In our sixth post on Understanding Kinetics, we consider pulse-chase experiments which are a common method to study bio-synthetic pathways. In pulse-chase experiments:
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Posted in Biochemistry, Models
In our fifth post on Understanding Kinetics, we consider the speed at which molecular complexes form. This is the fundamental mechanism underlying drug action (i.e. drugs inhibition their targets) and cellular signalling (i.e. ligands activate their receptors) and is probably the most important “kinetic effect” to consider in experimental design. Here again we use previously derived mathematical models1 to define some simple rules for the timescales/half-lives and magnitude of these reactions (figure above).
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Posted in Biochemistry, Models
In our fourth post in the Understanding Kinetics series we consider the speed at which proteins can turn off (A) or on (B). The dynamics of such processes are important to consider when designing experiments (i.e. How long should I wait to take a measurement?) and understanding Network Motifs in signalling cascades. Luckily we can use exact mathematical models (equations below1) for such processes to define some intuitive rules for the timescales/half-lives and magnitude of these reactions (figure above).
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Posted in Biochemistry, Models
It’s well known that physical rest is important for athletic training but mental rest/relaxation, is also a very important factor. An excellent article1 and primer2 on this topic was recently published and the major take away was: “muscle memory”/motor-control is set during rest through a “mental replaying” of the training-activity. In other words, if you want your final kick in a 5k or marathon to feel natural, make sure you get plenty of sleep after workouts!
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Posted in Running
Science, as we now think of it, only really started about 400 years ago when Francis Bacon unified theory, observation, and experiment in his: “A New Method.” Before this “unified procedure,” science was a patchwork of “lucky guesses” which over-emphasized one tool or another (For example, Aristotle loved reason and hated mathematics whereas Pythagoras believed the world could only be described by pure mathematics).
In addition, quantitative experimentation and the idea of “testing a hypothesis,” only really became practically feasible with inventions of the early Renaissance. Below we give more detail on some these key milestones in the advancement of research, physics, chemistry and biology.
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Posted in Timelines
Entropy (S) can be best understood as “the effect of probability on a physical or chemical processes”. This relationship is famously described by the Boltzmann entropy formula which relates the probability of a particular state (P1) to the chemical or mechanical work (ΔG) required to obtain that state.
Entropy changes(ΔS), are not probabilities per se but rather a conceptual bridge between probability and energy. In this equation, k is the Boltzmann constant, T is temperature, P is the probability of the considered state, ΔS is the entropy change and ΔG is the free energy change.
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Posted in Models, Physical Chemistry
“Model organisms” are the best-studied organisms in experimental biology. For a particular research questions a specific “model organism” is chosen for its balance of: (1) ease of use and (2) “generalizability” of results. For example
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Posted in Animal Work, Molecular Biology
The timescales over which organisms “mutate” or “evolve” varies dramatically from months(viruses) to millions of years(animals). The figure above plots the average genome size in base-pairs (x-axis) versus the average mutation-rate(y-axis) for various organisms. In addition, a second y-axis (“minimum time to 1% mutation”) illustrate approximate timescales corresponding to each rate.
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Posted in Biology, Models, Molecular Biology
Analogies are often the best tools to connect a program’s “functional logic” with the “abstract tools” of the programming language. Here, we outline the logical differences between common if-statement structures using a “Driving Analogy” drawn in parallel to a “Flow-Chart” and “Model Python Code.” We choose Python because it is one of the most intuitive and commonly used programming languages in biology.
Computers store information with transistors or “switches” that have an on (“1”) or off (“0”) state. As a result, they must perform mathematical and logical operations using a binary (base-2) numbering system. For example, as can be see above, computers represent our the decimal/base-10 number “113” with the binary/base-2 number: “1110001”.
Boolean algebra/digital logic is how computer hardware performs computations with a binary numbering system. In addition, Boolean operators (and, or, not, etc.) are critical components to all programming languages. The figure above conceptually summarizes the major Boolean Logic Gates using Venn Diagrams to represent the inputs (top) and outputs (bottom) that result.
Substitution and elimination reactions (between Lewis-bases and alkyl-halides) are some of the first reactions taught in organic chemistry. The figure above, organizes the main factors that distinquish: SN1, SN2, E1 or E2 mechanisms into a single, 4-quadrant spectrum. We describe the heirarchy of these factors in more detail below.
The chemistry that underlies the browning of bread. meats, etc. was first defined in 1912 by Louis-Camille Maillard and involves the polymerization of sugars and proteins. While this reaction is obviously messy (i.e. has many different pathways), the dominant chemical mechanisms were identified in a classic paper by John Hodge in 1953 and are outlined in the figure above. As you can see, bread-browning is mainly: amine(protein)-activated carbonyl(sugar) polymerization.
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Posted in Cooking
Historical reference points make understanding evolutionary time a lot easier by “calibrating” it. In the figure above we provide three parallel timelines depicting key events in the history of the universe (billions of years), animal evolution (millions of years) and human development (thousands of years). Major extinction events are marked by horizontal red lines. Additional details can be found below.
Quantitiative Reverse-Transcriptase PCR (RT-PCR) is different from regular PCR in that it does not measure genes(i.e. DNA) per se but rather measures the EXPRESSION of those gene (i.e. mRNA). Given the fact gene expression (mRNA) can vary dramatically between cell-types, it is important to first isolate a single cell-type by:
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Posted in Animal Work, Biology, Molecular Biology
