
  • Within-species interaction networks

 

Network Motifs:

  • Coherent Feedforward Loop (FFL)
  • Incoherent Feedforward Loop
  • Feedback Loop
  • Scaffold Motifs
  • Bi Fan
  • Multi Input Motifs (MIM)
  • Regulator Chains
  • Bi-Parallel
  • Single Input Module (SIM)
  • Dense Overlapping Regulon (DOR)

 

Biological Switches

  • Ultrasensitivity
  • Switches (Bistability)

 

Biological Oscillators

  • Clocks
  • Negative Feedback Only Oscillators
    • Repressilator
    • Pentilator
    • Goodwin Oscillator
    • Frazilator
    • Metabolator
  • Negative + Positive Feedback Oscillators
    • Meyer and Strayer model of Calcium Oscillations
    • van der Pol Oscillator
    • Fitzhugh-Nagumo Oscillator
    • Cyanobacteria Circadian Oscillator
  • Negative + Negative Feedback Oscillator
  • Negative and Positive + Negative Feedback cell cycle Oscillator
  • Fussenegger Oscillators
  • Smolen Oscillator
  • Amplified Negative Feedback Oscillators
  • Variable link Oscillators

 

Synthetic Biology study design of networks, switches, and oscillators.

 

From The dynamics and robustness of Network Motifs in transcription networks

Network Motifs

Even though biological systems are extremely complex, some of its complexity could be simplified. The study of a complex system in its entirety could prove impossible with current theories and technology. However, mathematical modelling has sought to distil the essence of complexity into concepts readily understandable by today’s science. One of such approaches has been reported by means of the study of pathways of interaction of biological networks. By concentrating on similar features that biological networks share, it has been recently discovered that at a cellular level, regulation and transcription Networks display certain patterns of connectivity at a much higher rate than expected in an equivalent randomized network. These recurring patterns of interaction, or network “Motifs”, can help us define bread classes of networks and their types of functional elements. In the same way, they can reveal the evolutionary aim by which they have been developed. Network Motifs can be interpreted as structures that have emerged as direct a reflection of the constraints under which the network has evolved. These network Motifs have been found in the biological networks of many systems, suggesting that they are the building blocks of transcription networks [4]. It has been suggested that in biological networks, these recurrent Network Motifs are responsible for carrying out key information processing tasks in the organism [5].

 

From Coupling oscillations and switches in genetic networks.

Switches (bistability) and oscillations (limit cycle) are omnipresent in biological networks. Synthetic genetic networks producing bistability and oscillations have been designed and constructed experimentally. However, in real biological systems, regulatory circuits are usually interconnected and the dynamics of those complex networks is often richer than the dynamics of simple modules. Here we couple the genetic Toggle switch and the Repressilator, two prototypic systems exhibiting bistability and oscillations, respectively. We study two types of coupling. In the first type, the bistable switch is under the control of the oscillator. Numerical simulation of this system allows us to determine the conditions under which a periodic switch between the two stable steady states of the Toggle switch occurs. In addition we show how birhythmicity characterized by the coexistence of two stable small-amplitude limit cycles, can easily be obtained in the system. In the second type of coupling, the oscillator is placed under the control of the Toggleswitch. Numerical simulation of this system shows that this construction could for example be exploited to generate a permanent transition from a stable steady state to self-sustained oscillations (and vice versa) after a transient external perturbation. Those results thus describe qualitative dynamical behaviors that can be generated through the coupling of two simple network modules. These results differ from the dynamical properties resulting from interlocked feedback loops systems in which a given variable is involved at the same time in both positive and negative feedbacks. Finally the models described here may be of interest in synthetic biology, as they give hints on how the coupling should be designed to get the required properties.

 

From Robust, Tunable Biological Oscillations from Interlinked Positive and Negative Feedback Loops

To test the generality of the idea that positive feedback enables an oscillator to have a tunable frequency and constant amplitude, we examined several other oscillator models, including five negative feedback–only models: (i) the Goodwin oscillator, a well-studied model relevant to circadian oscillations (18, 19); (ii) the Repressilator, a transcriptional triple-negative feedback loop constructed in Escherichia coli (20); (iii) the “Pentilator,” a Repressilator with five (rather than three) repressors; (iv) the Metabolator (21), a synthetic metabolic oscillator; and (v) the Frzilator, amodel of the control of gliding motions in myxobacteria (22). In four of the cases (Goodwin, Repressilator, Pentilator, and Metabolator), the amplitude/frequency curves were inverted U-shaped curves similar to that seen for the negative feedback–only cell cycle model (Figs. 1B and 3A). In the case of the Frzilator, the legs of the curve were truncated; the oscillator had a nonzero minimal amplitude (Fig. 3A). For all five of the negative feedback–only models, the oscillators functioned over only a narrow range of frequencies (Fig. 3A).

We also examined four positive-plus-negative feedback oscillators: (i) the van der Pol oscillator, inspired by studies of vacuum tubes (12); (ii) the Fitzhugh-Nagumo model of propagating action potentials (23, 24); (iii) the Meyer-Stryer model of calcium oscillations (25); and (iv) a model of circadian oscillations in the cyanobacterial KaiA/B/C system (26–28). In each case, we obtained a flat, wide amplitude/frequency curve (Fig. 3B). Thus, a tunable frequency plus constant amplitude can be obtained from many different positive-plusnegative feedback models; this feature is not peculiar to one particular topology or parameterization.

These findings rationalize why the positiveplus- negative feedback design might have been selected through evolution in cases where a tunable frequency and constant amplitude are important, such as heartbeats and cell cycles. However, it is not clear that an adjustable frequency would be advantageous for circadian oscillations, because frequency is fixed at one cycle per day. Nevertheless, the cyanobacterial circadian oscillator appears to rely on positive feedback (26), and positive feedback loops have been postulated for other circadian oscillators as well (Table 1). This raises the question of whether the positiveplus- negative feedback design might offer additional advantages.

One possibility is that the positive-plusnegative feedback design permits oscillations over a wider range of enzyme concentrations and kinetic constant values, making the oscillator easier to evolve and more robust to variations in its imperfect components. We tested this idea through a Monte Carlo approach.We formulated three simple oscillatormodels: (i) a three-variable triple negative feedback loop with no additional feedback (Fig. 4A), (ii) one with added positive feedback (Fig. 4B), or (iii) one with added negative feedback (Fig. 4C). We generated random parameter sets for the models and then for each set determined whether the model produced limit cycle oscillations.We continued generating parameter sets until we had amassed 500 that gave oscillations.

 

From Robust, Tunable Biological Oscillations from Interlinked Positive and Negative Feedback Loops

Sysbio

 

 

Key Terms:

  • Ultra-sensitivity
  • Bi-stability
  • Positive Feedback Loop
  • Negative Feedback Loop
  • Biological Oscillators
  • Biological Switches
  • Biological Networks
  • Network Motifs
  • Regulation Networks
  • Signalling Networks
  • Communication Networks
  • Biological Clocks
  • Circadian Rhythms
  • Harmonic Oscillators
  • Van der Pol Oscillator (Limit Cycle)
  • FitzHugh–Nagumo oscillators (Neural)
  • Limit Cycle Oscillator
  • Cell Cycle
  • Systems Biology
  • Synthetic Biology
  • Gene Regulatory Networks
  • Kuramoto Oscillators
  • Phase Coupled Oscillators
  • Cardic Pacemaker
  • Biochemical Networks
  • Synchronization
  • Goodwin Oscillator
  • Repressilators
  • Fussenegger Oscillators
  • Smolen Oscillators
  • Variable Link Oscillators
  • Metabolators
  • Amplified Negative Feedback Oscillators

 

 

 

Key Sources of Research:

 

 

Ultrasensitivity Part I: Michaelian responses and zero-order ultrasensitivity

James E. Ferrell Jr. and Sang Hoon Ha

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4214216/pdf/nihms-629459.pdf

 

 

 

 

Ultrasensitivity Part II: Multisite phosphorylation, stoichiometric inhibitors, and positive feedback

James E. Ferrell Jr. and Sang Hoon Ha

 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4435807/pdf/nihms686079.pdf

 

 

 

Ultrasensitivity part III: cascades, bistable switches, and oscillators

James E. Ferrell Jr and Sang Hoon Ha

 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4254632/pdf/nihms635216.pdf

 

 

 

Robust Network Topologies for Generating Switch-Like Cellular Responses

Najaf A. Shah1, Casim A. Sarkar

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3121696/pdf/pcbi.1002085.pdf

 

 

 

 

Feedback Loops Shape Cellular Signals in Space and Time

Onn Brandman1 and Tobias Meyer

 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2680159/pdf/nihms101299.pdf

 

 

 

Interlinked Fast and Slow Positive Feedback Loops Drive Reliable Cell Decisions

Onn Brandman, James E. Ferrell Jr, Rong Li2,3,4, and Tobias Meyer

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3175767/pdf/nihms180881.pdf

 

 

 

Positive feedback in cellular control systems

Alexander Y. Mitrophanov and Eduardo A. Groisman

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2486260/pdf/nihms-58057.pdf

 

 

 

Effect of positive feedback loops on the robustness of oscillations in the network of cyclin-dependent kinases driving the mammalian cell cycle

Claude Gerard, Didier Gonze and Albert Goldbeter

 

http://onlinelibrary.wiley.com/store/10.1111/j.1742-4658.2012.08585.x/asset/j.1742-4658.2012.08585.x.pdf?v=1&t=j0i1rfq0&s=54814f48d70da4b93bd1632677765a1a5673c8d6

 

 

Design Principles of Biochemical Oscillators

Béla Novak and John J. Tyson

 

 

 

Design principles underlying circadian clocks

D. A. Rand1,†, B. V. Shulgin1, D. Salazar1,2 and A. J. Millar

 

 

 

Positive Feedback Promotes Oscillations in Negative Feedback Loops

Bharath Ananthasubramaniam*, Hanspeter Herzel

 

 

 

Efficient Switches in Biology and Computer Science

Luca Cardelli1,2, Rosa D. Hernansaiz-Ballesteros3, Neil Dalchau1, Attila Csika ́sz-Nagy

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5215766/pdf/pcbi.1005100.pdf

 

 

 

Robust, Tunable Biological Oscillations from Interlinked Positive and Negative Feedback Loops

Tony Yu-Chen Tsai,1* Yoon Sup Choi,1,2* Wenzhe Ma,3,4 Joseph R. Pomerening,5 Chao Tang,3,4 James E. Ferrell Jr

https://www.researchgate.net/publication/5253202_Robust_Tunable_Biological_Oscillations_from_Interlinked_Positive_and_Negative_Feedback_Loops?el=1_x_8&enrichId=rgreq-3a45d550364998e0f57384dda12a695f-XXX&enrichSource=Y292ZXJQYWdlOzI0MTY5NjI3MjtBUzoxMzEyODEwMTg5NTM3MjhAMTQwODMxMTI0MjY2OQ==

 

 

 

Biological switches and clocks

John J. Tyson1,*, Reka Albert2, Albert Goldbeter3, Peter Ruoff4 and Jill Sibl

 

http://www.ulb.ac.be/sciences/utc/ARTICLES/2008_Tyson_J_R_Soc_Interface.pdf

https://www.kitp.ucsb.edu/activities/bioclocks07

http://online.kitp.ucsb.edu/online/bioclocks07/

 

 

 

Network thinking in ecology and evolution

Stephen R. Proulx1, Daniel E.L. Promislow2 and Patrick C. Phillips

 

https://pdfs.semanticscholar.org/5665/65601ed2a5c67143b6d4be7193c02235a279.pdf

 

 

 

Networks in ecology

Jordi Bascompte

 

http://izt.ciens.ucv.ve/ecologia/Archivos/ECO_POB%202007/ECOPO7_2007/Bascompte%202007.pdf

 

 

 

Network structure and the biology of populations

Robert M. May

 

https://www.sccs.swarthmore.edu/users/08/bblonder/phys120/docs/may.pdf

 

 

 

Biological networks: Motifs and modules

 

http://bioinfo.vanderbilt.edu/zhanglab/lectures/BMIF310_network_B_Motifs_2009.pdf

 

 

 

Analysis of Biological Networks: Network Motifs

 

http://www.cs.tau.ac.il/~roded/courses/bnet-a06/lec04.pdf

 

 

 

Regulatory networks & Functional motifs

Didier Gonze

 

http://homepages.ulb.ac.be/~dgonze/TEACHING/network_motifs.pdf

 

 

 

Structure and function of the feed-forward loop network motif

S. Mangan and U. Alon

 

http://www.pnas.org/content/100/21/11980.full.pdf

 

 

 

Network Motifs: Simple Building Blocks of Complex Networks

R. Milo, S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, U. Alon

 

http://wilson.med.harvard.edu/nb204/MiloAlon2002.pdf

 

 

 

The dynamics and robustness of Network Motifs in transcription networks

Arturo Araujo

http://www.ucl.ac.uk/~ucbpaar/flies_archivos/Network_Motifs.pdf

 

 

 

Formation of Regulatory Patterns During Signal Propagation in a Mammalian Cellular Network

Avi Ma’ayan, Sherry L. Jenkins, Susana Neves, Anthony Hasseldine, Elizabeth Grace, Benjamin Dubin-Thaler, Narat J. Eungdamrong, Gehzi Weng, Prahlad T. Ram, J. Jeremy Rice, Aaron Kershenbaum, Gustavo A. Stolovitzky, Robert D. Blitzer, and Ravi Iyengar

 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3032439/pdf/nihms266526.pdf

 

 

 

Toward Predictive Models of Mammalian Cells

Avi Ma’ayan, Robert D. Blitzer, and Ravi Iyengar

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3035045/pdf/nihms266522.pdf

 

 

 

Modeling Cell Signaling Networks

Narat J. Eungdamrong and Ravi Iyengar

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3620715/pdf/nihms453834.pdf

 

 

 

Bistability in Biochemical Signaling Models

Eric A. Sobie

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4118931/pdf/nihms-332970.pdf

 

 

An Introduction to Dynamical Systems

Eric A. Sobie

 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4118930/pdf/nihms-332968.pdf

 

 

 

Computational approaches for modeling regulatory cellular networks

Narat J. Eungdamrong and Ravi Iyengar

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3619405/pdf/nihms-453838.pdf

 

 

Systems Biology—Biomedical Modeling

Eric A. Sobie,* Young-Seon Lee, Sherry L. Jenkins, and Ravi Iyengar

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3188945/

 

 

 

Network analyses in systems pharmacology

 

Seth I. Berger and Ravi Iyengar

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2752618/pdf/btp465.pdf

 

 

Biological Networks: The Tinkerer as an Engineer

U Alon

 

http://dp.univr.it/~laudanna/Systems%20Biology/Publications/Reviews/Network%20analysis/Biological%20Networks%20The%20Tinkerer%20as%20an%20Engineer.pdf

 

 

Cell Biology: Networks, Regulation and Pathways

GAŠPER TKACˇ IK , WILLIAM BIALEK

 

https://www.princeton.edu/~wbialek/our_papers/tkacik+bialek_09b.pdf

 

 

 

Coupling oscillations and switches in genetic networks

Didier Gonze

 

https://pdfs.semanticscholar.org/0878/d29052b34bc3fe43649c826fd9fd0506e445.pdf

 

 

 

Biological Oscillators and Switches

 

http://faculty.washington.edu/hqian/amath4-523/Murray-Math-Biol-ch7.pdf

 

 

 

Design principles of biological oscillators

 

Didier Gonze

 Nonlinear Chemical Dynamics: Oscillations, Patterns, and Chaos

 

Irving R. Epstein

Kenneth Showalter

 

 

Modelling biological oscillations

 

Shan He

 

A comparative analysis of synthetic genetic oscillators

 

Oliver Purcell1,*, Nigel J. Savery3, Claire S. Grierson4 and Mario di Bernardo2,5

 

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Author: Mayank Chaturvedi

You can contact me using this email mchatur at the rate of AOL.COM. My professional profile is on Linkedin.com.

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