ﺗﻌﯿﯿﻦ ﺑﺮﻫﻢﮐﻨﺶﻫﺎي ﭘﺎﯾﺪار ﺑﺮ اﺳﺎس ژنﻫﺎي ﺧﺎﻧﻪدار

Stable Interactions Detection Based on Housekeeping Genes

ژنﻫﺎي ﺧﺎﻧﻪدار در ﺗﻤﺎﻣﯽ ﺷﺮاﯾﻂ زﯾﺴﺘﯽ و ﻣﺮاﺣﻞ رﺷﺪ ﺑﺎﻓﺖ ﺣﺎﺿﺮ ﺑﻮده و ﻫﻤﯿﺸﻪ ﺑﯿﺎن ﻣﯽﺷﻮﻧﺪ. ژن ﺧﺎﻧﻪدار، ژن ﺑﻨﯿﺎدي اﺳﺖ ﮐﻪ ﺑﺮاي ﻧﮕﻬﺪاري از ﻋﻤﻠﮑﺮد اوﻟﯿﻪ ﺳﻠﻮل ﻣﻮردﻧﯿﺎز ﺑﻮده و در ﺑﯿﺸﺘﺮ ﺳﻠﻮلﻫﺎي ﯾﮏ ارﮔﺎﻧﯿﺴﻢ ﺑﯿﺎنﺷﺪه و ﭘﺮوﺗﺌﯿﻦﻫﺎي ﻣﺘﻨﺎﻇﺮ را ﻣﯽﺳﺎزد. در ﺣﺎﻟﺖ ﻋﺎدي، ﺑﺮﺧﯽ از ژنﻫﺎ در ﺷﺮاﯾﻂ و ﮔﺎمﻫﺎي ﻣﺘﻔﺎوت از ﭼﺮﺧﻪ ﺳﻠﻮﻟﯽ ﺑﯿﺎن ﻧﻤﯽﺷﻮﻧﺪ ﮐﻪ ﻣﯽﺗﻮاﻧﺪ دﯾﻨﺎﻣﯿﮏ ﺣﻀﻮر ﯾﮏ ﭘﺮوﺗﺌﯿﻦ و ﺑﻪﺗﺒﻊ آن ﺑﺮﻫﻤﮑﻨﺶﻫﺎي ﮔﺬرا ﯾﺎ ﭘﺎﯾﺪار را ﻣﺸﺨﺺ ﮐﻨﺪ. ﺑﺮﻫﻤﮑﻨﺶﻫﺎي ﭘﺎﯾﺪار در ﺗﻌﯿﯿﻦ ﮐﻤﭙﻠﮑﺲﻫﺎي ﭘﺮوﺗﺌﯿﻨﯽ ﻧﻘﺶ ﻣﻬﻤﯽ دارﻧﺪ. ﯾﮑﯽ از ﭼﺎﻟﺶﻫﺎي ﻣﻮﺟﻮد در زﯾﺴﺖﺷﻨﺎﺳﯽ ﺳﯿﺴﺘﻤﯽ، ﺗﻌﯿﯿﻦ ﺣﺪ آﺳﺘﺎﻧﻪﻫﺎي ﻣﻨﺎﺳﺐ ﺑﺮاي ﺗﻌﯿﯿﻦ ﭘﺮوﺗﺌﯿﻦﻫﺎي ﻓﻌﺎل در ﻫﺮ زﻣﺎن و ﻣﺸﺨﺺ ﮐﺮدن ﺑﺮﻫﻢﮐﻨﺶﻫﺎي ﭘﺎﯾﺪار ﻫﺴﺖ. ﻫﺪف ﻣﺎ در اﯾﻦ ﻣﻘﺎﻟﻪ، ﺗﻌﯿﯿﻦ ﺣﺪ آﺳﺘﺎﻧﻪ ﻣﻨﺤﺼﺮ ﺑﻪ ﻫﺮ ژن ﺑﺮاي ﻣﺸﺨﺺ ﮐﺮدن ژنﻫﺎي ﺧﺎﻧﻪدار و ﺑﺮﻫﻢﮐﻨﺶﻫﺎي ﭘﺎﯾﺪار ﻫﺴﺖ. ﺑﺎ اﺳﺘﻔﺎده از اﻟﮕﻮرﯾﺘﻢ ﺑﻬﯿﻨﻪﺳﺎزي ﻓﺮا اﺑﺘﮑﺎري ﮐﺮم ﺷﺐﺗﺎب و ﺗﺎﺑﻊ ﺟﺬاﺑﯿﺖ ﻣﺒﺘﻨﯽ ﺑﺮ ﺗﺮﮐﯿﺐ ﻣﺠﻤﻮﻋﻪ ﮐﻤﭙﻠﮑﺲﻫﺎي اﺳﺘﺎﻧﺪارد و ﺑﯿﺎن ﻫﻢزﻣﺎن ژنﻫﺎ، ﺣﺪ آﺳﺘﺎﻧﻪ ﻣﺨﺼﻮص ﻫﺮ ژن ﺗﻌﯿﯿﻦ ﻣﯽﺷﻮد. ﻧﺘﺎﯾﺞ ﺗﺠﺮﺑﯽ رويدادهﻫﺎي ﻣﻮﺟﻮد ﻧﺸﺎن ﻣﯽدﻫﺪ ﮐﻪ ﺣﺪ آﺳﺘﺎﻧﻪﻫﺎي اﯾﺠﺎدﺷﺪه ﺑﺎ روش اﺑﺪاﻋﯽ ﻧﺘﺎﯾﺞ ﺑﻬﺘﺮي ﻧﺴﺒﺖ ﺑﻪ روشﻫﺎي ﻗﺒﻠﯽ داﺷﺘﻪ اﺳﺖ.

Housekeeping genes are expressed in all biological conditions and stages of tissue growth. A housekeeping gene is the basic gene required for maintaining the cell's primary function and is expressed in most cells of an organism to produces corresponding proteins. Normally, some genes are not expressed in different conditions and steps of the cell cycle that can determine the dynamics of the presence of a protein and consequently transient or stable interactions. Stable interactions play an important role in the determination of protein complexes. One of the challenges in system biology is to determine the appropriate thresholds to determine active proteins at each time point and to identify stable interactions. Our goal in this paper is to determine the unique threshold for each gene to characterize housekeeping genes and stable interactions. Determination of the specific threshold for each gene is determined by using the firefly optimization algorithm with the attraction function based on the combination of the standard protein complex and co-expression of genes. Experimental results on the existing dataset show that the thresholds created by the proposed method had better results than the previous methods