Formate gradients as a means for detecting the maximum carbon conversion efficiency of heterotrophic substrates:...

Estimating microbial growth is essential for understanding population dynamics and environmental adaptations. Indirect methods provide valuable insights by measuring parameters such as turbidity, metabolic activity, and biomass, enabling efficient and reproducible assessments.During exponential growth, microbial cells scatter light proportionally to their biomass, a principle used in turbidity measurements. About one million cells per milliliter produce detectable scattering, which a...

Chemolithotrophs are microorganisms that obtain energy by oxidizing inorganic molecules such as hydrogen gas (H₂), ammonia (NH₃), reduced sulfur compounds (H₂S, S²⁻), and ferrous iron (Fe²⁺). Unlike heterotrophic organisms that rely on organic carbon, chemolithotrophs transfer electrons from these inorganic donors to the electron transport chain (ETC), generating a proton motive force (PMF) that drives ATP synthesis through oxidative phosphorylation. However, because inorganic electron donors...

Trophic level transfer efficiency (TLTE) is a measure of the total energy transfer from one trophic level to the next. Due to extensive energy loss as metabolic heat, an average of only 10% of the original energy obtained is passed on to the next level. This pattern of energy loss severely limits the possible number of trophic levels in a food chain.

Direct methods for measuring microbial populations in a culture are essential tools in microbiology, providing quantitative data for various applications. Among these, microscopic counts, plate counts, and serial dilution are widely used techniques, each with unique principles and applications.Microscopic CountsMicroscopic counting involves the use of a Petroff-Hausser chamber, a specialized microscope slide with a grid and defined depth. By observing a liquid culture under a microscope,...

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...