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Computational genes: a tool for molecular diagnosis and therapy of aberrant mutational phenotype.

Martínez-Pérez IM, Zhang G, Ignatova Z, Zimmermann KH - BMC Bioinformatics (2007)

Bottom Line: The aberrant mutations trigger a cascade reaction: specific molecular markers as input are released and induce a spontaneous self-assembly of a wild type protein or peptide, while the mutational disease phenotype is silenced.We experimentally demostrated in in vitro translation system that a viable protein can be autonomously assembled.Our work demostrates the basic principles of computational genes and particularly, their potential to detect mutations, and as a response thereafter administer an output that suppresses the aberrant disease phenotype and/or restores the lost physiological function.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Computer Technology, Hamburg University of Technology, Hamburg 21073, Germany. martinez-perez@tu-harburg.de

ABSTRACT

Background: A finite state machine manipulating information-carrying DNA strands can be used to perform autonomous molecular-scale computations at the cellular level.

Results: We propose a new finite state machine able to detect and correct aberrant molecular phenotype given by mutated genetic transcripts. The aberrant mutations trigger a cascade reaction: specific molecular markers as input are released and induce a spontaneous self-assembly of a wild type protein or peptide, while the mutational disease phenotype is silenced. We experimentally demostrated in in vitro translation system that a viable protein can be autonomously assembled.

Conclusion: Our work demostrates the basic principles of computational genes and particularly, their potential to detect mutations, and as a response thereafter administer an output that suppresses the aberrant disease phenotype and/or restores the lost physiological function.

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Design of computational gene. A) Most eukaryotic genes are organized as alternating sequences of coding (exons) and non-coding (introns) segments. Conserved regions in the introns, e.g., pyrimidine rich region (poly(Y)), 5'-splice junction (AG/GTGAG), AG dinucleotide at the 3'-splice junction, and branch point sequence (CTCAT) guarantee proper splicing. These conserved regions are maintained in the structural moiety of the computational gene. B) Schematic representation of self-assembled (functional) gene encoding diagnostic rule (2), for n = 2. The initial state comprises promoter, first exon, and 5'-splicing site, the transition rules are placed in the intron region, and the final state includes branch site, poly(Y)-region, 3'-splice site, and second exon. C) Finite state automaton implementing diagnostic rule (2). The automaton starts in the initial state S0 and transits into the final state Sn if all mutations are present.
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Figure 1: Design of computational gene. A) Most eukaryotic genes are organized as alternating sequences of coding (exons) and non-coding (introns) segments. Conserved regions in the introns, e.g., pyrimidine rich region (poly(Y)), 5'-splice junction (AG/GTGAG), AG dinucleotide at the 3'-splice junction, and branch point sequence (CTCAT) guarantee proper splicing. These conserved regions are maintained in the structural moiety of the computational gene. B) Schematic representation of self-assembled (functional) gene encoding diagnostic rule (2), for n = 2. The initial state comprises promoter, first exon, and 5'-splicing site, the transition rules are placed in the intron region, and the final state includes branch site, poly(Y)-region, 3'-splice site, and second exon. C) Finite state automaton implementing diagnostic rule (2). The automaton starts in the initial state S0 and transits into the final state Sn if all mutations are present.

Mentions: Here we present a new molecular automaton, called computational gene, consisting of a structural and functional moiety which is designed such that it might work in a cellular environment. The structural part is a naturally occurring gene, which is used as a skeleton to encode the input and the transitions of the automaton (Figure 1A). The conserved features of a structural gene (e.g., DNA polymerase binding site, start and stop codons, and splicing sites) serve as constants of the computational gene, while the coding regions, the number of exons and introns, the position of start and stop codon, and the automata theoretical variables (symbols, states, and transitions) are the design parameters of the computational gene. The constants and the design parameters are linked by several logical and biochemical constraints (e.g., encoded automata theoretic variables must not be recognized as splicing junctions). The input of the automaton are molecular markers given by single stranded DNA (ssDNA) molecules. These markers are signalling aberrant (e.g., carcinogenic) molecular phenotype [13] and turn on the self-assembly of the functional gene. If the input is accepted, the output encodes a double stranded DNA (dsDNA) molecule, a functional gene which should be successfully integrated into the cellular transcription and translation machinery producing a wild type protein or an anti-drug (e.g., short peptide) (Figure 1B). Otherwise, a rejected input will assemble into a partially dsDNA molecule which cannot be translated. A computational gene can be described by a finite automaton M such that the set of strings accepted by M (language) is given by those dsDNA molecules that a translation system recognises as genes. To this end, observe that linear self-assembly is equivalent to regular languages [14], and regular languages are exactly those languages that are accepted by finite automata. Therefore, we can expect to construct a functional gene based only on the linear self-assembly of oligonucleotides or duplex DNA with sticky ends, controlled by a finite state machine. The structural gene resembles the natural structure of the genetic information in the cell and bears all the regulatory elements allowing the functional gene to be successfully translated into a protein. As there is no size restriction on the structural gene, there would be also no size limitation for the functional gene.


Computational genes: a tool for molecular diagnosis and therapy of aberrant mutational phenotype.

Martínez-Pérez IM, Zhang G, Ignatova Z, Zimmermann KH - BMC Bioinformatics (2007)

Design of computational gene. A) Most eukaryotic genes are organized as alternating sequences of coding (exons) and non-coding (introns) segments. Conserved regions in the introns, e.g., pyrimidine rich region (poly(Y)), 5'-splice junction (AG/GTGAG), AG dinucleotide at the 3'-splice junction, and branch point sequence (CTCAT) guarantee proper splicing. These conserved regions are maintained in the structural moiety of the computational gene. B) Schematic representation of self-assembled (functional) gene encoding diagnostic rule (2), for n = 2. The initial state comprises promoter, first exon, and 5'-splicing site, the transition rules are placed in the intron region, and the final state includes branch site, poly(Y)-region, 3'-splice site, and second exon. C) Finite state automaton implementing diagnostic rule (2). The automaton starts in the initial state S0 and transits into the final state Sn if all mutations are present.
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Related In: Results  -  Collection

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Figure 1: Design of computational gene. A) Most eukaryotic genes are organized as alternating sequences of coding (exons) and non-coding (introns) segments. Conserved regions in the introns, e.g., pyrimidine rich region (poly(Y)), 5'-splice junction (AG/GTGAG), AG dinucleotide at the 3'-splice junction, and branch point sequence (CTCAT) guarantee proper splicing. These conserved regions are maintained in the structural moiety of the computational gene. B) Schematic representation of self-assembled (functional) gene encoding diagnostic rule (2), for n = 2. The initial state comprises promoter, first exon, and 5'-splicing site, the transition rules are placed in the intron region, and the final state includes branch site, poly(Y)-region, 3'-splice site, and second exon. C) Finite state automaton implementing diagnostic rule (2). The automaton starts in the initial state S0 and transits into the final state Sn if all mutations are present.
Mentions: Here we present a new molecular automaton, called computational gene, consisting of a structural and functional moiety which is designed such that it might work in a cellular environment. The structural part is a naturally occurring gene, which is used as a skeleton to encode the input and the transitions of the automaton (Figure 1A). The conserved features of a structural gene (e.g., DNA polymerase binding site, start and stop codons, and splicing sites) serve as constants of the computational gene, while the coding regions, the number of exons and introns, the position of start and stop codon, and the automata theoretical variables (symbols, states, and transitions) are the design parameters of the computational gene. The constants and the design parameters are linked by several logical and biochemical constraints (e.g., encoded automata theoretic variables must not be recognized as splicing junctions). The input of the automaton are molecular markers given by single stranded DNA (ssDNA) molecules. These markers are signalling aberrant (e.g., carcinogenic) molecular phenotype [13] and turn on the self-assembly of the functional gene. If the input is accepted, the output encodes a double stranded DNA (dsDNA) molecule, a functional gene which should be successfully integrated into the cellular transcription and translation machinery producing a wild type protein or an anti-drug (e.g., short peptide) (Figure 1B). Otherwise, a rejected input will assemble into a partially dsDNA molecule which cannot be translated. A computational gene can be described by a finite automaton M such that the set of strings accepted by M (language) is given by those dsDNA molecules that a translation system recognises as genes. To this end, observe that linear self-assembly is equivalent to regular languages [14], and regular languages are exactly those languages that are accepted by finite automata. Therefore, we can expect to construct a functional gene based only on the linear self-assembly of oligonucleotides or duplex DNA with sticky ends, controlled by a finite state machine. The structural gene resembles the natural structure of the genetic information in the cell and bears all the regulatory elements allowing the functional gene to be successfully translated into a protein. As there is no size restriction on the structural gene, there would be also no size limitation for the functional gene.

Bottom Line: The aberrant mutations trigger a cascade reaction: specific molecular markers as input are released and induce a spontaneous self-assembly of a wild type protein or peptide, while the mutational disease phenotype is silenced.We experimentally demostrated in in vitro translation system that a viable protein can be autonomously assembled.Our work demostrates the basic principles of computational genes and particularly, their potential to detect mutations, and as a response thereafter administer an output that suppresses the aberrant disease phenotype and/or restores the lost physiological function.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Computer Technology, Hamburg University of Technology, Hamburg 21073, Germany. martinez-perez@tu-harburg.de

ABSTRACT

Background: A finite state machine manipulating information-carrying DNA strands can be used to perform autonomous molecular-scale computations at the cellular level.

Results: We propose a new finite state machine able to detect and correct aberrant molecular phenotype given by mutated genetic transcripts. The aberrant mutations trigger a cascade reaction: specific molecular markers as input are released and induce a spontaneous self-assembly of a wild type protein or peptide, while the mutational disease phenotype is silenced. We experimentally demostrated in in vitro translation system that a viable protein can be autonomously assembled.

Conclusion: Our work demostrates the basic principles of computational genes and particularly, their potential to detect mutations, and as a response thereafter administer an output that suppresses the aberrant disease phenotype and/or restores the lost physiological function.

Show MeSH