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Online Web Agency has technical product skills "Foundations of Computer Science, Computational Life Sciences, and Bioinformatics" in particular on: "security, formal methods, computational modeling, theory, and simulation" and transfers business culture and above all transfers experience.
Foundations of Computer Science – The basics
- First computers and algorithmic theory (30s-50s): Theoretical developments (e.g. Turing machine) and primitive hardware define the space to explore by formalizing what is computable and the complexity of execution.
- Programming Languages (50s+): From assembly languages to high-level tools (FORTRAN, COBOL, and many others), the creation of algorithms capable of “writing other algorithms” with greater human readability is explored.
- Basics of Data and Networks (60s+): The need to store and transmit systematically and efficiently data collected, leads to the development of basic systems data collected and the basic infrastructure for Internet.
Birth and evolution of Computational Life Sciences and Bioinformatics
- Molecular Biology and Sequencing (50s+): Decoding the structure of life at the molecular level (discoveries about DNA, RNA, proteins...) requires increasingly advanced techniques to archive and analyze this information.
- Human Genome Project (1990-2003): Large-scale collaborative effort to obtain the complete sequence of human DNA. The management of these vast data collected it is a breakthrough in the use of computing for life science applications.
- The “omics” explosion (2000+): In addition to the genome, proteins (proteomics) and transcription (transcriptomics) are now studied on a large scale. Algorithms specialize in the analysis of data collected biological and high-performance computing.
- Computational Systems Biology (2000+): Attempts to model biological processes at various levels with computer tools (e.g. from interactions between cells to the simulation of whole organs or organisms) are becoming a growing interdisciplinary field.
Symbiosis: information technology accelerating biology and vice versa
- Bio-inspired algorithms: Nature often contains efficient methods for solving complex problems. This spurs new classes of algorithms (e.g. brain-inspired neural networks, artificial evolution that finds analogies in mutations).
- Computationally Difficult (Hard) Problems: Many key tasks in biology (e.g. protein folding, sequence alignments, etc.) are "intrinsically" computationally complex problems, which pushes the limits in the creation of IT tools and the use of supercomputer resources.
- Personalized medicine and “Data Driven Discovery”: Bioinformatic techniques and analysis of large genetic datasets (from individuals and the general population) offer extraordinary opportunities in precision diagnosis, prevention and treatment.
Challenges and Future
- Big Data e Cloud Computing: The volume of data collected in the biological field it continues to grow exponentially. Efficient strategies are needed to process and extract relevant information from data deluges.
- Interpretability of the models: Approaches such as deep learning often produce highly accurate results, but their interpretation can be difficult. A better understanding of "why" they work is needed, especially in sensitive fields such as medicine.
- Privacy and Ethics: As the mass of data collected personal data with a genomic profile continues to grow, it is imperative to guarantee high standards of privacy, non-discrimination and their use for biomedical research in a fully conscious and transparent manner.
Conclusion
The Foundations of Computer Science provide the tools that enable breakthrough innovations in Computational Life Sciences and Bioinformatics. Biological problems, in turn, offer challenges that further advance the computing field. It is a true collaboration to understand and potentially improve life itself.
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