Introduction to Systemics with focus on Systems Biology
Download as PPTX, PDF3 likes853 views
The document provides an introduction to systemics with an emphasis on systems biology, highlighting its interdisciplinary nature and the importance of understanding complex systems. It covers key concepts such as systems analysis, design, dynamics, and various methodologies used in systems science, alongside historical development in the field. Additionally, it addresses challenges faced in systems science and concludes by advocating for systemics as a powerful methodology for integrating knowledge and designing models across scientific disciplines.
![A short history
• 1970s: Sequence Databases, Similarity matrices, Molecular
Evolution – prominent figures such as Margaret Dayhoff,
Carl Woese etc.
• 1980s: Sequence Alignment/ Search – David Lipmann,
Smith, Waterman etc.
• 1990s: HMMs, Protein Structure Prediction, Genomics and
Comparative Genomics
• 2000s: HGP, Synthetic Biology from the knowledge of
Biological Systems, High throughput sequencing,
Development of Computational Methods (Systems
methodologies, analysis techniques) [Rise of Systems
Biology]
• Early and Late 2010s: Next Generation Sequencing, Cellular
Modeling, Networks, Gene prediction, Synthetic biology
etc.](https://image.slidesharecdn.com/introductiontosystemsscience-170420161035/85/Introduction-to-Systemics-with-focus-on-Systems-Biology-7-320.jpg)
![%%% Statistics
tdiv=treset(end)-treset(end-1);
mdiv=max(mass(k:end));
division=[division ; tdiv mdiv]
fprintf('t_div=%g n',tdiv)
fprintf('m_div=%g n',mdiv)
function y = dxdt(t,v)
%Parameters
kSMPF=0.05;
kD1CYC=0.0235;
kD2CYC=0.75;
kASS=100;
kDISS=0.0025;
kDRUM1=0.125;
kIRUM1=2;
k1SLP1=0.8;
J1SLP1=0.001;
V2SLP1=0.2;
J2SLP1=0.001;
kDMPFRUM1=0.35;
k1IE=0.2;
a=0.05;
J1IE=0.001;
V2IE=0.05;
J2IE=0.001;
k12RUM1=50;
kARUM1=35;
VWEE1=0.125;
J1WEE1=0.01;
J2WEE1=0.01;
k1CDC25=0.05;
k2CDC25=2.5;
J1CDC25=0.01;
J2CDC25=0.01;
VCDC25=0.2;
k1WEE1=0.05;
k2WEE1=2.5;
m=0.005;
Cdc25T=1;
Wee1T=1;
IET=1;
Slp1T=1;
VSRUM1=0.06;
kDRUM1P=250;
kDX=1;
%CCP=1;
CCP=0; %%
Ki=105.76; %Vary the parameters here
PDC=0.0006;
%Variables
MPF=v(1);
MPFp=v(2);
Slp1A=v(3);
IEA=v(4);
MPFRum1=v(5);
Rum1=v(6);
Rum1p=v(7);
Wee1=v(8);
Cdc25p=v(9);
Mass=v(10);
Cdc25PDC=v(11); %% Vary the parameters here
%Algebric Equations
kWEE1=k1WEE1*Wee1T+(k2WEE1-k1WEE1)*Wee1;
kWEE1p=0.625; %%
kCDC25=k1CDC25*Cdc25T+(k2CDC25-k1CDC25)*Cdc25p;
kCDC25p=1; %%
Wee1p=Wee1T-Wee1;
Cdc25=Cdc25T-Cdc25p-Cdc25PDC;
IE=IET-IEA;
Slp1=Slp1T-Slp1A;
% ODEs
y = [
%MPF
kSMPF*Mass-kWEE1*MPF+kCDC25*MPFp-(kD1CYC+kD2CYC*Slp1A)*MPF-kASS*Rum1*MPF+(kDISS+kDRUM1+kIRUM1)*MPFRum1+kDX*CCP*MPFRum1;
%MPFp
kWEE1*MPF-kCDC25*MPFp-(kD1CYC+kD2CYC*Slp1A)*MPFp;
%Slp1A
k1SLP1*IEA*Slp1/(J1SLP1+Slp1)-V2SLP1*Slp1A/(J2SLP1+Slp1A);
%IEA
k1IE*(MPF+a*MPFp)*IE/(J1IE+IE)-V2IE*IEA/(J2IE+IEA);
%MPFRum1
kASS*Rum1*MPF-(kDISS+kDRUM1+kIRUM1+kDMPFRUM1)*MPFRum1-kDX*CCP*MPFRum1;
%Rum1
VSRUM1-kASS*Rum1*MPF+(kDISS+kDMPFRUM1)*MPFRum1-k12RUM1*MPFp*Rum1+kARUM1*Rum1p-kDRUM1*Rum1-kDX*CCP*Rum1;
%Rum1p
kIRUM1*MPFRum1+k12RUM1*MPFp*Rum1-kARUM1*Rum1p-kDRUM1P*(MPF+a*MPFp)*Rum1p-kDRUM1*Rum1p+kDX*CCP*Rum1+kDX*CCP*MPFRum1-kDX*CCP*Rum1p;
%Wee1
%VWEE1*Wee1p/(J1WEE1+Wee1p)-kWEE1*(MPF+a*MPFp)*Wee1/(J2WEE1+Wee1);
VWEE1*Wee1p/(J1WEE1+Wee1p)-kWEE1p*(MPF+a*MPFp)*Wee1/(J2WEE1+Wee1); %%
%Cdc25p
kCDC25p*(MPF+a*MPFp)*Cdc25/(J1CDC25+Cdc25)-VCDC25*Cdc25p/(J2CDC25+Cdc25p); %%
%Mass
m*Mass
%Cdc25PDC
Ki*PDC*Cdc25p;
] ;
% Event
function [value,isterminal,direction] = events(t,x)
% Locate the time when (testvar-threshold) passes through zero in a decreasing direction and stop integration.
MATLAB CODE
RESULTS](https://image.slidesharecdn.com/introductiontosystemsscience-170420161035/85/Introduction-to-Systemics-with-focus-on-Systems-Biology-13-320.jpg)
Introduction to Systemics with focus on Systems Biology
- 1. Introduction to Systemics with focus on Systems Biology Mrinal Vashisth Biotechnology Jaipur National University
- 2. What is Systems? • It is an interdisciplinary field concerned with the study of systems in nature, society and technology • We work from parts all the way up to the systems as a whole • The basic realization is: “the whole is greater than the sum of the parts” • The soul: what drives the given system, what will happen to it after certain time
- 3. Key Terms in Systems Science • Systems analysis: Interaction within systems and its surroundings before formulating a model • Systems design: Creating an efficient systems model based on analysis • Systems dynamics: Time lapse studies in complex systems: cause and effect, stock and flow etc. • Systems engineering: Development of complex systems by combining analysis, design and dynamics e.g. Signal processing, communication systems etc. • Systems methodologies: Methods for Systems analysis • Systems science: Scientific disciplines partly based on systems approach e.g. Systems biology, Systems chemistry etc. Systems science as an encompassing Term for Systems approach, in turn, Analysis, Design and Dynamical Studies
- 4. • Systems theory: Study of complex systems in nature, society, and science • Systemics: (1970, Mario Bunge) Study concerning the macroscopic view, an alternative research approach – systems thinking • Systems equivalence: Making analogies between parameters or components between different systems Flow variable: moves through the system Effort variable: puts the system into action Compliance: stores energy as potential Inductance: stores energy as kinetic Resistance: dissipates or uses energy System type Flow variable Effort variable Compliance Inductance Resistance Mechanical dx/dt F = force spring (k) mass (m) damper (c) Electrical i = current V = voltage capacitance (C) inductance (L) resistance (R)
- 5. Components and themes of Complex Systems (General Systems Theory) As systems scientists we are mostly interested in the study of Complex Systems. • Collective behaviour: Population dynamics, particle swarm behaviour etc. • Emergence: “life is an emergent property of chemistry” • Evolution and adaptation: Machine learning, genetic algorithms, AI, Evolutionary Developmental Biology etc. • Game theory: Rational choice theory, Darwinian competition, bounded rationality etc. • Networks: Dynamic, adaptive, robust, scale free, small scale etc. • Nonlinear dynamics: Curve fitting, Time lapse studies, Chaos (double rod pendulum), Multistability, ODEs etc. • Pattern formation: PDEs, geomorphology etc. • Self-organization: Collective consciousness (shared understanding of social norms) Koch snowflake 7 levels Branching Orthogonal Decomposition based Reduced Order Model for Vortex- Induced Vibrations Double rod pendulum
- 6. Systems Biology • Biology, computer science, engineering, bioinformatics, physics and many more – all come together to predict how biological systems change over time and under varying conditions • To bring it all together: We use Systems theory for the formulation of dynamical models with systems analysis, design principles and methodologies to gain novel insights • The research approach itself can be called systemics.
- 7. A short history • 1970s: Sequence Databases, Similarity matrices, Molecular Evolution – prominent figures such as Margaret Dayhoff, Carl Woese etc. • 1980s: Sequence Alignment/ Search – David Lipmann, Smith, Waterman etc. • 1990s: HMMs, Protein Structure Prediction, Genomics and Comparative Genomics • 2000s: HGP, Synthetic Biology from the knowledge of Biological Systems, High throughput sequencing, Development of Computational Methods (Systems methodologies, analysis techniques) [Rise of Systems Biology] • Early and Late 2010s: Next Generation Sequencing, Cellular Modeling, Networks, Gene prediction, Synthetic biology etc.
- 8. Motivating Questions • What instructions are there in the genome? • How are these genomic elements organized? (Mapping) • What genes and regulatory circuits are there? • Can we predict Transcriptome from Genome? • How to make sense out of Big Data in Biology? • How do we: design new therapies and redesign organisms? • How do we : – Determine the cause of the disease – Mechanism of drug action – Discover the metabolic pathways in microorganisms? How to integrate all this knowledge?
- 9. Systems Biology Method • It employs the Scientific method at its core. • Formulation – prediction – testing – refinement – repeat. • A panel of experts from interdisciplinary fields come together to lay out the framework for a model by the programmers • Analysis is performed on the Networks and gaps are predicted • Many such models are made and they are combined as multiscale models • Single cell analysis, Omics, and experimental quantitative methods all feed to the refinement of the model. “The whole of science is nothing more than a refinement of everyday thinking.“ -- Albert Einstein
- 10. Combines together Cellular Studies and Anthropological Studies
- 11. Comparison of Systems Biology with other fields We combine Experimental data and Computational techniques based on which we aim to synthesize novel information (gain new insights)
- 12. Example 1: Sorting pharmaceutical compounds based on their efficacy using Dynamical models Minmal Cell Network by Gérard et al.
- 13. %%% Statistics tdiv=treset(end)-treset(end-1); mdiv=max(mass(k:end)); division=[division ; tdiv mdiv] fprintf('t_div=%g n',tdiv) fprintf('m_div=%g n',mdiv) function y = dxdt(t,v) %Parameters kSMPF=0.05; kD1CYC=0.0235; kD2CYC=0.75; kASS=100; kDISS=0.0025; kDRUM1=0.125; kIRUM1=2; k1SLP1=0.8; J1SLP1=0.001; V2SLP1=0.2; J2SLP1=0.001; kDMPFRUM1=0.35; k1IE=0.2; a=0.05; J1IE=0.001; V2IE=0.05; J2IE=0.001; k12RUM1=50; kARUM1=35; VWEE1=0.125; J1WEE1=0.01; J2WEE1=0.01; k1CDC25=0.05; k2CDC25=2.5; J1CDC25=0.01; J2CDC25=0.01; VCDC25=0.2; k1WEE1=0.05; k2WEE1=2.5; m=0.005; Cdc25T=1; Wee1T=1; IET=1; Slp1T=1; VSRUM1=0.06; kDRUM1P=250; kDX=1; %CCP=1; CCP=0; %% Ki=105.76; %Vary the parameters here PDC=0.0006; %Variables MPF=v(1); MPFp=v(2); Slp1A=v(3); IEA=v(4); MPFRum1=v(5); Rum1=v(6); Rum1p=v(7); Wee1=v(8); Cdc25p=v(9); Mass=v(10); Cdc25PDC=v(11); %% Vary the parameters here %Algebric Equations kWEE1=k1WEE1*Wee1T+(k2WEE1-k1WEE1)*Wee1; kWEE1p=0.625; %% kCDC25=k1CDC25*Cdc25T+(k2CDC25-k1CDC25)*Cdc25p; kCDC25p=1; %% Wee1p=Wee1T-Wee1; Cdc25=Cdc25T-Cdc25p-Cdc25PDC; IE=IET-IEA; Slp1=Slp1T-Slp1A; % ODEs y = [ %MPF kSMPF*Mass-kWEE1*MPF+kCDC25*MPFp-(kD1CYC+kD2CYC*Slp1A)*MPF-kASS*Rum1*MPF+(kDISS+kDRUM1+kIRUM1)*MPFRum1+kDX*CCP*MPFRum1; %MPFp kWEE1*MPF-kCDC25*MPFp-(kD1CYC+kD2CYC*Slp1A)*MPFp; %Slp1A k1SLP1*IEA*Slp1/(J1SLP1+Slp1)-V2SLP1*Slp1A/(J2SLP1+Slp1A); %IEA k1IE*(MPF+a*MPFp)*IE/(J1IE+IE)-V2IE*IEA/(J2IE+IEA); %MPFRum1 kASS*Rum1*MPF-(kDISS+kDRUM1+kIRUM1+kDMPFRUM1)*MPFRum1-kDX*CCP*MPFRum1; %Rum1 VSRUM1-kASS*Rum1*MPF+(kDISS+kDMPFRUM1)*MPFRum1-k12RUM1*MPFp*Rum1+kARUM1*Rum1p-kDRUM1*Rum1-kDX*CCP*Rum1; %Rum1p kIRUM1*MPFRum1+k12RUM1*MPFp*Rum1-kARUM1*Rum1p-kDRUM1P*(MPF+a*MPFp)*Rum1p-kDRUM1*Rum1p+kDX*CCP*Rum1+kDX*CCP*MPFRum1-kDX*CCP*Rum1p; %Wee1 %VWEE1*Wee1p/(J1WEE1+Wee1p)-kWEE1*(MPF+a*MPFp)*Wee1/(J2WEE1+Wee1); VWEE1*Wee1p/(J1WEE1+Wee1p)-kWEE1p*(MPF+a*MPFp)*Wee1/(J2WEE1+Wee1); %% %Cdc25p kCDC25p*(MPF+a*MPFp)*Cdc25/(J1CDC25+Cdc25)-VCDC25*Cdc25p/(J2CDC25+Cdc25p); %% %Mass m*Mass %Cdc25PDC Ki*PDC*Cdc25p; ] ; % Event function [value,isterminal,direction] = events(t,x) % Locate the time when (testvar-threshold) passes through zero in a decreasing direction and stop integration. MATLAB CODE RESULTS
- 15. Example 2: Expression2Kinases: Identifying Upstream Pathways from Gene Expression
- 16. Challenges faced by Systems Science • Hyper-specialization results in formulation of narrow models. • Quick and short publications often are inadequate to develop the complex analyses that modern systems theory demands. • Funding sources may favour neat empirical research over complex (and often abstract) systems analyses. • Critics voicing charges of conservatism may fear that systems theory overemphasizes integration, and thus neglects the study of conflict and change. • Specialists who fear that the autonomy or identity of their discipline is threatened by systems theory • Fear that integration with weaker disciplines will either weaken their approach, or will mask its achievement, thus diminishing the visibility of their approach. • Some scholars still may feel intimidated by the systems approach. Even worse, they do not even know what general systems theory (GST) is. • Some scholars are simply unable or unwilling to obtain the background in multiple disciplines that is often required for true multidisciplinary research.
- 17. Conclusion • Systemics is an alternative research methodology. • Emergence, Evolution & adaptation, Network analysis, Dynamical analysis, Pattern analysis, Collective consciousness, Game Theory, Big-Data (Bioinformatics) all help us understand and design models of the system and taking appropriate decisions • We focus on the big-picture while we do take care of the parts • Systems design reduces the cost of experimentation and gives clues into where/ how to look for particular information and make sense out of it • Systems method can be applied to all of science if we have sufficient data to make calculative predictions.
- 18. Thank you for your precious time!
- 19. References • https://www.systemsbiology.org/about/what-is-systems-biology/, What is Systems Biology, Retrieved April, 11, 2017. • Bailey K D, Fifty Years of Systems Science: Further Reflections, Syst. Res.22, 355-361 (2005), DOI:10.1002/sres.711 • Stichweh R, Systems Theory, https://www.fiw.uni-bonn.de/, Retrieved April, 11, 2017 • Chen et al. Expression2Kinases: mRNA Profiling Linked to Multiple Upstream Regulatory Layers. Bioinformatics. 2011 Nov 10. • Lachmann et al. KEA: kinase enrichment analysis. Bioinformatics 25(5):684-6 (2009) • Sibel JC, Tyson JJ, Mathematical Modeling as a Tool for Investigation Cell Cucle Control Networks., 2007, Methods, Vol. 41, pp. 238-247. • Gérard C, Tyson JJ, Coudreuse D, Novák B, Cell Cycle Control by a Minimal Cdk Network PLoS Comput Biol , 2015, Vol. 11. # Image Credits: GIPHY, Wikimedia Commons, ISB, MATLAB generated