Systems Biology: A Brief Overview

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Systems Biology A Brief Overview

• Hiroaki Kitano
• Science 295 1662- 1664

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System-level understanding

• Approach advocated in systems biology
• Would allow researchers to
• Gather comprehensive data sets on system
  performance
• Gain information on underlying molecules
• Requires shift in notion of what to look for
• Areas of focus
• System structure
• System dynamics

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Component vs. system level in biology

• Component-level
• Genes and proteins
• Source of integral but limited knowledge
• Basically a list of parts

• System-level
• Structure and dynamics
• Enables understanding of system functions,
  patterns and control

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Key properties in understanding system-level
biology

• System structures
• System dynamics
• Control method
• Design method

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System structures

• Network of gene interactions
• Biochemical pathways
• Large-scale effects of interactions
• Modulation of physical properties of
  intracellular and multicellular structures

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System dynamics

• Behavior of system over varying conditions within
  a given timeframe
• Involves the following analysis methods
• Phase portrait analysis
• Bifurcation analysis
• Identification of mechanisms for specific
  behaviors

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Control method

• Involves the identification and modulation of
  points that influence the state of the cell
• Used in drug design

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Design method

• Used in the modification/construction of
  improved biological systems
• Principle used in genetic engineering
• Requires use of design principles and simulations
  as opposed to trial and error methods

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Gene-regulatory logic and Biochemical networks

• Conventional methods
• Perform experiments
• Conduct extensive literature surveys
• Expression profiling
• Clustering analysis is used to identify genes
  coexpressed with genes of known function

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Gene-regulatory logic and Biochemical networks

• Methods based on Microarray data
• Information is derived from mRNA abundance
• Transcriptional regulation analysis generates
  plausible hypotheses
• Hypotheses may be used to infer network structure

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Entropy-based decision making algorithm

• Theoretically suggests an experiment with the
  least number of ambiguous network hypotheses
• Progress would lead to increased emphasis on
  hypothesis-driven research in biology

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Network structure and dynamics

• Structure and dynamics are overlapping processes
• For dynamic analysis of cellular systems, we need
  to define the purpose of the model
• Obtain an in-depth understanding of systems
  behavior or
• Predict complex behaviors in response to stimuli

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Network structure and dynamics

• Flux-balance analysis

• Metabolic pathway switching in E. coli

Predictions of metabolic switching from network
structure
Experimental confirmation of predictions
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Classification and comparison of circuits

• Provide further insights on design patterns
• The hope is that intensive investigation will
  reveal a possible evolutionary family of circuits
  as well as a periodic table for functional
  regulatory circuits

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Robustness

• An essential property of biological systems
• Properties exhibited by a robust system
• (i) Adaptation
• (ii) Parameter insensitivity
• (iii) Graceful degradation

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Robustness

• In engineering systems
• (i) a form of system control
• (ii) redundancy
• (iii) structural stability
• (iv) modularity

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Robustness

• In biological systems

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Hypothesis-driven research in systems biology
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Requirements of systems-level analysis

• Comprehensive set of quantitative data
• High throughput and accurate measurements
• Software infrastructure

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Three aspects to consider for comprehensiveness

• Factor comprehensiveness
• Reflects the number of mRNA transcripts and
  proteins that can be measured at once
• Timeline comprehensiveness
• Represents the timeframe within which
  measurements are made
• Item comprehensiveness
• Simultaneous measurement of multiple items

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High throughput and accurate measurements

• Technical innovations in experimental devices
• Allow visualization of molecular interactions
• Methods that speed up measurements and encourage
  automation
• Microfluidic systems/micro-TAS prototypes for PCR
  and electrophoresis

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Software infrastructure

• Simulation software
• Analysis and computing software
• Systems Biology Mark-up Language (SBML)
• Would enable models to be exchanged between
  software tools
• Systems Biology Workbench (SBW)
• Would provide a framework of modular open-source
  software for systems biology research

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Simulation framework
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Possible applications of systems biology

• Mechanism-based drug discovery
• May help identify feedback mechanisms that offset
  the effects of drugs
• May predict systemic side-effects
• May allow the use of a multiple drug system
• Optimization of treatment regimes for individual
  patients

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The End