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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Feedback Thought in Economics and Finance

Feedback Thought in Economics and Finance

  • Negative Feedbacks
  • Positive Feedbacks
  • Stocks and Flows
  • Limiting Factors

 

Key People:

  • Jay Forrester
  • George Richardson
  • John Sterman
  • Michael Radzicki
  • Mikhail Oet
  • Oleg Pavlov
  • Eric D. Beinhocker
  • Stuart A. Umpleby
  • Khalid Saeed
  • Kaoru Yamaguchi

 

Reflexivity and Second order economics are closely related concepts.

 

From System Dynamics and Its Contribution to Economics and Economic Modeling

 

System dynamics is a computer simulation modeling methodology that is used to analyze complex nonlinear dynamic feedback systems for the purposes of generating insight and designing policies that will improve system performance. It was originally created in 1957 by Jay W. Forrester of the Massachusetts Institute of Technology as a methodology for building computer simulation models of problematic behavior within corporations. The models were used to design and test policies aimed at altering a corporation’s structure so that its behavior would improve and become more robust.

Today, system dynamics is applied to a large variety of problems in a multitude of academic disciplines, including economics. System dynamics models are created by identifying and linking the relevant pieces of a system’s structure and simulating the behavior generated by that structure. Through an iterative process of structure identification, mapping, and simulation a model emerges that can explain (mimic) a system’s problematic behavior and serve as a vehicle for policy design and testing. From a system dynamics perspective a system’s structure consists of stocks, flows, feedback loops, and limiting factors.

Stocks can be thought of as bathtubs that accumulate/de-cumulate a system’s flows over time. Flow can be thought of as pipe and faucet assemblies that fill or drain the stocks. Mathematically, the process of flows accumulating/de-cumulating in stocks is called integration. The integration process creates all dynamic behavior in the world be it in a physical system, a biological system, or a socioeconomic system. Examples of stocks and flows in economic systems include a stock of inventory and its inflow of production and its outflow of sales, a stock of the book value of a firm’s capital and its inflow of investment  spending and its outflow of depreciation, and a stock of employed labor and its inflow of hiring and its outflow of labor separations.

Feedback is the transmission and return of information about the amount of information or material that has accumulated in a system’s stocks. Information travels from a stock back to its flow(s) either directly or indirectly, and this movement of information causes the system’s faucets to open more, close a bit, close all the way, or stay in the same place. Every feedback loop has to contain at least one stock so that a simultaneous equation situation can be avoided and a model’s behavior can be revealed recursively. Loops with a single stock are termed minor, while loops containing more than one stock are termed major. 

Two types of feedback loops exist in system dynamics modeling: positive loops and negative loops. Generally speaking, positive loops generate self-reinforcing behavior and are responsible for the growth or decline of a system. Any relationship that can be termed a virtuous or vicious circle is thus a positive feedback loop. Examples of positive loops in economic systems include path dependent processes, increasing returns, speculative bubbles, learning by-doing, and many of the relationships found in macroeconomic growth theory. Forrester [12], Radzicki and Sterman [46],Moxnes [32], Sterman (Chap. 10 in [55]), Radzicki [44], Ryzhenkov [49], and Weber [58] describe system dynamics models of economic systems that possess dominant positive feedback processes.

Negative feedback loops generate goal-seeking behavior and are responsible for both stabilizing systems and causing them to oscillate. When a negative loop detects a gap between a stock and its goal it initiates corrective action aimed at closing the gap. When this is accomplished without a significant time delay, a system will adjust smoothly to its goal. On the other hand, if there are significant time lags in the corrective actions of a negative loop, it can overshoot or undershoot its goal and cause the system to oscillate. Examples of negative feedback processes in economic systems include equilibrating mechanisms (“auto-pilots”) such as simple supply and demand relationships, stock adjustment models for invetory control, any purposeful behavior, and many of the relationships found in macroeconomic business cycle theory. Meadows [27], Mass [26], Low [23], Forrester [12], and Sterman [54] provide examples of system dynamics models that generate cyclical behavior at the macro-economic and micro-economic levels.

From a system dynamics point of view, positive and negative feedback loops fight for control of a system’s behavior. The loops that are dominant at any given time determine a system’s time path and, if the system is nonlinear, the dominance of the loops can change over time as the system’s stocks fill and drain. From this perspective, the dynamic behavior of any economy that is, the interactions between the trend and the cycle in an economy over time can be explained as a fight for dominance between the economy’s most significant positive and negative feedback loops.

 

Key Sources of Research:

 

 

 

FEEDBACK MECHANISMS IN THE FINANCIAL SYSTEM: A MODERN VIEW

Mikhail V. Oet

Oleg V. Pavlov

http://www.systemdynamics.org/conferences/2014/proceed/papers/P1441.pdf

 

 

Mr. Hamilton, Mr. Forrester, and a Foundation for Evolutionary Economics

Michael J. Radzicki

 

https://www.researchgate.net/profile/Michael_Radzicki/publication/237782671_Mr._Hamilton_Mr._Forrester_and_a_Foundation_for_Evolutionary_Economics/links/0a85e52e41951a468c000000.pdf

 

European Contributions to Evolutionary Institutional Economics: The Cases of ‘Cumulative Circular Causation’ (CCC) and ‘Open Systems Approach’ (OSA).
Some Methodological and Policy Implications

 

Sebastian Berger and Wolfram Elsner

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.519.7169&rep=rep1&type=pdf

 

System Dynamicsand Its Contribution to Economics and Economic Modeling

MICHAEL J. RADZICKI

 

https://www.researchgate.net/profile/Michael_Radzicki/publication/227167378_System_Dynamics_and_Its_Contribution_to_Economics_and_Economic_Modeling/links/02e7e53331fe5f394b000000.pdf

 

 

Institutional Economics, Post Keynesian Economics, and System Dynamics: Three Strands of a Heterodox Economics Braid

Michael J. Radzicki, Ph.D.

https://www.researchgate.net/profile/Michael_Radzicki/publication/237138677_Institutional_Economics_Post_Keynesian_Economics_and_System_Dynamics_Three_Strands_of_a_Heterodox_Economics_Braid/links/02e7e53331eeea388c000000.pdf

 

 

Was Alfred Eichner a System Dynamicist?

by

Michael J. Radzicki

https://www.researchgate.net/profile/Michael_Radzicki/publication/239920399_Was_Alfred_Eichner_a_System_Dynamicist/links/0f317536d3f41a13fb000000.pdf

 

Second-Order Economics as an Example of Second-Order Cybernetics

Stuart A. Umpleby

 

http://www.univie.ac.at/constructivism/archive/fulltexts/890.pdf

 

Reflexivity, complexity, and the nature of social science

Eric D. Beinhocker

 

http://www.inet.ox.ac.uk/files/publications/Beinhocker%20(JEM%202013).pdf

 

Path dependence, its critics and the quest for ‘historical economics’

By

Paul A. David

https://www.researchgate.net/profile/Paul_David2/publication/23742679_Path_dependence_its_critics_and_the_quest_for_historical_economics/links/0deec53b482217c114000000.pdf

 

Endogenous Feedback Perspective on Money in a Stock-Flow Consistent Model

I. David Wheat
University of Bergen

 

http://www.wheatresources.com/WP/Wheat%20Endogenous%20Feedback%20Perspective%20on%20Money%20WP.pdf

 

Classical Economics on Limits to Growth

Khalid Saeed

 

http://haghshenas.com/PhD%20thesis/articel%20sources%201st/Classical%20Economics%20on%20Limits%20to%20Growth.pdf

 

 

Misperceptions of Feedback in Dynamic Decisionmaking

John D. Sterman

 

https://www.researchgate.net/profile/John_Sterman2/publication/37593529_Misperceptions_of_feedback_in_dynamic_decisionmaking/links/54359e4e0cf2bf1f1f2b3520.pdf

 

Learning in and about complex systems

John D. Sterman

 

http://atransdisciplinaryapproach.com/wp-content/uploads/2014/02/sterman-learning-in-and-about-complex-systems.pdf

 

Micro-worlds and Evolutionary Economics

Michael J. Radzicki

http://www.systemdynamics.org/conferences/1992/proceed/pdfs/radzi533.pdf

 

Feedback Thought in Social Science and Systems Theory

George Richardson

Pegasus Communications, Inc. ©1999
ISBN:1883823463

 

The Feedback concept in American Social Sciences 

George Richardson

1983

http://www.systemdynamics.org/conferences/1983/proceed/plenary/richa001.pdf

 

Evolutionary Economics and System Dynamics

Radzicki and Sterman

 

Effects of Feedback Complexity on Dynamic Decision Making
Ernst Diehl, John D. Sterman

Organizational Behavior and Human Decision Processes

Volume 62, Issue 2, May 1995, Pages 198-215

 

Old Wine in a New Bottle:
Towards a Common Language for Post-Keynesian Macroeconomics Model

Ginanjar Utama

2014

http://www.systemdynamics.org/conferences/2014/proceed/papers/P1307.pdf

 

On Component Based Modeling Approach using System Dynamics for The Financial System (With a Case Study of Keen-Minsky Model)

Ginanjar Utama

2013

http://www.systemdynamics.org/conferences/2013/proceed/papers/P1209.pdf

 

On the Monetary and Financial Stability under A Public Money System

– Modeling the American Monetary Act Simplified –

Kaoru Yamaguchi

 

http://www.systemdynamics.org/conferences/2012/proceed/papers/P1065.pdf

 

Integration of Real and Monetary Sectors with Labor Market
– SD Macroeconomic Modeling (3) –

Kaoru Yamaguchi

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.436.1085&rep=rep1&type=pdf

 

Balance of Payments and Foreign Exchange Dynamics

– SD Macroeconomic Modeling (4) –

Kaoru Yamaguchi, Ph.D

2007

http://www.systemdynamics.org/conferences/2007/proceed/papers/YAMAG211.pdf

 

 

Money and Macroeconomic Dynamics

Accounting System Dynamics Approach

Kaoru Yamaguchi, Ph.D

 

http://muratopia.org/Yamaguchi/macrodynamics/Macro%20Dynamics.pdf

 

Does Money Matter on the Formation of Business Cycles and Economic Recessions ?
– SD Simulations of A Monetary Goodwin Model –

 

Kaoru Yamaguchi

http://bs.doshisha.ac.jp/attach/page/BUSINESS-PAGE-JA-53/26830/file/DBS12-01.pdf

 

Head and Tail of Money Creation and its System Design Failures

– Toward the Alternative System Design –

JFRC Working Paper No. 01-2016

Kaoru Yamaguchi, Ph.D.

Yokei Yamaguchi

http://www.vollgeld-initiative.ch/fa/img/Vertiefung_deutsch/Head-and-Tail-2016_WP__-_Japan_Futures_Research_Center.pdf

 

Modelling the Great Transition

 

Emanuele Campiglio

New Economics Foundation

http://systemdynamics.org.uk/wp-content/uploads/Emanuel-SD-conference-9-2-12.pdf

 

The role of System Dynamics modelling to understand food chain complexity and address challenges for sustainability policies

Irene Monasterolo1, Roberto Pasqualino, Edoardo Mollona

 

http://www.fao.org/fileadmin/templates/ags/docs/MUFN/CALL_FILES_EXPERT_2015/CFP3-06_Full_Paper.pdf

 

Dynamic regional economic modeling: a systems approach

I. David Wheat

2014

 

http://www.zneiz.pb.edu.pl/data/magazine/article/434/en/1.17_wheat_pawluczuk.pdf

 

Expectation Formation and Parameter Estimation in Uncertain Dynamical Systems: The System Dynamics Approach to Post Keynesian-Institutional Economics

Introduction

 

Michael J. Radzicki

 

https://www.researchgate.net/profile/Michael_Radzicki/publication/254071516_Draft_Expectation_Formation_and_Parameter_Estimation_in_Uncertain_Dynamical_Systems_The_System_Dynamics_Approach_to_Post_Keynesian-Institutional_Economics/links/0deec536d3da974962000000.pdf

 

The Circular and Cumulative Structure of Administered Pricing

Mark Nichols, Oleg Pavlov, and Michael J. Radzicki

2006

https://www.researchgate.net/profile/Mark_Nichols3/publication/228273797_The_Circular_and_Cumulative_Structure_of_Administered_Pricing/links/02e7e5282d33c933df000000.pdf

 

A System Dynamics Approach to the Bhaduri‐Marglin Model

Klaus D. John

http://www.systemdynamics.org/conferences/2009/proceed/papers/P1306.pdf

 

An Institutional Dynamics Model of the Euro zone crisis: Greece as an Illustrative Example

Domen Zavrl

Miroljub Kljajić

http://www.systemdynamics.org/conferences/2010/proceed/papers/P1144.pdf

 

Is system dynamics modelling of relevance to neoclassical economists? 

Douglas J. Crookes Martin P. De Wit

https://www.researchgate.net/profile/Douglas_Crookes/publication/262674865_Is_System_Dynamics_Modelling_of_Relevance_to_Neoclassical_Economists/links/00b7d53861d6b14d9f000000.pdf

 

System dynamics modelling and simulating the effects of intellectual capital on economic growth

Ivona Milić Beran

http://hrcak.srce.hr/ojs/index.php/crorr/article/viewFile/2803/2121