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Research of Scientia Naturalis
Published by Yayasan Adra Karima Hubbi
ISSN : 30479932     EISSN : 30479940     DOI : 10.70177/scientia
Core Subject : Science, Agriculture,
Agriculture, Biological Sciences & Forestry Chemistry Physics
Research of Scientia Naturalis is an international forum for the publication of peer-reviewed integrative review articles, special thematic issues, reflections or comments on previous research or new research directions, interviews, replications, and intervention articles - all pertaining to the research fields of Mathematics and Natural Sciences. All publications provide breadth of coverage appropriate to a wide readership in Mathematics and Natural Sciences research depth to inform specialists in that area. We feel that the rapidly growing Research of Scientia Naturalis community is looking for a journal with this profile that we can achieve together. Submitted papers must be written in English for initial review stage by editors and further review process by minimum two international reviewers.
Arjuna Subject : Umum - Umum
Articles 2 Documents
 1 
Search results for , issue "Vol. 3 No. 1 (2026)" : 2 Documents clear
MICROBIAL CONSORTIA ENGINEERING: BRIDGING ENVIRONMENTAL MICROBIOLOGY AND SYNTHETIC BIOLOGY Salim, Achmad Agus; Wong, Lucas; Muller, Johannes
Research of Scientia Naturalis Vol. 3 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v3i1.3342

Abstract

Natural ecosystems rely on complex microbial interactions that surpass the metabolic capabilities of isolated monocultures, yet engineering stable multi-species systems remains a significant challenge in biotechnology. This research addresses the unpredictability of interspecies social dynamics by integrating principles from environmental microbiology with the precision of synthetic biology. The study aims to evaluate a rational design framework for “obligate syntrophy” to maintain community stability and enhance metabolic throughput during the processing of complex feedstocks. Utilizing a “bottom-up” methodology, a synthetic consortium of Escherichia coli and Pseudomonas putida was engineered with cross-feeding circuits and quorum-sensing feedback loops for real-time population regulation. Results demonstrate that the engineered consortia achieved a stable co-existence for over 240 hours, representing a 45% increase in biomass yield and a 70% improvement in detoxification efficiency compared to non-engineered mixed cultures. Statistical analysis confirms that the division of metabolic labor significantly reduces individual cellular burden while increasing overall community resilience. This research concludes that bridging ecological wisdom with genetic circuit design provides a superior architecture for robust industrial bioprocessing. The findings offer a scalable blueprint for “programmable ecology,” asserting that engineered microbial consortia are essential for unlocking the full potential of the global circular bioeconomy.
DATA-DRIVEN DISCOVERY IN CHEMICAL SCIENCES: INTEGRATING AI WITH EXPERIMENTAL AND COMPUTATIONAL CHEMISTRY Fitriani, Fitriani; Jun, Wang; Weber, Max
Research of Scientia Naturalis Vol. 3 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v3i1.3378

Abstract

The rapid growth of experimental and computational data in chemical sciences has created new opportunities and challenges for scientific discovery. Traditional hypothesis-driven approaches often struggle to efficiently explore complex chemical spaces characterized by high dimensionality, uncertainty, and resource constraints. Data-driven discovery, supported by artificial intelligence, offers a transformative paradigm by enabling the integration of experimental observations and computational insights into adaptive and scalable research workflows. This study aims to examine how artificial intelligence can be systematically integrated with experimental and computational chemistry to enhance discovery efficiency, predictive accuracy, and scientific interpretability. A mixed-methods research design was employed, combining curated experimental datasets, computational chemistry simulations, and machine learning models within an iterative feedback framework. Quantitative performance analysis and qualitative case studies were used to evaluate model accuracy, robustness, and practical utility. The results demonstrate that integrated AI models significantly outperform single-source approaches, showing lower prediction errors, improved generalization, and stronger alignment with chemical theory. Case-based evidence further indicates reductions in experimental trials and computational screening costs. The study concludes that data-driven discovery frameworks that tightly integrate artificial intelligence with experimental and computational chemistry represent a robust and sustainable approach for accelerating chemical innovation, supporting more informed decision-making, and advancing next-generation research methodologies in chemical sciences.

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