Lecture 12 Evolution of Physiological Systems

Lecture 12 Evolution of Physiological Systems - Stability...

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Stability, Robustness and Evolution of Electrophysiological Systems
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Nervous system has to do two somewhat contradictory things. Stability Personality, perceptions and memories remain relatively stable throughout adult life even though the underlying physical substrate will have changed: proteins will have turned over many thousands of times, there will have been rearrangement of synaptic connections and some local circuitry, and at least some neurons will have died and others born. Plasticity You can learn new skills and form new memories throughout life suggesting that the nervous system retains the ability able to reorganize certain aspects of its function.
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Sources of Stability Species Level 1. purifying selection acting on protein sequence/function 2. purifying selection acting on gene expression 3. evolution of robust networks underlying developmental, biochemical and physiological function 4. evolution of homeostatic feedback loops regulating developmental, biochemical and physiological function Organismal Level 1. Robust networks 2. Homeostatic feedback loops - during development - in the adult
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Structural Evolution - evolution of gene coding sequences Regulatory Evolution - evolution of gene regulatory sequences
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Two Components to Gene Regulation Cis regulatory sequences regions of DNA to which transcription factors bind Transcription factor network proteins that bind directly or indirectly to DNA to alter rates of gene transcription
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Wray et al, 2003
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Wray et al, 2003
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Structural Evolution - evolution of gene coding sequences Regulatory Evolution - evolution of gene regulatory sequences
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1. Evolution of gene regulation is more common and less problematic than the evolution of protein coding regions because it limits the problem of pleiotropy. 2. The molecular changes that produce regulatory evolution are more likely to be changes in cis-regulatory regions rather than the reorganization of transcription factor networks, again because this tends to limit pleiotropy.
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