Program Sessions

The "Fundamentals of network structure and metabolism" is a topic related to the field of systems biology, which involves the study of complex biological systems, such as cells, tissues, and organisms, using a holistic approach. Specifically, this topic refers to the study of the network of biochemical reactions that occur within a cell, and how this network is structured and regulated to allow for the proper functioning of the cell. 

Cellular metabolism is diverse and complex. Decoding biological principles behind metabolic networks is important to better understand rules of life and enable biosystems design to tackle challenging problems related to health, food, energy, and the environment. This session is focused on recent advances on analysis and fundamental understanding of network structure and metabolism in either monocultures or consortia of organisms. Studies that combine both computational analysis and experimental validation are highly encouraged.

The "Advances in methods, algorithms, and tools development " refers to the ongoing progress and improvements being made in the development of new methods, algorithms, and tools used in various fields such as cellular network modeling, cell engineering, metabolic engineering, microbial community and more. Accurate prediction of genotype-to-phenotype links under a given condition is fundamentally challenging. This session is focused on recent advances on developing methods, algorithms, and tools to analyze complex cellular networks, improving fundamental understanding of cellular metabolism and prediction of cellular phenotypes.

The "Economics in metabolism" is a concept that refers to the idea that metabolic pathways within living organisms are optimized to be as efficient as possible in terms of energy and resource usage, and that these optimizations can be analyzed using economic principles.

Metabolic pathways involve the conversion of one set of molecules into another set of molecules, which is driven by a set of enzymatic reactions. These reactions consume energy and require the use of resources such as cofactors and substrates. The idea behind economics in metabolism is that these metabolic pathways are structured in such a way as to minimize the costs associated with these energy and resource requirements, while still achieving the necessary cellular functions. Researchers have applied economic principles to the study of metabolic pathways, such as optimization theory, to understand how these pathways are structured and regulated. 

Metabolic pathways involve a series of biochemical reactions that occur within cells and are critical for various biological processes such as energy production, biosynthesis of cellular components, the breakdown of environmental toxins and so on.

One area of research in this field involves the analysis of large datasets from omics technologies such as genomics, transcriptomics, and metabolomics. These datasets can be used to identify genes and proteins that are involved in specific metabolic pathways and understand the underlying mechanisms of these pathways. ML, DL, and AI techniques can be used to analyze these datasets and develop predictive models of metabolic pathway activity.

Another area of research involves the use of big data and ML/DL/AI techniques to optimize metabolic pathways for bioproduction processes. These techniques can be used to analyze large datasets from experiments and simulations, identify key metabolic pathways that affect bioproduct yields and quality, and develop predictive models of metabolic pathway performance. This can help to improve the efficiency and scalability of bioproduction processes, leading to more sustainable and cost-effective production of bioproducts. 

The "Modeling, engineering, and applications of microbiome" is a multidisciplinary field of research that focuses on understanding the structure, function, and interactions of microbial communities, particularly those that live in and on the human body. Microbial communities, also known as microbiomes, play an essential role in human health and disease, and researchers in this field aim to develop new methods and technologies for manipulating and engineering these communities for therapeutic purposes.

One area of research in modeling, engineering, and applications of microbiome involves the use of computational models to simulate the behavior of microbial communities and predict how they will respond to various interventions. These models can help researchers better understand the complex interactions between different microbial species and identify potential targets for therapeutic interventions.

Another area of research involves the engineering of microbiomes to achieve specific functions, such as the production of therapeutic compounds or the degradation of environmental pollutants. This involves manipulating the composition of microbial communities through genetic engineering, synthetic biology, and other techniques to achieve a desired outcome. Applications of microbiome engineering are wide-ranging and include areas such as bioremediation, agriculture, and medicine.