Messages

Problem sheet can be downloaded from FROM here

In today's lecture, I compared two different mechanisms for chemotaxis - the Goldbetter-Segel fine tuned mechanism and the Barkai-Liebler Robust mechanism and showed that the robust mechanism is consistent with experimental data.

NEXT LECTURE IS ON THURSDAY 24TH MAY.

SYLLABUS FOR THE EXAM: Networks in the sensory transcription network: autoregulation, feed forward loops, SIMs and DORs.

Network motifs in the developmental transcription networks: feedback loops, cascades and regulated feedbacks.

Network motifs in the neuronal network: Multi-input FFLs.

In today's lecture, I introduced chemotaxis in Bacteria.

In the next lecture, I will elaborate on the chemotaxis protein circuit.

In today's lecture, I showed functional advantages of the multi-input FFL network motif in the neuronal network of C.Elegans.

In the next lecture, I describe bacterial chemotaxis.

In today's lecture, I presented the neuronal network of C.Elegans and explained the integrate and fire model of neural action.

In the next lecture, I will describe the dynamics of a multi-input FFL that arises in the nose-touch system of C.elegans. I will also start the description of the chemotaxis navigational system in E. Coli.

In today's lecture, I described sporulation in B. Subtilis using interlocked FFLs.

in the next lecture, I will present network motifs that arise in the neuronal network of C.elegans

In today's lecture, I described regulated feedback as a network motif in the developmental transcription network.

In the next lecture, I will model a developmental cascade using the sporulation network of a bacterium. I will also start with motifs in signal transduction networks.

In today's lecture, i described both double positive and double negative feedback loops which arise as motifs in developmental networks. The double negative feedback loop was illustrated using the two phases of the virus, lambda phage.

In the next lecture, we study regulated double positive feedback loops and interlocked FFLs in the developmental transcription network.

Next lecture is on 19th of April (after easter). We will study network motifs in the developmental transcription network.

In today's lecture, i completed the description of the sensory transcription networks by characterizing all possible network motifs.

In the next lecture: I will start with development networks and discuss the role of bistable switches.

In today's lecture, i described multi-output FFL as a network motif and showed how it is generates a temporal program with FIFO order.

In the next lecture, i will derive other functional advantages of the multi-output FFL and describe another network motif: the DOR (dense overlapping regulon).

In today's lecture, i presented SIM (single instruction module) as a network and showed how it generates a LIFO (last in first out) temporal order.

In the next lecture, i will present multi-output FFLs as a network motif.

IMPORTANT: THE OBLIGATORY EXERCISE SET FOR THE COURSE CAN BE DOWNLOADED FROM here

The due date is 29.03.2012.

In today's lecture, I described how incoherent type 1 FFL leads to pulse like response for a continuous signal and how it speeds up the response time. An experimental realization of this design in the galactose regulation in E. Coli was also described.

In the next lecture, I will discuss why other types of FFLs are rare and discuss evolutionary aspects of this network motif. Exercise set 2 will also be solved.

In today's lecture, i completed the description of C1 FFL network motifs of both the AND and OR input function types. An experimental realization in the flageller motor regulation system of E. Coli was also described.

In the next lecture, i will model IC1 FFL and describe its functional advantages.

Slides of today's lecture can be downloaded fromhere

In today's lecture, i showed that feed forward loops are a network motif in the transcription network. We also discussed one practical example of a coherent feed forward loop with AND input.

In the next lecture, i will present the implications of the dynamics of coherent type 1 feed forward loops and also demonstrate its realization in experiments.

In today's lecture, i described the evolutionary advantage of negative autoregulation and also briefly described positive autoregulation as a network motif. We also solved exercises from problem set 1.

In the next lecture, i will start describing feed forward loops as a network motif. The material will be from chapter 4 of Alon's book.

A practice problem set can be downloaded from here

In today's lecture, i modeled negative autoregulation using hill functions and showed that the response time is speeded up and the steady state protein concentration is more robust to fluctuations than simple regulation.

In the next lecture, i will describe evolutionary advantages of negative autoregulation and briefly describe positive autoregulation. We will also solve exercises from problem set 1.

Today, i showed that negative auto regulation is a network motif. We model negative auto regulation with a logic function and showed that the response time can be shortened by the fast activation strong repression dynamics.

In the next lecture, we will model negative autoregulation with hill functions and compare its evolutionary fitness with simple regulation.

A practice problem set can be downloaded from here

In today's lecture, we modeled a cascade of repressors in E.Coli. We also demonstrated that negative autoregulation is a network motif (beginning of chapter 3 from Alon's book)

In the next lecture, we continue with study of why negative auto-regulation serves as a network motif.

In today's lecture, i solved some exercises from Alon's book, chapter 2 -- the ones dealing with modeling of transcription and translation and one with activation cascades.

In the next lecture, i will solve the exercise on repression cascades and start chapter 3-- autoregulation as a network motif.

In today's lecture, I derived the ODE that models the dynamics of protein production, regulated by a single transcription factor, using the separation of time scales. Explicit solutions were derived and biologically relevant metrics such as the steady state protein expression level and response time characterized. The response time was shown to be similar to the cell cycle life time. Most of the material was from Chap 2 of Alon's book.

In the next lecture, i will start with some modeling exercises from chap 2 of Alon's book. Next, we will see how self regulation arises as a network motif and how it can increase robustness and speed up response time.

In today's lecture, i described transcription networks with particular emphasis on how gene expression is regulated by either activation or repression of transcription factors. The input Hill functions were also described. The material is from Alon, chapter 2.

In the next lecture, i will model the dynamics of protein formation in the basic building block of a transcription network and highlight the role of response time.

In today's lecture, I started with a gentle introduction to biology and covered theory of evolution and the basic chemical reactions that underly life. Transcription, translation, metabolism and cell division were introduced.

In the next lecture: i will continue describing the history of life with the evolution of eukaryotic cells, multicellular organisms and different phyla. Core cellular and developmental processes will be highlighted.

The reference book for the first part of the course is ''an introduction to systems biology" by Uri Alon, Chapman and Hall CRC.